On the three-quarter view advantage of familiar object recognition.

PubMed

Nonose, Kohei; Niimi, Ryosuke; Yokosawa, Kazuhiko

2016-11-01

A three-quarter view, i.e., an oblique view, of familiar objects often leads to a higher subjective goodness rating when compared with other orientations. What is the source of the high goodness for oblique views? First, we confirmed that object recognition performance was also best for oblique views around 30° view, even when the foreshortening disadvantage of front- and side-views was minimized (Experiments 1 and 2). In Experiment 3, we measured subjective ratings of view goodness and two possible determinants of view goodness: familiarity of view, and subjective impression of three-dimensionality. Three-dimensionality was measured as the subjective saliency of visual depth information. The oblique views were rated best, most familiar, and as approximating greatest three-dimensionality on average; however, the cluster analyses showed that the "best" orientation systematically varied among objects. We found three clusters of objects: front-preferred objects, oblique-preferred objects, and side-preferred objects. Interestingly, recognition performance and the three-dimensionality rating were higher for oblique views irrespective of the clusters. It appears that recognition efficiency is not the major source of the three-quarter view advantage. There are multiple determinants and variability among objects. This study suggests that the classical idea that a canonical view has a unique advantage in object perception requires further discussion.

Generation, recognition, and consistent fusion of partial boundary representations from range images

NASA Astrophysics Data System (ADS)

Kohlhepp, Peter; Hanczak, Andrzej M.; Li, Gang

1994-10-01

This paper presents SOMBRERO, a new system for recognizing and locating 3D, rigid, non- moving objects from range data. The objects may be polyhedral or curved, partially occluding, touching or lying flush with each other. For data collection, we employ 2D time- of-flight laser scanners mounted to a moving gantry robot. By combining sensor and robot coordinates, we obtain 3D cartesian coordinates. Boundary representations (Brep's) provide view independent geometry models that are both efficiently recognizable and derivable automatically from sensor data. SOMBRERO's methods for generating, matching and fusing Brep's are highly synergetic. A split-and-merge segmentation algorithm with dynamic triangular builds a partial (21/2D) Brep from scattered data. The recognition module matches this scene description with a model database and outputs recognized objects, their positions and orientations, and possibly surfaces corresponding to unknown objects. We present preliminary results in scene segmentation and recognition. Partial Brep's corresponding to different range sensors or viewpoints can be merged into a consistent, complete and irredundant 3D object or scene model. This fusion algorithm itself uses the recognition and segmentation methods.

Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

NASA Astrophysics Data System (ADS)

Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

2018-04-01

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

Real-time unconstrained object recognition: a processing pipeline based on the mammalian visual system.

PubMed

Aguilar, Mario; Peot, Mark A; Zhou, Jiangying; Simons, Stephen; Liao, Yuwei; Metwalli, Nader; Anderson, Mark B

2012-03-01

The mammalian visual system is still the gold standard for recognition accuracy, flexibility, efficiency, and speed. Ongoing advances in our understanding of function and mechanisms in the visual system can now be leveraged to pursue the design of computer vision architectures that will revolutionize the state of the art in computer vision.

Fast and efficient indexing approach for object recognition

NASA Astrophysics Data System (ADS)

Hefnawy, Alaa; Mashali, Samia A.; Rashwan, Mohsen; Fikri, Magdi

1999-08-01

This paper introduces a fast and efficient indexing approach for both 2D and 3D model-based object recognition in the presence of rotation, translation, and scale variations of objects. The indexing entries are computed after preprocessing the data by Haar wavelet decomposition. The scheme is based on a unified image feature detection approach based on Zernike moments. A set of low level features, e.g. high precision edges, gray level corners, are estimated by a set of orthogonal Zernike moments, calculated locally around every image point. A high dimensional, highly descriptive indexing entries are then calculated based on the correlation of these local features and employed for fast access to the model database to generate hypotheses. A list of the most candidate models is then presented by evaluating the hypotheses. Experimental results are included to demonstrate the effectiveness of the proposed indexing approach.

A Novel Locally Linear KNN Method With Applications to Visual Recognition.

PubMed

Liu, Qingfeng; Liu, Chengjun

2017-09-01

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients' truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.

Grouping in object recognition: the role of a Gestalt law in letter identification.

PubMed

Pelli, Denis G; Majaj, Najib J; Raizman, Noah; Christian, Christopher J; Kim, Edward; Palomares, Melanie C

2009-02-01

The Gestalt psychologists reported a set of laws describing how vision groups elements to recognize objects. The Gestalt laws "prescribe for us what we are to recognize 'as one thing'" (Kohler, 1920). Were they right? Does object recognition involve grouping? Tests of the laws of grouping have been favourable, but mostly assessed only detection, not identification, of the compound object. The grouping of elements seen in the detection experiments with lattices and "snakes in the grass" is compelling, but falls far short of the vivid everyday experience of recognizing a familiar, meaningful, named thing, which mediates the ordinary identification of an object. Thus, after nearly a century, there is hardly any evidence that grouping plays a role in ordinary object recognition. To assess grouping in object recognition, we made letters out of grating patches and measured threshold contrast for identifying these letters in visual noise as a function of perturbation of grating orientation, phase, and offset. We define a new measure, "wiggle", to characterize the degree to which these various perturbations violate the Gestalt law of good continuation. We find that efficiency for letter identification is inversely proportional to wiggle and is wholly determined by wiggle, independent of how the wiggle was produced. Thus the effects of three different kinds of shape perturbation on letter identifiability are predicted by a single measure of goodness of continuation. This shows that letter identification obeys the Gestalt law of good continuation and may be the first confirmation of the original Gestalt claim that object recognition involves grouping.

Grouping in object recognition: The role of a Gestalt law in letter identification

PubMed Central

Pelli, Denis G.; Majaj, Najib J.; Raizman, Noah; Christian, Christopher J.; Kim, Edward; Palomares, Melanie C.

2009-01-01

The Gestalt psychologists reported a set of laws describing how vision groups elements to recognize objects. The Gestalt laws “prescribe for us what we are to recognize ‘as one thing’” (Köhler, 1920). Were they right? Does object recognition involve grouping? Tests of the laws of grouping have been favourable, but mostly assessed only detection, not identification, of the compound object. The grouping of elements seen in the detection experiments with lattices and “snakes in the grass” is compelling, but falls far short of the vivid everyday experience of recognizing a familiar, meaningful, named thing, which mediates the ordinary identification of an object. Thus, after nearly a century, there is hardly any evidence that grouping plays a role in ordinary object recognition. To assess grouping in object recognition, we made letters out of grating patches and measured threshold contrast for identifying these letters in visual noise as a function of perturbation of grating orientation, phase, and offset. We define a new measure, “wiggle”, to characterize the degree to which these various perturbations violate the Gestalt law of good continuation. We find that efficiency for letter identification is inversely proportional to wiggle and is wholly determined by wiggle, independent of how the wiggle was produced. Thus the effects of three different kinds of shape perturbation on letter identifiability are predicted by a single measure of goodness of continuation. This shows that letter identification obeys the Gestalt law of good continuation and may be the first confirmation of the original Gestalt claim that object recognition involves grouping. PMID:19424881

Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

2017-04-01

In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition precision (90.6%) and recall (91.2%), particularly for incomplete and small objects.

Data-driven indexing mechanism for the recognition of polyhedral objects

NASA Astrophysics Data System (ADS)

McLean, Stewart; Horan, Peter; Caelli, Terry M.

1992-02-01

This paper is concerned with the problem of searching large model databases. To date, most object recognition systems have concentrated on the problem of matching using simple searching algorithms. This is quite acceptable when the number of object models is small. However, in the future, general purpose computer vision systems will be required to recognize hundreds or perhaps thousands of objects and, in such circumstances, efficient searching algorithms will be needed. The problem of searching a large model database is one which must be addressed if future computer vision systems are to be at all effective. In this paper we present a method we call data-driven feature-indexed hypothesis generation as one solution to the problem of searching large model databases.

Object/Shape Recognition Technology: An Assessment of the Feasibility of Implementation at Defense Logistics Agency Disposition Services

DTIC Science & Technology

2015-02-25

provide efficiency and effectively manufacture or inventory items. The industries that benefit from Cognex technology are automotive, food and beverage ...recognition tedmology, Tedmology Readiness Level, PAGES Cost Benefit Analysis, Tedmology Commercialization, Technology Transition 139 16. PRICE CODE 17...Technology Development & Transition Strategy Guidebook xvii UD Ultimate Disposal U.S. United States USAF United States Air Force xviii THIS

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-07-21

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images. PMID:27455264

Dilated contour extraction and component labeling algorithm for object vector representation

NASA Astrophysics Data System (ADS)

Skourikhine, Alexei N.

2005-08-01

Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Online graphic symbol recognition using neural network and ARG matching

NASA Astrophysics Data System (ADS)

Yang, Bing; Li, Changhua; Xie, Weixing

2001-09-01

This paper proposes a novel method for on-line recognition of line-based graphic symbol. The input strokes are usually warped into a cursive form due to the sundry drawing style, and classifying them is very difficult. To deal with this, an ART-2 neural network is used to classify the input strokes. It has the advantages of high recognition rate, less recognition time and forming classes in a self-organized manner. The symbol recognition is achieved by an Attribute Relational Graph (ARG) matching algorithm. The ARG is very efficient for representing complex objects, but computation cost is very high. To over come this, we suggest a fast graph matching algorithm using symbol structure information. The experimental results show that the proposed method is effective for recognition of symbols with hierarchical structure.

A novel binary shape context for 3D local surface description

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Liu, Yuan; Liang, Fuxun; Li, Bijun; Zang, Yufu

2017-08-01

3D local surface description is now at the core of many computer vision technologies, such as 3D object recognition, intelligent driving, and 3D model reconstruction. However, most of the existing 3D feature descriptors still suffer from low descriptiveness, weak robustness, and inefficiency in both time and memory. To overcome these challenges, this paper presents a robust and descriptive 3D Binary Shape Context (BSC) descriptor with high efficiency in both time and memory. First, a novel BSC descriptor is generated for 3D local surface description, and the performance of the BSC descriptor under different settings of its parameters is analyzed. Next, the descriptiveness, robustness, and efficiency in both time and memory of the BSC descriptor are evaluated and compared to those of several state-of-the-art 3D feature descriptors. Finally, the performance of the BSC descriptor for 3D object recognition is also evaluated on a number of popular benchmark datasets, and an urban-scene dataset is collected by a terrestrial laser scanner system. Comprehensive experiments demonstrate that the proposed BSC descriptor obtained high descriptiveness, strong robustness, and high efficiency in both time and memory and achieved high recognition rates of 94.8%, 94.1% and 82.1% on the considered UWA, Queen, and WHU datasets, respectively.

Auditory-visual object recognition time suggests specific processing for animal sounds.

PubMed

Suied, Clara; Viaud-Delmon, Isabelle

2009-01-01

Recognizing an object requires binding together several cues, which may be distributed across different sensory modalities, and ignoring competing information originating from other objects. In addition, knowledge of the semantic category of an object is fundamental to determine how we should react to it. Here we investigate the role of semantic categories in the processing of auditory-visual objects. We used an auditory-visual object-recognition task (go/no-go paradigm). We compared recognition times for two categories: a biologically relevant one (animals) and a non-biologically relevant one (means of transport). Participants were asked to react as fast as possible to target objects, presented in the visual and/or the auditory modality, and to withhold their response for distractor objects. A first main finding was that, when participants were presented with unimodal or bimodal congruent stimuli (an image and a sound from the same object), similar reaction times were observed for all object categories. Thus, there was no advantage in the speed of recognition for biologically relevant compared to non-biologically relevant objects. A second finding was that, in the presence of a biologically relevant auditory distractor, the processing of a target object was slowed down, whether or not it was itself biologically relevant. It seems impossible to effectively ignore an animal sound, even when it is irrelevant to the task. These results suggest a specific and mandatory processing of animal sounds, possibly due to phylogenetic memory and consistent with the idea that hearing is particularly efficient as an alerting sense. They also highlight the importance of taking into account the auditory modality when investigating the way object concepts of biologically relevant categories are stored and retrieved.

Cross-label Suppression: a Discriminative and Fast Dictionary Learning with Group Regularization.

PubMed

Wang, Xiudong; Gu, Yuantao

2017-05-10

This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose crosslabel suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the cross-label suppression, we don't resort to frequently-used `0-norm or `1- norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets including face recognition, object categorization, scene classification, texture recognition and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency.

Towards discrete wavelet transform-based human activity recognition

NASA Astrophysics Data System (ADS)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

The Initial Development of Object Knowledge by a Learning Robot

PubMed Central

Modayil, Joseph; Kuipers, Benjamin

2008-01-01

We describe how a robot can develop knowledge of the objects in its environment directly from unsupervised sensorimotor experience. The object knowledge consists of multiple integrated representations: trackers that form spatio-temporal clusters of sensory experience, percepts that represent properties for the tracked objects, classes that support efficient generalization from past experience, and actions that reliably change object percepts. We evaluate how well this intrinsically acquired object knowledge can be used to solve externally specified tasks including object recognition and achieving goals that require both planning and continuous control. PMID:19953188

ROBOSIGHT: Robotic Vision System For Inspection And Manipulation

NASA Astrophysics Data System (ADS)

Trivedi, Mohan M.; Chen, ChuXin; Marapane, Suresh

1989-02-01

Vision is an important sensory modality that can be used for deriving information critical to the proper, efficient, flexible, and safe operation of an intelligent robot. Vision systems are uti-lized for developing higher level interpretation of the nature of a robotic workspace using images acquired by cameras mounted on a robot. Such information can be useful for tasks such as object recognition, object location, object inspection, obstacle avoidance and navigation. In this paper we describe efforts directed towards developing a vision system useful for performing various robotic inspection and manipulation tasks. The system utilizes gray scale images and can be viewed as a model-based system. It includes general purpose image analysis modules as well as special purpose, task dependent object status recognition modules. Experiments are described to verify the robust performance of the integrated system using a robotic testbed.

Spoken word recognition by Latino children learning Spanish as their first language*

PubMed Central

HURTADO, NEREYDA; MARCHMAN, VIRGINIA A.; FERNALD, ANNE

2010-01-01

Research on the development of efficiency in spoken language understanding has focused largely on middle-class children learning English. Here we extend this research to Spanish-learning children (n=49; M=2;0; range=1;3–3;1) living in the USA in Latino families from primarily low socioeconomic backgrounds. Children looked at pictures of familiar objects while listening to speech naming one of the objects. Analyses of eye movements revealed developmental increases in the efficiency of speech processing. Older children and children with larger vocabularies were more efficient at processing spoken language as it unfolds in real time, as previously documented with English learners. Children whose mothers had less education tended to be slower and less accurate than children of comparable age and vocabulary size whose mothers had more schooling, consistent with previous findings of slower rates of language learning in children from disadvantaged backgrounds. These results add to the cross-linguistic literature on the development of spoken word recognition and to the study of the impact of socioeconomic status (SES) factors on early language development. PMID:17542157

The role of colour in implicit and explicit memory performance.

PubMed

Vernon, David; Lloyd-Jones, Toby J

2003-07-01

We present two experiments that examine the effects of colour transformation between study and test (from black and white to colour and vice versa, of from incorrectly coloured to correctly coloured and vice versa) on implicit and explicit measures of memory for diagnostically coloured natural objects (e.g., yellow banana). For naming and coloured-object decision (i.e., deciding whether an object is correctly coloured), there were shorter response times to correctly coloured-objects than to black-and-white and incorrectly coloured-objects. Repetition priming was equivalent for the different stimulus types. Colour transformation did not influence priming of picture naming, but for coloured-object decision priming was evident only for objects remaining the same from study to test. This was the case for both naming and coloured-object decision as study tasks. When participants were asked to consciously recognize objects that they had named or made coloured-object decisions to previously, whilst ignoring their colour, colour transformation reduced recognition efficiency. We discuss these results in terms of the flexibility of object representations that mediate priming and recognition.

Extreme Trust Region Policy Optimization for Active Object Recognition.

PubMed

Liu, Huaping; Wu, Yupei; Sun, Fuchun; Huaping Liu; Yupei Wu; Fuchun Sun; Sun, Fuchun; Liu, Huaping; Wu, Yupei

2018-06-01

In this brief, we develop a deep reinforcement learning method to actively recognize objects by choosing a sequence of actions for an active camera that helps to discriminate between the objects. The method is realized using trust region policy optimization, in which the policy is realized by an extreme learning machine and, therefore, leads to efficient optimization algorithm. The experimental results on the publicly available data set show the advantages of the developed extreme trust region optimization method.

The research of edge extraction and target recognition based on inherent feature of objects

NASA Astrophysics Data System (ADS)

Xie, Yu-chan; Lin, Yu-chi; Huang, Yin-guo

2008-03-01

Current research on computer vision often needs specific techniques for particular problems. Little use has been made of high-level aspects of computer vision, such as three-dimensional (3D) object recognition, that are appropriate for large classes of problems and situations. In particular, high-level vision often focuses mainly on the extraction of symbolic descriptions, and pays little attention to the speed of processing. In order to extract and recognize target intelligently and rapidly, in this paper we developed a new 3D target recognition method based on inherent feature of objects in which cuboid was taken as model. On the basis of analysis cuboid nature contour and greyhound distributing characteristics, overall fuzzy evaluating technique was utilized to recognize and segment the target. Then Hough transform was used to extract and match model's main edges, we reconstruct aim edges by stereo technology in the end. There are three major contributions in this paper. Firstly, the corresponding relations between the parameters of cuboid model's straight edges lines in an image field and in the transform field were summed up. By those, the aimless computations and searches in Hough transform processing can be reduced greatly and the efficiency is improved. Secondly, as the priori knowledge about cuboids contour's geometry character known already, the intersections of the component extracted edges are taken, and assess the geometry of candidate edges matches based on the intersections, rather than the extracted edges. Therefore the outlines are enhanced and the noise is depressed. Finally, a 3-D target recognition method is proposed. Compared with other recognition methods, this new method has a quick response time and can be achieved with high-level computer vision. The method present here can be used widely in vision-guide techniques to strengthen its intelligence and generalization, which can also play an important role in object tracking, port AGV, robots fields. The results of simulation experiments and theory analyzing demonstrate that the proposed method could suppress noise effectively, extracted target edges robustly, and achieve the real time need. Theory analysis and experiment shows the method is reasonable and efficient.

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications.

PubMed

Park, Seong-Wook; Park, Junyoung; Bong, Kyeongryeol; Shin, Dongjoo; Lee, Jinmook; Choi, Sungpill; Yoo, Hoi-Jun

2015-12-01

Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm × 4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.

Detecting objects in radiographs for homeland security

NASA Astrophysics Data System (ADS)

Prasad, Lakshman; Snyder, Hans

2005-05-01

We present a general scheme for segmenting a radiographic image into polygons that correspond to visual features. This decomposition provides a vectorized representation that is a high-level description of the image. The polygons correspond to objects or object parts present in the image. This characterization of radiographs allows the direct application of several shape recognition algorithms to identify objects. In this paper we describe the use of constrained Delaunay triangulations as a uniform foundational tool to achieve multiple visual tasks, namely image segmentation, shape decomposition, and parts-based shape matching. Shape decomposition yields parts that serve as tokens representing local shape characteristics. Parts-based shape matching enables the recognition of objects in the presence of occlusions, which commonly occur in radiographs. The polygonal representation of image features affords the efficient design and application of sophisticated geometric filtering methods to detect large-scale structural properties of objects in images. Finally, the representation of radiographs via polygons results in significant reduction of image file sizes and permits the scalable graphical representation of images, along with annotations of detected objects, in the SVG (scalable vector graphics) format that is proposed by the world wide web consortium (W3C). This is a textual representation that can be compressed and encrypted for efficient and secure transmission of information over wireless channels and on the Internet. In particular, our methods described here provide an algorithmic framework for developing image analysis tools for screening cargo at ports of entry for homeland security.

SigVox - A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Menenti, Massimo

2017-06-01

Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree.

ASERA: A Spectrum Eye Recognition Assistant

NASA Astrophysics Data System (ADS)

Yuan, Hailong; Zhang, Haotong; Zhang, Yanxia; Lei, Yajuan; Dong, Yiqiao; Zhao, Yongheng

2018-04-01

ASERA, ASpectrum Eye Recognition Assistant, aids in quasar spectral recognition and redshift measurement and can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). This interactive software allows users to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. ASERA is an efficient and user-friendly semi-automated toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope) and is available as a standalone Java application and as a Java applet. The software offers several functions, including wavelength and flux scale settings, zoom in and out, redshift estimation, and spectral line identification.

A new selective developmental deficit: Impaired object recognition with normal face recognition.

PubMed

Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

2011-05-01

Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual recognition. Copyright © 2010 Elsevier Srl. All rights reserved.

Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

NASA Astrophysics Data System (ADS)

Iqtait, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture.

Toward a Computer Vision-based Wayfinding Aid for Blind Persons to Access Unfamiliar Indoor Environments.

PubMed

Tian, Yingli; Yang, Xiaodong; Yi, Chucai; Arditi, Aries

2013-04-01

Independent travel is a well known challenge for blind and visually impaired persons. In this paper, we propose a proof-of-concept computer vision-based wayfinding aid for blind people to independently access unfamiliar indoor environments. In order to find different rooms (e.g. an office, a lab, or a bathroom) and other building amenities (e.g. an exit or an elevator), we incorporate object detection with text recognition. First we develop a robust and efficient algorithm to detect doors, elevators, and cabinets based on their general geometric shape, by combining edges and corners. The algorithm is general enough to handle large intra-class variations of objects with different appearances among different indoor environments, as well as small inter-class differences between different objects such as doors and door-like cabinets. Next, in order to distinguish intra-class objects (e.g. an office door from a bathroom door), we extract and recognize text information associated with the detected objects. For text recognition, we first extract text regions from signs with multiple colors and possibly complex backgrounds, and then apply character localization and topological analysis to filter out background interference. The extracted text is recognized using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, location, and text information are presented to the blind traveler as speech.

Toward a Computer Vision-based Wayfinding Aid for Blind Persons to Access Unfamiliar Indoor Environments

PubMed Central

Tian, YingLi; Yang, Xiaodong; Yi, Chucai; Arditi, Aries

2012-01-01

Independent travel is a well known challenge for blind and visually impaired persons. In this paper, we propose a proof-of-concept computer vision-based wayfinding aid for blind people to independently access unfamiliar indoor environments. In order to find different rooms (e.g. an office, a lab, or a bathroom) and other building amenities (e.g. an exit or an elevator), we incorporate object detection with text recognition. First we develop a robust and efficient algorithm to detect doors, elevators, and cabinets based on their general geometric shape, by combining edges and corners. The algorithm is general enough to handle large intra-class variations of objects with different appearances among different indoor environments, as well as small inter-class differences between different objects such as doors and door-like cabinets. Next, in order to distinguish intra-class objects (e.g. an office door from a bathroom door), we extract and recognize text information associated with the detected objects. For text recognition, we first extract text regions from signs with multiple colors and possibly complex backgrounds, and then apply character localization and topological analysis to filter out background interference. The extracted text is recognized using off-the-shelf optical character recognition (OCR) software products. The object type, orientation, location, and text information are presented to the blind traveler as speech. PMID:23630409

Neural network face recognition using wavelets

NASA Astrophysics Data System (ADS)

Karunaratne, Passant V.; Jouny, Ismail I.

1997-04-01

The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

Pattern Recognition Using Artificial Neural Network: A Review

NASA Astrophysics Data System (ADS)

Kim, Tai-Hoon

Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, artificial neural network techniques theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

Object recognition and localization from 3D point clouds by maximum-likelihood estimation

NASA Astrophysics Data System (ADS)

Dantanarayana, Harshana G.; Huntley, Jonathan M.

2017-08-01

We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike `interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

The impact of privacy protection filters on gender recognition

NASA Astrophysics Data System (ADS)

Ruchaud, Natacha; Antipov, Grigory; Korshunov, Pavel; Dugelay, Jean-Luc; Ebrahimi, Touradj; Berrani, Sid-Ahmed

2015-09-01

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or even outperform human visual system in its ability to detect and recognize objects and their features. In this paper, we focus on the specific task of gender recognition in images when they have been processed by privacy protection filters (e.g., blurring, masking, and pixelization) applied at different strengths. Assuming a privacy protection scenario, we compare the performance of state of the art deep learning algorithms with a subjective evaluation obtained via crowdsourcing to understand how privacy protection filters affect both machine and human vision.

Standard object recognition memory and "what" and "where" components: Improvement by post-training epinephrine in highly habituated rats.

PubMed

Jurado-Berbel, Patricia; Costa-Miserachs, David; Torras-Garcia, Meritxell; Coll-Andreu, Margalida; Portell-Cortés, Isabel

2010-02-11

The present work examined whether post-training systemic epinephrine (EPI) is able to modulate short-term (3h) and long-term (24 h and 48 h) memory of standard object recognition, as well as long-term (24 h) memory of separate "what" (object identity) and "where" (object location) components of object recognition. Although object recognition training is associated to low arousal levels, all the animals received habituation to the training box in order to further reduce emotional arousal. Post-training EPI improved long-term (24 h and 48 h), but not short-term (3 h), memory in the standard object recognition task, as well as 24 h memory for both object identity and object location. These data indicate that post-training epinephrine: (1) facilitates long-term memory for standard object recognition; (2) exerts separate facilitatory effects on "what" (object identity) and "where" (object location) components of object recognition; and (3) is capable of improving memory for a low arousing task even in highly habituated rats.

Experience moderates overlap between object and face recognition, suggesting a common ability

PubMed Central

Gauthier, Isabel; McGugin, Rankin W.; Richler, Jennifer J.; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E.

2014-01-01

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. PMID:24993021

Experience moderates overlap between object and face recognition, suggesting a common ability.

PubMed

Gauthier, Isabel; McGugin, Rankin W; Richler, Jennifer J; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E

2014-07-03

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. © 2014 ARVO.

A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-Rigid Shape Matching

DTIC Science & Technology

2009-02-01

topology changes. We used a subset of the TOSCA shape database , [10], consisting of four different objects: cat, dog, male, and female. Each of the...often encountered as acquisition imperfections when the shapes are acquired using a 3D scanner. We used a subset of the TOSCA shape database , consisting...object recognition, Point Based Graphics, Prague, 2007. 18 44. A. Spira and R. Kimmel, An efficient solution to the eikonal equation on parametric

Generalization between canonical and non-canonical views in object recognition

PubMed Central

Ghose, Tandra; Liu, Zili

2013-01-01

Viewpoint generalization in object recognition is the process that allows recognition of a given 3D object from many different viewpoints despite variations in its 2D projections. We used the canonical view effects as a foundation to empirically test the validity of a major theory in object recognition, the view-approximation model (Poggio & Edelman, 1990). This model predicts that generalization should be better when an object is first seen from a non-canonical view and then a canonical view than when seen in the reversed order. We also manipulated object similarity to study the degree to which this view generalization was constrained by shape details and task instructions (object vs. image recognition). Old-new recognition performance for basic and subordinate level objects was measured in separate blocks. We found that for object recognition, view generalization between canonical and non-canonical views was comparable for basic level objects. For subordinate level objects, recognition performance was more accurate from non-canonical to canonical views than the other way around. When the task was changed from object recognition to image recognition, the pattern of the results reversed. Interestingly, participants responded “old” to “new” images of “old” objects with a substantially higher rate than to “new” objects, despite instructions to the contrary, thereby indicating involuntary view generalization. Our empirical findings are incompatible with the prediction of the view-approximation theory, and argue against the hypothesis that views are stored independently. PMID:23283692

Introducing memory and association mechanism into a biologically inspired visual model.

PubMed

Qiao, Hong; Li, Yinlin; Tang, Tang; Wang, Peng

2014-09-01

A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.

Target recognitions in multiple-camera closed-circuit television using color constancy

NASA Astrophysics Data System (ADS)

Soori, Umair; Yuen, Peter; Han, Ji Wen; Ibrahim, Izzati; Chen, Wentao; Hong, Kan; Merfort, Christian; James, David; Richardson, Mark

2013-04-01

People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people's dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve target recognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of target recognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of target recognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of target recognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged target recognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.

A Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing

PubMed Central

Long, Chengjiang; Hua, Gang; Kapoor, Ashish

2015-01-01

We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. PMID:26924892

Use of the recognition heuristic depends on the domain's recognition validity, not on the recognition validity of selected sets of objects.

PubMed

Pohl, Rüdiger F; Michalkiewicz, Martha; Erdfelder, Edgar; Hilbig, Benjamin E

2017-07-01

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

Eye movements during object recognition in visual agnosia.

PubMed

Charles Leek, E; Patterson, Candy; Paul, Matthew A; Rafal, Robert; Cristino, Filipe

2012-07-01

This paper reports the first ever detailed study about eye movement patterns during single object recognition in visual agnosia. Eye movements were recorded in a patient with an integrative agnosic deficit during two recognition tasks: common object naming and novel object recognition memory. The patient showed normal directional biases in saccades and fixation dwell times in both tasks and was as likely as controls to fixate within object bounding contour regardless of recognition accuracy. In contrast, following initial saccades of similar amplitude to controls, the patient showed a bias for short saccades. In object naming, but not in recognition memory, the similarity of the spatial distributions of patient and control fixations was modulated by recognition accuracy. The study provides new evidence about how eye movements can be used to elucidate the functional impairments underlying object recognition deficits. We argue that the results reflect a breakdown in normal functional processes involved in the integration of shape information across object structure during the visual perception of shape. Copyright © 2012 Elsevier Ltd. All rights reserved.

The role of color information on object recognition: a review and meta-analysis.

PubMed

Bramão, Inês; Reis, Alexandra; Petersson, Karl Magnus; Faísca, Luís

2011-09-01

In this study, we systematically review the scientific literature on the effect of color on object recognition. Thirty-five independent experiments, comprising 1535 participants, were included in a meta-analysis. We found a moderate effect of color on object recognition (d=0.28). Specific effects of moderator variables were analyzed and we found that color diagnosticity is the factor with the greatest moderator effect on the influence of color in object recognition; studies using color diagnostic objects showed a significant color effect (d=0.43), whereas a marginal color effect was found in studies that used non-color diagnostic objects (d=0.18). The present study did not permit the drawing of specific conclusions about the moderator effect of the object recognition task; while the meta-analytic review showed that color information improves object recognition mainly in studies using naming tasks (d=0.36), the literature review revealed a large body of evidence showing positive effects of color information on object recognition in studies using a large variety of visual recognition tasks. We also found that color is important for the ability to recognize artifacts and natural objects, to recognize objects presented as types (line-drawings) or as tokens (photographs), and to recognize objects that are presented without surface details, such as texture or shadow. Taken together, the results of the meta-analysis strongly support the contention that color plays a role in object recognition. This suggests that the role of color should be taken into account in models of visual object recognition. Copyright © 2011 Elsevier B.V. All rights reserved.

The role of perceptual load in object recognition.

PubMed

Lavie, Nilli; Lin, Zhicheng; Zokaei, Nahid; Thoma, Volker

2009-10-01

Predictions from perceptual load theory (Lavie, 1995, 2005) regarding object recognition across the same or different viewpoints were tested. Results showed that high perceptual load reduces distracter recognition levels despite always presenting distracter objects from the same view. They also showed that the levels of distracter recognition were unaffected by a change in the distracter object view under conditions of low perceptual load. These results were found both with repetition priming measures of distracter recognition and with performance on a surprise recognition memory test. The results support load theory proposals that distracter recognition critically depends on the level of perceptual load. The implications for the role of attention in object recognition theories are discussed. PsycINFO Database Record (c) 2009 APA, all rights reserved.

Automatic target recognition and detection in infrared imagery under cluttered background

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Koç, Aykut; Alatan, A. Aydın.

2017-10-01

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

NASA Astrophysics Data System (ADS)

Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

2016-06-01

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

Under what conditions is recognition spared relative to recall after selective hippocampal damage in humans?

PubMed

Holdstock, J S; Mayes, A R; Roberts, N; Cezayirli, E; Isaac, C L; O'Reilly, R C; Norman, K A

2002-01-01

The claim that recognition memory is spared relative to recall after focal hippocampal damage has been disputed in the literature. We examined this claim by investigating object and object-location recall and recognition memory in a patient, YR, who has adult-onset selective hippocampal damage. Our aim was to identify the conditions under which recognition was spared relative to recall in this patient. She showed unimpaired forced-choice object recognition but clearly impaired recall, even when her control subjects found the object recognition task to be numerically harder than the object recall task. However, on two other recognition tests, YR's performance was not relatively spared. First, she was clearly impaired at an equivalently difficult yes/no object recognition task, but only when targets and foils were very similar. Second, YR was clearly impaired at forced-choice recognition of object-location associations. This impairment was also unrelated to difficulty because this task was no more difficult than the forced-choice object recognition task for control subjects. The clear impairment of yes/no, but not of forced-choice, object recognition after focal hippocampal damage, when targets and foils are very similar, is predicted by the neural network-based Complementary Learning Systems model of recognition. This model postulates that recognition is mediated by hippocampally dependent recollection and cortically dependent familiarity; thus hippocampal damage should not impair item familiarity. The model postulates that familiarity is ineffective when very similar targets and foils are shown one at a time and subjects have to identify which items are old (yes/no recognition). In contrast, familiarity is effective in discriminating which of similar targets and foils, seen together, is old (forced-choice recognition). Independent evidence from the remember/know procedure also indicates that YR's familiarity is normal. The Complementary Learning Systems model can also accommodate the clear impairment of forced-choice object-location recognition memory if it incorporates the view that the most complete convergence of spatial and object information, represented in different cortical regions, occurs in the hippocampus.

Application of robust face recognition in video surveillance systems

NASA Astrophysics Data System (ADS)

Zhang, De-xin; An, Peng; Zhang, Hao-xiang

2018-03-01

In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis (FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.

Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly; Reid, Max B.

1994-01-01

A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

In search of a recognition memory engram

PubMed Central

Brown, M.W.; Banks, P.J.

2015-01-01

A large body of data from human and animal studies using psychological, recording, imaging, and lesion techniques indicates that recognition memory involves at least two separable processes: familiarity discrimination and recollection. Familiarity discrimination for individual visual stimuli seems to be effected by a system centred on the perirhinal cortex of the temporal lobe. The fundamental change that encodes prior occurrence within the perirhinal cortex is a reduction in the responses of neurones when a stimulus is repeated. Neuronal network modelling indicates that a system based on such a change in responsiveness is potentially highly efficient in information theoretic terms. A review is given of findings indicating that perirhinal cortex acts as a storage site for recognition memory of objects and that such storage depends upon processes producing synaptic weakening. PMID:25280908

Method and System for Object Recognition Search

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor); Duong, Vu A. (Inventor); Stubberud, Allen R. (Inventor)

2012-01-01

A method for object recognition using shape and color features of the object to be recognized. An adaptive architecture is used to recognize and adapt the shape and color features for moving objects to enable object recognition.

Object memory effects on figure assignment: conscious object recognition is not necessary or sufficient.

PubMed

Peterson, M A; de Gelder, B; Rapcsak, S Z; Gerhardstein, P C; Bachoud-Lévi, A

2000-01-01

In three experiments we investigated whether conscious object recognition is necessary or sufficient for effects of object memories on figure assignment. In experiment 1, we examined a brain-damaged participant, AD, whose conscious object recognition is severely impaired. AD's responses about figure assignment do reveal effects from memories of object structure, indicating that conscious object recognition is not necessary for these effects, and identifying the figure-ground test employed here as a new implicit test of access to memories of object structure. In experiments 2 and 3, we tested a second brain-damaged participant, WG, for whom conscious object recognition was relatively spared. Nevertheless, effects from memories of object structure on figure assignment were not evident in WG's responses about figure assignment in experiment 2, indicating that conscious object recognition is not sufficient for effects of object memories on figure assignment. WG's performance sheds light on AD's performance, and has implications for the theoretical understanding of object memory effects on figure assignment.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

Texture- and deformability-based surface recognition by tactile image analysis.

PubMed

Khasnobish, Anwesha; Pal, Monalisa; Tibarewala, D N; Konar, Amit; Pal, Kunal

2016-08-01

Deformability and texture are two unique object characteristics which are essential for appropriate surface recognition by tactile exploration. Tactile sensation is required to be incorporated in artificial arms for rehabilitative and other human-computer interface applications to achieve efficient and human-like manoeuvring. To accomplish the same, surface recognition by tactile data analysis is one of the prerequisites. The aim of this work is to develop effective technique for identification of various surfaces based on deformability and texture by analysing tactile images which are obtained during dynamic exploration of the item by artificial arms whose gripper is fitted with tactile sensors. Tactile data have been acquired, while human beings as well as a robot hand fitted with tactile sensors explored the objects. The tactile images are pre-processed, and relevant features are extracted from the tactile images. These features are provided as input to the variants of support vector machine (SVM), linear discriminant analysis and k-nearest neighbour (kNN) for classification. Based on deformability, six household surfaces are recognized from their corresponding tactile images. Moreover, based on texture five surfaces of daily use are classified. The method adopted in the former two cases has also been applied for deformability- and texture-based recognition of four biomembranes, i.e. membranes prepared from biomaterials which can be used for various applications such as drug delivery and implants. Linear SVM performed best for recognizing surface deformability with an accuracy of 83 % in 82.60 ms, whereas kNN classifier recognizes surfaces of daily use having different textures with an accuracy of 89 % in 54.25 ms and SVM with radial basis function kernel recognizes biomembranes with an accuracy of 78 % in 53.35 ms. The classifiers are observed to generalize well on the unseen test datasets with very high performance to achieve efficient material recognition based on its deformability and texture.

Real-Time Laser Ultrasound Tomography for Profilometry of Solids

NASA Astrophysics Data System (ADS)

Zarubin, V. P.; Bychkov, A. S.; Karabutov, A. A.; Simonova, V. A.; Kudinov, I. A.; Cherepetskaya, E. B.

2018-01-01

We studied the possibility of applying laser ultrasound tomography for profilometry of solids. The proposed approach provides high spatial resolution and efficiency, as well as profilometry of contaminated objects or objects submerged in liquids. The algorithms for the construction of tomograms and recognition of the profiles of studied objects using the parallel programming technology NDIVIA CUDA are proposed. A prototype of the real-time laser ultrasound profilometer was used to obtain the profiles of solid surfaces of revolution. The proposed method allows the real-time determination of the surface position for cylindrical objects with an approximation accuracy of up to 16 μm.

The development of newborn object recognition in fast and slow visual worlds

PubMed Central

Wood, Justin N.; Wood, Samantha M. W.

2016-01-01

Object recognition is central to perception and cognition. Yet relatively little is known about the environmental factors that cause invariant object recognition to emerge in the newborn brain. Is this ability a hardwired property of vision? Or does the development of invariant object recognition require experience with a particular kind of visual environment? Here, we used a high-throughput controlled-rearing method to examine whether newborn chicks (Gallus gallus) require visual experience with slowly changing objects to develop invariant object recognition abilities. When newborn chicks were raised with a slowly rotating virtual object, the chicks built invariant object representations that generalized across novel viewpoints and rotation speeds. In contrast, when newborn chicks were raised with a virtual object that rotated more quickly, the chicks built viewpoint-specific object representations that failed to generalize to novel viewpoints and rotation speeds. Moreover, there was a direct relationship between the speed of the object and the amount of invariance in the chick's object representation. Thus, visual experience with slowly changing objects plays a critical role in the development of invariant object recognition. These results indicate that invariant object recognition is not a hardwired property of vision, but is learned rapidly when newborns encounter a slowly changing visual world. PMID:27097925

Viewpoint dependence in the recognition of non-elongated familiar objects: testing the effects of symmetry, front-back axis, and familiarity.

PubMed

Niimi, Ryosuke; Yokosawa, Kazuhiko

2009-01-01

Visual recognition of three-dimensional (3-D) objects is relatively impaired for some particular views, called accidental views. For most familiar objects, the front and top views are considered to be accidental views. Previous studies have shown that foreshortening of the axes of elongation of objects in these views impairs recognition, but the influence of other possible factors is largely unknown. Using familiar objects without a salient axis of elongation, we found that a foreshortened symmetry plane of the object and low familiarity of the viewpoint accounted for the relatively worse recognition for front views and top views, independently of the effect of a foreshortened axis of elongation. We found no evidence that foreshortened front-back axes impaired recognition in front views. These results suggest that the viewpoint dependence of familiar object recognition is not a unitary phenomenon. The possible role of symmetry (either 2-D or 3-D) in familiar object recognition is also discussed.

Automatic anatomy recognition via multiobject oriented active shape models.

PubMed

Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

2010-12-01

This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a recognition accuracy of > or = 90% yielded a TPVF > or = 95% and FPVF < or = 0.5%. Over the three data sets and over all tested objects, in 97% of the cases, the optimal solutions found by the proposed method constituted the true global optimum. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy recognition system. Increasing the number of objects in the model can significantly improve both recognition and delineation accuracy. More spread out arrangement of objects in the model can lead to improved recognition and delineation accuracy. Including larger objects in the model also improved recognition and delineation. The proposed method almost always finds globally optimum solutions.

DORSAL HIPPOCAMPAL PROGESTERONE INFUSIONS ENHANCE OBJECT RECOGNITION IN YOUNG FEMALE MICE

PubMed Central

Orr, Patrick T.; Lewis, Michael C.; Frick, Karyn M.

2009-01-01

The effects of progesterone on memory are not nearly as well studied as the effects of estrogens. Although progesterone can reportedly enhance spatial and/or object recognition in female rodents when given immediately after training, previous studies have injected progesterone systemically, and therefore, the brain regions mediating this enhancement are not clear. As such, this study was designed to determine the role of the dorsal hippocampus in mediating the beneficial effect of progesterone on object recognition. Young ovariectomized C57BL/6 mice were trained in a hippocampal-dependent object recognition task utilizing two identical objects, and then immediately or 2 hrs afterwards, received bilateral dorsal hippocampal infusions of vehicle or 0.01, 0.1, or 1.0 μg/μl water-soluble progesterone. Forty-eight hours later, object recognition memory was tested using a previously explored object and a novel object. Relative to the vehicle group, memory for the familiar object was enhanced in all groups receiving immediate infusions of progesterone. Progesterone infusion delayed 2 hrs after training did not affect object recognition. These data suggest that the dorsal hippocampus may play a critical role in progesterone-induced enhancement of object recognition. PMID:19477194

Interactive object recognition assistance: an approach to recognition starting from target objects

NASA Astrophysics Data System (ADS)

Geisler, Juergen; Littfass, Michael

1999-07-01

Recognition of target objects in remotely sensed imagery required detailed knowledge about the target object domain as well as about mapping properties of the sensing system. The art of object recognition is to combine both worlds appropriately and to provide models of target appearance with respect to sensor characteristics. Common approaches to support interactive object recognition are either driven from the sensor point of view and address the problem of displaying images in a manner adequate to the sensing system. Or they focus on target objects and provide exhaustive encyclopedic information about this domain. Our paper discusses an approach to assist interactive object recognition based on knowledge about target objects and taking into account the significance of object features with respect to characteristics of the sensed imagery, e.g. spatial and spectral resolution. An `interactive recognition assistant' takes the image analyst through the interpretation process by indicating step-by-step the respectively most significant features of objects in an actual set of candidates. The significance of object features is expressed by pregenerated trees of significance, and by the dynamic computation of decision relevance for every feature at each step of the recognition process. In the context of this approach we discuss the question of modeling and storing the multisensorial/multispectral appearances of target objects and object classes as well as the problem of an adequate dynamic human-machine-interface that takes into account various mental models of human image interpretation.

Infant Visual Attention and Object Recognition

PubMed Central

Reynolds, Greg D.

2015-01-01

This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. PMID:25596333

Efficient visual object and word recognition relies on high spatial frequency coding in the left posterior fusiform gyrus: evidence from a case-series of patients with ventral occipito-temporal cortex damage.

PubMed

Roberts, Daniel J; Woollams, Anna M; Kim, Esther; Beeson, Pelagie M; Rapcsak, Steven Z; Lambon Ralph, Matthew A

2013-11-01

Recent visual neuroscience investigations suggest that ventral occipito-temporal cortex is retinotopically organized, with high acuity foveal input projecting primarily to the posterior fusiform gyrus (pFG), making this region crucial for coding high spatial frequency information. Because high spatial frequencies are critical for fine-grained visual discrimination, we hypothesized that damage to the left pFG should have an adverse effect not only on efficient reading, as observed in pure alexia, but also on the processing of complex non-orthographic visual stimuli. Consistent with this hypothesis, we obtained evidence that a large case series (n = 20) of patients with lesions centered on left pFG: 1) Exhibited reduced sensitivity to high spatial frequencies; 2) demonstrated prolonged response latencies both in reading (pure alexia) and object naming; and 3) were especially sensitive to visual complexity and similarity when discriminating between novel visual patterns. These results suggest that the patients' dual reading and non-orthographic recognition impairments have a common underlying mechanism and reflect the loss of high spatial frequency visual information normally coded in the left pFG.

Recognition-induced forgetting is not due to category-based set size.

PubMed

Maxcey, Ashleigh M

2016-01-01

What are the consequences of accessing a visual long-term memory representation? Previous work has shown that accessing a long-term memory representation via retrieval improves memory for the targeted item and hurts memory for related items, a phenomenon called retrieval-induced forgetting. Recently we found a similar forgetting phenomenon with recognition of visual objects. Recognition-induced forgetting occurs when practice recognizing an object during a two-alternative forced-choice task, from a group of objects learned at the same time, leads to worse memory for objects from that group that were not practiced. An alternative explanation of this effect is that category-based set size is inducing forgetting, not recognition practice as claimed by some researchers. This alternative explanation is possible because during recognition practice subjects make old-new judgments in a two-alternative forced-choice task, and are thus exposed to more objects from practiced categories, potentially inducing forgetting due to set-size. Herein I pitted the category-based set size hypothesis against the recognition-induced forgetting hypothesis. To this end, I parametrically manipulated the amount of practice objects received in the recognition-induced forgetting paradigm. If forgetting is due to category-based set size, then the magnitude of forgetting of related objects will increase as the number of practice trials increases. If forgetting is recognition induced, the set size of exemplars from any given category should not be predictive of memory for practiced objects. Consistent with this latter hypothesis, additional practice systematically improved memory for practiced objects, but did not systematically affect forgetting of related objects. These results firmly establish that recognition practice induces forgetting of related memories. Future directions and important real-world applications of using recognition to access our visual memories of previously encountered objects are discussed.

Mechanisms of object recognition: what we have learned from pigeons

PubMed Central

Soto, Fabian A.; Wasserman, Edward A.

2014-01-01

Behavioral studies of object recognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood. Here, we review this research and suggest that a core set of mechanisms for object recognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of object recognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of object recognition in the primate ventral stream, plays a role in object recognition by pigeons. We also highlight differences between pigeons and people in object recognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of object recognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human object recognition from studying the “simple” brains of pigeons. PMID:25352784

Development of visuo-haptic transfer for object recognition in typical preschool and school-aged children.

PubMed

Purpura, Giulia; Cioni, Giovanni; Tinelli, Francesca

2018-07-01

Object recognition is a long and complex adaptive process and its full maturation requires combination of many different sensory experiences as well as cognitive abilities to manipulate previous experiences in order to develop new percepts and subsequently to learn from the environment. It is well recognized that the transfer of visual and haptic information facilitates object recognition in adults, but less is known about development of this ability. In this study, we explored the developmental course of object recognition capacity in children using unimodal visual information, unimodal haptic information, and visuo-haptic information transfer in children from 4 years to 10 years and 11 months of age. Participants were tested through a clinical protocol, involving visual exploration of black-and-white photographs of common objects, haptic exploration of real objects, and visuo-haptic transfer of these two types of information. Results show an age-dependent development of object recognition abilities for visual, haptic, and visuo-haptic modalities. A significant effect of time on development of unimodal and crossmodal recognition skills was found. Moreover, our data suggest that multisensory processes for common object recognition are active at 4 years of age. They facilitate recognition of common objects, and, although not fully mature, are significant in adaptive behavior from the first years of age. The study of typical development of visuo-haptic processes in childhood is a starting point for future studies regarding object recognition in impaired populations.

In search of a recognition memory engram.

PubMed

Brown, M W; Banks, P J

2015-03-01

A large body of data from human and animal studies using psychological, recording, imaging, and lesion techniques indicates that recognition memory involves at least two separable processes: familiarity discrimination and recollection. Familiarity discrimination for individual visual stimuli seems to be effected by a system centred on the perirhinal cortex of the temporal lobe. The fundamental change that encodes prior occurrence within the perirhinal cortex is a reduction in the responses of neurones when a stimulus is repeated. Neuronal network modelling indicates that a system based on such a change in responsiveness is potentially highly efficient in information theoretic terms. A review is given of findings indicating that perirhinal cortex acts as a storage site for recognition memory of objects and that such storage depends upon processes producing synaptic weakening. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

NASA Astrophysics Data System (ADS)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Multivariate fMRI and Eye Tracking Reveal Differential Effects of Visual Interference on Recognition Memory Judgments for Objects and Scenes.

PubMed

O'Neil, Edward B; Watson, Hilary C; Dhillon, Sonya; Lobaugh, Nancy J; Lee, Andy C H

2015-09-01

Recent work has demonstrated that the perirhinal cortex (PRC) supports conjunctive object representations that aid object recognition memory following visual object interference. It is unclear, however, how these representations interact with other brain regions implicated in mnemonic retrieval and how congruent and incongruent interference influences the processing of targets and foils during object recognition. To address this, multivariate partial least squares was applied to fMRI data acquired during an interference match-to-sample task, in which participants made object or scene recognition judgments after object or scene interference. This revealed a pattern of activity sensitive to object recognition following congruent (i.e., object) interference that included PRC, prefrontal, and parietal regions. Moreover, functional connectivity analysis revealed a common pattern of PRC connectivity across interference and recognition conditions. Examination of eye movements during the same task in a separate study revealed that participants gazed more at targets than foils during correct object recognition decisions, regardless of interference congruency. By contrast, participants viewed foils more than targets for incorrect object memory judgments, but only after congruent interference. Our findings suggest that congruent interference makes object foils appear familiar and that a network of regions, including PRC, is recruited to overcome the effects of interference.

The Role of Perceptual Load in Object Recognition

ERIC Educational Resources Information Center

Lavie, Nilli; Lin, Zhicheng; Zokaei, Nahid; Thoma, Volker

2009-01-01

Predictions from perceptual load theory (Lavie, 1995, 2005) regarding object recognition across the same or different viewpoints were tested. Results showed that high perceptual load reduces distracter recognition levels despite always presenting distracter objects from the same view. They also showed that the levels of distracter recognition were…

Object Recognition and Localization: The Role of Tactile Sensors

PubMed Central

Aggarwal, Achint; Kirchner, Frank

2014-01-01

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving object recognition and localization. This paper presents two approaches for haptic object recognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic object recognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments. PMID:24553087

View-invariant object recognition ability develops after discrimination, not mere exposure, at several viewing angles.

PubMed

Yamashita, Wakayo; Wang, Gang; Tanaka, Keiji

2010-01-01

One usually fails to recognize an unfamiliar object across changes in viewing angle when it has to be discriminated from similar distractor objects. Previous work has demonstrated that after long-term experience in discriminating among a set of objects seen from the same viewing angle, immediate recognition of the objects across 30-60 degrees changes in viewing angle becomes possible. The capability for view-invariant object recognition should develop during the within-viewing-angle discrimination, which includes two kinds of experience: seeing individual views and discriminating among the objects. The aim of the present study was to determine the relative contribution of each factor to the development of view-invariant object recognition capability. Monkeys were first extensively trained in a task that required view-invariant object recognition (Object task) with several sets of objects. The animals were then exposed to a new set of objects over 26 days in one of two preparatory tasks: one in which each object view was seen individually, and a second that required discrimination among the objects at each of four viewing angles. After the preparatory period, we measured the monkeys' ability to recognize the objects across changes in viewing angle, by introducing the object set to the Object task. Results indicated significant view-invariant recognition after the second but not first preparatory task. These results suggest that discrimination of objects from distractors at each of several viewing angles is required for the development of view-invariant recognition of the objects when the distractors are similar to the objects.

Remote Safety Monitoring for Elderly Persons Based on Omni-Vision Analysis

PubMed Central

Xiang, Yun; Tang, Yi-ping; Ma, Bao-qing; Yan, Hang-chen; Jiang, Jun; Tian, Xu-yuan

2015-01-01

Remote monitoring service for elderly persons is important as the aged populations in most developed countries continue growing. To monitor the safety and health of the elderly population, we propose a novel omni-directional vision sensor based system, which can detect and track object motion, recognize human posture, and analyze human behavior automatically. In this work, we have made the following contributions: (1) we develop a remote safety monitoring system which can provide real-time and automatic health care for the elderly persons and (2) we design a novel motion history or energy images based algorithm for motion object tracking. Our system can accurately and efficiently collect, analyze, and transfer elderly activity information and provide health care in real-time. Experimental results show that our technique can improve the data analysis efficiency by 58.5% for object tracking. Moreover, for the human posture recognition application, the success rate can reach 98.6% on average. PMID:25978761

Infant visual attention and object recognition.

PubMed

Reynolds, Greg D

2015-05-15

This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. Copyright © 2015 Elsevier B.V. All rights reserved.

Three-dimensional deformable-model-based localization and recognition of road vehicles.

PubMed

Zhang, Zhaoxiang; Tan, Tieniu; Huang, Kaiqi; Wang, Yunhong

2012-01-01

We address the problem of model-based object recognition. Our aim is to localize and recognize road vehicles from monocular images or videos in calibrated traffic scenes. A 3-D deformable vehicle model with 12 shape parameters is set up as prior information, and its pose is determined by three parameters, which are its position on the ground plane and its orientation about the vertical axis under ground-plane constraints. An efficient local gradient-based method is proposed to evaluate the fitness between the projection of the vehicle model and image data, which is combined into a novel evolutionary computing framework to estimate the 12 shape parameters and three pose parameters by iterative evolution. The recovery of pose parameters achieves vehicle localization, whereas the shape parameters are used for vehicle recognition. Numerous experiments are conducted in this paper to demonstrate the performance of our approach. It is shown that the local gradient-based method can evaluate accurately and efficiently the fitness between the projection of the vehicle model and the image data. The evolutionary computing framework is effective for vehicles of different types and poses is robust to all kinds of occlusion.

Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

NASA Astrophysics Data System (ADS)

Li, Y. H.; Shinohara, T.; Satoh, T.; Tachibana, K.

2016-06-01

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

Distinct roles of basal forebrain cholinergic neurons in spatial and object recognition memory.

PubMed

Okada, Kana; Nishizawa, Kayo; Kobayashi, Tomoko; Sakata, Shogo; Kobayashi, Kazuto

2015-08-06

Recognition memory requires processing of various types of information such as objects and locations. Impairment in recognition memory is a prominent feature of amnesia and a symptom of Alzheimer's disease (AD). Basal forebrain cholinergic neurons contain two major groups, one localized in the medial septum (MS)/vertical diagonal band of Broca (vDB), and the other in the nucleus basalis magnocellularis (NBM). The roles of these cell groups in recognition memory have been debated, and it remains unclear how they contribute to it. We use a genetic cell targeting technique to selectively eliminate cholinergic cell groups and then test spatial and object recognition memory through different behavioural tasks. Eliminating MS/vDB neurons impairs spatial but not object recognition memory in the reference and working memory tasks, whereas NBM elimination undermines only object recognition memory in the working memory task. These impairments are restored by treatment with acetylcholinesterase inhibitors, anti-dementia drugs for AD. Our results highlight that MS/vDB and NBM cholinergic neurons are not only implicated in recognition memory but also have essential roles in different types of recognition memory.

Appearance-based face recognition and light-fields.

PubMed

Gross, Ralph; Matthews, Iain; Baker, Simon

2004-04-01

Arguably the most important decision to be made when developing an object recognition algorithm is selecting the scene measurements or features on which to base the algorithm. In appearance-based object recognition, the features are chosen to be the pixel intensity values in an image of the object. These pixel intensities correspond directly to the radiance of light emitted from the object along certain rays in space. The set of all such radiance values over all possible rays is known as the plenoptic function or light-field. In this paper, we develop a theory of appearance-based object recognition from light-fields. This theory leads directly to an algorithm for face recognition across pose that uses as many images of the face as are available, from one upwards. All of the pixels, whichever image they come from, are treated equally and used to estimate the (eigen) light-field of the object. The eigen light-field is then used as the set of features on which to base recognition, analogously to how the pixel intensities are used in appearance-based face and object recognition.

Orientation congruency effects for familiar objects: coordinate transformations in object recognition.

PubMed

Graf, M; Kaping, D; Bülthoff, H H

2005-03-01

How do observers recognize objects after spatial transformations? Recent neurocomputational models have proposed that object recognition is based on coordinate transformations that align memory and stimulus representations. If the recognition of a misoriented object is achieved by adjusting a coordinate system (or reference frame), then recognition should be facilitated when the object is preceded by a different object in the same orientation. In the two experiments reported here, two objects were presented in brief masked displays that were in close temporal contiguity; the objects were in either congruent or incongruent picture-plane orientations. Results showed that naming accuracy was higher for congruent than for incongruent orientations. The congruency effect was independent of superordinate category membership (Experiment 1) and was found for objects with different main axes of elongation (Experiment 2). The results indicate congruency effects for common familiar objects even when they have dissimilar shapes. These findings are compatible with models in which object recognition is achieved by an adjustment of a perceptual coordinate system.

Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment.

PubMed

Qiao, Hong; Xi, Xuanyang; Li, Yinlin; Wu, Wei; Li, Fengfu

2015-11-01

Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational methods.

Measuring the Speed of Newborn Object Recognition in Controlled Visual Worlds

ERIC Educational Resources Information Center

Wood, Justin N.; Wood, Samantha M. W.

2017-01-01

How long does it take for a newborn to recognize an object? Adults can recognize objects rapidly, but measuring object recognition speed in newborns has not previously been possible. Here we introduce an automated controlled-rearing method for measuring the speed of newborn object recognition in controlled visual worlds. We raised newborn chicks…

Deletion of the GluA1 AMPA receptor subunit impairs recency-dependent object recognition memory

PubMed Central

Sanderson, David J.; Hindley, Emma; Smeaton, Emily; Denny, Nick; Taylor, Amy; Barkus, Chris; Sprengel, Rolf; Seeburg, Peter H.; Bannerman, David M.

2011-01-01

Deletion of the GluA1 AMPA receptor subunit impairs short-term spatial recognition memory. It has been suggested that short-term recognition depends upon memory caused by the recent presentation of a stimulus that is independent of contextual–retrieval processes. The aim of the present set of experiments was to test whether the role of GluA1 extends to nonspatial recognition memory. Wild-type and GluA1 knockout mice were tested on the standard object recognition task and a context-independent recognition task that required recency-dependent memory. In a first set of experiments it was found that GluA1 deletion failed to impair performance on either of the object recognition or recency-dependent tasks. However, GluA1 knockout mice displayed increased levels of exploration of the objects in both the sample and test phases compared to controls. In contrast, when the time that GluA1 knockout mice spent exploring the objects was yoked to control mice during the sample phase, it was found that GluA1 deletion now impaired performance on both the object recognition and the recency-dependent tasks. GluA1 deletion failed to impair performance on a context-dependent recognition task regardless of whether object exposure in knockout mice was yoked to controls or not. These results demonstrate that GluA1 is necessary for nonspatial as well as spatial recognition memory and plays an important role in recency-dependent memory processes. PMID:21378100

Window-based method for approximating the Hausdorff in three-dimensional range imagery

DOEpatents

Koch, Mark W [Albuquerque, NM

2009-06-02

One approach to pattern recognition is to use a template from a database of objects and match it to a probe image containing the unknown. Accordingly, the Hausdorff distance can be used to measure the similarity of two sets of points. In particular, the Hausdorff can measure the goodness of a match in the presence of occlusion, clutter, and noise. However, existing 3D algorithms for calculating the Hausdorff are computationally intensive, making them impractical for pattern recognition that requires scanning of large databases. The present invention is directed to a new method that can efficiently, in time and memory, compute the Hausdorff for 3D range imagery. The method uses a window-based approach.

Dopamine D1 receptor stimulation modulates the formation and retrieval of novel object recognition memory: Role of the prelimbic cortex

PubMed Central

Pezze, Marie A.; Marshall, Hayley J.; Fone, Kevin C.F.; Cassaday, Helen J.

2015-01-01

Previous studies have shown that dopamine D1 receptor antagonists impair novel object recognition memory but the effects of dopamine D1 receptor stimulation remain to be determined. This study investigated the effects of the selective dopamine D1 receptor agonist SKF81297 on acquisition and retrieval in the novel object recognition task in male Wistar rats. SKF81297 (0.4 and 0.8 mg/kg s.c.) given 15 min before the sampling phase impaired novel object recognition evaluated 10 min or 24 h later. The same treatments also reduced novel object recognition memory tested 24 h after the sampling phase and when given 15 min before the choice session. These data indicate that D1 receptor stimulation modulates both the encoding and retrieval of object recognition memory. Microinfusion of SKF81297 (0.025 or 0.05 μg/side) into the prelimbic sub-region of the medial prefrontal cortex (mPFC) in this case 10 min before the sampling phase also impaired novel object recognition memory, suggesting that the mPFC is one important site mediating the effects of D1 receptor stimulation on visual recognition memory. PMID:26277743

The Joint Role of Trained, Untrained, and Observed Actions at the Origins of Goal Recognition

PubMed Central

Gerson, Sarah A.; Woodward, Amanda L.

2014-01-01

Recent findings across a variety of domains reveal the benefits of self-produced experience on object exploration, object knowledge, attention, and action perception. The influence of active experience may be particularly important in infancy, when motor development is undergoing great changes. Despite the importance of self-produced experience, we know that infants and young children are eventually able to gain knowledge through purely observational experience. In the current work, three-month-old infants were given experience with object-directed actions in one of three forms and their recognition of the goal of grasping actions was then assessed in a habituation paradigm. All infants were given the chance to manually interact with the toys without assistance (a difficult task for most three-month-olds). Two of the three groups were then given additional experience with object-directed actions, either through active training (in which Velcro mittens helped infants act more efficiently) or observational training. Findings support the conclusion that self-produced experience is uniquely informative for action perception and suggest that individual differences in spontaneous motor activity may interact with observational experience to inform action perception early in life. PMID:24468646

Safe trajectory estimation at a pedestrian crossing to assist visually impaired people.

PubMed

Alghamdi, Saleh; van Schyndel, Ron; Khalil, Ibrahim

2012-01-01

The aim of this paper is to present a service for blind and people with low vision to assist them to cross the street independently. The presented approach provides the user with significant information such as detection of pedestrian crossing signal from any point of view, when the pedestrian crossing signal light is green, the detection of dynamic and fixed obstacles, predictions of the movement of fellow pedestrians and information on objects which may intersect his path. Our approach is based on capturing multiple frames using a depth camera which is attached to a user's headgear. Currently a testbed system is built on a helmet and is connected to a laptop in the user's backpack. In this paper, we discussed efficiency of using Speeded-Up Robust Features (SURF) algorithm for object recognition for purposes of blind people assistance. The system predicts the movement of objects of interest to provide the user with information on the safest path to navigate and information on the surrounding area. Evaluation of this approach on real sequence video frames provides 90% of human detection and more than 80% for recognition of other related objects.

Improving Efficiency of Passive RFID Tag Anti-Collision Protocol Using Dynamic Frame Adjustment and Optimal Splitting.

PubMed

Memon, Muhammad Qasim; He, Jingsha; Yasir, Mirza Ammar; Memon, Aasma

2018-04-12

Radio frequency identification is a wireless communication technology, which enables data gathering and identifies recognition from any tagged object. The number of collisions produced during wireless communication would lead to a variety of problems including unwanted number of iterations and reader-induced idle slots, computational complexity in terms of estimation as well as recognition of the number of tags. In this work, dynamic frame adjustment and optimal splitting are employed together in the proposed algorithm. In the dynamic frame adjustment method, the length of frames is based on the quantity of tags to yield optimal efficiency. The optimal splitting method is conceived with smaller duration of idle slots using an optimal value for splitting level M o p t , where (M > 2), to vary slot sizes to get the minimal identification time for the idle slots. The application of the proposed algorithm offers the advantages of not going for the cumbersome estimation of the quantity of tags incurred and the size (number) of tags has no effect on its performance efficiency. Our experiment results show that using the proposed algorithm, the efficiency curve remains constant as the number of tags varies from 50 to 450, resulting in an overall theoretical gain in the efficiency of 0.032 compared to system efficiency of 0.441 and thus outperforming both dynamic binary tree slotted ALOHA (DBTSA) and binary splitting protocols.

ASERA: A spectrum eye recognition assistant for quasar spectra

NASA Astrophysics Data System (ADS)

Yuan, Hailong; Zhang, Haotong; Zhang, Yanxia; Lei, Yajuan; Dong, Yiqiao; Zhao, Yongheng

2013-11-01

Spectral type recognition is an important and fundamental step of large sky survey projects in the data reduction for further scientific research, like parameter measurement and statistic work. It tends out to be a huge job to manually inspect the low quality spectra produced from the massive spectroscopic survey, where the automatic pipeline may not provide confident type classification results. In order to improve the efficiency and effectiveness of spectral classification, we develop a semi-automated toolkit named ASERA, ASpectrum Eye Recognition Assistant. The main purpose of ASERA is to help the user in quasar spectral recognition and redshift measurement. Furthermore it can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). It is an interactive software allowing the user to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. It is an efficient and user-friendly toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope). The toolkit is available in two modes: a Java standalone application and a Java applet. ASERA has a few functions, such as wavelength and flux scale setting, zoom in and out, redshift estimation, spectral line identification, which helps user to improve the spectral classification accuracy especially for low quality spectra and reduce the labor of eyeball check. The function and performance of this tool is displayed through the recognition of several quasar spectra and a late type stellar spectrum from the LAMOST Pilot survey. Its future expansion capabilities are discussed.

Cognitive object recognition system (CORS)

NASA Astrophysics Data System (ADS)

Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

2010-04-01

We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

Shape and Color Features for Object Recognition Search

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Duong, Vu A.; Stubberud, Allen R.

2012-01-01

A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for object recognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the object recognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired object recognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future.

Recognition-induced forgetting of faces in visual long-term memory.

PubMed

Rugo, Kelsi F; Tamler, Kendall N; Woodman, Geoffrey F; Maxcey, Ashleigh M

2017-10-01

Despite more than a century of evidence that long-term memory for pictures and words are different, much of what we know about memory comes from studies using words. Recent research examining visual long-term memory has demonstrated that recognizing an object induces the forgetting of objects from the same category. This recognition-induced forgetting has been shown with a variety of everyday objects. However, unlike everyday objects, faces are objects of expertise. As a result, faces may be immune to recognition-induced forgetting. However, despite excellent memory for such stimuli, we found that faces were susceptible to recognition-induced forgetting. Our findings have implications for how models of human memory account for recognition-induced forgetting as well as represent objects of expertise and consequences for eyewitness testimony and the justice system.

Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

PubMed Central

Li, Xin; Guo, Rui; Chen, Chao

2014-01-01

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach. PMID:24961216

Object recognition of ladar with support vector machine

NASA Astrophysics Data System (ADS)

Sun, Jian-Feng; Li, Qi; Wang, Qi

2005-01-01

Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

A Robust and Device-Free System for the Recognition and Classification of Elderly Activities.

PubMed

Li, Fangmin; Al-Qaness, Mohammed Abdulaziz Aide; Zhang, Yong; Zhao, Bihai; Luan, Xidao

2016-12-01

Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise.

Coding visual features extracted from video sequences.

PubMed

Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2014-05-01

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

Robust Real-Time and Rotation-Invariant American Sign Language Alphabet Recognition Using Range Camera

NASA Astrophysics Data System (ADS)

Lahamy, H.; Lichti, D.

2012-07-01

The automatic interpretation of human gestures can be used for a natural interaction with computers without the use of mechanical devices such as keyboards and mice. The recognition of hand postures have been studied for many years. However, most of the literature in this area has considered 2D images which cannot provide a full description of the hand gestures. In addition, a rotation-invariant identification remains an unsolved problem even with the use of 2D images. The objective of the current study is to design a rotation-invariant recognition process while using a 3D signature for classifying hand postures. An heuristic and voxelbased signature has been designed and implemented. The tracking of the hand motion is achieved with the Kalman filter. A unique training image per posture is used in the supervised classification. The designed recognition process and the tracking procedure have been successfully evaluated. This study has demonstrated the efficiency of the proposed rotation invariant 3D hand posture signature which leads to 98.24% recognition rate after testing 12723 samples of 12 gestures taken from the alphabet of the American Sign Language.

Financial and workflow analysis of radiology reporting processes in the planning phase of implementation of a speech recognition system

NASA Astrophysics Data System (ADS)

Whang, Tom; Ratib, Osman M.; Umamoto, Kathleen; Grant, Edward G.; McCoy, Michael J.

2002-05-01

The goal of this study is to determine the financial value and workflow improvements achievable by replacing traditional transcription services with a speech recognition system in a large, university hospital setting. Workflow metrics were measured at two hospitals, one of which exclusively uses a transcription service (UCLA Medical Center), and the other which exclusively uses speech recognition (West Los Angeles VA Hospital). Workflow metrics include time spent per report (the sum of time spent interpreting, dictating, reviewing, and editing), transcription turnaround, and total report turnaround. Compared to traditional transcription, speech recognition resulted in radiologists spending 13-32% more time per report, but it also resulted in reduction of report turnaround time by 22-62% and reduction of marginal cost per report by 94%. The model developed here helps justify the introduction of a speech recognition system by showing that the benefits of reduced operating costs and decreased turnaround time outweigh the cost of increased time spent per report. Whether the ultimate goal is to achieve a financial objective or to improve operational efficiency, it is important to conduct a thorough analysis of workflow before implementation.

Feedforward object-vision models only tolerate small image variations compared to human

PubMed Central

Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2014-01-01

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e., briefly presented masked stimuli with complex image variations), human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modeling. We show that this approach is not of significant help in solving the computational crux of object recognition (i.e., invariant object recognition) when the identity-preserving image variations become more complex. PMID:25100986

Automatic anatomy recognition on CT images with pathology

NASA Astrophysics Data System (ADS)

Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Decreased acetylcholine release delays the consolidation of object recognition memory.

PubMed

De Jaeger, Xavier; Cammarota, Martín; Prado, Marco A M; Izquierdo, Iván; Prado, Vania F; Pereira, Grace S

2013-02-01

Acetylcholine (ACh) is important for different cognitive functions such as learning, memory and attention. The release of ACh depends on its vesicular loading by the vesicular acetylcholine transporter (VAChT). It has been demonstrated that VAChT expression can modulate object recognition memory. However, the role of VAChT expression on object recognition memory persistence still remains to be understood. To address this question we used distinct mouse lines with reduced expression of VAChT, as well as pharmacological manipulations of the cholinergic system. We showed that reduction of cholinergic tone impairs object recognition memory measured at 24h. Surprisingly, object recognition memory, measured at 4 days after training, was impaired by substantial, but not moderate, reduction in VAChT expression. Our results suggest that levels of acetylcholine release strongly modulate object recognition memory consolidation and appear to be of particular importance for memory persistence 4 days after training. Copyright © 2012 Elsevier B.V. All rights reserved.

Intraperirhinal cortex administration of the synthetic cannabinoid, HU210, disrupts object recognition memory in rats.

PubMed

Sticht, Martin A; Jacklin, Derek L; Mechoulam, Raphael; Parker, Linda A; Winters, Boyer D

2015-03-25

Cannabinoids disrupt learning and memory in human and nonhuman participants. Object recognition memory, which is particularly susceptible to the impairing effects of cannabinoids, relies critically on the perirhinal cortex (PRh); however, to date, the effects of cannabinoids within PRh have not been assessed. In the present study, we evaluated the effects of localized administration of the synthetic cannabinoid, HU210 (0.01, 1.0 μg/hemisphere), into PRh on spontaneous object recognition in Long-Evans rats. Animals received intra-PRh infusions of HU210 before the sample phase, and object recognition memory was assessed at various delays in a subsequent retention test. We found that presample intra-PRh HU210 dose dependently (1.0 μg but not 0.01 μg) interfered with spontaneous object recognition performance, exerting an apparently more pronounced effect when memory demands were increased. These novel findings show that cannabinoid agonists in PRh disrupt object recognition memory. Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

Breaking object correspondence across saccades impairs object recognition: The role of color and luminance.

PubMed

Poth, Christian H; Schneider, Werner X

2016-09-01

Rapid saccadic eye movements bring the foveal region of the eye's retina onto objects for high-acuity vision. Saccades change the location and resolution of objects' retinal images. To perceive objects as visually stable across saccades, correspondence between the objects before and after the saccade must be established. We have previously shown that breaking object correspondence across the saccade causes a decrement in object recognition (Poth, Herwig, & Schneider, 2015). Color and luminance can establish object correspondence, but it is unknown how these surface features contribute to transsaccadic visual processing. Here, we investigated whether changing the surface features color-and-luminance and color alone across saccades impairs postsaccadic object recognition. Participants made saccades to peripheral objects, which either maintained or changed their surface features across the saccade. After the saccade, participants briefly viewed a letter within the saccade target object (terminated by a pattern mask). Postsaccadic object recognition was assessed as participants' accuracy in reporting the letter. Experiment A used the colors green and red with different luminances as surface features, Experiment B blue and yellow with approximately the same luminances. Changing the surface features across the saccade deteriorated postsaccadic object recognition in both experiments. These findings reveal a link between object recognition and object correspondence relying on the surface features colors and luminance, which is currently not addressed in theories of transsaccadic perception. We interpret the findings within a recent theory ascribing this link to visual attention (Schneider, 2013).

Rapid effects of dorsal hippocampal G-protein coupled estrogen receptor on learning in female mice.

PubMed

Lymer, Jennifer; Robinson, Alana; Winters, Boyer D; Choleris, Elena

2017-03-01

Through rapid mechanisms of action, estrogens affect learning and memory processes. It has been shown that 17β-estradiol and an Estrogen Receptor (ER) α agonist enhances performance in social recognition, object recognition, and object placement tasks when administered systemically or infused in the dorsal hippocampus. In contrast, systemic and dorsal hippocampal ERβ activation only promote spatial learning. In addition, 17β-estradiol, the ERα and the G-protein coupled estrogen receptor (GPER) agonists increase dendritic spine density in the CA1 hippocampus. Recently, we have shown that selective systemic activation of the GPER also rapidly facilitated social recognition, object recognition, and object placement learning in female mice. Whether activation the GPER specifically in the dorsal hippocampus can also rapidly improve learning and memory prior to acquisition is unknown. Here, we investigated the rapid effects of infusion of the GPER agonist, G-1 (dose: 50nM, 100nM, 200nM), in the dorsal hippocampus on social recognition, object recognition, and object placement learning tasks in home cage. These paradigms were completed within 40min, which is within the range of rapid estrogenic effects. Dorsal hippocampal administration of G-1 improved social (doses: 50nM, 200nM G-1) and object (dose: 200nM G-1) recognition with no effect on object placement. Additionally, when spatial cues were minimized by testing in a Y-apparatus, G-1 administration promoted social (doses: 100nM, 200nM G-1) and object (doses: 50nM, 100nM, 200nM G-1) recognition. Therefore, like ERα, the GPER in the hippocampus appears to be sufficient for the rapid facilitation of social and object recognition in female mice, but not for the rapid facilitation of object placement learning. Thus, the GPER in the dorsal hippocampus is involved in estrogenic mediation of learning and memory and these effects likely occur through rapid signalling mechanisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Vanderbilt Expertise Test Reveals Domain-General and Domain-Specific Sex Effects in Object Recognition

PubMed Central

McGugin, Rankin W.; Richler, Jennifer J.; Herzmann, Grit; Speegle, Magen; Gauthier, Isabel

2012-01-01

Individual differences in face recognition are often contrasted with differences in object recognition using a single object category. Likewise, individual differences in perceptual expertise for a given object domain have typically been measured relative to only a single category baseline. In Experiment 1, we present a new test of object recognition, the Vanderbilt Expertise Test (VET), which is comparable in methods to the Cambridge Face Memory Task (CFMT) but uses eight different object categories. Principal component analysis reveals that the underlying structure of the VET can be largely explained by two independent factors, which demonstrate good reliability and capture interesting sex differences inherent in the VET structure. In Experiment 2, we show how the VET can be used to separate domain-specific from domain-general contributions to a standard measure of perceptual expertise. While domain-specific contributions are found for car matching for both men and women and for plane matching in men, women in this sample appear to use more domain-general strategies to match planes. In Experiment 3, we use the VET to demonstrate that holistic processing of faces predicts face recognition independently of general object recognition ability, which has a sex-specific contribution to face recognition. Overall, the results suggest that the VET is a reliable and valid measure of object recognition abilities and can measure both domain-general skills and domain-specific expertise, which were both found to depend on the sex of observers. PMID:22877929

Object, spatial and social recognition testing in a single test paradigm.

PubMed

Lian, Bin; Gao, Jun; Sui, Nan; Feng, Tingyong; Li, Ming

2018-07-01

Animals have the ability to process information about an object or a conspecific's physical features and location, and alter its behavior when such information is updated. In the laboratory, the object, spatial and social recognition are often studied in separate tasks, making them unsuitable to study the potential dissociations and interactions among various types of recognition memories. The present study introduced a single paradigm to detect the object and spatial recognition, and social recognition of a familiar and novel conspecific. Specifically, male and female Sprague-Dawley adult (>75 days old) or preadolescent (25-28 days old) rats were tested with two objects and one social partner in an open-field arena for four 10-min sessions with a 20-min inter-session interval. After the first sample session, a new object replaced one of the sampled objects in the second session, and the location of one of the old objects was changed in the third session. Finally, a new social partner was introduced in the fourth session and replaced the familiar one. Exploration time with each stimulus was recorded and measures for the three recognitions were calculated based on the discrimination ratio. Overall results show that adult and preadolescent male and female rats spent more time exploring the social partner than the objects, showing a clear preference for social stimulus over nonsocial one. They also did not differ in their abilities to discriminate a new object, a new location and a new social partner from a familiar one, and to recognize a familiar conspecific. Acute administration of MK-801 (a NMDA receptor antagonist, 0.025 and 0.10 mg/kg, i.p.) after the sample session dose-dependently reduced the total time spent on exploring the social partner and objects in the adult rats, and had a significantly larger effect in the females than in the males. MK-801 also dose-dependently increased motor activity. However, it did not alter the object, spatial and social recognitions. These findings indicate that the new triple recognition paradigm is capable of recording the object, spatial location and social recognition together and revealing potential sex and age differences. This paradigm is also useful for the study of object and social exploration concurrently and can be used to evaluate cognition-altering drugs in various stages of recognition memories. Copyright © 2018. Published by Elsevier Inc.

Score-Level Fusion of Phase-Based and Feature-Based Fingerprint Matching Algorithms

NASA Astrophysics Data System (ADS)

Ito, Koichi; Morita, Ayumi; Aoki, Takafumi; Nakajima, Hiroshi; Kobayashi, Koji; Higuchi, Tatsuo

This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. In our previous work, we have already proposed an efficient fingerprint recognition algorithm using Phase-Only Correlation (POC), and developed commercial fingerprint verification units for access control applications. The use of Fourier phase information of fingerprint images makes it possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. This paper presents an idea of improving the performance of POC-based fingerprint matching by combining it with feature-based matching, where feature-based matching is introduced in order to improve recognition efficiency for images with nonlinear distortion. Experimental evaluation using two different types of fingerprint image databases demonstrates efficient recognition performance of the combination of the POC-based algorithm and the feature-based algorithm.

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

PubMed Central

Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo

2015-01-01

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

HWDA: A coherence recognition and resolution algorithm for hybrid web data aggregation

NASA Astrophysics Data System (ADS)

Guo, Shuhang; Wang, Jian; Wang, Tong

2017-09-01

Aiming at the object confliction recognition and resolution problem for hybrid distributed data stream aggregation, a distributed data stream object coherence solution technology is proposed. Firstly, the framework was defined for the object coherence conflict recognition and resolution, named HWDA. Secondly, an object coherence recognition technology was proposed based on formal language description logic and hierarchical dependency relationship between logic rules. Thirdly, a conflict traversal recognition algorithm was proposed based on the defined dependency graph. Next, the conflict resolution technology was prompted based on resolution pattern matching including the definition of the three types of conflict, conflict resolution matching pattern and arbitration resolution method. At last, the experiment use two kinds of web test data sets to validate the effect of application utilizing the conflict recognition and resolution technology of HWDA.

Adaptive learning compressive tracking based on Markov location prediction

NASA Astrophysics Data System (ADS)

Zhou, Xingyu; Fu, Dongmei; Yang, Tao; Shi, Yanan

2017-03-01

Object tracking is an interdisciplinary research topic in image processing, pattern recognition, and computer vision which has theoretical and practical application value in video surveillance, virtual reality, and automatic navigation. Compressive tracking (CT) has many advantages, such as efficiency and accuracy. However, when there are object occlusion, abrupt motion and blur, similar objects, and scale changing, the CT has the problem of tracking drift. We propose the Markov object location prediction to get the initial position of the object. Then CT is used to locate the object accurately, and the classifier parameter adaptive updating strategy is given based on the confidence map. At the same time according to the object location, extract the scale features, which is able to deal with object scale variations effectively. Experimental results show that the proposed algorithm has better tracking accuracy and robustness than current advanced algorithms and achieves real-time performance.

A Novel Energy-Efficient Approach for Human Activity Recognition.

PubMed

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

2017-09-08

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Research on improving image recognition robustness by combining multiple features with associative memory

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

Vision-based obstacle recognition system for automated lawn mower robot development

NASA Astrophysics Data System (ADS)

Mohd Zin, Zalhan; Ibrahim, Ratnawati

2011-06-01

Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system's performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.

Human recognition based on head-shoulder contour extraction and BP neural network

NASA Astrophysics Data System (ADS)

Kong, Xiao-fang; Wang, Xiu-qin; Gu, Guohua; Chen, Qian; Qian, Wei-xian

2014-11-01

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target's contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

Post-Training Reversible Inactivation of the Hippocampus Enhances Novel Object Recognition Memory

ERIC Educational Resources Information Center

Oliveira, Ana M. M.; Hawk, Joshua D.; Abel, Ted; Havekes, Robbert

2010-01-01

Research on the role of the hippocampus in object recognition memory has produced conflicting results. Previous studies have used permanent hippocampal lesions to assess the requirement for the hippocampus in the object recognition task. However, permanent hippocampal lesions may impact performance through effects on processes besides memory…

Shape and texture fused recognition of flying targets

NASA Astrophysics Data System (ADS)

Kovács, Levente; Utasi, Ákos; Kovács, Andrea; Szirányi, Tamás

2011-06-01

This paper presents visual detection and recognition of flying targets (e.g. planes, missiles) based on automatically extracted shape and object texture information, for application areas like alerting, recognition and tracking. Targets are extracted based on robust background modeling and a novel contour extraction approach, and object recognition is done by comparisons to shape and texture based query results on a previously gathered real life object dataset. Application areas involve passive defense scenarios, including automatic object detection and tracking with cheap commodity hardware components (CPU, camera and GPS).

Development of novel tasks for studying view-invariant object recognition in rodents: Sensitivity to scopolamine.

PubMed

Mitchnick, Krista A; Wideman, Cassidy E; Huff, Andrew E; Palmer, Daniel; McNaughton, Bruce L; Winters, Boyer D

2018-05-15

The capacity to recognize objects from different view-points or angles, referred to as view-invariance, is an essential process that humans engage in daily. Currently, the ability to investigate the neurobiological underpinnings of this phenomenon is limited, as few ethologically valid view-invariant object recognition tasks exist for rodents. Here, we report two complementary, novel view-invariant object recognition tasks in which rodents physically interact with three-dimensional objects. Prior to experimentation, rats and mice were given extensive experience with a set of 'pre-exposure' objects. In a variant of the spontaneous object recognition task, novelty preference for pre-exposed or new objects was assessed at various angles of rotation (45°, 90° or 180°); unlike control rodents, for whom the objects were novel, rats and mice tested with pre-exposed objects did not discriminate between rotated and un-rotated objects in the choice phase, indicating substantial view-invariant object recognition. Secondly, using automated operant touchscreen chambers, rats were tested on pre-exposed or novel objects in a pairwise discrimination task, where the rewarded stimulus (S+) was rotated (180°) once rats had reached acquisition criterion; rats tested with pre-exposed objects re-acquired the pairwise discrimination following S+ rotation more effectively than those tested with new objects. Systemic scopolamine impaired performance on both tasks, suggesting involvement of acetylcholine at muscarinic receptors in view-invariant object processing. These tasks present novel means of studying the behavioral and neural bases of view-invariant object recognition in rodents. Copyright © 2018 Elsevier B.V. All rights reserved.

Incidental Memory of Younger and Older Adults for Objects Encountered in a Real World Context

PubMed Central

Qin, Xiaoyan; Bochsler, Tiana M.; Aizpurua, Alaitz; Cheong, Allen M. Y.; Koutstaal, Wilma; Legge, Gordon E.

2014-01-01

Effects of context on the perception of, and incidental memory for, real-world objects have predominantly been investigated in younger individuals, under conditions involving a single static viewpoint. We examined the effects of prior object context and object familiarity on both older and younger adults’ incidental memory for real objects encountered while they traversed a conference room. Recognition memory for context-typical and context-atypical objects was compared with a third group of unfamiliar objects that were not readily named and that had no strongly associated context. Both older and younger adults demonstrated a typicality effect, showing significantly lower 2-alternative-forced-choice recognition of context-typical than context-atypical objects; for these objects, the recognition of older adults either significantly exceeded, or numerically surpassed, that of younger adults. Testing-awareness elevated recognition but did not interact with age or with object type. Older adults showed significantly higher recognition for context-atypical objects than for unfamiliar objects that had no prior strongly associated context. The observation of a typicality effect in both age groups is consistent with preserved semantic schemata processing in aging. The incidental recognition advantage of older over younger adults for the context-typical and context-atypical objects may reflect aging-related differences in goal-related processing, with older adults under comparatively more novel circumstances being more likely to direct their attention to the external environment, or age-related differences in top-down effortful distraction regulation, with older individuals’ attention more readily captured by salient objects in the environment. Older adults’ reduced recognition of unfamiliar objects compared to context-atypical objects may reflect possible age differences in contextually driven expectancy violations. The latter finding underscores the theoretical and methodological value of including a third type of objects–that are comparatively neutral with respect to their contextual associations–to help differentiate between contextual integration effects (for schema-consistent objects) and expectancy violations (for schema-inconsistent objects). PMID:24941065

Correlated activity supports efficient cortical processing

PubMed Central

Hung, Chou P.; Cui, Ding; Chen, Yueh-peng; Lin, Chia-pei; Levine, Matthew R.

2015-01-01

Visual recognition is a computational challenge that is thought to occur via efficient coding. An important concept is sparseness, a measure of coding efficiency. The prevailing view is that sparseness supports efficiency by minimizing redundancy and correlations in spiking populations. Yet, we recently reported that “choristers”, neurons that behave more similarly (have correlated stimulus preferences and spontaneous coincident spiking), carry more generalizable object information than uncorrelated neurons (“soloists”) in macaque inferior temporal (IT) cortex. The rarity of choristers (as low as 6% of IT neurons) indicates that they were likely missed in previous studies. Here, we report that correlation strength is distinct from sparseness (choristers are not simply broadly tuned neurons), that choristers are located in non-granular output layers, and that correlated activity predicts human visual search efficiency. These counterintuitive results suggest that a redundant correlational structure supports efficient processing and behavior. PMID:25610392

Neuropeptide S interacts with the basolateral amygdala noradrenergic system in facilitating object recognition memory consolidation.

PubMed

Han, Ren-Wen; Xu, Hong-Jiao; Zhang, Rui-San; Wang, Pei; Chang, Min; Peng, Ya-Li; Deng, Ke-Yu; Wang, Rui

2014-01-01

The noradrenergic activity in the basolateral amygdala (BLA) was reported to be involved in the regulation of object recognition memory. As the BLA expresses high density of receptors for Neuropeptide S (NPS), we investigated whether the BLA is involved in mediating NPS's effects on object recognition memory consolidation and whether such effects require noradrenergic activity. Intracerebroventricular infusion of NPS (1nmol) post training facilitated 24-h memory in a mouse novel object recognition task. The memory-enhancing effect of NPS could be blocked by the β-adrenoceptor antagonist propranolol. Furthermore, post-training intra-BLA infusions of NPS (0.5nmol/side) improved 24-h memory for objects, which was impaired by co-administration of propranolol (0.5μg/side). Taken together, these results indicate that NPS interacts with the BLA noradrenergic system in improving object recognition memory during consolidation. Copyright © 2013 Elsevier Inc. All rights reserved.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Automated Recognition of Vegetation and Water Bodies on the Territory of Megacities in Satellite Images of Visible and IR Bands

NASA Astrophysics Data System (ADS)

Mozgovoy, Dmitry k.; Hnatushenko, Volodymyr V.; Vasyliev, Volodymyr V.

2018-04-01

Vegetation and water bodies are a fundamental element of urban ecosystems, and water mapping is critical for urban and landscape planning and management. A methodology of automated recognition of vegetation and water bodies on the territory of megacities in satellite images of sub-meter spatial resolution of the visible and IR bands is proposed. By processing multispectral images from the satellite SuperView-1A, vector layers of recognized plant and water objects were obtained. Analysis of the results of image processing showed a sufficiently high accuracy of the delineation of the boundaries of recognized objects and a good separation of classes. The developed methodology provides a significant increase of the efficiency and reliability of updating maps of large cities while reducing financial costs. Due to the high degree of automation, the proposed methodology can be implemented in the form of a geo-information web service functioning in the interests of a wide range of public services and commercial institutions.

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

In search of memory tests equivalent for experiments on animals and humans.

PubMed

Brodziak, Andrzej; Kołat, Estera; Różyk-Myrta, Alicja

2014-12-19

Older people often exhibit memory impairments. Contemporary demographic trends cause aging of the society. In this situation, it is important to conduct clinical trials of drugs and use training methods to improve memory capacity. Development of new memory tests requires experiments on animals and then clinical trials in humans. Therefore, we decided to review the assessment methods and search for tests that evaluate analogous cognitive processes in animals and humans. This review has enabled us to propose 2 pairs of tests of the efficiency of working memory capacity in animals and humans. We propose a basic set of methods for complex clinical trials of drugs and training methods to improve memory, consisting of 2 pairs of tests: 1) the Novel Object Recognition Test - Sternberg Item Recognition Test and 2) the Object-Location Test - Visuospatial Memory Test. We postulate that further investigations of methods that are equivalent in animals experiments and observations performed on humans are necessary.

The medial dorsal thalamic nucleus and the medial prefrontal cortex of the rat function together to support associative recognition and recency but not item recognition.

PubMed

Cross, Laura; Brown, Malcolm W; Aggleton, John P; Warburton, E Clea

2012-12-21

In humans recognition memory deficits, a typical feature of diencephalic amnesia, have been tentatively linked to mediodorsal thalamic nucleus (MD) damage. Animal studies have occasionally investigated the role of the MD in single-item recognition, but have not systematically analyzed its involvement in other recognition memory processes. In Experiment 1 rats with bilateral excitotoxic lesions in the MD or the medial prefrontal cortex (mPFC) were tested in tasks that assessed single-item recognition (novel object preference), associative recognition memory (object-in-place), and recency discrimination (recency memory task). Experiment 2 examined the functional importance of the interactions between the MD and mPFC using disconnection techniques. Unilateral excitotoxic lesions were placed in both the MD and the mPFC in either the same (MD + mPFC Ipsi) or opposite hemispheres (MD + mPFC Contra group). Bilateral lesions in the MD or mPFC impaired object-in-place and recency memory tasks, but had no effect on novel object preference. In Experiment 2 the MD + mPFC Contra group was significantly impaired in the object-in-place and recency memory tasks compared with the MD + mPFC Ipsi group, but novel object preference was intact. Thus, connections between the MD and mPFC are critical for recognition memory when the discriminations involve associative or recency information. However, the rodent MD is not necessary for single-item recognition memory.

Vision-based object detection and recognition system for intelligent vehicles

NASA Astrophysics Data System (ADS)

Ran, Bin; Liu, Henry X.; Martono, Wilfung

1999-01-01

Recently, a proactive crash mitigation system is proposed to enhance the crash avoidance and survivability of the Intelligent Vehicles. Accurate object detection and recognition system is a prerequisite for a proactive crash mitigation system, as system component deployment algorithms rely on accurate hazard detection, recognition, and tracking information. In this paper, we present a vision-based approach to detect and recognize vehicles and traffic signs, obtain their information, and track multiple objects by using a sequence of color images taken from a moving vehicle. The entire system consist of two sub-systems, the vehicle detection and recognition sub-system and traffic sign detection and recognition sub-system. Both of the sub- systems consist of four models: object detection model, object recognition model, object information model, and object tracking model. In order to detect potential objects on the road, several features of the objects are investigated, which include symmetrical shape and aspect ratio of a vehicle and color and shape information of the signs. A two-layer neural network is trained to recognize different types of vehicles and a parameterized traffic sign model is established in the process of recognizing a sign. Tracking is accomplished by combining the analysis of single image frame with the analysis of consecutive image frames. The analysis of the single image frame is performed every ten full-size images. The information model will obtain the information related to the object, such as time to collision for the object vehicle and relative distance from the traffic sings. Experimental results demonstrated a robust and accurate system in real time object detection and recognition over thousands of image frames.

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 3 2012-01-01 2012-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 3 2014-01-01 2014-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 3 2011-01-01 2011-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 3 2013-01-01 2013-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

10 CFR 431.19 - Department of Energy recognition of accreditation bodies.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 3 2010-01-01 2010-01-01 false Department of Energy recognition of accreditation bodies. 431.19 Section 431.19 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR... Methods of Determining Efficiency § 431.19 Department of Energy recognition of accreditation bodies. (a...

The Consolidation of Object and Context Recognition Memory Involve Different Regions of the Temporal Lobe

ERIC Educational Resources Information Center

Balderas, Israela; Rodriguez-Ortiz, Carlos J.; Salgado-Tonda, Paloma; Chavez-Hurtado, Julio; McGaugh, James L.; Bermudez-Rattoni, Federico

2008-01-01

These experiments investigated the involvement of several temporal lobe regions in consolidation of recognition memory. Anisomycin, a protein synthesis inhibitor, was infused into the hippocampus, perirhinal cortex, insular cortex, or basolateral amygdala of rats immediately after the sample phase of object or object-in-context recognition memory…

Object recognition contributions to figure-ground organization: operations on outlines and subjective contours.

PubMed

Peterson, M A; Gibson, B S

1994-11-01

In previous research, replicated here, we found that some object recognition processes influence figure-ground organization. We have proposed that these object recognition processes operate on edges (or contours) detected early in visual processing, rather than on regions. Consistent with this proposal, influences from object recognition on figure-ground organization were previously observed in both pictures and stereograms depicting regions of different luminance, but not in random-dot stereograms, where edges arise late in processing (Peterson & Gibson, 1993). In the present experiments, we examined whether or not two other types of contours--outlines and subjective contours--enable object recognition influences on figure-ground organization. For both types of contours we observed a pattern of effects similar to that originally obtained with luminance edges. The results of these experiments are valuable for distinguishing between alternative views of the mechanisms mediating object recognition influences on figure-ground organization. In addition, in both Experiments 1 and 2, fixated regions were seen as figure longer than nonfixated regions, suggesting that fixation location must be included among the variables relevant to figure-ground organization.

Changes in Visual Object Recognition Precede the Shape Bias in Early Noun Learning

PubMed Central

Yee, Meagan; Jones, Susan S.; Smith, Linda B.

2012-01-01

Two of the most formidable skills that characterize human beings are language and our prowess in visual object recognition. They may also be developmentally intertwined. Two experiments, a large sample cross-sectional study and a smaller sample 6-month longitudinal study of 18- to 24-month-olds, tested a hypothesized developmental link between changes in visual object representation and noun learning. Previous findings in visual object recognition indicate that children’s ability to recognize common basic level categories from sparse structural shape representations of object shape emerges between the ages of 18 and 24 months, is related to noun vocabulary size, and is lacking in children with language delay. Other research shows in artificial noun learning tasks that during this same developmental period, young children systematically generalize object names by shape, that this shape bias predicts future noun learning, and is lacking in children with language delay. The two experiments examine the developmental relation between visual object recognition and the shape bias for the first time. The results show that developmental changes in visual object recognition systematically precede the emergence of the shape bias. The results suggest a developmental pathway in which early changes in visual object recognition that are themselves linked to category learning enable the discovery of higher-order regularities in category structure and thus the shape bias in novel noun learning tasks. The proposed developmental pathway has implications for understanding the role of specific experience in the development of both visual object recognition and the shape bias in early noun learning. PMID:23227015

Method of synthesized phase objects for pattern recognition with rotation invariance

NASA Astrophysics Data System (ADS)

Ostroukh, Alexander P.; Butok, Alexander M.; Shvets, Rostislav A.; Yezhov, Pavel V.; Kim, Jin-Tae; Kuzmenko, Alexander V.

2015-11-01

We present a development of the method of synthesized phase objects (SPO-method) [1] for the rotation-invariant pattern recognition. For the standard method of recognition and the SPO-method, the comparison of the parameters of correlation signals for a number of amplitude objects is executed at the realization of a rotation in an optical-digital correlator with the joint Fourier transformation. It is shown that not only the invariance relative to a rotation at a realization of the joint correlation for synthesized phase objects (SP-objects) but also the main advantage of the method of SP-objects over the reference one such as the unified δ-like recognition signal with the largest possible signal-to-noise ratio independent of the type of an object are attained.

Quantifying the Energy Efficiency of Object Recognition and Optical Flow

DTIC Science & Technology

2014-03-28

other linear solvers, such as conjugate- gradient (CG), preconditioned conjugate-gradient (PCG), and red-black Gauss Seidel (RB). We have also... Seidel , and conjugate gradient solvers. We are interested in the energy it takes to get a given solution quality. In Figure 6, we plot the quality of...in terms of Joules. Conversely, our implementation of red-black Gauss Seidel proves to be very inefficient when we consider Joules instead of just

Short temporal asynchrony disrupts visual object recognition

PubMed Central

Singer, Jedediah M.; Kreiman, Gabriel

2014-01-01

Humans can recognize objects and scenes in a small fraction of a second. The cascade of signals underlying rapid recognition might be disrupted by temporally jittering different parts of complex objects. Here we investigated the time course over which shape information can be integrated to allow for recognition of complex objects. We presented fragments of object images in an asynchronous fashion and behaviorally evaluated categorization performance. We observed that visual recognition was significantly disrupted by asynchronies of approximately 30 ms, suggesting that spatiotemporal integration begins to break down with even small deviations from simultaneity. However, moderate temporal asynchrony did not completely obliterate recognition; in fact, integration of visual shape information persisted even with an asynchrony of 100 ms. We describe the data with a concise model based on the dynamic reduction of uncertainty about what image was presented. These results emphasize the importance of timing in visual processing and provide strong constraints for the development of dynamical models of visual shape recognition. PMID:24819738

Modeling recall memory for emotional objects in Alzheimer's disease.

PubMed

Sundstrøm, Martin

2011-07-01

To examine whether emotional memory (EM) of objects with self-reference in Alzheimer's disease (AD) can be modeled with binomial logistic regression in a free recall and an object recognition test to predict EM enhancement. Twenty patients with AD and twenty healthy controls were studied. Six objects (three presented as gifts) were shown to each participant. Ten minutes later, a free recall and a recognition test were applied. The recognition test had target-objects mixed with six similar distracter objects. Participants were asked to name any object in the recall test and identify each object in the recognition test as known or unknown. The total of gift objects recalled in AD patients (41.6%) was larger than neutral objects (13.3%) and a significant EM recall effect for gifts was found (Wilcoxon: p < .003). EM was not found for recognition in AD patients due to a ceiling effect. Healthy older adults scored overall higher in recall and recognition but showed no EM enhancement due to a ceiling effect. A logistic regression showed that likelihood of emotional recall memory can be modeled as a function of MMSE score (p < .014) and object status (p < .0001) as gift or non-gift. Recall memory was enhanced in AD patients for emotional objects indicating that EM in mild to moderate AD although impaired can be provoked with strong emotional load. The logistic regression model suggests that EM declines with the progression of AD rather than disrupts and may be a useful tool for evaluating magnitude of emotional load.

Novel images and novel locations of familiar images as sensitive translational cognitive tests in humans.

PubMed

Raber, Jacob

2015-05-15

Object recognition is a sensitive cognitive test to detect effects of genetic and environmental factors on cognition in rodents. There are various versions of object recognition that have been used since the original test was reported by Ennaceur and Delacour in 1988. There are nonhuman primate and human primate versions of object recognition as well, allowing cross-species comparisons. As no language is required for test performance, object recognition is a very valuable test for human research studies in distinct parts of the world, including areas where there might be less years of formal education. The main focus of this review is to illustrate how object recognition can be used to assess cognition in humans under normal physiological and neurological conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

PubMed Central

Liu, Zhong; Zhao, Changchen; Wu, Xingming; Chen, Weihai

2017-01-01

RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. PMID:28245553

Trajectory Recognition as the Basis for Object Individuation: A Functional Model of Object File Instantiation and Object-Token Encoding

PubMed Central

Fields, Chris

2011-01-01

The perception of persisting visual objects is mediated by transient intermediate representations, object files, that are instantiated in response to some, but not all, visual trajectories. The standard object file concept does not, however, provide a mechanism sufficient to account for all experimental data on visual object persistence, object tracking, and the ability to perceive spatially disconnected stimuli as continuously existing objects. Based on relevant anatomical, functional, and developmental data, a functional model is constructed that bases visual object individuation on the recognition of temporal sequences of apparent center-of-mass positions that are specifically identified as trajectories by dedicated “trajectory recognition networks” downstream of the medial–temporal motion-detection area. This model is shown to account for a wide range of data, and to generate a variety of testable predictions. Individual differences in the recognition, abstraction, and encoding of trajectory information are expected to generate distinct object persistence judgments and object recognition abilities. Dominance of trajectory information over feature information in stored object tokens during early infancy, in particular, is expected to disrupt the ability to re-identify human and other individuals across perceptual episodes, and lead to developmental outcomes with characteristics of autism spectrum disorders. PMID:21716599

Efficient search for a face by chimpanzees (Pan troglodytes).

PubMed

Tomonaga, Masaki; Imura, Tomoko

2015-07-16

The face is quite an important stimulus category for human and nonhuman primates in their social lives. Recent advances in comparative-cognitive research clearly indicate that chimpanzees and humans process faces in a special manner; that is, using holistic or configural processing. Both species exhibit the face-inversion effect in which the inverted presentation of a face deteriorates their perception and recognition. Furthermore, recent studies have shown that humans detect human faces among non-facial objects rapidly. We report that chimpanzees detected chimpanzee faces among non-facial objects quite efficiently. This efficient search was not limited to own-species faces. They also found human adult and baby faces--but not monkey faces--efficiently. Additional testing showed that a front-view face was more readily detected than a profile, suggesting the important role of eye-to-eye contact. Chimpanzees also detected a photograph of a banana as efficiently as a face, but a further examination clearly indicated that the banana was detected mainly due to a low-level feature (i.e., color). Efficient face detection was hampered by an inverted presentation, suggesting that configural processing of faces is a critical element of efficient face detection in both species. This conclusion was supported by a simple simulation experiment using the saliency model.

Efficient search for a face by chimpanzees (Pan troglodytes)

PubMed Central

Tomonaga, Masaki; Imura, Tomoko

2015-01-01

The face is quite an important stimulus category for human and nonhuman primates in their social lives. Recent advances in comparative-cognitive research clearly indicate that chimpanzees and humans process faces in a special manner; that is, using holistic or configural processing. Both species exhibit the face-inversion effect in which the inverted presentation of a face deteriorates their perception and recognition. Furthermore, recent studies have shown that humans detect human faces among non-facial objects rapidly. We report that chimpanzees detected chimpanzee faces among non-facial objects quite efficiently. This efficient search was not limited to own-species faces. They also found human adult and baby faces-but not monkey faces-efficiently. Additional testing showed that a front-view face was more readily detected than a profile, suggesting the important role of eye-to-eye contact. Chimpanzees also detected a photograph of a banana as efficiently as a face, but a further examination clearly indicated that the banana was detected mainly due to a low-level feature (i.e., color). Efficient face detection was hampered by an inverted presentation, suggesting that configural processing of faces is a critical element of efficient face detection in both species. This conclusion was supported by a simple simulation experiment using the saliency model. PMID:26180944

A method of object recognition for single pixel imaging

NASA Astrophysics Data System (ADS)

Li, Boxuan; Zhang, Wenwen

2018-01-01

Computational ghost imaging(CGI), utilizing a single-pixel detector, has been extensively used in many fields. However, in order to achieve a high-quality reconstructed image, a large number of iterations are needed, which limits the flexibility of using CGI in practical situations, especially in the field of object recognition. In this paper, we purpose a method utilizing the feature matching to identify the number objects. In the given system, approximately 90% of accuracy of recognition rates can be achieved, which provides a new idea for the application of single pixel imaging in the field of object recognition

Dopamine D1 receptor activation leads to object recognition memory in a coral reef fish.

PubMed

Hamilton, Trevor J; Tresguerres, Martin; Kline, David I

2017-07-01

Object recognition memory is the ability to identify previously seen objects and is an adaptive mechanism that increases survival for many species throughout the animal kingdom. Previously believed to be possessed by only the highest order mammals, it is now becoming clear that fish are also capable of this type of memory formation. Similar to the mammalian hippocampus, the dorsolateral pallium regulates distinct memory processes and is modulated by neurotransmitters such as dopamine. Caribbean bicolour damselfish ( Stegastes partitus ) live in complex environments dominated by coral reef structures and thus likely possess many types of complex memory abilities including object recognition. This study used a novel object recognition test in which fish were first presented two identical objects, then after a retention interval of 10 min with no objects, the fish were presented with a novel object and one of the objects they had previously encountered in the first trial. We demonstrate that the dopamine D 1 -receptor agonist (SKF 38393) induces the formation of object recognition memories in these fish. Thus, our results suggest that dopamine-receptor mediated enhancement of spatial memory formation in fish represents an evolutionarily conserved mechanism in vertebrates. © 2017 The Author(s).

Superior voice recognition in a patient with acquired prosopagnosia and object agnosia.

PubMed

Hoover, Adria E N; Démonet, Jean-François; Steeves, Jennifer K E

2010-11-01

Anecdotally, it has been reported that individuals with acquired prosopagnosia compensate for their inability to recognize faces by using other person identity cues such as hair, gait or the voice. Are they therefore superior at the use of non-face cues, specifically voices, to person identity? Here, we empirically measure person and object identity recognition in a patient with acquired prosopagnosia and object agnosia. We quantify person identity (face and voice) and object identity (car and horn) recognition for visual, auditory, and bimodal (visual and auditory) stimuli. The patient is unable to recognize faces or cars, consistent with his prosopagnosia and object agnosia, respectively. He is perfectly able to recognize people's voices and car horns and bimodal stimuli. These data show a reverse shift in the typical weighting of visual over auditory information for audiovisual stimuli in a compromised visual recognition system. Moreover, the patient shows selectively superior voice recognition compared to the controls revealing that two different stimulus domains, persons and objects, may not be equally affected by sensory adaptation effects. This also implies that person and object identity recognition are processed in separate pathways. These data demonstrate that an individual with acquired prosopagnosia and object agnosia can compensate for the visual impairment and become quite skilled at using spared aspects of sensory processing. In the case of acquired prosopagnosia it is advantageous to develop a superior use of voices for person identity recognition in everyday life. Copyright © 2010 Elsevier Ltd. All rights reserved.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

PubMed Central

Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

2015-01-01

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

Detection and recognition of targets by using signal polarization properties

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Peralta-Fabi, Ricardo; Popov, Anatoly V.; Babakov, Mikhail F.

1999-08-01

The quality of radar target recognition can be enhanced by exploiting its polarization signatures. A specialized X-band polarimetric radar was used for target recognition in experimental investigations. The following polarization characteristics connected to the object geometrical properties were investigated: the amplitudes of the polarization matrix elements; an anisotropy coefficient; depolarization coefficient; asymmetry coefficient; the energy of a backscattering signal; object shape factor. A large quantity of polarimetric radar data was measured and processed to form a database of different object and different weather conditions. The histograms of polarization signatures were approximated by a Nakagami distribution, then used for real- time target recognition. The Neyman-Pearson criterion was used for the target detection, and the criterion of the maximum of a posterior probability was used for recognition problem. Some results of experimental verification of pattern recognition and detection of objects with different electrophysical and geometrical characteristics urban in clutter are presented in this paper.

Learned Non-Rigid Object Motion is a View-Invariant Cue to Recognizing Novel Objects

PubMed Central

Chuang, Lewis L.; Vuong, Quoc C.; Bülthoff, Heinrich H.

2012-01-01

There is evidence that observers use learned object motion to recognize objects. For instance, studies have shown that reversing the learned direction in which a rigid object rotated in depth impaired recognition accuracy. This motion reversal can be achieved by playing animation sequences of moving objects in reverse frame order. In the current study, we used this sequence-reversal manipulation to investigate whether observers encode the motion of dynamic objects in visual memory, and whether such dynamic representations are encoded in a way that is dependent on the viewing conditions. Participants first learned dynamic novel objects, presented as animation sequences. Following learning, they were then tested on their ability to recognize these learned objects when their animation sequence was shown in the same sequence order as during learning or in the reverse sequence order. In Experiment 1, we found that non-rigid motion contributed to recognition performance; that is, sequence-reversal decreased sensitivity across different tasks. In subsequent experiments, we tested the recognition of non-rigidly deforming (Experiment 2) and rigidly rotating (Experiment 3) objects across novel viewpoints. Recognition performance was affected by viewpoint changes for both experiments. Learned non-rigid motion continued to contribute to recognition performance and this benefit was the same across all viewpoint changes. By comparison, learned rigid motion did not contribute to recognition performance. These results suggest that non-rigid motion provides a source of information for recognizing dynamic objects, which is not affected by changes to viewpoint. PMID:22661939

A Novel Energy-Efficient Approach for Human Activity Recognition

PubMed Central

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Tang, Biyu; Lu, Hai; Shi, Haibin

2017-01-01

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. PMID:28885560

Parallel and distributed computation for fault-tolerant object recognition

NASA Technical Reports Server (NTRS)

Wechsler, Harry

1988-01-01

The distributed associative memory (DAM) model is suggested for distributed and fault-tolerant computation as it relates to object recognition tasks. The fault-tolerance is with respect to geometrical distortions (scale and rotation), noisy inputs, occulsion/overlap, and memory faults. An experimental system was developed for fault-tolerant structure recognition which shows the feasibility of such an approach. The approach is futher extended to the problem of multisensory data integration and applied successfully to the recognition of colored polyhedral objects.

Visual Object Detection, Categorization, and Identification Tasks Are Associated with Different Time Courses and Sensitivities

ERIC Educational Resources Information Center

de la Rosa, Stephan; Choudhery, Rabia N.; Chatziastros, Astros

2011-01-01

Recent evidence suggests that the recognition of an object's presence and its explicit recognition are temporally closely related. Here we re-examined the time course (using a fine and a coarse temporal resolution) and the sensitivity of three possible component processes of visual object recognition. In particular, participants saw briefly…

An ERP Study on Self-Relevant Object Recognition

ERIC Educational Resources Information Center

Miyakoshi, Makoto; Nomura, Michio; Ohira, Hideki

2007-01-01

We performed an event-related potential study to investigate the self-relevance effect in object recognition. Three stimulus categories were prepared: SELF (participant's own objects), FAMILIAR (disposable and public objects, defined as objects with less-self-relevant familiarity), and UNFAMILIAR (others' objects). The participants' task was to…

Aging and solid shape recognition: Vision and haptics.

PubMed

Norman, J Farley; Cheeseman, Jacob R; Adkins, Olivia C; Cox, Andrea G; Rogers, Connor E; Dowell, Catherine J; Baxter, Michael W; Norman, Hideko F; Reyes, Cecia M

2015-10-01

The ability of 114 younger and older adults to recognize naturally-shaped objects was evaluated in three experiments. The participants viewed or haptically explored six randomly-chosen bell peppers (Capsicum annuum) in a study session and were later required to judge whether each of twelve bell peppers was "old" (previously presented during the study session) or "new" (not presented during the study session). When recognition memory was tested immediately after study, the younger adults' (Experiment 1) performance for vision and haptics was identical when the individual study objects were presented once. Vision became superior to haptics, however, when the individual study objects were presented multiple times. When 10- and 20-min delays (Experiment 2) were inserted in between study and test sessions, no significant differences occurred between vision and haptics: recognition performance in both modalities was comparable. When the recognition performance of older adults was evaluated (Experiment 3), a negative effect of age was found for visual shape recognition (younger adults' overall recognition performance was 60% higher). There was no age effect, however, for haptic shape recognition. The results of the present experiments indicate that the visual recognition of natural object shape is different from haptic recognition in multiple ways: visual shape recognition can be superior to that of haptics and is affected by aging, while haptic shape recognition is less accurate and unaffected by aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mice deficient for striatal Vesicular Acetylcholine Transporter (VAChT) display impaired short-term but normal long-term object recognition memory.

PubMed

Palmer, Daniel; Creighton, Samantha; Prado, Vania F; Prado, Marco A M; Choleris, Elena; Winters, Boyer D

2016-09-15

Substantial evidence implicates Acetylcholine (ACh) in the acquisition of object memories. While most research has focused on the role of the cholinergic basal forebrain and its cortical targets, there are additional cholinergic networks that may contribute to object recognition. The striatum contains an independent cholinergic network comprised of interneurons. In the current study, we investigated the role of this cholinergic signalling in object recognition using mice deficient for Vesicular Acetylcholine Transporter (VAChT) within interneurons of the striatum. We tested whether these striatal VAChT(D2-Cre-flox/flox) mice would display normal short-term (5 or 15min retention delay) and long-term (3h retention delay) object recognition memory. In a home cage object recognition task, male and female VAChT(D2-Cre-flox/flox) mice were impaired selectively with a 15min retention delay. When tested on an object location task, VAChT(D2-Cre-flox/flox) mice displayed intact spatial memory. Finally, when object recognition was tested in a Y-shaped apparatus, designed to minimize the influence of spatial and contextual cues, only females displayed impaired recognition with a 5min retention delay, but when males were challenged with a 15min retention delay, they were also impaired; neither males nor females were impaired with the 3h delay. The pattern of results suggests that striatal cholinergic transmission plays a role in the short-term memory for object features, but not spatial location. Copyright © 2016 Elsevier B.V. All rights reserved.

Modeling guidance and recognition in categorical search: bridging human and computer object detection.

PubMed

Zelinsky, Gregory J; Peng, Yifan; Berg, Alexander C; Samaras, Dimitris

2013-10-08

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery.

Modeling guidance and recognition in categorical search: Bridging human and computer object detection

PubMed Central

Zelinsky, Gregory J.; Peng, Yifan; Berg, Alexander C.; Samaras, Dimitris

2013-01-01

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery. PMID:24105460

Representation of 3-Dimenstional Objects by the Rat Perirhinal Cortex

PubMed Central

Burke, S.N.; Maurer, A.P.; Hartzell, A.L.; Nematollahi, S.; Uprety, A.; Wallace, J.L.; Barnes, C.A.

2012-01-01

The perirhinal cortex (PRC) is known to play an important role in object recognition. Little is known, however, regarding the activity of PRC neurons during the presentation of stimuli that are commonly used for recognition memory tasks in rodents, that is, 3-dimensional objects. Rats in the present study were exposed to 3-dimensional objects while they traversed a circular track for food reward. Under some behavioral conditions the track contained novel objects, familiar objects, or no objects. Approximately 38% of PRC neurons demonstrated ‘object fields’ (a selective increase in firing at the location of one or more objects). Although the rats spent more time exploring the objects when they were novel compared to familiar, indicating successful recognition memory, the proportion of object fields and the firing rates of PRC neurons were not affected by the rats’ previous experience with the objects. Together these data indicate that the activity of PRC cells is powerfully affected by the presence of objects while animals navigate through an environment, but under these conditions, the firing patterns are not altered by the relative novelty of objects during successful object recognition. PMID:22987680

The relationship between change detection and recognition of centrally attended objects in motion pictures.

PubMed

Angelone, Bonnie L; Levin, Daniel T; Simons, Daniel J

2003-01-01

Observers typically detect changes to central objects more readily than changes to marginal objects, but they sometimes miss changes to central, attended objects as well. However, even if observers do not report such changes, they may be able to recognize the changed object. In three experiments we explored change detection and recognition memory for several types of changes to central objects in motion pictures. Observers who failed to detect a change still performed at above chance levels on a recognition task in almost all conditions. In addition, observers who detected the change were no more accurate in their recognition than those who did not detect the change. Despite large differences in the detectability of changes across conditions, those observers who missed the change did not vary in their ability to recognize the changing object.

Recognition Of Complex Three Dimensional Objects Using Three Dimensional Moment Invariants

NASA Astrophysics Data System (ADS)

Sadjadi, Firooz A.

1985-01-01

A technique for the recognition of complex three dimensional objects is presented. The complex 3-D objects are represented in terms of their 3-D moment invariants, algebraic expressions that remain invariant independent of the 3-D objects' orientations and locations in the field of view. The technique of 3-D moment invariants has been used successfully for simple 3-D object recognition in the past. In this work we have extended this method for the representation of more complex objects. Two complex objects are represented digitally; their 3-D moment invariants have been calculated, and then the invariancy of these 3-D invariant moment expressions is verified by changing the orientation and the location of the objects in the field of view. The results of this study have significant impact on 3-D robotic vision, 3-D target recognition, scene analysis and artificial intelligence.

Effects of compression and individual variability on face recognition performance

NASA Astrophysics Data System (ADS)

McGarry, Delia P.; Arndt, Craig M.; McCabe, Steven A.; D'Amato, Donald P.

2004-08-01

The Enhanced Border Security and Visa Entry Reform Act of 2002 requires that the Visa Waiver Program be available only to countries that have a program to issue to their nationals machine-readable passports incorporating biometric identifiers complying with applicable standards established by the International Civil Aviation Organization (ICAO). In June 2002, the New Technologies Working Group of ICAO unanimously endorsed the use of face recognition (FR) as the globally interoperable biometric for machine-assisted identity confirmation with machine-readable travel documents (MRTDs), although Member States may elect to use fingerprint and/or iris recognition as additional biometric technologies. The means and formats are still being developed through which biometric information might be stored in the constrained space of integrated circuit chips embedded within travel documents. Such information will be stored in an open, yet unalterable and very compact format, probably as digitally signed and efficiently compressed images. The objective of this research is to characterize the many factors that affect FR system performance with respect to the legislated mandates concerning FR. A photograph acquisition environment and a commercial face recognition system have been installed at Mitretek, and over 1,400 images have been collected of volunteers. The image database and FR system are being used to analyze the effects of lossy image compression, individual differences, such as eyeglasses and facial hair, and the acquisition environment on FR system performance. Images are compressed by varying ratios using JPEG2000 to determine the trade-off points between recognition accuracy and compression ratio. The various acquisition factors that contribute to differences in FR system performance among individuals are also being measured. The results of this study will be used to refine and test efficient face image interchange standards that ensure highly accurate recognition, both for automated FR systems and human inspectors. Working within the M1-Biometrics Technical Committee of the InterNational Committee for Information Technology Standards (INCITS) organization, a standard face image format will be tested and submitted to organizations such as ICAO.

Exogenous temporal cues enhance recognition memory in an object-based manner.

PubMed

Ohyama, Junji; Watanabe, Katsumi

2010-11-01

Exogenous attention enhances the perception of attended items in both a space-based and an object-based manner. Exogenous attention also improves recognition memory for attended items in the space-based mode. However, it has not been examined whether object-based exogenous attention enhances recognition memory. To address this issue, we examined whether a sudden visual change in a task-irrelevant stimulus (an exogenous cue) would affect participants' recognition memory for items that were serially presented around a cued time. The results showed that recognition accuracy for an item was strongly enhanced when the visual cue occurred at the same location and time as the item (Experiments 1 and 2). The memory enhancement effect occurred when the exogenous visual cue and an item belonged to the same object (Experiments 3 and 4) and even when the cue was counterpredictive of the timing of an item to be asked about (Experiment 5). The present study suggests that an exogenous temporal cue automatically enhances the recognition accuracy for an item that is presented at close temporal proximity to the cue and that recognition memory enhancement occurs in an object-based manner.

Toward a unified model of face and object recognition in the human visual system

PubMed Central

Wallis, Guy

2013-01-01

Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects. PMID:23966963

Parts and Relations in Young Children's Shape-Based Object Recognition

ERIC Educational Resources Information Center

Augustine, Elaine; Smith, Linda B.; Jones, Susan S.

2011-01-01

The ability to recognize common objects from sparse information about geometric shape emerges during the same period in which children learn object names and object categories. Hummel and Biederman's (1992) theory of object recognition proposes that the geometric shapes of objects have two components--geometric volumes representing major object…

EMG-based speech recognition using hidden markov models with global control variables.

PubMed

Lee, Ki-Seung

2008-03-01

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.

Individual differences in cortical face selectivity predict behavioral performance in face recognition

PubMed Central

Huang, Lijie; Song, Yiying; Li, Jingguang; Zhen, Zonglei; Yang, Zetian; Liu, Jia

2014-01-01

In functional magnetic resonance imaging studies, object selectivity is defined as a higher neural response to an object category than other object categories. Importantly, object selectivity is widely considered as a neural signature of a functionally-specialized area in processing its preferred object category in the human brain. However, the behavioral significance of the object selectivity remains unclear. In the present study, we used the individual differences approach to correlate participants' face selectivity in the face-selective regions with their behavioral performance in face recognition measured outside the scanner in a large sample of healthy adults. Face selectivity was defined as the z score of activation with the contrast of faces vs. non-face objects, and the face recognition ability was indexed as the normalized residual of the accuracy in recognizing previously-learned faces after regressing out that for non-face objects in an old/new memory task. We found that the participants with higher face selectivity in the fusiform face area (FFA) and the occipital face area (OFA), but not in the posterior part of the superior temporal sulcus (pSTS), possessed higher face recognition ability. Importantly, the association of face selectivity in the FFA and face recognition ability cannot be accounted for by FFA response to objects or behavioral performance in object recognition, suggesting that the association is domain-specific. Finally, the association is reliable, confirmed by the replication from another independent participant group. In sum, our finding provides empirical evidence on the validity of using object selectivity as a neural signature in defining object-selective regions in the human brain. PMID:25071513

A reciprocal model of face recognition and autistic traits: evidence from an individual differences perspective.

PubMed

Halliday, Drew W R; MacDonald, Stuart W S; Scherf, K Suzanne; Sherf, Suzanne K; Tanaka, James W

2014-01-01

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals.

A Reciprocal Model of Face Recognition and Autistic Traits: Evidence from an Individual Differences Perspective

PubMed Central

Halliday, Drew W. R.; MacDonald, Stuart W. S.; Sherf, Suzanne K.; Tanaka, James W.

2014-01-01

Although not a core symptom of the disorder, individuals with autism often exhibit selective impairments in their face processing abilities. Importantly, the reciprocal connection between autistic traits and face perception has rarely been examined within the typically developing population. In this study, university participants from the social sciences, physical sciences, and humanities completed a battery of measures that assessed face, object and emotion recognition abilities, general perceptual-cognitive style, and sub-clinical autistic traits (the Autism Quotient (AQ)). We employed separate hierarchical multiple regression analyses to evaluate which factors could predict face recognition scores and AQ scores. Gender, object recognition performance, and AQ scores predicted face recognition behaviour. Specifically, males, individuals with more autistic traits, and those with lower object recognition scores performed more poorly on the face recognition test. Conversely, university major, gender and face recognition performance reliably predicted AQ scores. Science majors, males, and individuals with poor face recognition skills showed more autistic-like traits. These results suggest that the broader autism phenotype is associated with lower face recognition abilities, even among typically developing individuals. PMID:24853862

Mechanisms and neural basis of object and pattern recognition: a study with chess experts.

PubMed

Bilalić, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-11-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved object recognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific object recognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception.

Face Recognition Is Affected by Similarity in Spatial Frequency Range to a Greater Degree Than Within-Category Object Recognition

ERIC Educational Resources Information Center

Collin, Charles A.; Liu, Chang Hong; Troje, Nikolaus F.; McMullen, Patricia A.; Chaudhuri, Avi

2004-01-01

Previous studies have suggested that face identification is more sensitive to variations in spatial frequency content than object recognition, but none have compared how sensitive the 2 processes are to variations in spatial frequency overlap (SFO). The authors tested face and object matching accuracy under varying SFO conditions. Their results…

Face Memory and Object Recognition in Children with High-Functioning Autism or Asperger Syndrome and in Their Parents

ERIC Educational Resources Information Center

Kuusikko-Gauffin, Sanna; Jansson-Verkasalo, Eira; Carter, Alice; Pollock-Wurman, Rachel; Jussila, Katja; Mattila, Marja-Leena; Rahko, Jukka; Ebeling, Hanna; Pauls, David; Moilanen, Irma

2011-01-01

Children with Autism Spectrum Disorders (ASDs) have reported to have impairments in face, recognition and face memory, but intact object recognition and object memory. Potential abnormalities, in these fields at the family level of high-functioning children with ASD remains understudied despite, the ever-mounting evidence that ASDs are genetic and…

OPTICAL INFORMATION PROCESSING: Synthesis of an object recognition system based on the profile of the envelope of a laser pulse in pulsed lidars

NASA Astrophysics Data System (ADS)

Buryi, E. V.

1998-05-01

The main problems in the synthesis of an object recognition system, based on the principles of operation of neuron networks, are considered. Advantages are demonstrated of a hierarchical structure of the recognition algorithm. The use of reading of the amplitude spectrum of signals as information tags is justified and a method is developed for determination of the dimensionality of the tag space. Methods are suggested for ensuring the stability of object recognition in the optical range. It is concluded that it should be possible to recognise perspectives of complex objects.

Lateral entorhinal cortex is necessary for associative but not nonassociative recognition memory

PubMed Central

Wilson, David IG; Watanabe, Sakurako; Milner, Helen; Ainge, James A

2013-01-01

The lateral entorhinal cortex (LEC) provides one of the two major input pathways to the hippocampus and has been suggested to process the nonspatial contextual details of episodic memory. Combined with spatial information from the medial entorhinal cortex it is hypothesised that this contextual information is used to form an integrated spatially selective, context-specific response in the hippocampus that underlies episodic memory. Recently, we reported that the LEC is required for recognition of objects that have been experienced in a specific context (Wilson et al. (2013) Hippocampus 23:352-366). Here, we sought to extend this work to assess the role of the LEC in recognition of all associative combinations of objects, places and contexts within an episode. Unlike controls, rats with excitotoxic lesions of the LEC showed no evidence of recognizing familiar combinations of object in place, place in context, or object in place and context. However, LEC lesioned rats showed normal recognition of objects and places independently from each other (nonassociative recognition). Together with our previous findings, these data suggest that the LEC is critical for associative recognition memory and may bind together information relating to objects, places, and contexts needed for episodic memory formation. PMID:23836525

Coordinate Transformations in Object Recognition

ERIC Educational Resources Information Center

Graf, Markus

2006-01-01

A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation…

Representation, Classification and Information Fusion for Robust and Efficient Multimodal Human States Recognition

ERIC Educational Resources Information Center

Li, Ming

2013-01-01

The goal of this work is to enhance the robustness and efficiency of the multimodal human states recognition task. Human states recognition can be considered as a joint term for identifying/verifing various kinds of human related states, such as biometric identity, language spoken, age, gender, emotion, intoxication level, physical activity, vocal…

Modeling global scene factors in attention

NASA Astrophysics Data System (ADS)

Torralba, Antonio

2003-07-01

Models of visual attention have focused predominantly on bottom-up approaches that ignored structured contextual and scene information. I propose a model of contextual cueing for attention guidance based on the global scene configuration. It is shown that the statistics of low-level features across the whole image can be used to prime the presence or absence of objects in the scene and to predict their location, scale, and appearance before exploring the image. In this scheme, visual context information can become available early in the visual processing chain, which allows modulation of the saliency of image regions and provides an efficient shortcut for object detection and recognition. 2003 Optical Society of America

RecceMan: an interactive recognition assistance for image-based reconnaissance: synergistic effects of human perception and computational methods for object recognition, identification, and infrastructure analysis

NASA Astrophysics Data System (ADS)

El Bekri, Nadia; Angele, Susanne; Ruckhäberle, Martin; Peinsipp-Byma, Elisabeth; Haelke, Bruno

2015-10-01

This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The infrastructure analysis mode pursues the goal to analyze the function of the infrastructure. The image analyst extracts visually certain target object signatures, assigns them to corresponding object features and is finally able to recognize the object type. The system offers him the possibility to assign the image signatures to features given by sample images. The underlying data set contains a wide range of objects features and object types for different domains like ships or land vehicles. Each domain has its own feature tree developed by aerial image analyst experts. By selecting the corresponding features, the possible solution set of objects is automatically reduced and matches only the objects that contain the selected features. Moreover, we give an outlook of current research in the field of ground target analysis in which we deal with partly automated methods to extract image signatures and assign them to the corresponding features. This research includes methods for automatically determining the orientation of an object and geometric features like width and length of the object. This step enables to reduce automatically the possible object types offered to the image analyst by the interactive recognition assistance system.

Bioinspired Pollen-Like Hierarchical Surface for Efficient Recognition of Target Cancer Cells.

PubMed

Wang, Wenshuo; Yang, Gao; Cui, Haijun; Meng, Jingxin; Wang, Shutao; Jiang, Lei

2017-08-01

The efficient recognition and isolation of rare cancer cells holds great promise for cancer diagnosis and prognosis. In nature, pollens exploit spiky structures to realize recognition and adhesion to stigma. Herein, a bioinspired pollen-like hierarchical surface is developed by replicating the assembly of pollen grains, and efficient and specific recognition to target cancer cells is achieved. The pollen-like surface is fabricated by combining filtering-assisted assembly and soft lithography-based replication of pollen grains of wild chrysanthemum. After modification with a capture agent specific to cancer cells, the pollen-like surface enables the capture of target cancer cells with high efficiency and specificity. In addition, the pollen-like surface not only assures high viability of captured cells but also performs well in cell mixture system and at low cell density. This study represents a good example of constructing cell recognition biointerfaces inspired by pollen-stigma adhesion. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

10 CFR 431.21 - Procedures for recognition and withdrawal of recognition of accreditation bodies and...

Code of Federal Regulations, 2011 CFR

2011-01-01

... ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR CERTAIN COMMERCIAL AND INDUSTRIAL EQUIPMENT Electric Motors Test Procedures, Materials Incorporated and Methods of Determining Efficiency § 431.21 Procedures... Assistant Secretary for Energy Efficiency and Renewable Energy, U.S. Department of Energy, Forrestal...

Semantic and visual determinants of face recognition in a prosopagnosic patient.

PubMed

Dixon, M J; Bub, D N; Arguin, M

1998-05-01

Prosopagnosia is the neuropathological inability to recognize familiar people by their faces. It can occur in isolation or can coincide with recognition deficits for other nonface objects. Often, patients whose prosopagnosia is accompanied by object recognition difficulties have more trouble identifying certain categories of objects relative to others. In previous research, we demonstrated that objects that shared multiple visual features and were semantically close posed severe recognition difficulties for a patient with temporal lobe damage. We now demonstrate that this patient's face recognition is constrained by these same parameters. The prosopagnosic patient ELM had difficulties pairing faces to names when the faces shared visual features and the names were semantically related (e.g., Tonya Harding, Nancy Kerrigan, and Josee Chouinard -three ice skaters). He made tenfold fewer errors when the exact same faces were associated with semantically unrelated people (e.g., singer Celine Dion, actress Betty Grable, and First Lady Hillary Clinton). We conclude that prosopagnosia and co-occurring category-specific recognition problems both stem from difficulties disambiguating the stored representations of objects that share multiple visual features and refer to semantically close identities or concepts.

Mid-level perceptual features contain early cues to animacy.

PubMed

Long, Bria; Störmer, Viola S; Alvarez, George A

2017-06-01

While substantial work has focused on how the visual system achieves basic-level recognition, less work has asked about how it supports large-scale distinctions between objects, such as animacy and real-world size. Previous work has shown that these dimensions are reflected in our neural object representations (Konkle & Caramazza, 2013), and that objects of different real-world sizes have different mid-level perceptual features (Long, Konkle, Cohen, & Alvarez, 2016). Here, we test the hypothesis that animates and manmade objects also differ in mid-level perceptual features. To do so, we generated synthetic images of animals and objects that preserve some texture and form information ("texforms"), but are not identifiable at the basic level. We used visual search efficiency as an index of perceptual similarity, as search is slower when targets are perceptually similar to distractors. Across three experiments, we find that observers can find animals faster among objects than among other animals, and vice versa, and that these results hold when stimuli are reduced to unrecognizable texforms. Electrophysiological evidence revealed that this mixed-animacy search advantage emerges during early stages of target individuation, and not during later stages associated with semantic processing. Lastly, we find that perceived curvature explains part of the mixed-animacy search advantage and that observers use perceived curvature to classify texforms as animate/inanimate. Taken together, these findings suggest that mid-level perceptual features, including curvature, contain cues to whether an object may be animate versus manmade. We propose that the visual system capitalizes on these early cues to facilitate object detection, recognition, and classification.

Examining object recognition and object-in-Place memory in plateau zokors, Eospalax baileyi.

PubMed

Hegab, Ibrahim M; Tan, Yuchen; Wang, Chan; Yao, Baohui; Wang, Haifang; Ji, Weihong; Su, Junhu

2018-01-01

Recognition memory is important for the survival and fitness of subterranean rodents due to the barren underground conditions that require avoiding the burden of higher energy costs or possible conflict with conspecifics. Our study aims to examine the object and object/place recognition memories in plateau zokors (Eospalax baileyi) and test whether their underground life exerts sex-specific differences in memory functions using Novel Object Recognition (NOR) and Object-in-Place (OiP) paradigms. Animals were tested in the NOR with short (10min) and long-term (24h) inter-trial intervals (ITI) and in the OiP for a 30-min ITI between the familiarization and testing sessions. Plateau zokors showed a strong preference for novel objects manifested by a longer exploration time for the novel object after 10min ITI but failed to remember the familiar object when tested after 24h, suggesting a lack of long-term memory. In the OiP test, zokors effectively formed an association between the objects and the place where they were formerly encountered, resulting in a higher duration of exploration to the switched objects. However, both sexes showed equivalent results in exploration time during the NOR and OiP tests, which eliminates the possibility of discovering sex-specific variations in memory performance. Taken together, our study illustrates robust novelty preference and an effective short-term recognition memory without marked sex-specific differences, which might elucidate the dynamics of recognition memory formation and retrieval in plateau zokors. Copyright © 2017 Elsevier B.V. All rights reserved.

Implicit and Explicit Contributions to Object Recognition: Evidence from Rapid Perceptual Learning

PubMed Central

Hassler, Uwe; Friese, Uwe; Gruber, Thomas

2012-01-01

The present study investigated implicit and explicit recognition processes of rapidly perceptually learned objects by means of steady-state visual evoked potentials (SSVEP). Participants were initially exposed to object pictures within an incidental learning task (living/non-living categorization). Subsequently, degraded versions of some of these learned pictures were presented together with degraded versions of unlearned pictures and participants had to judge, whether they recognized an object or not. During this test phase, stimuli were presented at 15 Hz eliciting an SSVEP at the same frequency. Source localizations of SSVEP effects revealed for implicit and explicit processes overlapping activations in orbito-frontal and temporal regions. Correlates of explicit object recognition were additionally found in the superior parietal lobe. These findings are discussed to reflect facilitation of object-specific processing areas within the temporal lobe by an orbito-frontal top-down signal as proposed by bi-directional accounts of object recognition. PMID:23056558

Single prolonged stress impairs social and object novelty recognition in rats.

PubMed

Eagle, Andrew L; Fitzpatrick, Chris J; Perrine, Shane A

2013-11-01

Posttraumatic stress disorder (PTSD) results from exposure to a traumatic event and manifests as re-experiencing, arousal, avoidance, and negative cognition/mood symptoms. Avoidant symptoms, as well as the newly defined negative cognitions/mood, are a serious complication leading to diminished interest in once important or positive activities, such as social interaction; however, the basis of these symptoms remains poorly understood. PTSD patients also exhibit impaired object and social recognition, which may underlie the avoidance and symptoms of negative cognition, such as social estrangement or diminished interest in activities. Previous studies have demonstrated that single prolonged stress (SPS), models PTSD phenotypes, including impairments in learning and memory. Therefore, it was hypothesized that SPS would impair social and object recognition memory. Male Sprague Dawley rats were exposed to SPS then tested in the social choice test (SCT) or novel object recognition test (NOR). These tests measure recognition of novelty over familiarity, a natural preference of rodents. Results show that SPS impaired preference for both social and object novelty. In addition, SPS impairment in social recognition may be caused by impaired behavioral flexibility, or an inability to shift behavior during the SCT. These results demonstrate that traumatic stress can impair social and object recognition memory, which may underlie certain avoidant symptoms or negative cognition in PTSD and be related to impaired behavioral flexibility. Copyright © 2013 Elsevier B.V. All rights reserved.

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

10 CFR 431.21 - Procedures for recognition and withdrawal of recognition of accreditation bodies and...

Code of Federal Regulations, 2010 CFR

2010-01-01

... ENERGY CONSERVATION ENERGY EFFICIENCY PROGRAM FOR CERTAIN COMMERCIAL AND INDUSTRIAL EQUIPMENT Electric... Assistant Secretary for Energy Efficiency and Renewable Energy, U.S. Department of Energy, Forrestal... comments in a written statement submitted to the Assistant Secretary for Energy Efficiency and Renewable...

10 CFR 431.20 - Department of Energy recognition of nationally recognized certification programs.

Code of Federal Regulations, 2010 CFR

2010-01-01

... certification programs. 431.20 Section 431.20 Energy DEPARTMENT OF ENERGY ENERGY CONSERVATION ENERGY EFFICIENCY... Incorporated and Methods of Determining Efficiency § 431.20 Department of Energy recognition of nationally... similar procedures and methodologies for determining the energy efficiency of electric motors. It must...

Neural-Network Object-Recognition Program

NASA Technical Reports Server (NTRS)

Spirkovska, L.; Reid, M. B.

1993-01-01

HONTIOR computer program implements third-order neural network exhibiting invariance under translation, change of scale, and in-plane rotation. Invariance incorporated directly into architecture of network. Only one view of each object needed to train network for two-dimensional-translation-invariant recognition of object. Also used for three-dimensional-transformation-invariant recognition by training network on only set of out-of-plane rotated views. Written in C language.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

PubMed

Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

2015-09-30

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

Human action recognition based on point context tensor shape descriptor

NASA Astrophysics Data System (ADS)

Li, Jianjun; Mao, Xia; Chen, Lijiang; Wang, Lan

2017-07-01

Motion trajectory recognition is one of the most important means to determine the identity of a moving object. A compact and discriminative feature representation method can improve the trajectory recognition accuracy. This paper presents an efficient framework for action recognition using a three-dimensional skeleton kinematic joint model. First, we put forward a rotation-scale-translation-invariant shape descriptor based on point context (PC) and the normal vector of hypersurface to jointly characterize local motion and shape information. Meanwhile, an algorithm for extracting the key trajectory based on the confidence coefficient is proposed to reduce the randomness and computational complexity. Second, to decrease the eigenvalue decomposition time complexity, a tensor shape descriptor (TSD) based on PC that can globally capture the spatial layout and temporal order to preserve the spatial information of each frame is proposed. Then, a multilinear projection process is achieved by tensor dynamic time warping to map the TSD to a low-dimensional tensor subspace of the same size. Experimental results show that the proposed shape descriptor is effective and feasible, and the proposed approach obtains considerable performance improvement over the state-of-the-art approaches with respect to accuracy on a public action dataset.

A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture.

PubMed

Zhong, Yuanhong; Gao, Junyuan; Lei, Qilun; Zhou, Yao

2018-05-09

Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.

A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

PubMed Central

Zhong, Yuanhong; Gao, Junyuan; Lei, Qilun; Zhou, Yao

2018-01-01

Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications. PMID:29747429

Qualitatively similar processing for own- and other-race faces: Evidence from efficiency and equivalent input noise.

PubMed

Shafai, Fakhri; Oruc, Ipek

2018-02-01

The other-race effect is the finding of diminished performance in recognition of other-race faces compared to those of own-race. It has been suggested that the other-race effect stems from specialized expert processes being tuned exclusively to own-race faces. In the present study, we measured recognition contrast thresholds for own- and other-race faces as well as houses for Caucasian observers. We have factored face recognition performance into two invariant aspects of visual function: efficiency, which is related to neural computations and processing demanded by the task, and equivalent input noise, related to signal degradation within the visual system. We hypothesized that if expert processes are available only to own-race faces, this should translate into substantially greater recognition efficiencies for own-race compared to other-race faces. Instead, we found similar recognition efficiencies for both own- and other-race faces. The other-race effect manifested as increased equivalent input noise. These results argue against qualitatively distinct perceptual processes. Instead they suggest that for Caucasian observers, similar neural computations underlie recognition of own- and other-race faces. Copyright © 2018 Elsevier Ltd. All rights reserved.

HDAC inhibition modulates hippocampus-dependent long-term memory for object location in a CBP-dependent manner

PubMed Central

Haettig, Jakob; Stefanko, Daniel P.; Multani, Monica L.; Figueroa, Dario X.; McQuown, Susan C.; Wood, Marcelo A.

2011-01-01

Transcription of genes required for long-term memory not only involves transcription factors, but also enzymatic protein complexes that modify chromatin structure. Chromatin-modifying enzymes, such as the histone acetyltransferase (HAT) CREB (cyclic-AMP response element binding) binding protein (CBP), are pivotal for the transcriptional regulation required for long-term memory. Several studies have shown that CBP and histone acetylation are necessary for hippocampus-dependent long-term memory and hippocampal long-term potentiation (LTP). Importantly, every genetically modified Cbp mutant mouse exhibits long-term memory impairments in object recognition. However, the role of the hippocampus in object recognition is controversial. To better understand how chromatin-modifying enzymes modulate long-term memory for object recognition, we first examined the role of the hippocampus in retrieval of long-term memory for object recognition or object location. Muscimol inactivation of the dorsal hippocampus prior to retrieval had no effect on long-term memory for object recognition, but completely blocked long-term memory for object location. This was consistent with experiments showing that muscimol inactivation of the hippocampus had no effect on long-term memory for the object itself, supporting the idea that the hippocampus encodes spatial information about an object (such as location or context), whereas cortical areas (such as the perirhinal or insular cortex) encode information about the object itself. Using location-dependent object recognition tasks that engage the hippocampus, we demonstrate that CBP is essential for the modulation of long-term memory via HDAC inhibition. Together, these results indicate that HDAC inhibition modulates memory in the hippocampus via CBP and that different brain regions utilize different chromatin-modifying enzymes to regulate learning and memory. PMID:21224411

Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

NASA Astrophysics Data System (ADS)

Yan, Fengxia; Udupa, Jayaram K.; Tong, Yubing; Xu, Guoping; Odhner, Dewey; Torigian, Drew A.

2018-03-01

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

Dietary effects on object recognition: The impact of high-fat high-sugar diets on recollection and familiarity-based memory.

PubMed

Tran, Dominic M D; Westbrook, R Frederick

2018-05-31

Exposure to a high-fat high-sugar (HFHS) diet rapidly impairs novel-place- but not novel-object-recognition memory in rats (Tran & Westbrook, 2015, 2017). Three experiments sought to investigate the generality of diet-induced cognitive deficits by examining whether there are conditions under which object-recognition memory is impaired. Experiments 1 and 3 tested the strength of short- and long-term object-memory trace, respectively, by varying the interval of time between object familiarization and subsequent novel object test. Experiment 2 tested the effect of increasing working memory load on object-recognition memory by interleaving additional object exposures between familiarization and test in an n-back style task. Experiments 1-3 failed to detect any differences in object recognition between HFHS and control rats. Experiment 4 controlled for object novelty by separately familiarizing both objects presented at test, which included one remote-familiar and one recent-familiar object. Under these conditions, when test objects differed in their relative recency, HFHS rats showed a weaker memory trace for the remote object compared to chow rats. This result suggests that the diet leaves intact recollection judgments, but impairs familiarity judgments. We speculate that the HFHS diet adversely affects "where" memories as well as the quality of "what" memories, and discuss these effects in relation to recollection and familiarity memory models, hippocampal-dependent functions, and episodic food memories. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Motion Imagery Processing and Exploitation (MIPE)

DTIC Science & Technology

2013-01-01

facial recognition —i.e., the identification of a specific person.37 Object detection is often (but not always) considered a prerequisite for instance...The goal of segmentation is to distinguish objects and identify boundaries in images. Some of the earliest approaches to facial recognition involved...methods of instance recognition are at varying levels of maturity. Facial recognition methods are arguably the most mature; the technology is well

Perirhinal Cortex Lesions in Rats: Novelty Detection and Sensitivity to Interference

PubMed Central

2015-01-01

Rats with perirhinal cortex lesions received multiple object recognition trials within a continuous session to examine whether they show false memories. Experiment 1 focused on exploration patterns during the first object recognition test postsurgery, in which each trial contained 1 novel and 1 familiar object. The perirhinal cortex lesions reduced time spent exploring novel objects, but did not affect overall time spent exploring the test objects (novel plus familiar). Replications with subsequent cohorts of rats (Experiments 2, 3, 4.1) repeated this pattern of results. When all recognition memory data were combined (Experiments 1–4), giving totals of 44 perirhinal lesion rats and 40 surgical sham controls, the perirhinal cortex lesions caused a marginal reduction in total exploration time. That decrease in time with novel objects was often compensated by increased exploration of familiar objects. Experiment 4 also assessed the impact of proactive interference on recognition memory. Evidence emerged that prior object experience could additionally impair recognition performance in rats with perirhinal cortex lesions. Experiment 5 examined exploration levels when rats were just given pairs of novel objects to explore. Despite their perirhinal cortex lesions, exploration levels were comparable with those of control rats. While the results of Experiment 4 support the notion that perirhinal lesions can increase sensitivity to proactive interference, the overall findings question whether rats lacking a perirhinal cortex typically behave as if novel objects are familiar, that is, show false recognition. Rather, the rats retain a signal of novelty but struggle to discriminate the identity of that signal. PMID:26030425

Running Improves Pattern Separation during Novel Object Recognition.

PubMed

Bolz, Leoni; Heigele, Stefanie; Bischofberger, Josef

2015-10-09

Running increases adult neurogenesis and improves pattern separation in various memory tasks including context fear conditioning or touch-screen based spatial learning. However, it is unknown whether pattern separation is improved in spontaneous behavior, not emotionally biased by positive or negative reinforcement. Here we investigated the effect of voluntary running on pattern separation during novel object recognition in mice using relatively similar or substantially different objects.We show that running increases hippocampal neurogenesis but does not affect object recognition memory with 1.5 h delay after sample phase. By contrast, at 24 h delay, running significantly improves recognition memory for similar objects, whereas highly different objects can be distinguished by both, running and sedentary mice. These data show that physical exercise improves pattern separation, independent of negative or positive reinforcement. In sedentary mice there is a pronounced temporal gradient for remembering object details. In running mice, however, increased neurogenesis improves hippocampal coding and temporally preserves distinction of novel objects from familiar ones.

What pharmacological interventions indicate concerning the role of the perirhinal cortex in recognition memory

PubMed Central

Brown, M.W.; Barker, G.R.I.; Aggleton, J.P.; Warburton, E.C.

2012-01-01

Findings of pharmacological studies that have investigated the involvement of specific regions of the brain in recognition memory are reviewed. The particular emphasis of the review concerns what such studies indicate concerning the role of the perirhinal cortex in recognition memory. Most of the studies involve rats and most have investigated recognition memory for objects. Pharmacological studies provide a large body of evidence supporting the essential role of the perirhinal cortex in the acquisition, consolidation and retrieval of object recognition memory. Such studies provide increasingly detailed evidence concerning both the neurotransmitter systems and the underlying intracellular mechanisms involved in recognition memory processes. They have provided evidence in support of synaptic weakening as a major synaptic plastic process within perirhinal cortex underlying object recognition memory. They have also supplied confirmatory evidence that that there is more than one synaptic plastic process involved. The demonstrated necessity to long-term recognition memory of intracellular signalling mechanisms related to synaptic modification within perirhinal cortex establishes a central role for the region in the information storage underlying such memory. Perirhinal cortex is thereby established as an information storage site rather than solely a processing station. Pharmacological studies have also supplied new evidence concerning the detailed roles of other regions, including the hippocampus and the medial prefrontal cortex in different types of recognition memory tasks that include a spatial or temporal component. In so doing, they have also further defined the contribution of perirhinal cortex to such tasks. To date it appears that the contribution of perirhinal cortex to associative and temporal order memory reflects that in simple object recognition memory, namely that perirhinal cortex provides information concerning objects and their prior occurrence (novelty/familiarity). PMID:22841990

Three-dimensional obstacle classification in laser range data

NASA Astrophysics Data System (ADS)

Armbruster, Walter; Bers, Karl-Heinz

1998-10-01

The threat of hostile surveillance and weapon systems require military aircraft to fly under extreme conditions such as low altitude, high speed, poor visibility and incomplete terrain information. The probability of collision with natural and man-made obstacles during such contour missions is high if detection capability is restricted to conventional vision aids. Forward-looking scanning laser rangefinders which are presently being flight tested and evaluated at German proving grounds, provide a possible solution, having a large field of view, high angular and range resolution, a high pulse repetition rate, and sufficient pulse energy to register returns from wires at over 500 m range (depends on the system) with a high hit-and-detect probability. Despite the efficiency of the sensor, acceptance of current obstacle warning systems by test pilots is not very high, mainly due to the systems' inadequacies in obstacle recognition and visualization. This has motivated the development and the testing of more advanced 3d-scene analysis algorithm at FGAN-FIM to replace the obstacle recognition component of current warning systems. The basic ideas are to increase the recognition probability and to reduce the false alarm rate for hard-to-extract obstacles such as wires, by using more readily recognizable objects such as terrain, poles, pylons, trees, etc. by implementing a hierarchical classification procedure to generate a parametric description of the terrain surface as well as the class, position, orientation, size and shape of all objects in the scene. The algorithms can be used for other applications such as terrain following, autonomous obstacle avoidance, and automatic target recognition.

A Critical Role for the Nucleus Reuniens in Long-Term, But Not Short-Term Associative Recognition Memory Formation.

PubMed

Barker, Gareth R I; Warburton, Elizabeth Clea

2018-03-28

Recognition memory for single items requires the perirhinal cortex (PRH), whereas recognition of an item and its associated location requires a functional interaction among the PRH, hippocampus (HPC), and medial prefrontal cortex (mPFC). Although the precise mechanisms through which these interactions are effected are unknown, the nucleus reuniens (NRe) has bidirectional connections with each regions and thus may play a role in recognition memory. Here we investigated, in male rats, whether specific manipulations of NRe function affected performance of recognition memory for single items, object location, or object-in-place associations. Permanent lesions in the NRe significantly impaired long-term, but not short-term, object-in-place associative recognition memory, whereas single item recognition memory and object location memory were unaffected. Temporary inactivation of the NRe during distinct phases of the object-in-place task revealed its importance in both the encoding and retrieval stages of long-term associative recognition memory. Infusions of specific receptor antagonists showed that encoding was dependent on muscarinic and nicotinic cholinergic neurotransmission, whereas NMDA receptor neurotransmission was not required. Finally, we found that long-term object-in-place memory required protein synthesis within the NRe. These data reveal a specific role for the NRe in long-term associative recognition memory through its interactions with the HPC and mPFC, but not the PRH. The delay-dependent involvement of the NRe suggests that it is not a simple relay station between brain regions, but, rather, during high mnemonic demand, facilitates interactions between the mPFC and HPC, a process that requires both cholinergic neurotransmission and protein synthesis. SIGNIFICANCE STATEMENT Recognizing an object and its associated location, which is fundamental to our everyday memory, requires specific hippocampal-cortical interactions, potentially facilitated by the nucleus reuniens (NRe) of the thalamus. However, the role of the NRe itself in associative recognition memory is unknown. Here, we reveal the crucial role of the NRe in encoding and retrieval of long-term object-in-place memory, but not for remembrance of an individual object or individual location and such involvement is cholinergic receptor and protein synthesis dependent. This is the first demonstration that the NRe is a key node within an associative recognition memory network and is not just a simple relay for information within the network. Rather, we argue, the NRe actively modulates information processing during long-term associative memory formation. Copyright © 2018 the authors 0270-6474/18/383208-10$15.00/0.

Differential effects of spaced vs. massed training in long-term object-identity and object-location recognition memory.

PubMed

Bello-Medina, Paola C; Sánchez-Carrasco, Livia; González-Ornelas, Nadia R; Jeffery, Kathryn J; Ramírez-Amaya, Víctor

2013-08-01

Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently. Copyright © 2013 Elsevier B.V. All rights reserved.

Northeast Artificial Intelligence Consortium Annual Report. Volume 7. 1988 Research in Automated Photointerpretation

DTIC Science & Technology

1989-10-01

weight based on how powerful the corresponding feature is for object recognition and discrimination. For example, consider an arbitrary weight, denoted...quality of the segmentation, how powerful the features and spatial constraints in the knowledge base are (as far as object recognition is concern...that are powerful for object recognition and discrimination. At this point, this selection is performed heuristically through trial-and-error. As a

Haptic Exploration in Humans and Machines: Attribute Integration and Machine Recognition/Implementation.

DTIC Science & Technology

1988-04-30

side it necessary and Identify’ by’ block n~nmbot) haptic hand, touch , vision, robot, object recognition, categorization 20. AGSTRPACT (Continue an...established that the haptic system has remarkable capabilities for object recognition. We define haptics as purposive touch . The basic tactual system...gathered ratings of the importance of dimensions for categorizing common objects by touch . Texture and hardness ratings strongly co-vary, which is

High speed optical object recognition processor with massive holographic memory

NASA Technical Reports Server (NTRS)

Chao, T.; Zhou, H.; Reyes, G.

2002-01-01

Real-time object recognition using a compact grayscale optical correlator will be introduced. A holographic memory module for storing a large bank of optimum correlation filters, to accommodate the large data throughput rate needed for many real-world applications, has also been developed. System architecture of the optical processor and the holographic memory will be presented. Application examples of this object recognition technology will also be demonstrated.

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

General object recognition is specific: Evidence from novel and familiar objects.

PubMed

Richler, Jennifer J; Wilmer, Jeremy B; Gauthier, Isabel

2017-09-01

In tests of object recognition, individual differences typically correlate modestly but nontrivially across familiar categories (e.g. cars, faces, shoes, birds, mushrooms). In theory, these correlations could reflect either global, non-specific mechanisms, such as general intelligence (IQ), or more specific mechanisms. Here, we introduce two separate methods for effectively capturing category-general performance variation, one that uses novel objects and one that uses familiar objects. In each case, we show that category-general performance variance is unrelated to IQ, thereby implicating more specific mechanisms. The first approach examines three newly developed novel object memory tests (NOMTs). We predicted that NOMTs would exhibit more shared, category-general variance than familiar object memory tests (FOMTs) because novel objects, unlike familiar objects, lack category-specific environmental influences (e.g. exposure to car magazines or botany classes). This prediction held, and remarkably, virtually none of the substantial shared variance among NOMTs was explained by IQ. Also, while NOMTs correlated nontrivially with two FOMTs (faces, cars), these correlations were smaller than among NOMTs and no larger than between the face and car tests themselves, suggesting that the category-general variance captured by NOMTs is specific not only relative to IQ, but also, to some degree, relative to both face and car recognition. The second approach averaged performance across multiple FOMTs, which we predicted would increase category-general variance by averaging out category-specific factors. This prediction held, and as with NOMTs, virtually none of the shared variance among FOMTs was explained by IQ. Overall, these results support the existence of object recognition mechanisms that, though category-general, are specific relative to IQ and substantially separable from face and car recognition. They also add sensitive, well-normed NOMTs to the tools available to study object recognition. Copyright © 2017 Elsevier B.V. All rights reserved.

Involvement of hippocampal NMDA receptors in encoding and consolidation, but not retrieval, processes of spontaneous object location memory in rats.

PubMed

Yamada, Kazuo; Arai, Misaki; Suenaga, Toshiko; Ichitani, Yukio

2017-07-28

The hippocampus is thought to be involved in object location recognition memory, yet the contribution of hippocampal NMDA receptors to the memory processes, such as encoding, retention and retrieval, is unknown. First, we confirmed that hippocampal infusion of a competitive NMDA receptor antagonist, AP5 (2-amino-5-phosphonopentanoic acid, 20-40nmol), impaired performance of spontaneous object location recognition test but not that of novel object recognition test in Wistar rats. Next, the effects of hippocampal AP5 treatment on each process of object location recognition memory were examined with three different injection times using a 120min delay-interposed test: 15min before the sample phase (Time I), immediately after the sample phase (Time II), and 15min before the test phase (Time III). The blockade of hippocampal NMDA receptors before and immediately after the sample phase, but not before the test phase, markedly impaired performance of object location recognition test, suggesting that hippocampal NMDA receptors play an important role in encoding and consolidation/retention, but not retrieval, of spontaneous object location memory. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis of objects in binary images. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Leonard, Desiree M.

1991-01-01

Digital image processing techniques are typically used to produce improved digital images through the application of successive enhancement techniques to a given image or to generate quantitative data about the objects within that image. In support of and to assist researchers in a wide range of disciplines, e.g., interferometry, heavy rain effects on aerodynamics, and structure recognition research, it is often desirable to count objects in an image and compute their geometric properties. Therefore, an image analysis application package, focusing on a subset of image analysis techniques used for object recognition in binary images, was developed. This report describes the techniques and algorithms utilized in three main phases of the application and are categorized as: image segmentation, object recognition, and quantitative analysis. Appendices provide supplemental formulas for the algorithms employed as well as examples and results from the various image segmentation techniques and the object recognition algorithm implemented.

The Last Meter: Blind Visual Guidance to a Target.

PubMed

Manduchi, Roberto; Coughlan, James M

2014-01-01

Smartphone apps can use object recognition software to provide information to blind or low vision users about objects in the visual environment. A crucial challenge for these users is aiming the camera properly to take a well-framed picture of the desired target object. We investigate the effects of two fundamental constraints of object recognition - frame rate and camera field of view - on a blind person's ability to use an object recognition smartphone app. The app was used by 18 blind participants to find visual targets beyond arm's reach and approach them to within 30 cm. While we expected that a faster frame rate or wider camera field of view should always improve search performance, our experimental results show that in many cases increasing the field of view does not help, and may even hurt, performance. These results have important implications for the design of object recognition systems for blind users.

Object recognition with severe spatial deficits in Williams syndrome: sparing and breakdown.

PubMed

Landau, Barbara; Hoffman, James E; Kurz, Nicole

2006-07-01

Williams syndrome (WS) is a rare genetic disorder that results in severe visual-spatial cognitive deficits coupled with relative sparing in language, face recognition, and certain aspects of motion processing. Here, we look for evidence for sparing or impairment in another cognitive system-object recognition. Children with WS, normal mental-age (MA) and chronological age-matched (CA) children, and normal adults viewed pictures of a large range of objects briefly presented under various conditions of degradation, including canonical and unusual orientations, and clear or blurred contours. Objects were shown as either full-color views (Experiment 1) or line drawings (Experiment 2). Across both experiments, WS and MA children performed similarly in all conditions while CA children performed better than both WS group and MA groups with unusual views. This advantage, however, was eliminated when images were also blurred. The error types and relative difficulty of different objects were similar across all participant groups. The results indicate selective sparing of basic mechanisms of object recognition in WS, together with developmental delay or arrest in recognition of objects from unusual viewpoints. These findings are consistent with the growing literature on brain abnormalities in WS which points to selective impairment in the parietal areas of the brain. As a whole, the results lend further support to the growing literature on the functional separability of object recognition mechanisms from other spatial functions, and raise intriguing questions about the link between genetic deficits and cognition.

Central administration of angiotensin IV rapidly enhances novel object recognition among mice.

PubMed

Paris, Jason J; Eans, Shainnel O; Mizrachi, Elisa; Reilley, Kate J; Ganno, Michelle L; McLaughlin, Jay P

2013-07-01

Angiotensin IV (Val(1)-Tyr(2)-Ile(3)-His(4)-Pro(5)-Phe(6)) has demonstrated potential cognitive-enhancing effects. The present investigation assessed and characterized: (1) dose-dependency of angiotensin IV's cognitive enhancement in a C57BL/6J mouse model of novel object recognition, (2) the time-course for these effects, (3) the identity of residues in the hexapeptide important to these effects and (4) the necessity of actions at angiotensin IV receptors for procognitive activity. Assessment of C57BL/6J mice in a novel object recognition task demonstrated that prior administration of angiotensin IV (0.1, 1.0, or 10.0, but not 0.01 nmol, i.c.v.) significantly enhanced novel object recognition in a dose-dependent manner. These effects were time dependent, with improved novel object recognition observed when angiotensin IV (0.1 nmol, i.c.v.) was administered 10 or 20, but not 30 min prior to the onset of the novel object recognition testing. An alanine scan of the angiotensin IV peptide revealed that replacement of the Val(1), Ile(3), His(4), or Phe(6) residues with Ala attenuated peptide-induced improvements in novel object recognition, whereas Tyr(2) or Pro(5) replacement did not significantly affect performance. Administration of the angiotensin IV receptor antagonist, divalinal-Ang IV (20 nmol, i.c.v.), reduced (but did not abolish) novel object recognition; however, this antagonist completely blocked the procognitive effects of angiotensin IV (0.1 nmol, i.c.v.) in this task. Rotorod testing demonstrated no locomotor effects with any angiotensin IV or divalinal-Ang IV dose tested. These data demonstrate that angiotensin IV produces a rapid enhancement of associative learning and memory performance in a mouse model that was dependent on the angiotensin IV receptor. Copyright © 2013 Elsevier Ltd. All rights reserved.

Central administration of angiotensin IV rapidly enhances novel object recognition among mice

PubMed Central

Paris, Jason J.; Eans, Shainnel O.; Mizrachi, Elisa; Reilley, Kate J.; Ganno, Michelle L.; McLaughlin, Jay P.

2013-01-01

Angiotensin IV (Val1-Tyr2-Ile3-His4-Pro5-Phe6) has demonstrated potential cognitive-enhancing effects. The present investigation assessed and characterized: (1) dose-dependency of angiotensin IV's cognitive enhancement in a C57BL/6J mouse model of novel object recognition, (2) the time-course for these effects, (3) the identity of residues in the hexapeptide important to these effects and (4) the necessity of actions at angiotensin IV receptors for pro-cognitive activity. Assessment of C57BL/6J mice in a novel object recognition task demonstrated that prior administration of angiotensin IV (0.1, 1.0, or 10.0, but not 0.01, nmol, i.c.v.) significantly enhanced novel object recognition in a dose-dependent manner. These effects were time dependent, with improved novel object recognition observed when angiotensin IV (0.1 nmol, i.c.v.) was administered 10 or 20, but not 30, min prior to the onset of the novel object recognition testing. An alanine scan of the angiotensin IV peptide revealed that replacement of the Val1, Ile3, His4, or Phe6 residues with Ala attenuated peptide-induced improvements in novel object recognition, whereas Tyr2 or Pro5 replacement did not significantly affect performance. Administration of the angiotensin IV receptor antagonist, divalinal-Ang IV (20 nmol, i.c.v.), reduced (but did not abolish) novel object recognition; however, this antagonist completely blocked the pro-cognitive effects of angiotensin IV (0.1 nmol, i.c.v.) in this task. Rotorod testing demonstrated no locomotor effects for any angiotensin IV or divalinal-Ang IV dose tested. These data demonstrate that angiotensin IV produces a rapid enhancement of associative learning and memory performance in a mouse model that was dependent on the angiotensin IV receptor. PMID:23416700

Three Dimensional Object Recognition Using an Unsupervised Neural Network: Understanding the Distinguishing Features

DTIC Science & Technology

1992-12-23

predominance of structural models of recognition, of which a recent example is the Recognition By Components (RBC) theory ( Biederman , 1987 ). Structural...related to recent statistical theory (Huber, 1985; Friedman, 1987 ) and is derived from a biologically motivated computational theory (Bienenstock et...dimensional object recognition (Intrator and Gold, 1991). The method is related to recent statistical theory (Huber, 1985; Friedman, 1987 ) and is derived

Object Recognition Memory and the Rodent Hippocampus

ERIC Educational Resources Information Center

Broadbent, Nicola J.; Gaskin, Stephane; Squire, Larry R.; Clark, Robert E.

2010-01-01

In rodents, the novel object recognition task (NOR) has become a benchmark task for assessing recognition memory. Yet, despite its widespread use, a consensus has not developed about which brain structures are important for task performance. We assessed both the anterograde and retrograde effects of hippocampal lesions on performance in the NOR…

Self-Recognition in Autistic Children.

ERIC Educational Resources Information Center

Dawson, Geraldine; McKissick, Fawn Celeste

1984-01-01

Fifteen autistic children (four to six years old) were assessed for visual self-recognition ability, as well as for object permanence and gestural imitation. It was found that 13 of 15 autistic children showed evidence of self-recognition. Consistent relationships were suggested between self-cognition and object permanence but not between…

Developmental Commonalities between Object and Face Recognition in Adolescence

PubMed Central

Jüttner, Martin; Wakui, Elley; Petters, Dean; Davidoff, Jules

2016-01-01

In the visual perception literature, the recognition of faces has often been contrasted with that of non-face objects, in terms of differences with regard to the role of parts, part relations and holistic processing. However, recent evidence from developmental studies has begun to blur this sharp distinction. We review evidence for a protracted development of object recognition that is reminiscent of the well-documented slow maturation observed for faces. The prolonged development manifests itself in a retarded processing of metric part relations as opposed to that of individual parts and offers surprising parallels to developmental accounts of face recognition, even though the interpretation of the data is less clear with regard to holistic processing. We conclude that such results might indicate functional commonalities between the mechanisms underlying the recognition of faces and non-face objects, which are modulated by different task requirements in the two stimulus domains. PMID:27014176

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Human-inspired sound environment recognition system for assistive vehicles

NASA Astrophysics Data System (ADS)

González Vidal, Eduardo; Fredes Zarricueta, Ernesto; Auat Cheein, Fernando

2015-02-01

Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance. The proposed sound-based system is very efficient at providing general descriptions of the environment. Such descriptions are focused on vulnerable situations described by the ICF. The volunteers answered a questionnaire regarding the importance of constraining the vehicle velocities in risky environments, showing that all the volunteers felt comfortable with the system and its performance.

Correlations of recognition memory performance with expression and methylation of brain-derived neurotrophic factor in rats.

PubMed

Muñoz, Pablo C; Aspé, Mauricio A; Contreras, Luis S; Palacios, Adrián G

2010-01-01

Object recognition memory allows discrimination between novel and familiar objects. This kind of memory consists of two components: recollection, which depends on the hippocampus, and familiarity, which depends on the perirhinal cortex (Pcx). The importance of brain-derived neurotrophic factor (BDNF) for recognition memory has already been recognized. Recent evidence suggests that DNA methylation regulates the expression of BDNF and memory. Behavioral and molecular approaches were used to understand the potential contribution of DNA methylation to recognition memory. To that end, rats were tested for their ability to distinguish novel from familiar objects by using a spontaneous object recognition task. Furthermore, the level of DNA methylation was estimated after trials with a methyl-sensitive PCR. We found a significant correlation between performance on the novel object task and the expression of BDNF, negatively in hippocampal slices and positively in perirhinal cortical slices. By contrast, methylation of DNA in CpG island 1 in the promoter of exon 1 in BDNF only correlated in hippocampal slices, but not in the Pxc cortical slices from trained animals. These results suggest that DNA methylation may be involved in the regulation of the BDNF gene during recognition memory, at least in the hippocampus.

Threat as a feature in visual semantic object memory.

PubMed

Calley, Clifford S; Motes, Michael A; Chiang, H-Sheng; Buhl, Virginia; Spence, Jeffrey S; Abdi, Hervé; Anand, Raksha; Maguire, Mandy; Estevez, Leonardo; Briggs, Richard; Freeman, Thomas; Kraut, Michael A; Hart, John

2013-08-01

Threatening stimuli have been found to modulate visual processes related to perception and attention. The present functional magnetic resonance imaging (fMRI) study investigated whether threat modulates visual object recognition of man-made and naturally occurring categories of stimuli. Compared with nonthreatening pictures, threatening pictures of real items elicited larger fMRI BOLD signal changes in medial visual cortices extending inferiorly into the temporo-occipital (TO) "what" pathways. This region elicited greater signal changes for threatening items compared to nonthreatening from both the natural-occurring and man-made stimulus supraordinate categories, demonstrating a featural component to these visual processing areas. Two additional loci of signal changes within more lateral inferior TO areas (bilateral BA18 and 19 as well as the right ventral temporal lobe) were detected for a category-feature interaction, with stronger responses to man-made (category) threatening (feature) stimuli than to natural threats. The findings are discussed in terms of visual recognition of processing efficiently or rapidly groups of items that confer an advantage for survival. Copyright © 2012 Wiley Periodicals, Inc.

Non-accidental properties, metric invariance, and encoding by neurons in a model of ventral stream visual object recognition, VisNet.

PubMed

Rolls, Edmund T; Mills, W Patrick C

2018-05-01

When objects transform into different views, some properties are maintained, such as whether the edges are convex or concave, and these non-accidental properties are likely to be important in view-invariant object recognition. The metric properties, such as the degree of curvature, may change with different views, and are less likely to be useful in object recognition. It is shown that in a model of invariant visual object recognition in the ventral visual stream, VisNet, non-accidental properties are encoded much more than metric properties by neurons. Moreover, it is shown how with the temporal trace rule training in VisNet, non-accidental properties of objects become encoded by neurons, and how metric properties are treated invariantly. We also show how VisNet can generalize between different objects if they have the same non-accidental property, because the metric properties are likely to overlap. VisNet is a 4-layer unsupervised model of visual object recognition trained by competitive learning that utilizes a temporal trace learning rule to implement the learning of invariance using views that occur close together in time. A second crucial property of this model of object recognition is, when neurons in the level corresponding to the inferior temporal visual cortex respond selectively to objects, whether neurons in the intermediate layers can respond to combinations of features that may be parts of two or more objects. In an investigation using the four sides of a square presented in every possible combination, it was shown that even though different layer 4 neurons are tuned to encode each feature or feature combination orthogonally, neurons in the intermediate layers can respond to features or feature combinations present is several objects. This property is an important part of the way in which high capacity can be achieved in the four-layer ventral visual cortical pathway. These findings concerning non-accidental properties and the use of neurons in intermediate layers of the hierarchy help to emphasise fundamental underlying principles of the computations that may be implemented in the ventral cortical visual stream used in object recognition. Copyright © 2018 Elsevier Inc. All rights reserved.

Technology analysis for internet of things using big data learning

NASA Astrophysics Data System (ADS)

Senthilkumar, K.; Ellappan, Vijayan; Ajay

2017-11-01

We implemented a n efficient smart home automation system through the Internet of Things (IoT) including different type of sensors, this whole module will helps to the human beings to understand and provide the information about their home security system we are also going to apply Big Data Analysis to analyze the data that we are getting from different type of sensors in this module. We are using some sensors in our module to sense some type of things or object that makes our home standard and also introducing the face recognition system with an efficient algorithm into the module to make it more impressive and provide standardization in advance era.

Efficient LIDAR Point Cloud Data Managing and Processing in a Hadoop-Based Distributed Framework

NASA Astrophysics Data System (ADS)

Wang, C.; Hu, F.; Sha, D.; Han, X.

2017-10-01

Light Detection and Ranging (LiDAR) is one of the most promising technologies in surveying and mapping city management, forestry, object recognition, computer vision engineer and others. However, it is challenging to efficiently storage, query and analyze the high-resolution 3D LiDAR data due to its volume and complexity. In order to improve the productivity of Lidar data processing, this study proposes a Hadoop-based framework to efficiently manage and process LiDAR data in a distributed and parallel manner, which takes advantage of Hadoop's storage and computing ability. At the same time, the Point Cloud Library (PCL), an open-source project for 2D/3D image and point cloud processing, is integrated with HDFS and MapReduce to conduct the Lidar data analysis algorithms provided by PCL in a parallel fashion. The experiment results show that the proposed framework can efficiently manage and process big LiDAR data.

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

PubMed Central

dos Santos, Matheus; Ribeiro, Pedro Otávio; Núñez, Pedro; Botelho, Silvia

2017-01-01

The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot’s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper. PMID:28961163

Sensor agnostic object recognition using a map seeking circuit

NASA Astrophysics Data System (ADS)

Overman, Timothy L.; Hart, Michael

2012-05-01

Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

Contributions of Low and High Spatial Frequency Processing to Impaired Object Recognition Circuitry in Schizophrenia

PubMed Central

Calderone, Daniel J.; Hoptman, Matthew J.; Martínez, Antígona; Nair-Collins, Sangeeta; Mauro, Cristina J.; Bar, Moshe; Javitt, Daniel C.; Butler, Pamela D.

2013-01-01

Patients with schizophrenia exhibit cognitive and sensory impairment, and object recognition deficits have been linked to sensory deficits. The “frame and fill” model of object recognition posits that low spatial frequency (LSF) information rapidly reaches the prefrontal cortex (PFC) and creates a general shape of an object that feeds back to the ventral temporal cortex to assist object recognition. Visual dysfunction findings in schizophrenia suggest a preferential loss of LSF information. This study used functional magnetic resonance imaging (fMRI) and resting state functional connectivity (RSFC) to investigate the contribution of visual deficits to impaired object “framing” circuitry in schizophrenia. Participants were shown object stimuli that were intact or contained only LSF or high spatial frequency (HSF) information. For controls, fMRI revealed preferential activation to LSF information in precuneus, superior temporal, and medial and dorsolateral PFC areas, whereas patients showed a preference for HSF information or no preference. RSFC revealed a lack of connectivity between early visual areas and PFC for patients. These results demonstrate impaired processing of LSF information during object recognition in schizophrenia, with patients instead displaying increased processing of HSF information. This is consistent with findings of a preference for local over global visual information in schizophrenia. PMID:22735157

Visual recognition and inference using dynamic overcomplete sparse learning.

PubMed

Murray, Joseph F; Kreutz-Delgado, Kenneth

2007-09-01

We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.

Automaticity of Basic-Level Categorization Accounts for Labeling Effects in Visual Recognition Memory

ERIC Educational Resources Information Center

Richler, Jennifer J.; Gauthier, Isabel; Palmeri, Thomas J.

2011-01-01

Are there consequences of calling objects by their names? Lupyan (2008) suggested that overtly labeling objects impairs subsequent recognition memory because labeling shifts stored memory representations of objects toward the category prototype (representational shift hypothesis). In Experiment 1, we show that processing objects at the basic…

Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions.

PubMed

Contini, Erika W; Wardle, Susan G; Carlson, Thomas A

2017-10-01

Visual object recognition is a complex, dynamic process. Multivariate pattern analysis methods, such as decoding, have begun to reveal how the brain processes complex visual information. Recently, temporal decoding methods for EEG and MEG have offered the potential to evaluate the temporal dynamics of object recognition. Here we review the contribution of M/EEG time-series decoding methods to understanding visual object recognition in the human brain. Consistent with the current understanding of the visual processing hierarchy, low-level visual features dominate decodable object representations early in the time-course, with more abstract representations related to object category emerging later. A key finding is that the time-course of object processing is highly dynamic and rapidly evolving, with limited temporal generalisation of decodable information. Several studies have examined the emergence of object category structure, and we consider to what degree category decoding can be explained by sensitivity to low-level visual features. Finally, we evaluate recent work attempting to link human behaviour to the neural time-course of object processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Augmented reality three-dimensional object visualization and recognition with axially distributed sensing.

PubMed

Markman, Adam; Shen, Xin; Hua, Hong; Javidi, Bahram

2016-01-15

An augmented reality (AR) smartglass display combines real-world scenes with digital information enabling the rapid growth of AR-based applications. We present an augmented reality-based approach for three-dimensional (3D) optical visualization and object recognition using axially distributed sensing (ADS). For object recognition, the 3D scene is reconstructed, and feature extraction is performed by calculating the histogram of oriented gradients (HOG) of a sliding window. A support vector machine (SVM) is then used for classification. Once an object has been identified, the 3D reconstructed scene with the detected object is optically displayed in the smartglasses allowing the user to see the object, remove partial occlusions of the object, and provide critical information about the object such as 3D coordinates, which are not possible with conventional AR devices. To the best of our knowledge, this is the first report on combining axially distributed sensing with 3D object visualization and recognition for applications to augmented reality. The proposed approach can have benefits for many applications, including medical, military, transportation, and manufacturing.

Formal implementation of a performance evaluation model for the face recognition system.

PubMed

Shin, Yong-Nyuo; Kim, Jason; Lee, Yong-Jun; Shin, Woochang; Choi, Jin-Young

2008-01-01

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

Communication target object recognition for D2D connection with feature size limit

NASA Astrophysics Data System (ADS)

Ok, Jiheon; Kim, Soochang; Kim, Young-hoon; Lee, Chulhee

2015-03-01

Recently, a new concept of device-to-device (D2D) communication, which is called "point-and-link communication" has attracted great attentions due to its intuitive and simple operation. This approach enables user to communicate with target devices without any pre-identification information such as SSIDs, MAC addresses by selecting the target image displayed on the user's own device. In this paper, we present an efficient object matching algorithm that can be applied to look(point)-and-link communications for mobile services. Due to the limited channel bandwidth and low computational power of mobile terminals, the matching algorithm should satisfy low-complexity, low-memory and realtime requirements. To meet these requirements, we propose fast and robust feature extraction by considering the descriptor size and processing time. The proposed algorithm utilizes a HSV color histogram, SIFT (Scale Invariant Feature Transform) features and object aspect ratios. To reduce the descriptor size under 300 bytes, a limited number of SIFT key points were chosen as feature points and histograms were binarized while maintaining required performance. Experimental results show the robustness and the efficiency of the proposed algorithm.

Recognition vs Reverse Engineering in Boolean Concepts Learning

ERIC Educational Resources Information Center

Shafat, Gabriel; Levin, Ilya

2012-01-01

This paper deals with two types of logical problems--recognition problems and reverse engineering problems, and with the interrelations between these types of problems. The recognition problems are modeled in the form of a visual representation of various objects in a common pattern, with a composition of represented objects in the pattern.…

Ball-scale based hierarchical multi-object recognition in 3D medical images

NASA Astrophysics Data System (ADS)

Bağci, Ulas; Udupa, Jayaram K.; Chen, Xinjian

2010-03-01

This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.

Critical object recognition in millimeter-wave images with robustness to rotation and scale.

PubMed

Mohammadzade, Hoda; Ghojogh, Benyamin; Faezi, Sina; Shabany, Mahdi

2017-06-01

Locating critical objects is crucial in various security applications and industries. For example, in security applications, such as in airports, these objects might be hidden or covered under shields or secret sheaths. Millimeter-wave images can be utilized to discover and recognize the critical objects out of the hidden cases without any health risk due to their non-ionizing features. However, millimeter-wave images usually have waves in and around the detected objects, making object recognition difficult. Thus, regular image processing and classification methods cannot be used for these images and additional pre-processings and classification methods should be introduced. This paper proposes a novel pre-processing method for canceling rotation and scale using principal component analysis. In addition, a two-layer classification method is introduced and utilized for recognition. Moreover, a large dataset of millimeter-wave images is collected and created for experiments. Experimental results show that a typical classification method such as support vector machines can recognize 45.5% of a type of critical objects at 34.2% false alarm rate (FAR), which is a drastically poor recognition. The same method within the proposed recognition framework achieves 92.9% recognition rate at 0.43% FAR, which indicates a highly significant improvement. The significant contribution of this work is to introduce a new method for analyzing millimeter-wave images based on machine vision and learning approaches, which is not yet widely noted in the field of millimeter-wave image analysis.

Functional cross‐hemispheric shift between object‐place paired associate memory and spatial memory in the human hippocampus

PubMed Central

Lee, Choong‐Hee; Ryu, Jungwon; Lee, Sang‐Hun; Kim, Hakjin

2016-01-01

ABSTRACT The hippocampus plays critical roles in both object‐based event memory and spatial navigation, but it is largely unknown whether the left and right hippocampi play functionally equivalent roles in these cognitive domains. To examine the hemispheric symmetry of human hippocampal functions, we used an fMRI scanner to measure BOLD activity while subjects performed tasks requiring both object‐based event memory and spatial navigation in a virtual environment. Specifically, the subjects were required to form object‐place paired associate memory after visiting four buildings containing discrete objects in a virtual plus maze. The four buildings were visually identical, and the subjects used distal visual cues (i.e., scenes) to differentiate the buildings. During testing, the subjects were required to identify one of the buildings when cued with a previously associated object, and when shifted to a random place, the subject was expected to navigate to the previously chosen building. We observed that the BOLD activity foci changed from the left hippocampus to the right hippocampus as task demand changed from identifying a previously seen object (object‐cueing period) to searching for its paired‐associate place (object‐cued place recognition period). Furthermore, the efficient retrieval of object‐place paired associate memory (object‐cued place recognition period) was correlated with the BOLD response of the left hippocampus, whereas the efficient retrieval of relatively pure spatial memory (spatial memory period) was correlated with the right hippocampal BOLD response. These findings suggest that the left and right hippocampi in humans might process qualitatively different information for remembering episodic events in space. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc. PMID:27009679

Fat segmentation on chest CT images via fuzzy models

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, Jayaram K.; Wu, Caiyun; Pednekar, Gargi; Subramanian, Janani Rajan; Lederer, David J.; Christie, Jason; Torigian, Drew A.

2016-03-01

Quantification of fat throughout the body is vital for the study of many diseases. In the thorax, it is important for lung transplant candidates since obesity and being underweight are contraindications to lung transplantation given their associations with increased mortality. Common approaches for thoracic fat segmentation are all interactive in nature, requiring significant manual effort to draw the interfaces between fat and muscle with low efficiency and questionable repeatability. The goal of this paper is to explore a practical way for the segmentation of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) components of chest fat based on a recently developed body-wide automatic anatomy recognition (AAR) methodology. The AAR approach involves 3 main steps: building a fuzzy anatomy model of the body region involving all its major representative objects, recognizing objects in any given test image, and delineating the objects. We made several modifications to these steps to develop an effective solution to delineate SAT/VAT components of fat. Two new objects representing interfaces of SAT and VAT regions with other tissues, SatIn and VatIn are defined, rather than using directly the SAT and VAT components as objects for constructing the models. A hierarchical arrangement of these new and other reference objects is built to facilitate their recognition in the hierarchical order. Subsequently, accurate delineations of the SAT/VAT components are derived from these objects. Unenhanced CT images from 40 lung transplant candidates were utilized in experimentally evaluating this new strategy. Mean object location error achieved was about 2 voxels and delineation error in terms of false positive and false negative volume fractions were, respectively, 0.07 and 0.1 for SAT and 0.04 and 0.2 for VAT.

Integration trumps selection in object recognition.

PubMed

Saarela, Toni P; Landy, Michael S

2015-03-30

Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. Copyright © 2015 Elsevier Ltd. All rights reserved.

Compensation for Blur Requires Increase in Field of View and Viewing Time

PubMed Central

Kwon, MiYoung; Liu, Rong; Chien, Lillian

2016-01-01

Spatial resolution is an important factor for human pattern recognition. In particular, low resolution (blur) is a defining characteristic of low vision. Here, we examined spatial (field of view) and temporal (stimulus duration) requirements for blurry object recognition. The spatial resolution of an image such as letter or face, was manipulated with a low-pass filter. In experiment 1, studying spatial requirement, observers viewed a fixed-size object through a window of varying sizes, which was repositioned until object identification (moving window paradigm). Field of view requirement, quantified as the number of “views” (window repositions) for correct recognition, was obtained for three blur levels, including no blur. In experiment 2, studying temporal requirement, we determined threshold viewing time, the stimulus duration yielding criterion recognition accuracy, at six blur levels, including no blur. For letter and face recognition, we found blur significantly increased the number of views, suggesting a larger field of view is required to recognize blurry objects. We also found blur significantly increased threshold viewing time, suggesting longer temporal integration is necessary to recognize blurry objects. The temporal integration reflects the tradeoff between stimulus intensity and time. While humans excel at recognizing blurry objects, our findings suggest compensating for blur requires increased field of view and viewing time. The need for larger spatial and longer temporal integration for recognizing blurry objects may further challenge object recognition in low vision. Thus, interactions between blur and field of view should be considered for developing low vision rehabilitation or assistive aids. PMID:27622710

Integration trumps selection in object recognition

PubMed Central

Saarela, Toni P.; Landy, Michael S.

2015-01-01

Summary Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several “cues” (color, luminance, texture etc.), and humans can integrate sensory cues to improve detection and recognition [1–3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue-invariance by responding to a given shape independent of the visual cue defining it [5–8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10,11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11,12], imaging [13–16], and single-cell and neural population recordings [17,18]. Besides single features, attention can select whole objects [19–21]. Objects are among the suggested “units” of attention because attention to a single feature of an object causes the selection of all of its features [19–21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near-optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

Complementary Hemispheric Asymmetries in Object Naming and Recognition: A Voxel-Based Correlational Study

ERIC Educational Resources Information Center

Acres, K.; Taylor, K. I.; Moss, H. E.; Stamatakis, E. A.; Tyler, L. K.

2009-01-01

Cognitive neuroscientific research proposes complementary hemispheric asymmetries in naming and recognising visual objects, with a left temporal lobe advantage for object naming and a right temporal lobe advantage for object recognition. Specifically, it has been proposed that the left inferior temporal lobe plays a mediational role linking…

Priming Contour-Deleted Images: Evidence for Immediate Representations in Visual Object Recognition.

ERIC Educational Resources Information Center

Biederman, Irving; Cooper, Eric E.

1991-01-01

Speed and accuracy of identification of pictures of objects are facilitated by prior viewing. Contributions of image features, convex or concave components, and object models in a repetition priming task were explored in 2 studies involving 96 college students. Results provide evidence of intermediate representations in visual object recognition.…

Electrophysiological evidence for effects of color knowledge in object recognition.

PubMed

Lu, Aitao; Xu, Guiping; Jin, Hua; Mo, Lei; Zhang, Jijia; Zhang, John X

2010-01-29

Knowledge about the typical colors associated with familiar everyday objects (i.e., strawberries are red) is well-known to be represented in the conceptual semantic system. Evidence that such knowledge may also play a role in early perceptual processes for object recognition is scant. In the present ERP study, participants viewed a list of object pictures and detected infrequent stimulus repetitions. Results show that shortly after stimulus onset, ERP components indexing early perceptual processes, including N1, P2, and N2, differentiated between objects in their appropriate or congruent color from these objects in an inappropriate or incongruent color. Such congruence effect also occurred in N3 associated with semantic processing of pictures but not in N4 for domain-general semantic processing. Our results demonstrate a clear effect of color knowledge in early object recognition stages and support the following proposal-color as a surface property is stored in a multiple-memory system where pre-semantic perceptual and semantic conceptual representations interact during object recognition. (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Coding of visual object features and feature conjunctions in the human brain.

PubMed

Martinovic, Jasna; Gruber, Thomas; Müller, Matthias M

2008-01-01

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object's features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process--while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200-400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.

[Recognition of visual objects under forward masking. Effects of cathegorial similarity of test and masking stimuli].

PubMed

Gerasimenko, N Iu; Slavutskaia, A V; Kalinin, S A; Kulikov, M A; Mikhaĭlova, E S

2013-01-01

In 38 healthy subjects accuracy and response time were examined during recognition of two categories of images--animals andnonliving objects--under forward masking. We revealed new data that masking effects depended of categorical similarity of target and masking stimuli. The recognition accuracy was the lowest and the response time was the most slow, when the target and masking stimuli belongs to the same category, that was combined with high dispersion of response times. The revealed effects were more clear in the task of animal recognition in comparison with the recognition of nonliving objects. We supposed that the revealed effects connected with interference between cortical representations of the target and masking stimuli and discussed our results in context of cortical interference and negative priming.

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

PubMed

Wong, Sebastien C; Stamatescu, Victor; Gatt, Adam; Kearney, David; Lee, Ivan; McDonnell, Mark D

2017-10-01

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.

How Redundant Are Redundant Color Adjectives? An Efficiency-Based Analysis of Color Overspecification

PubMed Central

Rubio-Fernández, Paula

2016-01-01

Color adjectives tend to be used redundantly in referential communication. I propose that redundant color adjectives (RCAs) are often intended to exploit a color contrast in the visual context and hence facilitate object identification, despite not being necessary to establish unique reference. Two language-production experiments investigated two types of factors that may affect the use of RCAs: factors related to the efficiency of color in the visual context and factors related to the semantic category of the noun. The results of Experiment 1 confirmed that people produce RCAs when color may facilitate object recognition; e.g., they do so more often in polychrome displays than in monochrome displays, and more often in English (pre-nominal position) than in Spanish (post-nominal position). RCAs are also used when color is a central property of the object category; e.g., people referred to the color of clothes more often than to the color of geometrical figures (Experiment 1), and they overspecified atypical colors more often than variable and stereotypical colors (Experiment 2). These results are relevant for pragmatic models of referential communication based on Gricean pragmatics and informativeness. An alternative analysis is proposed, which focuses on the efficiency and pertinence of color in a given referential situation. PMID:26924999

Neurophysiological indices of perceptual object priming in the absence of explicit recognition memory.

PubMed

Harris, Jill D; Cutmore, Tim R H; O'Gorman, John; Finnigan, Simon; Shum, David

2009-02-01

The aim of this study was to identify ERP correlates of perceptual object priming that are insensitive to factors affecting explicit, episodic memory. EEG was recorded from 21 participants while they performed a visual object recognition test on a combination of unstudied items and old items that were previously encountered during either a 'deep' or 'shallow' levels-of-processing (LOP) study task. The results demonstrated a midline P150 old/new effect which was sensitive only to objects' old/new status and not to the accuracy of recognition responses to old items, or to the LOP manipulation. Similar outcomes were observed for the subsequent P200 and N400 effects, the former of which had a parietal scalp maximum and the latter, a broadly distributed topography. In addition an LPC old/new effect typical of those reported in past ERP recognition studies was observed. These outcomes support the proposal that the P150 effect is reflective of perceptual object priming and moreover, provide novel evidence that this and the P200 effect are independent of explicit recognition memory process(es).

Improved Object Detection Using a Robotic Sensing Antenna with Vibration Damping Control

PubMed Central

Feliu-Batlle, Vicente; Feliu-Talegon, Daniel; Castillo-Berrio, Claudia Fernanda

2017-01-01

Some insects or mammals use antennae or whiskers to detect by the sense of touch obstacles or recognize objects in environments in which other senses like vision cannot work. Artificial flexible antennae can be used in robotics to mimic this sense of touch in these recognition tasks. We have designed and built a two-degree of freedom (2DOF) flexible antenna sensor device to perform robot navigation tasks. This device is composed of a flexible beam, two servomotors that drive the beam and a load cell sensor that detects the contact of the beam with an object. It is found that the efficiency of such a device strongly depends on the speed and accuracy achieved by the antenna positioning system. These issues are severely impaired by the vibrations that appear in the antenna during its movement. However, these antennae are usually moved without taking care of these undesired vibrations. This article proposes a new closed-loop control schema that cancels vibrations and improves the free movements of the antenna. Moreover, algorithms to estimate the 3D beam position and the instant and point of contact with an object are proposed. Experiments are reported that illustrate the efficiency of these proposed algorithms and the improvements achieved in object detection tasks using a control system that cancels beam vibrations. PMID:28406449

Improved Object Detection Using a Robotic Sensing Antenna with Vibration Damping Control.

PubMed

Feliu-Batlle, Vicente; Feliu-Talegon, Daniel; Castillo-Berrio, Claudia Fernanda

2017-04-13

Some insects or mammals use antennae or whiskers to detect by the sense of touch obstacles or recognize objects in environments in which other senses like vision cannot work. Artificial flexible antennae can be used in robotics to mimic this sense of touch in these recognition tasks. We have designed and built a two-degree of freedom (2DOF) flexible antenna sensor device to perform robot navigation tasks. This device is composed of a flexible beam, two servomotors that drive the beam and a load cell sensor that detects the contact of the beam with an object. It is found that the efficiency of such a device strongly depends on the speed and accuracy achieved by the antenna positioning system. These issues are severely impaired by the vibrations that appear in the antenna during its movement. However, these antennae are usually moved without taking care of these undesired vibrations. This article proposes a new closed-loop control schema that cancels vibrations and improves the free movements of the antenna. Moreover, algorithms to estimate the 3D beam position and the instant and point of contact with an object are proposed. Experiments are reported that illustrate the efficiency of these proposed algorithms and the improvements achieved in object detection tasks using a control system that cancels beam vibrations.

Handheld laser scanner automatic registration based on random coding

NASA Astrophysics Data System (ADS)

He, Lei; Yu, Chun-ping; Wang, Li

2011-06-01

Current research on Laser Scanner often focuses mainly on the static measurement. Little use has been made of dynamic measurement, that are appropriate for more problems and situations. In particular, traditional Laser Scanner must Keep stable to scan and measure coordinate transformation parameters between different station. In order to make the scanning measurement intelligently and rapidly, in this paper ,we developed a new registration algorithm for handleheld laser scanner based on the positon of target, which realize the dynamic measurement of handheld laser scanner without any more complex work. the double camera on laser scanner can take photograph of the artificial target points to get the three-dimensional coordinates, this points is designed by random coding. And then, a set of matched points is found from control points to realize the orientation of scanner by the least-square common points transformation. After that the double camera can directly measure the laser point cloud in the surface of object and get the point cloud data in an unified coordinate system. There are three major contributions in the paper. Firstly, a laser scanner based on binocular vision is designed with double camera and one laser head. By those, the real-time orientation of laser scanner is realized and the efficiency is improved. Secondly, the coding marker is introduced to solve the data matching, a random coding method is proposed. Compared with other coding methods,the marker with this method is simple to match and can avoid the shading for the object. Finally, a recognition method of coding maker is proposed, with the use of the distance recognition, it is more efficient. The method present here can be used widely in any measurement from small to huge obiect, such as vehicle, airplane which strengthen its intelligence and efficiency. The results of experiments and theory analzing demonstrate that proposed method could realize the dynamic measurement of handheld laser scanner. Theory analysis and experiment shows the method is reasonable and efficient.

A validated set of tool pictures with matched objects and non-objects for laterality research.

PubMed

Verma, Ark; Brysbaert, Marc

2015-01-01

Neuropsychological and neuroimaging research has established that knowledge related to tool use and tool recognition is lateralized to the left cerebral hemisphere. Recently, behavioural studies with the visual half-field technique have confirmed the lateralization. A limitation of this research was that different sets of stimuli had to be used for the comparison of tools to other objects and objects to non-objects. Therefore, we developed a new set of stimuli containing matched triplets of tools, other objects and non-objects. With the new stimulus set, we successfully replicated the findings of no visual field advantage for objects in an object recognition task combined with a significant right visual field advantage for tools in a tool recognition task. The set of stimuli is available as supplemental data to this article.

The Role of Sensory-Motor Information in Object Recognition: Evidence from Category-Specific Visual Agnosia

ERIC Educational Resources Information Center

Wolk, D.A.; Coslett, H.B.; Glosser, G.

2005-01-01

The role of sensory-motor representations in object recognition was investigated in experiments involving AD, a patient with mild visual agnosia who was impaired in the recognition of visually presented living as compared to non-living entities. AD named visually presented items for which sensory-motor information was available significantly more…

Use of Authentic-Speech Technique for Teaching Sound Recognition to EFL Students

ERIC Educational Resources Information Center

Sersen, William J.

2011-01-01

The main objective of this research was to test an authentic-speech technique for improving the sound-recognition skills of EFL (English as a foreign language) students at Roi-Et Rajabhat University. The secondary objective was to determine the correlation, if any, between students' self-evaluation of sound-recognition progress and the actual…

Ordinal measures for iris recognition.

PubMed

Sun, Zhenan; Tan, Tieniu

2009-12-01

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.

Combining color and shape information for illumination-viewpoint invariant object recognition.

PubMed

Diplaros, Aristeidis; Gevers, Theo; Patras, Ioannis

2006-01-01

In this paper, we propose a new scheme that merges color- and shape-invariant information for object recognition. To obtain robustness against photometric changes, color-invariant derivatives are computed first. Color invariance is an important aspect of any object recognition scheme, as color changes considerably with the variation in illumination, object pose, and camera viewpoint. These color invariant derivatives are then used to obtain similarity invariant shape descriptors. Shape invariance is equally important as, under a change in camera viewpoint and object pose, the shape of a rigid object undergoes a perspective projection on the image plane. Then, the color and shape invariants are combined in a multidimensional color-shape context which is subsequently used as an index. As the indexing scheme makes use of a color-shape invariant context, it provides a high-discriminative information cue robust against varying imaging conditions. The matching function of the color-shape context allows for fast recognition, even in the presence of object occlusion and cluttering. From the experimental results, it is shown that the method recognizes rigid objects with high accuracy in 3-D complex scenes and is robust against changing illumination, camera viewpoint, object pose, and noise.

Eye Movements to Pictures Reveal Transient Semantic Activation during Spoken Word Recognition

ERIC Educational Resources Information Center

Yee, Eiling; Sedivy, Julie C.

2006-01-01

Two experiments explore the activation of semantic information during spoken word recognition. Experiment 1 shows that as the name of an object unfolds (e.g., lock), eye movements are drawn to pictorial representations of both the named object and semantically related objects (e.g., key). Experiment 2 shows that objects semantically related to an…

It's all connected: Pathways in visual object recognition and early noun learning.

PubMed

Smith, Linda B

2013-11-01

A developmental pathway may be defined as the route, or chain of events, through which a new structure or function forms. For many human behaviors, including object name learning and visual object recognition, these pathways are often complex and multicausal and include unexpected dependencies. This article presents three principles of development that suggest the value of a developmental psychology that explicitly seeks to trace these pathways and uses empirical evidence on developmental dependencies among motor development, action on objects, visual object recognition, and object name learning in 12- to 24-month-old infants to make the case. The article concludes with a consideration of the theoretical implications of this approach. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Development of a sonar-based object recognition system

NASA Astrophysics Data System (ADS)

Ecemis, Mustafa Ihsan

2001-02-01

Sonars are used extensively in mobile robotics for obstacle detection, ranging and avoidance. However, these range-finding applications do not exploit the full range of information carried in sonar echoes. In addition, mobile robots need robust object recognition systems. Therefore, a simple and robust object recognition system using ultrasonic sensors may have a wide range of applications in robotics. This dissertation develops and analyzes an object recognition system that uses ultrasonic sensors of the type commonly found on mobile robots. Three principal experiments are used to test the sonar recognition system: object recognition at various distances, object recognition during unconstrained motion, and softness discrimination. The hardware setup, consisting of an inexpensive Polaroid sonar and a data acquisition board, is described first. The software for ultrasound signal generation, echo detection, data collection, and data processing is then presented. Next, the dissertation describes two methods to extract information from the echoes, one in the frequency domain and the other in the time domain. The system uses the fuzzy ARTMAP neural network to recognize objects on the basis of the information content of their echoes. In order to demonstrate that the performance of the system does not depend on the specific classification method being used, the K- Nearest Neighbors (KNN) Algorithm is also implemented. KNN yields a test accuracy similar to fuzzy ARTMAP in all experiments. Finally, the dissertation describes a method for extracting features from the envelope function in order to reduce the dimension of the input vector used by the classifiers. Decreasing the size of the input vectors reduces the memory requirements of the system and makes it run faster. It is shown that this method does not affect the performance of the system dramatically and is more appropriate for some tasks. The results of these experiments demonstrate that sonar can be used to develop a low-cost, low-computation system for real-time object recognition tasks on mobile robots. This system differs from all previous approaches in that it is relatively simple, robust, fast, and inexpensive.

The roles of perceptual and conceptual information in face recognition.

PubMed

Schwartz, Linoy; Yovel, Galit

2016-11-01

The representation of familiar objects is comprised of perceptual information about their visual properties as well as the conceptual knowledge that we have about them. What is the relative contribution of perceptual and conceptual information to object recognition? Here, we examined this question by designing a face familiarization protocol during which participants were either exposed to rich perceptual information (viewing each face in different angles and illuminations) or with conceptual information (associating each face with a different name). Both conditions were compared with single-view faces presented with no labels. Recognition was tested on new images of the same identities to assess whether learning generated a view-invariant representation. Results showed better recognition of novel images of the learned identities following association of a face with a name label, but no enhancement following exposure to multiple face views. Whereas these findings may be consistent with the role of category learning in object recognition, face recognition was better for labeled faces only when faces were associated with person-related labels (name, occupation), but not with person-unrelated labels (object names or symbols). These findings suggest that association of meaningful conceptual information with an image shifts its representation from an image-based percept to a view-invariant concept. They further indicate that the role of conceptual information should be considered to account for the superior recognition that we have for familiar faces and objects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise

PubMed Central

Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

2016-01-01

Vocabulary size has been suggested as a useful measure of “verbal abilities” that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18–35 years) and 22 older (60–78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults’ poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access; with an average vocabulary size similar to that of younger adults, they were still slower in lexical access. PMID:27458400

Age-Related Differences in Lexical Access Relate to Speech Recognition in Noise.

PubMed

Carroll, Rebecca; Warzybok, Anna; Kollmeier, Birger; Ruigendijk, Esther

2016-01-01

Vocabulary size has been suggested as a useful measure of "verbal abilities" that correlates with speech recognition scores. Knowing more words is linked to better speech recognition. How vocabulary knowledge translates to general speech recognition mechanisms, how these mechanisms relate to offline speech recognition scores, and how they may be modulated by acoustical distortion or age, is less clear. Age-related differences in linguistic measures may predict age-related differences in speech recognition in noise performance. We hypothesized that speech recognition performance can be predicted by the efficiency of lexical access, which refers to the speed with which a given word can be searched and accessed relative to the size of the mental lexicon. We tested speech recognition in a clinical German sentence-in-noise test at two signal-to-noise ratios (SNRs), in 22 younger (18-35 years) and 22 older (60-78 years) listeners with normal hearing. We also assessed receptive vocabulary, lexical access time, verbal working memory, and hearing thresholds as measures of individual differences. Age group, SNR level, vocabulary size, and lexical access time were significant predictors of individual speech recognition scores, but working memory and hearing threshold were not. Interestingly, longer accessing times were correlated with better speech recognition scores. Hierarchical regression models for each subset of age group and SNR showed very similar patterns: the combination of vocabulary size and lexical access time contributed most to speech recognition performance; only for the younger group at the better SNR (yielding about 85% correct speech recognition) did vocabulary size alone predict performance. Our data suggest that successful speech recognition in noise is mainly modulated by the efficiency of lexical access. This suggests that older adults' poorer performance in the speech recognition task may have arisen from reduced efficiency in lexical access; with an average vocabulary size similar to that of younger adults, they were still slower in lexical access.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

Application of syntactic methods of pattern recognition for data mining and knowledge discovery in medicine

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-04-01

This paper presents and discusses possibilities of application of selected algorithms belonging to the group of syntactic methods of patten recognition used to analyze and extract features of shapes and to diagnose morphological lesions seen on selected medical images. This method is particularly useful for specialist morphological analysis of shapes of selected organs of abdominal cavity conducted to diagnose disease symptoms occurring in the main pancreatic ducts, upper segments of ureters and renal pelvis. Analysis of the correct morphology of these organs is possible with the application of the sequential and tree method belonging to the group of syntactic methods of pattern recognition. The objective of this analysis is to support early diagnosis of disease lesions, mainly characteristic for carcinoma and pancreatitis, based on examinations of ERCP images and a diagnosis of morphological lesions in ureters as well as renal pelvis based on an analysis of urograms. In the analysis of ERCP images the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis, while in the case of kidney radiogram analysis the aim is to diagnose local irregularities of ureter lumen and to examine the morphology of renal pelvis and renal calyxes. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of description of features of shapes and context-free sequential attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing of width diagrams of the examined structures. Additionally, in order to support the analysis of the correct structure of renal pelvis a method using the tree grammar for syntactic pattern recognition to define its correct morphological shapes has been presented.

Progestogens’ effects and mechanisms for object recognition memory across the lifespan

PubMed Central

Walf, Alicia A.; Koonce, Carolyn J.; Frye, Cheryl A.

2016-01-01

This review explores the effects of female reproductive hormones, estrogens and progestogens, with a focus on progesterone and allopregnanolone, on object memory. Progesterone and its metabolites, in particular allopregnanolone, exert various effects on both cognitive and non-mnemonic functions in females. The well-known object recognition task is a valuable experimental paradigm that can be used to determine the effects and mechanisms of progestogens for mnemonic effects across the lifespan, which will be discussed herein. In this task there is little test-decay when different objects are used as targets and baseline valance for objects is controlled. This allows repeated testing, within-subjects designs, and longitudinal assessments, which aid understanding of changes in hormonal milieu. Objects are not aversive or food-based, which are hormone-sensitive factors. This review focuses on published data from our laboratory, and others, using the object recognition task in rodents to assess the role and mechanisms of progestogens throughout the lifespan. Improvements in object recognition performance of rodents are often associated with higher hormone levels in the hippocampus and prefrontal cortex during natural cycles, with hormone replacement following ovariectomy in young animals, or with aging. The capacity for reversal of age- and reproductive senescence-related decline in cognitive performance, and changes in neural plasticity that may be dissociated from peripheral effects with such decline, are discussed. The focus here will be on the effects of brain-derived factors, such as the neurosteroid, allopregnanolone, and other hormones, for enhancing object recognition across the lifespan. PMID:26235328

Ordinal feature selection for iris and palmprint recognition.

PubMed

Sun, Zhenan; Wang, Libin; Tan, Tieniu

2014-09-01

Ordinal measures have been demonstrated as an effective feature representation model for iris and palmprint recognition. However, ordinal measures are a general concept of image analysis and numerous variants with different parameter settings, such as location, scale, orientation, and so on, can be derived to construct a huge feature space. This paper proposes a novel optimization formulation for ordinal feature selection with successful applications to both iris and palmprint recognition. The objective function of the proposed feature selection method has two parts, i.e., misclassification error of intra and interclass matching samples and weighted sparsity of ordinal feature descriptors. Therefore, the feature selection aims to achieve an accurate and sparse representation of ordinal measures. And, the optimization subjects to a number of linear inequality constraints, which require that all intra and interclass matching pairs are well separated with a large margin. Ordinal feature selection is formulated as a linear programming (LP) problem so that a solution can be efficiently obtained even on a large-scale feature pool and training database. Extensive experimental results demonstrate that the proposed LP formulation is advantageous over existing feature selection methods, such as mRMR, ReliefF, Boosting, and Lasso for biometric recognition, reporting state-of-the-art accuracy on CASIA and PolyU databases.

Search algorithm complexity modeling with application to image alignment and matching

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2014-05-01

Search algorithm complexity modeling, in the form of penetration rate estimation, provides a useful way to estimate search efficiency in application domains which involve searching over a hypothesis space of reference templates or models, as in model-based object recognition, automatic target recognition, and biometric recognition. The penetration rate quantifies the expected portion of the database that must be searched, and is useful for estimating search algorithm computational requirements. In this paper we perform mathematical modeling to derive general equations for penetration rate estimates that are applicable to a wide range of recognition problems. We extend previous penetration rate analyses to use more general probabilistic modeling assumptions. In particular we provide penetration rate equations within the framework of a model-based image alignment application domain in which a prioritized hierarchical grid search is used to rank subspace bins based on matching probability. We derive general equations, and provide special cases based on simplifying assumptions. We show how previously-derived penetration rate equations are special cases of the general formulation. We apply the analysis to model-based logo image alignment in which a hierarchical grid search is used over a geometric misalignment transform hypothesis space. We present numerical results validating the modeling assumptions and derived formulation.

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report

PubMed Central

Poth, Christian H.; Schneider, Werner X.

2016-01-01

Human vision is organized in discrete processing episodes (e.g., eye fixations or task-steps). Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM), which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of 10 letters and reported as many as possible after a retention interval (whole report). Next, participants viewed a probe letter and indicated whether it had been one of the 10 letters (probe recognition). In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters) compared with non-encoded letters (non-reported letters). Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2, participants reported only one of 10 letters (partial report) and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM. PMID:27713722

Episodic Short-Term Recognition Requires Encoding into Visual Working Memory: Evidence from Probe Recognition after Letter Report.

PubMed

Poth, Christian H; Schneider, Werner X

2016-01-01

Human vision is organized in discrete processing episodes (e.g., eye fixations or task-steps). Object information must be transmitted across episodes to enable episodic short-term recognition: recognizing whether a current object has been seen in a previous episode. We ask whether episodic short-term recognition presupposes that objects have been encoded into capacity-limited visual working memory (VWM), which retains visual information for report. Alternatively, it could rely on the activation of visual features or categories that occurs before encoding into VWM. We assessed the dependence of episodic short-term recognition on VWM by a new paradigm combining letter report and probe recognition. Participants viewed displays of 10 letters and reported as many as possible after a retention interval (whole report). Next, participants viewed a probe letter and indicated whether it had been one of the 10 letters (probe recognition). In Experiment 1, probe recognition was more accurate for letters that had been encoded into VWM (reported letters) compared with non-encoded letters (non-reported letters). Interestingly, those letters that participants reported in their whole report had been near to one another within the letter displays. This suggests that the encoding into VWM proceeded in a spatially clustered manner. In Experiment 2, participants reported only one of 10 letters (partial report) and probes either referred to this letter, to letters that had been near to it, or far from it. Probe recognition was more accurate for near than for far letters, although none of these letters had to be reported. These findings indicate that episodic short-term recognition is constrained to a small number of simultaneously presented objects that have been encoded into VWM.

Bimodal Benefits on Objective and Subjective Outcomes for Adult Cochlear Implant Users

PubMed Central

Heo, Ji-Hye; Lee, Won-Sang

2013-01-01

Background and Objectives Given that only a few studies have focused on the bimodal benefits on objective and subjective outcomes and emphasized the importance of individual data, the present study aimed to measure the bimodal benefits on the objective and subjective outcomes for adults with cochlear implant. Subjects and Methods Fourteen listeners with bimodal devices were tested on the localization and recognition abilities using environmental sounds, 1-talker, and 2-talker speech materials. The localization ability was measured through an 8-loudspeaker array. For the recognition measures, listeners were asked to repeat the sentences or say the environmental sounds the listeners heard. As a subjective questionnaire, three domains of Korean-version of Speech, Spatial, Qualities of Hearing scale (K-SSQ) were used to explore any relationships between objective and subjective outcomes. Results Based on the group-mean data, the bimodal hearing enhanced both localization and recognition regardless of test material. However, the inter- and intra-subject variability appeared to be large across test materials for both localization and recognition abilities. Correlation analyses revealed that the relationships were not always consistent between the objective outcomes and the subjective self-reports with bimodal devices. Conclusions Overall, this study supports significant bimodal advantages on localization and recognition measures, yet the large individual variability in bimodal benefits should be considered carefully for the clinical assessment as well as counseling. The discrepant relations between objective and subjective results suggest that the bimodal benefits in traditional localization or recognition measures might not necessarily correspond to the self-reported subjective advantages in everyday listening environments. PMID:24653909

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

NASA Astrophysics Data System (ADS)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

Ventromedial prefrontal cortex is obligatory for consolidation and reconsolidation of object recognition memory.

PubMed

Akirav, Irit; Maroun, Mouna

2006-12-01

Once consolidated, a long-term memory item could regain susceptibility to consolidation blockers, that is, reconsolidate, upon its reactivation. Both consolidation and reconsolidation require protein synthesis, but it is not yet known how similar these processes are in terms of molecular, cellular, and neural circuit mechanisms. Whereas most previous studies focused on aversive conditioning in the amygdala and the hippocampus, here we examine the role of the ventromedial prefrontal cortex (vmPFC) in consolidation and reconsolidation of object recognition memory. Object recognition memory is the ability to discriminate the familiarity of previously encountered objects. We found that microinfusion of the protein synthesis inhibitor anisomycin or the N-methyl-D-aspartate (NMDA) receptor antagonist D,L-2-amino-5-phosphonovaleric acid (APV) into the vmPFC, immediately after training, resulted in impairment of long-term (24 h) but not short-term (3 h) recognition memory. Similarly, microinfusion of anisomycin or APV into the vmPFC immediately after reactivation of the long-term memory impaired recognition memory 24 h, but not 3 h, post-reactivation. These results indicate that both protein synthesis and NMDA receptors are required for consolidation and reconsolidation of recognition memory in the vmPFC.

Bimodal benefits on objective and subjective outcomes for adult cochlear implant users.

PubMed

Heo, Ji-Hye; Lee, Jae-Hee; Lee, Won-Sang

2013-09-01

Given that only a few studies have focused on the bimodal benefits on objective and subjective outcomes and emphasized the importance of individual data, the present study aimed to measure the bimodal benefits on the objective and subjective outcomes for adults with cochlear implant. Fourteen listeners with bimodal devices were tested on the localization and recognition abilities using environmental sounds, 1-talker, and 2-talker speech materials. The localization ability was measured through an 8-loudspeaker array. For the recognition measures, listeners were asked to repeat the sentences or say the environmental sounds the listeners heard. As a subjective questionnaire, three domains of Korean-version of Speech, Spatial, Qualities of Hearing scale (K-SSQ) were used to explore any relationships between objective and subjective outcomes. Based on the group-mean data, the bimodal hearing enhanced both localization and recognition regardless of test material. However, the inter- and intra-subject variability appeared to be large across test materials for both localization and recognition abilities. Correlation analyses revealed that the relationships were not always consistent between the objective outcomes and the subjective self-reports with bimodal devices. Overall, this study supports significant bimodal advantages on localization and recognition measures, yet the large individual variability in bimodal benefits should be considered carefully for the clinical assessment as well as counseling. The discrepant relations between objective and subjective results suggest that the bimodal benefits in traditional localization or recognition measures might not necessarily correspond to the self-reported subjective advantages in everyday listening environments.

Ultra-fast Object Recognition from Few Spikes

DTIC Science & Technology

2005-07-06

Computer Science and Artificial Intelligence Laboratory Ultra-fast Object Recognition from Few Spikes Chou Hung, Gabriel Kreiman , Tomaso Poggio...neural code for different kinds of object-related information. *The authors, Chou Hung and Gabriel Kreiman , contributed equally to this work...Supplementary Material is available at http://ramonycajal.mit.edu/ kreiman /resources/ultrafast

The Neural Regions Sustaining Episodic Encoding and Recognition of Objects

ERIC Educational Resources Information Center

Hofer, Alex; Siedentopf, Christian M.; Ischebeck, Anja; Rettenbacher, Maria A.; Widschwendter, Christian G.; Verius, Michael; Golaszewski, Stefan M.; Koppelstaetter, Florian; Felber, Stephan; Wolfgang Fleischhacker, W.

2007-01-01

In this functional MRI experiment, encoding of objects was associated with activation in left ventrolateral prefrontal/insular and right dorsolateral prefrontal and fusiform regions as well as in the left putamen. By contrast, correct recognition of previously learned objects (R judgments) produced activation in left superior frontal, bilateral…

Implications of Animal Object Memory Research for Human Amnesia

ERIC Educational Resources Information Center

Winters, Boyer D.; Saksida, Lisa M.; Bussey, Timothy J.

2010-01-01

Damage to structures in the human medial temporal lobe causes severe memory impairment. Animal object recognition tests gained prominence from attempts to model "global" human medial temporal lobe amnesia, such as that observed in patient HM. These tasks, such as delayed nonmatching-to-sample and spontaneous object recognition, for assessing…

Crowded and Sparse Domains in Object Recognition: Consequences for Categorization and Naming

ERIC Educational Resources Information Center

Gale, Tim M.; Laws, Keith R.; Foley, Kerry

2006-01-01

Some models of object recognition propose that items from structurally crowded categories (e.g., living things) permit faster access to superordinate semantic information than structurally dissimilar categories (e.g., nonliving things), but slower access to individual object information when naming items. We present four experiments that utilize…

Automatic anatomy recognition in whole-body PET/CT images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Huiqian; Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity ofmore » anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.« less

Reversibility of object recognition but not spatial memory impairment following binge-like alcohol exposure in rats

PubMed Central

Cippitelli, Andrea; Zook, Michelle; Bell, Lauren; Damadzic, Ruslan; Eskay, Robert L.; Schwandt, Melanie; Heilig, Markus

2010-01-01

Excessive alcohol use leads to neurodegeneration in several brain structures including the hippocampal dentate gyrus and the entorhinal cortex. Cognitive deficits that result are among the most insidious and debilitating consequences of alcoholism. The object exploration task (OET) provides a sensitive measurement of spatial memory impairment induced by hippocampal and cortical damage. In this study, we examine whether the observed neurotoxicity produced by a 4-day binge ethanol treatment results in long-term memory impairment by observing the time course of reactions to spatial change (object configuration) and non-spatial change (object recognition). Wistar rats were assessed for their abilities to detect spatial configuration in the OET at 1 week and 10 weeks following the ethanol treatment, in which ethanol groups received 9–15 g/kg/day and achieved blood alcohol levels over 300 mg/dl. At 1 week, results indicated that the binge alcohol treatment produced impairment in both spatial memory and non-spatial object recognition performance. Unlike the controls, ethanol treated rats did not increase the duration or number of contacts with the displaced object in the spatial memory task, nor did they increase the duration of contacts with the novel object in the object recognition task. After 10 weeks, spatial memory remained impaired in the ethanol treated rats but object recognition ability was recovered. Our data suggest that episodes of binge-like alcohol exposure result in long-term and possibly permanent impairments in memory for the configuration of objects during exploration, whereas the ability to detect non-spatial changes is only temporarily affected. PMID:20849966

Intelligent data processing of an ultrasonic sensor system for pattern recognition improvements

NASA Astrophysics Data System (ADS)

Na, Seung You; Park, Min-Sang; Hwang, Won-Gul; Kee, Chang-Doo

1999-05-01

Though conventional time-of-flight ultrasonic sensor systems are popular due to the advantages of low cost and simplicity, the usage of the sensors is rather narrowly restricted within object detection and distance readings. There is a strong need to enlarge the amount of environmental information for mobile applications to provide intelligent autonomy. Wide sectors of such neighboring object recognition problems can be satisfactorily handled with coarse vision data such as sonar maps instead of accurate laser or optic measurements. For the usage of object pattern recognition, ultrasonic senors have inherent shortcomings of poor directionality and specularity which result in low spatial resolution and indistinctiveness of object patterns. To resolve these problems an array of increased number of sensor elements has been used for large objects. In this paper we propose a method of sensor array system with improved recognition capability using electronic circuits accompanying the sensor array and neuro-fuzzy processing of data fusion. The circuit changes transmitter output voltages of array elements in several steps. Relying upon the known sensor characteristics, a set of different return signals from neighboring senors is manipulated to provide an enhanced pattern recognition in the aspects of inclination angle, size and shift as well as distance of objects. The results show improved resolution of the measurements for smaller targets.

A Comparison of the Effects of Depth Rotation on Visual and Haptic Three-Dimensional Object Recognition

ERIC Educational Resources Information Center

Lawson, Rebecca

2009-01-01

A sequential matching task was used to compare how the difficulty of shape discrimination influences the achievement of object constancy for depth rotations across haptic and visual object recognition. Stimuli were nameable, 3-dimensional plastic models of familiar objects (e.g., bed, chair) and morphs midway between these endpoint shapes (e.g., a…

Single-pixel non-imaging object recognition by means of Fourier spectrum acquisition

NASA Astrophysics Data System (ADS)

Chen, Huichao; Shi, Jianhong; Liu, Xialin; Niu, Zhouzhou; Zeng, Guihua

2018-04-01

Single-pixel imaging has emerged over recent years as a novel imaging technique, which has significant application prospects. In this paper, we propose and experimentally demonstrate a scheme that can achieve single-pixel non-imaging object recognition by acquiring the Fourier spectrum. In an experiment, a four-step phase-shifting sinusoid illumination light is used to irradiate the object image, the value of the light intensity is measured with a single-pixel detection unit, and the Fourier coefficients of the object image are obtained by a differential measurement. The Fourier coefficients are first cast into binary numbers to obtain the hash value. We propose a new method of perceptual hashing algorithm, which is combined with a discrete Fourier transform to calculate the hash value. The hash distance is obtained by calculating the difference of the hash value between the object image and the contrast images. By setting an appropriate threshold, the object image can be quickly and accurately recognized. The proposed scheme realizes single-pixel non-imaging perceptual hashing object recognition by using fewer measurements. Our result might open a new path for realizing object recognition with non-imaging.

Lateral Entorhinal Cortex is Critical for Novel Object-Context Recognition

PubMed Central

Wilson, David IG; Langston, Rosamund F; Schlesiger, Magdalene I; Wagner, Monica; Watanabe, Sakurako; Ainge, James A

2013-01-01

Episodic memory incorporates information about specific events or occasions including spatial locations and the contextual features of the environment in which the event took place. It has been modeled in rats using spontaneous exploration of novel configurations of objects, their locations, and the contexts in which they are presented. While we have a detailed understanding of how spatial location is processed in the brain relatively little is known about where the nonspatial contextual components of episodic memory are processed. Initial experiments measured c-fos expression during an object-context recognition (OCR) task to examine which networks within the brain process contextual features of an event. Increased c-fos expression was found in the lateral entorhinal cortex (LEC; a major hippocampal afferent) during OCR relative to control conditions. In a subsequent experiment it was demonstrated that rats with lesions of LEC were unable to recognize object-context associations yet showed normal object recognition and normal context recognition. These data suggest that contextual features of the environment are integrated with object identity in LEC and demonstrate that recognition of such object-context associations requires the LEC. This is consistent with the suggestion that contextual features of an event are processed in LEC and that this information is combined with spatial information from medial entorhinal cortex to form episodic memory in the hippocampus. © 2013 Wiley Periodicals, Inc. PMID:23389958

Object Recognition using Feature- and Color-Based Methods

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu; Stubberud, Allen

2008-01-01

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

Optical Pattern Recognition

NASA Astrophysics Data System (ADS)

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

NASA Technical Reports Server (NTRS)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Short- and long-term effects of nicotine and the histone deacetylase inhibitor phenylbutyrate on novel object recognition in zebrafish.

PubMed

Faillace, M P; Pisera-Fuster, A; Medrano, M P; Bejarano, A C; Bernabeu, R O

2017-03-01

Zebrafish have a sophisticated color- and shape-sensitive visual system, so we examined color cue-based novel object recognition in zebrafish. We evaluated preference in the absence or presence of drugs that affect attention and memory retention in rodents: nicotine and the histone deacetylase inhibitor (HDACi) phenylbutyrate (PhB). The objective of this study was to evaluate whether nicotine and PhB affect innate preferences of zebrafish for familiar and novel objects after short- and long-retention intervals. We developed modified object recognition (OR) tasks using neutral novel and familiar objects in different colors. We also tested objects which differed with respect to the exploratory behavior they elicited from naïve zebrafish. Zebrafish showed an innate preference for exploring red or green objects rather than yellow or blue objects. Zebrafish were better at discriminating color changes than changes in object shape or size. Nicotine significantly enhanced or changed short-term innate novel object preference whereas PhB had similar effects when preference was assessed 24 h after training. Analysis of other zebrafish behaviors corroborated these results. Zebrafish were innately reluctant or prone to explore colored novel objects, so drug effects on innate preference for objects can be evaluated changing the color of objects with a simple geometry. Zebrafish exhibited recognition memory for novel objects with similar innate significance. Interestingly, nicotine and PhB significantly modified innate object preference.

Does object view influence the scene consistency effect?

PubMed

Sastyin, Gergo; Niimi, Ryosuke; Yokosawa, Kazuhiko

2015-04-01

Traditional research on the scene consistency effect only used clearly recognizable object stimuli to show mutually interactive context effects for both the object and background components on scene perception (Davenport & Potter in Psychological Science, 15, 559-564, 2004). However, in real environments, objects are viewed from multiple viewpoints, including an accidental, hard-to-recognize one. When the observers named target objects in scenes (Experiments 1a and 1b, object recognition task), we replicated the scene consistency effect (i.e., there was higher accuracy for the objects with consistent backgrounds). However, there was a significant interaction effect between consistency and object viewpoint, which indicated that the scene consistency effect was more important for identifying objects in the accidental view condition than in the canonical view condition. Therefore, the object recognition system may rely more on the scene context when the object is difficult to recognize. In Experiment 2, the observers identified the background (background recognition task) while the scene consistency and object views were manipulated. The results showed that object viewpoint had no effect, while the scene consistency effect was observed. More specifically, the canonical and accidental views both equally provided contextual information for scene perception. These findings suggested that the mechanism for conscious recognition of objects could be dissociated from the mechanism for visual analysis of object images that were part of a scene. The "context" that the object images provided may have been derived from its view-invariant, relatively low-level visual features (e.g., color), rather than its semantic information.

Three-dimensional object recognition based on planar images

NASA Astrophysics Data System (ADS)

Mital, Dinesh P.; Teoh, Eam-Khwang; Au, K. C.; Chng, E. K.

1993-01-01

This paper presents the development and realization of a robotic vision system for the recognition of 3-dimensional (3-D) objects. The system can recognize a single object from among a group of known regular convex polyhedron objects that is constrained to lie on a calibrated flat platform. The approach adopted comprises a series of image processing operations on a single 2-dimensional (2-D) intensity image to derive an image line drawing. Subsequently, a feature matching technique is employed to determine 2-D spatial correspondences of the image line drawing with the model in the database. Besides its identification ability, the system can also provide important position and orientation information of the recognized object. The system was implemented on an IBM-PC AT machine executing at 8 MHz without the 80287 Maths Co-processor. In our overall performance evaluation based on a 600 recognition cycles test, the system demonstrated an accuracy of above 80% with recognition time well within 10 seconds. The recognition time is, however, indirectly dependent on the number of models in the database. The reliability of the system is also affected by illumination conditions which must be clinically controlled as in any industrial robotic vision system.

Exploration computer applications to primary dispersion halos: Kougarok tin prospect, Seward Peninsula, Alaska, USA

USGS Publications Warehouse

Reid, Jeffrey C.

1989-01-01

Computer processing and high resolution graphics display of geochemical data were used to quickly, accurately, and efficiently obtain important decision-making information for tin (cassiterite) exploration, Seward Peninsula, Alaska (USA). Primary geochemical dispersion patterns were determined for tin-bearing intrusive granite phases of Late Cretaceous age with exploration bedrock lithogeochemistry at the Kougarok tin prospect. Expensive diamond drilling footage was required to reach exploration objectives. Recognition of element distribution and dispersion patterns was useful in subsurface interpretation and correlation, and to aid location of other holes.

Image processing and machine learning in the morphological analysis of blood cells.

PubMed

Rodellar, J; Alférez, S; Acevedo, A; Molina, A; Merino, A

2018-05-01

This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies. © 2018 John Wiley & Sons Ltd.

Between Efficiency, Capability and Recognition: Competing Epistemes in Global Governance Reforms

ERIC Educational Resources Information Center

Chan, Jennifer

2007-01-01

This article examines global governance reforms as a site of contestation between three different "truths"/epistemes (the market, human rights principles, and cultural identity) in terms of the competing principles of efficiency, capability, and recognition. Nancy Fraser's conceptions of participation parity and a dialogical approach of…

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Super-recognition in development: A case study of an adolescent with extraordinary face recognition skills.

PubMed

Bennetts, Rachel J; Mole, Joseph; Bate, Sarah

2017-09-01

Face recognition abilities vary widely. While face recognition deficits have been reported in children, it is unclear whether superior face recognition skills can be encountered during development. This paper presents O.B., a 14-year-old female with extraordinary face recognition skills: a "super-recognizer" (SR). O.B. demonstrated exceptional face-processing skills across multiple tasks, with a level of performance that is comparable to adult SRs. Her superior abilities appear to be specific to face identity: She showed an exaggerated face inversion effect and her superior abilities did not extend to object processing or non-identity aspects of face recognition. Finally, an eye-movement task demonstrated that O.B. spent more time than controls examining the nose - a pattern previously reported in adult SRs. O.B. is therefore particularly skilled at extracting and using identity-specific facial cues, indicating that face and object recognition are dissociable during development, and that super recognition can be detected in adolescence.

Preserved Haptic Shape Processing after Bilateral LOC Lesions.

PubMed

Snow, Jacqueline C; Goodale, Melvyn A; Culham, Jody C

2015-10-07

The visual and haptic perceptual systems are understood to share a common neural representation of object shape. A region thought to be critical for recognizing visual and haptic shape information is the lateral occipital complex (LOC). We investigated whether LOC is essential for haptic shape recognition in humans by studying behavioral responses and brain activation for haptically explored objects in a patient (M.C.) with bilateral lesions of the occipitotemporal cortex, including LOC. Despite severe deficits in recognizing objects using vision, M.C. was able to accurately recognize objects via touch. M.C.'s psychophysical response profile to haptically explored shapes was also indistinguishable from controls. Using fMRI, M.C. showed no object-selective visual or haptic responses in LOC, but her pattern of haptic activation in other brain regions was remarkably similar to healthy controls. Although LOC is routinely active during visual and haptic shape recognition tasks, it is not essential for haptic recognition of object shape. The lateral occipital complex (LOC) is a brain region regarded to be critical for recognizing object shape, both in vision and in touch. However, causal evidence linking LOC with haptic shape processing is lacking. We studied recognition performance, psychophysical sensitivity, and brain response to touched objects, in a patient (M.C.) with extensive lesions involving LOC bilaterally. Despite being severely impaired in visual shape recognition, M.C. was able to identify objects via touch and she showed normal sensitivity to a haptic shape illusion. M.C.'s brain response to touched objects in areas of undamaged cortex was also very similar to that observed in neurologically healthy controls. These results demonstrate that LOC is not necessary for recognizing objects via touch. Copyright © 2015 the authors 0270-6474/15/3513745-16$15.00/0.

Ciproxifan, an H3 receptor antagonist, improves short-term recognition memory impaired by isoflurane anesthesia.

PubMed

Ding, Fang; Zheng, Limin; Liu, Min; Chen, Rongfa; Leung, L Stan; Luo, Tao

2016-08-01

Exposure to volatile anesthetics has been reported to cause temporary or sustained impairments in learning and memory in pre-clinical studies. The selective antagonists of the histamine H3 receptors (H3R) are considered to be a promising group of novel therapeutic agents for the treatment of cognitive disorders. The aim of this study was to evaluate the effect of H3R antagonist ciproxifan on isoflurane-induced deficits in an object recognition task. Adult C57BL/6 J mice were exposed to isoflurane (1.3 %) or vehicle gas for 2 h. The object recognition tests were carried at 24 h or 7 days after exposure to anesthesia to exploit the tendency of mice to prefer exploring novel objects in an environment when a familiar object is also present. During the training phase, two identical objects were placed in two defined sites of the chamber. During the test phase, performed 1 or 24 h after the training phase, one of the objects was replaced by a new object with a different shape. The time spent exploring each object was recorded. A robust deficit in object recognition memory occurred 1 day after exposure to isoflurane anesthesia. Isoflurane-treated mice spent significantly less time exploring a novel object at 1 h but not at 24 h after the training phase. The deficit in short-term memory was reversed by the administration of ciproxifan 30 min before behavioral training. Isoflurane exposure induces reversible deficits in object recognition memory. Ciproxifan appears to be a potential therapeutic agent for improving post-anesthesia cognitive memory performance.

An observational study of implicit motor imagery using laterality recognition of the hand after stroke.

PubMed

Amesz, Sarah; Tessari, Alessia; Ottoboni, Giovanni; Marsden, Jon

2016-01-01

To explore the relationship between laterality recognition after stroke and impairments in attention, 3D object rotation and functional ability. Observational cross-sectional study. Acute care teaching hospital. Thirty-two acute and sub-acute people with stroke and 36 healthy, age-matched controls. Laterality recognition, attention and mental rotation of objects. Within the stroke group, the relationship between laterality recognition and functional ability, neglect, hemianopia and dyspraxia were further explored. People with stroke were significantly less accurate (69% vs 80%) and showed delayed reaction times (3.0 vs 1.9 seconds) when determining the laterality of a pictured hand. Deficits either in accuracy or reaction times were seen in 53% of people with stroke. The accuracy of laterality recognition was associated with reduced functional ability (R(2) = 0.21), less accurate mental rotation of objects (R(2) = 0.20) and dyspraxia (p = 0.03). Implicit motor imagery is affected in a significant number of patients after stroke with these deficits related to lesions to the motor networks as well as other deficits seen after stroke. This research provides new insights into how laterality recognition is related to a number of other deficits after stroke, including the mental rotation of 3D objects, attention and dyspraxia. Further research is required to determine if treatment programmes can improve deficits in laterality recognition and impact functional outcomes after stroke.

Dissociated active and passive tactile shape recognition: a case study of pure tactile apraxia.

PubMed

Valenza, N; Ptak, R; Zimine, I; Badan, M; Lazeyras, F; Schnider, A

2001-11-01

Disorders of tactile object recognition (TOR) may result from primary motor or sensory deficits or higher cognitive impairment of tactile shape representations or semantic memory. Studies with healthy participants suggest the existence of exploratory motor procedures directly linked to the extraction of specific properties of objects. A pure deficit of these procedures without concomitant gnostic disorders has never been described in a brain-damaged patient. Here, we present a patient with a right hemispheric infarction who, in spite of intact sensorimotor functions, had impaired TOR with the left hand. Recognition of 2D shapes and objects was severely deficient under the condition of spontaneous exploration. Tactile exploration of shapes was disorganized and exploratory procedures, such as the contour-following strategy, which is necessary to identify the precise shape of an object, were severely disturbed. However, recognition of 2D shapes under manually or verbally guided exploration and the recognition of shapes traced on the skin were intact, indicating a dissociation in shape recognition between active and passive touch. Functional MRI during sensory stimulation of the left hand showed preserved activation of the spared primary sensory cortex in the right hemisphere. We interpret the deficit of our patient as a pure tactile apraxia without tactile agnosia, i.e. a specific inability to use tactile feedback to generate the exploratory procedures necessary for tactile shape recognition.

Processing statistics: an examination of focused and distributed attention using event related potentials.

PubMed

Baijal, Shruti; Nakatani, Chie; van Leeuwen, Cees; Srinivasan, Narayanan

2013-06-07

Human observers show remarkable efficiency in statistical estimation; they are able, for instance, to estimate the mean size of visual objects, even if their number exceeds the capacity limits of focused attention. This ability has been understood as the result of a distinct mode of attention, i.e. distributed attention. Compared to the focused attention mode, working memory representations under distributed attention are proposed to be more compressed, leading to reduced working memory loads. An alternate proposal is that distributed attention uses less structured, feature-level representations. These would fill up working memory (WM) more, even when target set size is low. Using event-related potentials, we compared WM loading in a typical distributed attention task (mean size estimation) to that in a corresponding focused attention task (object recognition), using a measure called contralateral delay activity (CDA). Participants performed both tasks on 2, 4, or 8 different-sized target disks. In the recognition task, CDA amplitude increased with set size; notably, however, in the mean estimation task the CDA amplitude was high regardless of set size. In particular for set-size 2, the amplitude was higher in the mean estimation task than in the recognition task. The result showed that the task involves full WM loading even with a low target set size. This suggests that in the distributed attention mode, representations are not compressed, but rather less structured than under focused attention conditions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Short-term blueberry-enriched antioxidant diet prevents and reverses object recognition memory loss in aged rats

USDA-ARS?s Scientific Manuscript database

Objective Previously, four months of a blueberry-enriched (BB) antioxidant diet prevented impaired object recognition memory in aged rats. Experiment 1 determined whether one and two-month BB diets would have a similar effect and whether the benefits would disappear promptly after terminating the d...

Qualitative Differences in the Representation of Spatial Relations for Different Object Classes

ERIC Educational Resources Information Center

Cooper, Eric E.; Brooks, Brian E.

2004-01-01

Two experiments investigated whether the representations used for animal, produce, and object recognition code spatial relations in a similar manner. Experiment 1 tested the effects of planar rotation on the recognition of animals and nonanimal objects. Response times for recognizing animals followed an inverted U-shaped function, whereas those…

Object memory and change detection: dissociation as a function of visual and conceptual similarity.

PubMed

Yeh, Yei-Yu; Yang, Cheng-Ta

2008-01-01

People often fail to detect a change between two visual scenes, a phenomenon referred to as change blindness. This study investigates how a post-change object's similarity to the pre-change object influences memory of the pre-change object and affects change detection. The results of Experiment 1 showed that similarity lowered detection sensitivity but did not affect the speed of identifying the pre-change object, suggesting that similarity between the pre- and post-change objects does not degrade the pre-change representation. Identification speed for the pre-change object was faster than naming the new object regardless of detection accuracy. Similarity also decreased detection sensitivity in Experiment 2 but improved the recognition of the pre-change object under both correct detection and detection failure. The similarity effect on recognition was greatly reduced when 20% of each pre-change stimulus was masked by random dots in Experiment 3. Together the results suggest that the level of pre-change representation under detection failure is equivalent to the level under correct detection and that the pre-change representation is almost complete. Similarity lowers detection sensitivity but improves explicit access in recognition. Dissociation arises between recognition and change detection as the two judgments rely on the match-to-mismatch signal and mismatch-to-match signal, respectively.

Chronic methylphenidate-effects over circadian cycle of young and adult rats submitted to open-field and object recognition tests.

PubMed

Gomes, Karin M; Souza, Renan P; Valvassori, Samira S; Réus, Gislaine Z; Inácio, Cecília G; Martins, Márcio R; Comim, Clarissa M; Quevedo, João

2009-11-01

In this study age-, circadian rhythm- and methylphenidate administration- effect on open field habituation and object recognition were analyzed. Young and adult male Wistar rats were treated with saline or methylphenidate 2.0 mg/kg for 28 days. Experiments were performed during the light and the dark cycle. Locomotor activity was significantly altered by circadian cycle and methylphenidate treatment during the training session and by drug treatment during the testing session. Exploratory activity was significantly modulated by age during the training session and by age and drug treatment during the testing session. Object recognition memory was altered by cycle at the training session; by age 1.5 h later and by cycle and age 24 h after the training session. These results show that methylphenidate treatment was the major modulator factor on open-field test while cycle and age had an important effect on object recognition experiment.

Anatomical medial surfaces with efficient resolution of branches singularities.

PubMed

Gil, Debora; Vera, Sergio; Borràs, Agnés; Andaluz, Albert; González Ballester, Miguel A

2017-01-01

Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility of existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a confident application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an efficient GPU-CPU implementation using standard image processing tools. We show the method computational efficiency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies. Copyright © 2016 Elsevier B.V. All rights reserved.

Two speed factors of visual recognition independently correlated with fluid intelligence.

PubMed

Tachibana, Ryosuke; Namba, Yuri; Noguchi, Yasuki

2014-01-01

Growing evidence indicates a moderate but significant relationship between processing speed in visuo-cognitive tasks and general intelligence. On the other hand, findings from neuroscience proposed that the primate visual system consists of two major pathways, the ventral pathway for objects recognition and the dorsal pathway for spatial processing and attentive analysis. Previous studies seeking for visuo-cognitive factors of human intelligence indicated a significant correlation between fluid intelligence and the inspection time (IT), an index for a speed of object recognition performed in the ventral pathway. We thus presently examined a possibility that neural processing speed in the dorsal pathway also represented a factor of intelligence. Specifically, we used the mental rotation (MR) task, a popular psychometric measure for mental speed of spatial processing in the dorsal pathway. We found that the speed of MR was significantly correlated with intelligence scores, while it had no correlation with one's IT (recognition speed of visual objects). Our results support the new possibility that intelligence could be explained by two types of mental speed, one related to object recognition (IT) and another for manipulation of mental images (MR).

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images

PubMed Central

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-01-01

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition. PMID:29786665

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images.

PubMed

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-05-22

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition.

Advanced optical correlation and digital methods for pattern matching—50th anniversary of Vander Lugt matched filter

NASA Astrophysics Data System (ADS)

Millán, María S.

2012-10-01

On the verge of the 50th anniversary of Vander Lugt’s formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical-digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption-decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical-digital solutions.

Syntactic methods of shape feature description and its application in analysis of medical images

NASA Astrophysics Data System (ADS)

Ogiela, Marek R.; Tadeusiewicz, Ryszard

2000-02-01

The paper presents specialist algorithms of morphologic analysis of shapes of selected organs of abdominal cavity proposed in order to diagnose disease symptoms occurring in the main pancreatic ducts and upper segments of ureters. Analysis of the correct morphology of these structures has been conducted with the use of syntactic methods of pattern recognition. Its main objective is computer-aided support to early diagnosis of neoplastic lesions and pancreatitis based on images taken in the course of examination with the endoscopic retrograde cholangiopancreatography (ERCP) method and a diagnosis of morphological lesions in ureter based on kidney radiogram analysis. In the analysis of ERCP images, the main objective is to recognize morphological lesions in pancreas ducts characteristic for carcinoma and chronic pancreatitis. In the case of kidney radiogram analysis the aim is to diagnose local irregularity of ureter lumen. Diagnosing the above mentioned lesion has been conducted with the use of syntactic methods of pattern recognition, in particular the languages of shape features description and context-free attributed grammars. These methods allow to recognize and describe in a very efficient way the aforementioned lesions on images obtained as a result of initial image processing into diagrams of widths of the examined structures.

Association and dissociation between detection and discrimination of objects of expertise: Evidence from visual search.

PubMed

Golan, Tal; Bentin, Shlomo; DeGutis, Joseph M; Robertson, Lynn C; Harel, Assaf

2014-02-01

Expertise in face recognition is characterized by high proficiency in distinguishing between individual faces. However, faces also enjoy an advantage at the early stage of basic-level detection, as demonstrated by efficient visual search for faces among nonface objects. In the present study, we asked (1) whether the face advantage in detection is a unique signature of face expertise, or whether it generalizes to other objects of expertise, and (2) whether expertise in face detection is intrinsically linked to expertise in face individuation. We compared how groups with varying degrees of object and face expertise (typical adults, developmental prosopagnosics [DP], and car experts) search for objects within and outside their domains of expertise (faces, cars, airplanes, and butterflies) among a variable set of object distractors. Across all three groups, search efficiency (indexed by reaction time slopes) was higher for faces and airplanes than for cars and butterflies. Notably, the search slope for car targets was considerably shallower in the car experts than in nonexperts. Although the mean face slope was slightly steeper among the DPs than in the other two groups, most of the DPs' search slopes were well within the normative range. This pattern of results suggests that expertise in object detection is indeed associated with expertise at the subordinate level, that it is not specific to faces, and that the two types of expertise are distinct facilities. We discuss the potential role of experience in bridging between low-level discriminative features and high-level naturalistic categories.

Reversibility of object recognition but not spatial memory impairment following binge-like alcohol exposure in rats.

PubMed

Cippitelli, Andrea; Zook, Michelle; Bell, Lauren; Damadzic, Ruslan; Eskay, Robert L; Schwandt, Melanie; Heilig, Markus

2010-11-01

Excessive alcohol use leads to neurodegeneration in several brain structures including the hippocampal dentate gyrus and the entorhinal cortex. Cognitive deficits that result are among the most insidious and debilitating consequences of alcoholism. The object exploration task (OET) provides a sensitive measurement of spatial memory impairment induced by hippocampal and cortical damage. In this study, we examine whether the observed neurotoxicity produced by a 4-day binge ethanol treatment results in long-term memory impairment by observing the time course of reactions to spatial change (object configuration) and non-spatial change (object recognition). Wistar rats were assessed for their abilities to detect spatial configuration in the OET at 1 week and 10 weeks following the ethanol treatment, in which ethanol groups received 9-15 g/kg/day and achieved blood alcohol levels over 300 mg/dl. At 1 week, results indicated that the binge alcohol treatment produced impairment in both spatial memory and non-spatial object recognition performance. Unlike the controls, ethanol treated rats did not increase the duration or number of contacts with the displaced object in the spatial memory task, nor did they increase the duration of contacts with the novel object in the object recognition task. After 10 weeks, spatial memory remained impaired in the ethanol treated rats but object recognition ability was recovered. Our data suggest that episodes of binge-like alcohol exposure result in long-term and possibly permanent impairments in memory for the configuration of objects during exploration, whereas the ability to detect non-spatial changes is only temporarily affected. Copyright © 2010 Elsevier Inc. All rights reserved.

Neurocomputational bases of object and face recognition.

PubMed Central

Biederman, I; Kalocsai, P

1997-01-01

A number of behavioural phenomena distinguish the recognition of faces and objects, even when members of a set of objects are highly similar. Because faces have the same parts in approximately the same relations, individuation of faces typically requires specification of the metric variation in a holistic and integral representation of the facial surface. The direct mapping of a hypercolumn-like pattern of activation onto a representation layer that preserves relative spatial filter values in a two-dimensional (2D) coordinate space, as proposed by C. von der Malsburg and his associates, may account for many of the phenomena associated with face recognition. An additional refinement, in which each column of filters (termed a 'jet') is centred on a particular facial feature (or fiducial point), allows selectivity of the input into the holistic representation to avoid incorporation of occluding or nearby surfaces. The initial hypercolumn representation also characterizes the first stage of object perception, but the image variation for objects at a given location in a 2D coordinate space may be too great to yield sufficient predictability directly from the output of spatial kernels. Consequently, objects can be represented by a structural description specifying qualitative (typically, non-accidental) characterizations of an object's parts, the attributes of the parts, and the relations among the parts, largely based on orientation and depth discontinuities (as shown by Hummel & Biederman). A series of experiments on the name priming or physical matching of complementary images (in the Fourier domain) of objects and faces documents that whereas face recognition is strongly dependent on the original spatial filter values, evidence from object recognition indicates strong invariance to these values, even when distinguishing among objects that are as similar as faces. PMID:9304687

Genetic specificity of face recognition.

PubMed

Shakeshaft, Nicholas G; Plomin, Robert

2015-10-13

Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

Genetic specificity of face recognition

PubMed Central

Shakeshaft, Nicholas G.; Plomin, Robert

2015-01-01

Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities. PMID:26417086

Deficits in long-term recognition memory reveal dissociated subtypes in congenital prosopagnosia.

PubMed

Stollhoff, Rainer; Jost, Jürgen; Elze, Tobias; Kennerknecht, Ingo

2011-01-25

The study investigates long-term recognition memory in congenital prosopagnosia (CP), a lifelong impairment in face identification that is present from birth. Previous investigations of processing deficits in CP have mostly relied on short-term recognition tests to estimate the scope and severity of individual deficits. We firstly report on a controlled test of long-term (one year) recognition memory for faces and objects conducted with a large group of participants with CP. Long-term recognition memory is significantly impaired in eight CP participants (CPs). In all but one case, this deficit was selective to faces and didn't extend to intra-class recognition of object stimuli. In a test of famous face recognition, long-term recognition deficits were less pronounced, even after accounting for differences in media consumption between controls and CPs. Secondly, we combined test results on long-term and short-term recognition of faces and objects, and found a large heterogeneity in severity and scope of individual deficits. Analysis of the observed heterogeneity revealed a dissociation of CP into subtypes with a homogeneous phenotypical profile. Thirdly, we found that among CPs self-assessment of real-life difficulties, based on a standardized questionnaire, and experimentally assessed face recognition deficits are strongly correlated. Our results demonstrate that controlled tests of long-term recognition memory are needed to fully assess face recognition deficits in CP. Based on controlled and comprehensive experimental testing, CP can be dissociated into subtypes with a homogeneous phenotypical profile. The CP subtypes identified align with those found in prosopagnosia caused by cortical lesions; they can be interpreted with respect to a hierarchical neural system for face perception.

Deficits in Long-Term Recognition Memory Reveal Dissociated Subtypes in Congenital Prosopagnosia

PubMed Central

Stollhoff, Rainer; Jost, Jürgen; Elze, Tobias; Kennerknecht, Ingo

2011-01-01

The study investigates long-term recognition memory in congenital prosopagnosia (CP), a lifelong impairment in face identification that is present from birth. Previous investigations of processing deficits in CP have mostly relied on short-term recognition tests to estimate the scope and severity of individual deficits. We firstly report on a controlled test of long-term (one year) recognition memory for faces and objects conducted with a large group of participants with CP. Long-term recognition memory is significantly impaired in eight CP participants (CPs). In all but one case, this deficit was selective to faces and didn't extend to intra-class recognition of object stimuli. In a test of famous face recognition, long-term recognition deficits were less pronounced, even after accounting for differences in media consumption between controls and CPs. Secondly, we combined test results on long-term and short-term recognition of faces and objects, and found a large heterogeneity in severity and scope of individual deficits. Analysis of the observed heterogeneity revealed a dissociation of CP into subtypes with a homogeneous phenotypical profile. Thirdly, we found that among CPs self-assessment of real-life difficulties, based on a standardized questionnaire, and experimentally assessed face recognition deficits are strongly correlated. Our results demonstrate that controlled tests of long-term recognition memory are needed to fully assess face recognition deficits in CP. Based on controlled and comprehensive experimental testing, CP can be dissociated into subtypes with a homogeneous phenotypical profile. The CP subtypes identified align with those found in prosopagnosia caused by cortical lesions; they can be interpreted with respect to a hierarchical neural system for face perception. PMID:21283572

Figure-ground organization and object recognition processes: an interactive account.

PubMed

Vecera, S P; O'Reilly, R C

1998-04-01

Traditional bottom-up models of visual processing assume that figure-ground organization precedes object recognition. This assumption seems logically necessary: How can object recognition occur before a region is labeled as figure? However, some behavioral studies find that familiar regions are more likely to be labeled figure than less familiar regions, a problematic finding for bottom-up models. An interactive account is proposed in which figure-ground processes receive top-down input from object representations in a hierarchical system. A graded, interactive computational model is presented that accounts for behavioral results in which familiarity effects are found. The interactive model offers an alternative conception of visual processing to bottom-up models.

Orientation estimation of anatomical structures in medical images for object recognition

NASA Astrophysics Data System (ADS)

Bağci, Ulaş; Udupa, Jayaram K.; Chen, Xinjian

2011-03-01

Recognition of anatomical structures is an important step in model based medical image segmentation. It provides pose estimation of objects and information about "where" roughly the objects are in the image and distinguishing them from other object-like entities. In,1 we presented a general method of model-based multi-object recognition to assist in segmentation (delineation) tasks. It exploits the pose relationship that can be encoded, via the concept of ball scale (b-scale), between the binary training objects and their associated grey images. The goal was to place the model, in a single shot, close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. Unlike position and scale parameters, we observe that orientation parameters require more attention when estimating the pose of the model as even small differences in orientation parameters can lead to inappropriate recognition. Motivated from the non-Euclidean nature of the pose information, we propose in this paper the use of non-Euclidean metrics to estimate orientation of the anatomical structures for more accurate recognition and segmentation. We statistically analyze and evaluate the following metrics for orientation estimation: Euclidean, Log-Euclidean, Root-Euclidean, Procrustes Size-and-Shape, and mean Hermitian metrics. The results show that mean Hermitian and Cholesky decomposition metrics provide more accurate orientation estimates than other Euclidean and non-Euclidean metrics.

Cultural differences in visual object recognition in 3-year-old children

PubMed Central

Kuwabara, Megumi; Smith, Linda B.

2016-01-01

Recent research indicates that culture penetrates fundamental processes of perception and cognition (e.g. Nisbett & Miyamoto, 2005). Here, we provide evidence that these influences begin early and influence how preschool children recognize common objects. The three tasks (n=128) examined the degree to which nonface object recognition by 3 year olds was based on individual diagnostic features versus more configural and holistic processing. Task 1 used a 6-alternative forced choice task in which children were asked to find a named category in arrays of masked objects in which only 3 diagnostic features were visible for each object. U.S. children outperformed age-matched Japanese children. Task 2 presented pictures of objects to children piece by piece. U.S. children recognized the objects given fewer pieces than Japanese children and likelihood of recognition increased for U.S., but not Japanese children when the piece added was rated by both U.S. and Japanese adults as highly defining. Task 3 used a standard measure of configural progressing, asking the degree to which recognition of matching pictures was disrupted by the rotation of one picture. Japanese children’s recognition was more disrupted by inversion than was that of U.S. children, indicating more configural processing by Japanese than U.S. children. The pattern suggests early cross-cultural differences in visual processing; findings that raise important questions about how visual experiences differ across cultures and about universal patterns of cognitive development. PMID:26985576

Cultural differences in visual object recognition in 3-year-old children.

PubMed

Kuwabara, Megumi; Smith, Linda B

2016-07-01

Recent research indicates that culture penetrates fundamental processes of perception and cognition. Here, we provide evidence that these influences begin early and influence how preschool children recognize common objects. The three tasks (N=128) examined the degree to which nonface object recognition by 3-year-olds was based on individual diagnostic features versus more configural and holistic processing. Task 1 used a 6-alternative forced choice task in which children were asked to find a named category in arrays of masked objects where only three diagnostic features were visible for each object. U.S. children outperformed age-matched Japanese children. Task 2 presented pictures of objects to children piece by piece. U.S. children recognized the objects given fewer pieces than Japanese children, and the likelihood of recognition increased for U.S. children, but not Japanese children, when the piece added was rated by both U.S. and Japanese adults as highly defining. Task 3 used a standard measure of configural progressing, asking the degree to which recognition of matching pictures was disrupted by the rotation of one picture. Japanese children's recognition was more disrupted by inversion than was that of U.S. children, indicating more configural processing by Japanese than U.S. children. The pattern suggests early cross-cultural differences in visual processing; findings that raise important questions about how visual experiences differ across cultures and about universal patterns of cognitive development. Copyright © 2016 Elsevier Inc. All rights reserved.

Associative (prosop)agnosia without (apparent) perceptual deficits: a case-study.

PubMed

Anaki, David; Kaufman, Yakir; Freedman, Morris; Moscovitch, Morris

2007-04-09

In associative agnosia early perceptual processing of faces or objects are considered to be intact, while the ability to access stored semantic information about the individual face or object is impaired. Recent claims, however, have asserted that associative agnosia is also characterized by deficits at the perceptual level, which are too subtle to be detected by current neuropsychological tests. Thus, the impaired identification of famous faces or common objects in associative agnosia stems from difficulties in extracting the minute perceptual details required to identify a face or an object. In the present study, we report the case of a patient DBO with a left occipital infarct, who shows impaired object and famous face recognition. Despite his disability, he exhibits a face inversion effect, and is able to select a famous face from among non-famous distractors. In addition, his performance is normal in an immediate and delayed recognition memory for faces, whose external features were deleted. His deficits in face recognition are apparent only when he is required to name a famous face, or select two faces from among a triad of famous figures based on their semantic relationships (a task which does not require access to names). The nature of his deficits in object perception and recognition are similar to his impairments in the face domain. This pattern of behavior supports the notion that apperceptive and associative agnosia reflect distinct and dissociated deficits, which result from damage to different stages of the face and object recognition process.

Tc1 mouse model of trisomy-21 dissociates properties of short- and long-term recognition memory.

PubMed

Hall, Jessica H; Wiseman, Frances K; Fisher, Elizabeth M C; Tybulewicz, Victor L J; Harwood, John L; Good, Mark A

2016-04-01

The present study examined memory function in Tc1 mice, a transchromosomic model of Down syndrome (DS). Tc1 mice demonstrated an unusual delay-dependent deficit in recognition memory. More specifically, Tc1 mice showed intact immediate (30sec), impaired short-term (10-min) and intact long-term (24-h) memory for objects. A similar pattern was observed for olfactory stimuli, confirming the generality of the pattern across sensory modalities. The specificity of the behavioural deficits in Tc1 mice was confirmed using APP overexpressing mice that showed the opposite pattern of object memory deficits. In contrast to object memory, Tc1 mice showed no deficit in either immediate or long-term memory for object-in-place information. Similarly, Tc1 mice showed no deficit in short-term memory for object-location information. The latter result indicates that Tc1 mice were able to detect and react to spatial novelty at the same delay interval that was sensitive to an object novelty recognition impairment. These results demonstrate (1) that novelty detection per se and (2) the encoding of visuo-spatial information was not disrupted in adult Tc1 mice. The authors conclude that the task specific nature of the short-term recognition memory deficit suggests that the trisomy of genes on human chromosome 21 in Tc1 mice impacts on (perirhinal) cortical systems supporting short-term object and olfactory recognition memory. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

Behavioral model of visual perception and recognition

NASA Astrophysics Data System (ADS)

Rybak, Ilya A.; Golovan, Alexander V.; Gusakova, Valentina I.

1993-09-01

In the processes of visual perception and recognition human eyes actively select essential information by way of successive fixations at the most informative points of the image. A behavioral program defining a scanpath of the image is formed at the stage of learning (object memorizing) and consists of sequential motor actions, which are shifts of attention from one to another point of fixation, and sensory signals expected to arrive in response to each shift of attention. In the modern view of the problem, invariant object recognition is provided by the following: (1) separated processing of `what' (object features) and `where' (spatial features) information at high levels of the visual system; (2) mechanisms of visual attention using `where' information; (3) representation of `what' information in an object-based frame of reference (OFR). However, most recent models of vision based on OFR have demonstrated the ability of invariant recognition of only simple objects like letters or binary objects without background, i.e. objects to which a frame of reference is easily attached. In contrast, we use not OFR, but a feature-based frame of reference (FFR), connected with the basic feature (edge) at the fixation point. This has provided for our model, the ability for invariant representation of complex objects in gray-level images, but demands realization of behavioral aspects of vision described above. The developed model contains a neural network subsystem of low-level vision which extracts a set of primary features (edges) in each fixation, and high- level subsystem consisting of `what' (Sensory Memory) and `where' (Motor Memory) modules. The resolution of primary features extraction decreases with distances from the point of fixation. FFR provides both the invariant representation of object features in Sensor Memory and shifts of attention in Motor Memory. Object recognition consists in successive recall (from Motor Memory) and execution of shifts of attention and successive verification of the expected sets of features (stored in Sensory Memory). The model shows the ability of recognition of complex objects (such as faces) in gray-level images invariant with respect to shift, rotation, and scale.

Comparison of Object Recognition Behavior in Human and Monkey

PubMed Central

Rajalingham, Rishi; Schmidt, Kailyn

2015-01-01

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys. PMID:26338324

Recognition memory in tree shrew (Tupaia belangeri) after repeated familiarization sessions.

PubMed

Khani, Abbas; Rainer, Gregor

2012-07-01

Recognition memories are formed during perceptual experience and allow subsequent recognition of previously encountered objects as well as their distinction from novel objects. As a consequence, novel objects are generally explored longer than familiar objects by many species. This novelty preference has been documented in rodents using the novel object recognition (NOR) test, as well is in primates including humans using preferential looking time paradigms. Here, we examine novelty preference using the NOR task in tree shrew, a small animal species that is considered to be an intermediary between rodents and primates. Our paradigm consisted of three phases: arena familiarization, object familiarization sessions with two identical objects in the arena and finally a test session following a 24-h retention period with a familiar and a novel object in the arena. We employed two different object familiarization durations: one and three sessions on consecutive days. After three object familiarization sessions, tree shrews exhibited robust preference for novel objects on the test day. This was accompanied by significant reduction in familiar object exploration time, occurring largely between the first and second day of object familiarization. By contrast, tree shrews did not show a significant preference for the novel object after a one-session object familiarization. Nonetheless, they spent significantly less time exploring the familiar object on the test day compared to the object familiarization day, indicating that they did maintain a memory trace for the familiar object. Our study revealed different time courses for familiar object habituation and emergence of novelty preference, suggesting that novelty preference is dependent on well-consolidated memory of the competing familiar object. Taken together, our results demonstrate robust novelty preference of tree shrews, in general similarity to previous findings in rodents and primates. Copyright © 2012 Elsevier B.V. All rights reserved.

Visual memory in unilateral spatial neglect: immediate recall versus delayed recognition.

PubMed

Moreh, Elior; Malkinson, Tal Seidel; Zohary, Ehud; Soroker, Nachum

2014-09-01

Patients with unilateral spatial neglect (USN) often show impaired performance in spatial working memory tasks, apart from the difficulty retrieving "left-sided" spatial data from long-term memory, shown in the "piazza effect" by Bisiach and colleagues. This study's aim was to compare the effect of the spatial position of a visual object on immediate and delayed memory performance in USN patients. Specifically, immediate verbal recall performance, tested using a simultaneous presentation of four visual objects in four quadrants, was compared with memory in a later-provided recognition task, in which objects were individually shown at the screen center. Unlike healthy controls, USN patients showed a left-side disadvantage and a vertical bias in the immediate free recall task (69% vs. 42% recall for right- and left-sided objects, respectively). In the recognition task, the patients correctly recognized half of "old" items, and their correct rejection rate was 95.5%. Importantly, when the analysis focused on previously recalled items (in the immediate task), no statistically significant difference was found in the delayed recognition of objects according to their original quadrant of presentation. Furthermore, USN patients were able to recollect the correct original location of the recognized objects in 60% of the cases, well beyond chance level. This suggests that the memory trace formed in these cases was not only semantic but also contained a visuospatial tag. Finally, successful recognition of objects missed in recall trials points to formation of memory traces for neglected contralesional objects, which may become accessible to retrieval processes in explicit memory.

Memantine and recognition memory: possible facilitation of its behavioral effects by the nitric oxide (NO) donor molsidomine.

PubMed

Pitsikas, Nikolaos; Sakellaridis, Nikolaos

2007-10-01

The effects of the non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist memantine on recognition memory were investigated in the rat by using the object recognition task. In addition, a possible interaction between memantine and the nitric oxide (NO) donor molsidomine in antagonizing extinction of recognition memory was also evaluated utilizing the same behavioral procedure. In a first dose-response study, post-training administration of memantine (10 and 20, but not 3 mg/kg) antagonized recognition memory deficits in the rat, suggesting that memantine modulates storage and/or retrieval of information. In a subsequent study, combination of sub-threshold doses of memantine (3 mg/kg) and the NO donor molsidomine (1 mg/kg) counteracted delay-dependent impairments in the same task. Neither memantine (3 mg/kg) nor molsidomine (1 mg/kg) alone reduced object recognition performance deficits. The present findings indicate a) that memantine is involved in recognition memory and b) support a functional interaction between memantine and molsidomine on recognition memory mechanisms.

Spatiotemporal information during unsupervised learning enhances viewpoint invariant object recognition

PubMed Central

Tian, Moqian; Grill-Spector, Kalanit

2015-01-01

Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning is used to link among object views. Specifically, researchers argue whether temporal proximity, motion, or spatiotemporal continuity among object views during unsupervised learning is beneficial. Here, we untangled the role of each of these factors in unsupervised learning of novel three-dimensional (3-D) objects. We found that after unsupervised training with 24 object views spanning a 180° view space, participants showed significant improvement in their ability to recognize 3-D objects across rotation. Surprisingly, there was no advantage to unsupervised learning with spatiotemporal continuity or motion information than training with temporal proximity. However, we discovered that when participants were trained with just a third of the views spanning the same view space, unsupervised learning via spatiotemporal continuity yielded significantly better recognition performance on novel views than learning via temporal proximity. These results suggest that while it is possible to obtain view-invariant recognition just from observing many views of an object presented in temporal proximity, spatiotemporal information enhances performance by producing representations with broader view tuning than learning via temporal association. Our findings have important implications for theories of object recognition and for the development of computational algorithms that learn from examples. PMID:26024454

Multiple degree of freedom optical pattern recognition

NASA Technical Reports Server (NTRS)

Casasent, D.

1987-01-01

Three general optical approaches to multiple degree of freedom object pattern recognition (where no stable object rest position exists) are advanced. These techniques include: feature extraction, correlation, and artificial intelligence. The details of the various processors are advanced together with initial results.

Products recognition on shop-racks from local scale-invariant features

NASA Astrophysics Data System (ADS)

Zawistowski, Jacek; Kurzejamski, Grzegorz; Garbat, Piotr; Naruniec, Jacek

2016-04-01

This paper presents a system designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. System uses well known binary keypoint detection algorithms for finding characteristic points in the image. One of the main idea is object recognition based on Implicit Shape Model method. Authors of the article proposed many improvements of the algorithm. Originally fiducial points are matched with a very simple function. This leads to the limitations in the number of objects parts being success- fully separated, while various methods of classification may be validated in order to achieve higher performance. Such an extension implies research on training procedure able to deal with many objects categories. Proposed solution opens a new possibilities for many algorithms demanding fast and robust multi-object recognition.

Recognition of Frequency Modulated Whistle-Like Sounds by a Bottlenose Dolphin (Tursiops truncatus) and Humans with Transformations in Amplitude, Duration and Frequency.

PubMed

Branstetter, Brian K; DeLong, Caroline M; Dziedzic, Brandon; Black, Amy; Bakhtiari, Kimberly

2016-01-01

Bottlenose dolphins (Tursiops truncatus) use the frequency contour of whistles produced by conspecifics for individual recognition. Here we tested a bottlenose dolphin's (Tursiops truncatus) ability to recognize frequency modulated whistle-like sounds using a three alternative matching-to-sample paradigm. The dolphin was first trained to select a specific object (object A) in response to a specific sound (sound A) for a total of three object-sound associations. The sounds were then transformed by amplitude, duration, or frequency transposition while still preserving the frequency contour of each sound. For comparison purposes, 30 human participants completed an identical task with the same sounds, objects, and training procedure. The dolphin's ability to correctly match objects to sounds was robust to changes in amplitude with only a minor decrement in performance for short durations. The dolphin failed to recognize sounds that were frequency transposed by plus or minus ½ octaves. Human participants demonstrated robust recognition with all acoustic transformations. The results indicate that this dolphin's acoustic recognition of whistle-like sounds was constrained by absolute pitch. Unlike human speech, which varies considerably in average frequency, signature whistles are relatively stable in frequency, which may have selected for a whistle recognition system invariant to frequency transposition.

Recognition of Frequency Modulated Whistle-Like Sounds by a Bottlenose Dolphin (Tursiops truncatus) and Humans with Transformations in Amplitude, Duration and Frequency

PubMed Central

Branstetter, Brian K.; DeLong, Caroline M.; Dziedzic, Brandon; Black, Amy; Bakhtiari, Kimberly

2016-01-01

Bottlenose dolphins (Tursiops truncatus) use the frequency contour of whistles produced by conspecifics for individual recognition. Here we tested a bottlenose dolphin’s (Tursiops truncatus) ability to recognize frequency modulated whistle-like sounds using a three alternative matching-to-sample paradigm. The dolphin was first trained to select a specific object (object A) in response to a specific sound (sound A) for a total of three object-sound associations. The sounds were then transformed by amplitude, duration, or frequency transposition while still preserving the frequency contour of each sound. For comparison purposes, 30 human participants completed an identical task with the same sounds, objects, and training procedure. The dolphin’s ability to correctly match objects to sounds was robust to changes in amplitude with only a minor decrement in performance for short durations. The dolphin failed to recognize sounds that were frequency transposed by plus or minus ½ octaves. Human participants demonstrated robust recognition with all acoustic transformations. The results indicate that this dolphin’s acoustic recognition of whistle-like sounds was constrained by absolute pitch. Unlike human speech, which varies considerably in average frequency, signature whistles are relatively stable in frequency, which may have selected for a whistle recognition system invariant to frequency transposition. PMID:26863519

The effect of colour congruency on shape discriminations of novel objects.

PubMed

Nicholson, Karen G; Humphrey, G Keith

2004-01-01

Although visual object recognition is primarily shape driven, colour assists the recognition of some objects. It is unclear, however, just how colour information is coded with respect to shape in long-term memory and how the availability of colour in the visual image facilitates object recognition. We examined the role of colour in the recognition of novel, 3-D objects by manipulating the congruency of object colour across the study and test phases, using an old/new shape-identification task. In experiment 1, we found that participants were faster at correctly identifying old objects on the basis of shape information when these objects were presented in their original colour, rather than in a different colour. In experiments 2 and 3, we found that participants were faster at correctly identifying old objects on the basis of shape information when these objects were presented with their original part-colour conjunctions, rather than in different or in reversed part-colour conjunctions. In experiment 4, we found that participants were quite poor at the verbal recall of part-colour conjunctions for correctly identified old objects, presented as grey-scale images at test. In experiment 5, we found that participants were significantly slower at correctly identifying old objects when object colour was incongruent across study and test, than when background colour was incongruent across study and test. The results of these experiments suggest that both shape and colour information are stored as part of the long-term representation of these novel objects. Results are discussed in terms of how colour might be coded with respect to shape in stored object representations.

Automatic textual annotation of video news based on semantic visual object extraction

NASA Astrophysics Data System (ADS)

Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

2003-12-01

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

Exploratory behavior and recognition memory in medial septal electrolytic, neuro- and immunotoxic lesioned rats.

PubMed

Dashniani, M G; Burjanadze, M A; Naneishvili, T L; Chkhikvishvili, N C; Beselia, G V; Kruashvili, L B; Pochkhidze, N O; Chighladze, M R

2015-01-01

In the present study, the effect of the medial septal (MS) lesions on exploratory activity in the open field and the spatial and object recognition memory has been investigated. This experiment compares three types of MS lesions: electrolytic lesions that destroy cells and fibers of passage, neurotoxic - ibotenic acid lesions that spare fibers of passage but predominantly affect the septal noncholinergic neurons, and immunotoxin - 192 IgG-saporin infusions that only eliminate cholinergic neurons. The main results are: the MS electrolytic lesioned rats were impaired in habituating to the environment in the repeated spatial environment, but rats with immuno- or neurotoxic lesions of the MS did not differ from control ones; the MS electrolytic and ibotenic acid lesioned rats showed an increase in their exploratory activity to the objects and were impaired in habituating to the objects in the repeated spatial environment; rats with immunolesions of the MS did not differ from control rats; electrolytic lesions of the MS disrupt spatial recognition memory; rats with immuno- or neurotoxic lesions of the MS were normal in detecting spatial novelty; all of the MS-lesioned and control rats clearly reacted to the object novelty by exploring the new object more than familiar ones. Results observed across lesion techniques indicate that: (i) the deficits after nonselective damage of MS are limited to a subset of cognitive processes dependent on the hippocampus, (ii) MS is substantial for spatial, but not for object recognition memory - the object recognition memory can be supported outside the septohippocampal system; (iii) the selective loss of septohippocampal cholinergic or noncholinergic projections does not disrupt the function of the hippocampus to a sufficient extent to impair spatial recognition memory; (iv) there is dissociation between the two major components (cholinergic and noncholinergic) of the septohippocampal pathway in exploratory behavior assessed in the open field - the memory exhibited by decrements in exploration of repeated object presentations is affected by either electrolytic or ibotenic lesions, but not saporin.

Picture object recognition in an American black bear (Ursus americanus).

PubMed

Johnson-Ulrich, Zoe; Vonk, Jennifer; Humbyrd, Mary; Crowley, Marilyn; Wojtkowski, Ela; Yates, Florence; Allard, Stephanie

2016-11-01

Many animals have been tested for conceptual discriminations using two-dimensional images as stimuli, and many of these species appear to transfer knowledge from 2D images to analogous real life objects. We tested an American black bear for picture-object recognition using a two alternative forced choice task. She was presented with four unique sets of objects and corresponding pictures. The bear showed generalization from both objects to pictures and pictures to objects; however, her transfer was superior when transferring from real objects to pictures, suggesting that bears can recognize visual features from real objects within photographic images during discriminations.

Object recognition with hierarchical discriminant saliency networks.

PubMed

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.

Depth rotation and mirror-image reflection reduce affective preference as well as recognition memory for pictures of novel objects.

PubMed

Lawson, Rebecca

2004-10-01

In two experiments, the identification of novel 3-D objects was worse for depth-rotated and mirror-reflected views, compared with the study view in an implicit affective preference memory task, as well as in an explicit recognition memory task. In Experiment 1, recognition was worse and preference was lower when depth-rotated views of an object were paired with an unstudied object relative to trials when the study view of that object was shown. There was a similar trend for mirror-reflected views. In Experiment 2, the study view of an object was both recognized and preferred above chance when it was paired with either depth-rotated or mirror-reflected views of that object. These results suggest that view-sensitive representations of objects mediate performance in implicit, as well as explicit, memory tasks. The findings do not support the claim that separate episodic and structural description representations underlie performance in implicit and explicit memory tasks, respectively.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

NASA Astrophysics Data System (ADS)

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution.

PubMed

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-06

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining.

Transfer learning for visual categorization: a survey.

PubMed

Shao, Ling; Zhu, Fan; Li, Xuelong

2015-05-01

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.

Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.

PubMed

Xu, Dong; Yan, Shuicheng; Tao, Dacheng; Lin, Stephen; Zhang, Hong-Jiang

2007-11-01

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications.

An on-chip imaging droplet-sorting system: a real-time shape recognition method to screen target cells in droplets with single cell resolution

PubMed Central

Girault, Mathias; Kim, Hyonchol; Arakawa, Hisayuki; Matsuura, Kenji; Odaka, Masao; Hattori, Akihiro; Terazono, Hideyuki; Yasuda, Kenji

2017-01-01

A microfluidic on-chip imaging cell sorter has several advantages over conventional cell sorting methods, especially to identify cells with complex morphologies such as clusters. One of the remaining problems is how to efficiently discriminate targets at the species level without labelling. Hence, we developed a label-free microfluidic droplet-sorting system based on image recognition of cells in droplets. To test the applicability of this method, a mixture of two plankton species with different morphologies (Dunaliella tertiolecta and Phaeodactylum tricornutum) were successfully identified and discriminated at a rate of 10 Hz. We also examined the ability to detect the number of objects encapsulated in a droplet. Single cell droplets sorted into collection channels showed 91 ± 4.5% and 90 ± 3.8% accuracy for D. tertiolecta and P. tricornutum, respectively. Because we used image recognition to confirm single cell droplets, we achieved highly accurate single cell sorting. The results indicate that the integrated method of droplet imaging cell sorting can provide a complementary sorting approach capable of isolating single target cells from a mixture of cells with high accuracy without any staining. PMID:28059147

Ignorance- versus evidence-based decision making: a decision time analysis of the recognition heuristic.

PubMed

Hilbig, Benjamin E; Pohl, Rüdiger F

2009-09-01

According to part of the adaptive toolbox notion of decision making known as the recognition heuristic (RH), the decision process in comparative judgments-and its duration-is determined by whether recognition discriminates between objects. By contrast, some recently proposed alternative models predict that choices largely depend on the amount of evidence speaking for each of the objects and that decision times thus depend on the evidential difference between objects, or the degree of conflict between options. This article presents 3 experiments that tested predictions derived from the RH against those from alternative models. All experiments used naturally recognized objects without teaching participants any information and thus provided optimal conditions for application of the RH. However, results supported the alternative, evidence-based models and often conflicted with the RH. Recognition was not the key determinant of decision times, whereas differences between objects with respect to (both positive and negative) evidence predicted effects well. In sum, alternative models that allow for the integration of different pieces of information may well provide a better account of comparative judgments. (c) 2009 APA, all rights reserved.

Using Prosopagnosia to Test and Modify Visual Recognition Theory.

PubMed

O'Brien, Alexander M

2018-02-01

Biederman's contemporary theory of basic visual object recognition (Recognition-by-Components) is based on structural descriptions of objects and presumes 36 visual primitives (geons) people can discriminate, but there has been no empirical test of the actual use of these 36 geons to visually distinguish objects. In this study, we tested for the actual use of these geons in basic visual discrimination by comparing object discrimination performance patterns (when distinguishing varied stimuli) of an acquired prosopagnosia patient (LB) and healthy control participants. LB's prosopagnosia left her heavily reliant on structural descriptions or categorical object differences in visual discrimination tasks versus the control participants' additional ability to use face recognition or coordinate systems (Coordinate Relations Hypothesis). Thus, when LB performed comparably to control participants with a given stimulus, her restricted reliance on basic or categorical discriminations meant that the stimuli must be distinguishable on the basis of a geon feature. By varying stimuli in eight separate experiments and presenting all 36 geons, we discerned that LB coded only 12 (vs. 36) distinct visual primitives (geons), apparently reflective of human visual systems generally.

Object recognition for autonomous robot utilizing distributed knowledge database

NASA Astrophysics Data System (ADS)

Takatori, Jiro; Suzuki, Kenji; Hartono, Pitoyo; Hashimoto, Shuji

2003-10-01

In this paper we present a novel method of object recognition utilizing a remote knowledge database for an autonomous robot. The developed robot has three robot arms with different sensors; two CCD cameras and haptic sensors. It can see, touch and move the target object from different directions. Referring to remote knowledge database of geometry and material, the robot observes and handles the objects to understand them including their physical characteristics.

What Three-Year-Olds Remember from Their Past: Long-Term Memory for Persons, Objects, and Actions

ERIC Educational Resources Information Center

Hirte, Monika; Graf, Frauke; Kim, Ziyon; Knopf, Monika

2017-01-01

From birth on, infants show long-term recognition memory for persons. Furthermore, infants from six months onwards are able to store and retrieve demonstrated actions over long-term intervals in deferred imitation tasks. Thus, information about the model demonstrating the object-related actions is stored and recognition memory for the objects as…

Representations of Shape in Object Recognition and Long-Term Visual Memory

DTIC Science & Technology

1993-02-11

in anything other than linguistic terms ( Biederman , 1987 , for example). STATUS 1. Viewpoint-Dependent Features in Object Representation Tarr and...is object- based orientation-independent representations sufficient for "basic-level" categorization ( Biederman , 1987 ; Corballis, 1988). Alternatively...space. REFERENCES Biederman , I. ( 1987 ). Recognition-by-components: A theory of human image understanding. Psychological Review, 94,115-147. Cooper, L

The Dark Side of Context: Context Reinstatement Can Distort Memory.

PubMed

Doss, Manoj K; Picart, Jamila K; Gallo, David A

2018-04-01

It is widely assumed that context reinstatement benefits memory, but our experiments revealed that context reinstatement can systematically distort memory. Participants viewed pictures of objects superimposed over scenes, and we later tested their ability to differentiate these old objects from similar new objects. Context reinstatement was manipulated by presenting objects on the reinstated or switched scene at test. Not only did context reinstatement increase correct recognition of old objects, but it also consistently increased incorrect recognition of similar objects as old ones. This false recognition effect was robust, as it was found in several experiments, occurred after both immediate and delayed testing, and persisted with high confidence even after participants were warned to avoid the distorting effects of context. To explain this memory illusion, we propose that context reinstatement increases the likelihood of confusing conceptual and perceptual information, potentially in medial temporal brain regions that integrate this information.

Constraints in distortion-invariant target recognition system simulation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Razzaque, Md A.

2000-11-01

Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

Impaired recognition of faces and objects in dyslexia: Evidence for ventral stream dysfunction?

PubMed

Sigurdardottir, Heida Maria; Ívarsson, Eysteinn; Kristinsdóttir, Kristjana; Kristjánsson, Árni

2015-09-01

The objective of this study was to establish whether or not dyslexics are impaired at the recognition of faces and other complex nonword visual objects. This would be expected based on a meta-analysis revealing that children and adult dyslexics show functional abnormalities within the left fusiform gyrus, a brain region high up in the ventral visual stream, which is thought to support the recognition of words, faces, and other objects. 20 adult dyslexics (M = 29 years) and 20 matched typical readers (M = 29 years) participated in the study. One dyslexic-typical reader pair was excluded based on Adult Reading History Questionnaire scores and IS-FORM reading scores. Performance was measured on 3 high-level visual processing tasks: the Cambridge Face Memory Test, the Vanderbilt Holistic Face Processing Test, and the Vanderbilt Expertise Test. People with dyslexia are impaired in their recognition of faces and other visually complex objects. Their holistic processing of faces appears to be intact, suggesting that dyslexics may instead be specifically impaired at part-based processing of visual objects. The difficulty that people with dyslexia experience with reading might be the most salient manifestation of a more general high-level visual deficit. (c) 2015 APA, all rights reserved).

The medial prefrontal cortex-lateral entorhinal cortex circuit is essential for episodic-like memory and associative object-recognition.

PubMed

Chao, Owen Y; Huston, Joseph P; Li, Jay-Shake; Wang, An-Li; de Souza Silva, Maria A

2016-05-01

The prefrontal cortex directly projects to the lateral entorhinal cortex (LEC), an important substrate for engaging item-associated information and relaying the information to the hippocampus. Here we ask to what extent the communication between the prefrontal cortex and LEC is critically involved in the processing of episodic-like memory. We applied a disconnection procedure to test whether the interaction between the medial prefrontal cortex (mPFC) and LEC is essential for the expression of recognition memory. It was found that male rats that received unilateral NMDA lesions of the mPFC and LEC in the same hemisphere, exhibited intact episodic-like (what-where-when) and object-recognition memories. When these lesions were placed in the opposite hemispheres (disconnection), episodic-like and associative memories for object identity, location and context were impaired. However, the disconnection did not impair the components of episodic memory, namely memory for novel object (what), object place (where) and temporal order (when), per se. Thus, the present findings suggest that the mPFC and LEC are a critical part of a neural circuit that underlies episodic-like and associative object-recognition memory. © 2015 Wiley Periodicals, Inc.

Nicotine Administration Attenuates Methamphetamine-Induced Novel Object Recognition Deficits

PubMed Central

Vieira-Brock, Paula L.; McFadden, Lisa M.; Nielsen, Shannon M.; Smith, Misty D.; Hanson, Glen R.

2015-01-01

Background: Previous studies have demonstrated that methamphetamine abuse leads to memory deficits and these are associated with relapse. Furthermore, extensive evidence indicates that nicotine prevents and/or improves memory deficits in different models of cognitive dysfunction and these nicotinic effects might be mediated by hippocampal or cortical nicotinic acetylcholine receptors. The present study investigated whether nicotine attenuates methamphetamine-induced novel object recognition deficits in rats and explored potential underlying mechanisms. Methods: Adolescent or adult male Sprague-Dawley rats received either nicotine water (10–75 μg/mL) or tap water for several weeks. Methamphetamine (4×7.5mg/kg/injection) or saline was administered either before or after chronic nicotine exposure. Novel object recognition was evaluated 6 days after methamphetamine or saline. Serotonin transporter function and density and α4β2 nicotinic acetylcholine receptor density were assessed on the following day. Results: Chronic nicotine intake via drinking water beginning during either adolescence or adulthood attenuated the novel object recognition deficits caused by a high-dose methamphetamine administration. Similarly, nicotine attenuated methamphetamine-induced deficits in novel object recognition when administered after methamphetamine treatment. However, nicotine did not attenuate the serotonergic deficits caused by methamphetamine in adults. Conversely, nicotine attenuated methamphetamine-induced deficits in α4β2 nicotinic acetylcholine receptor density in the hippocampal CA1 region. Furthermore, nicotine increased α4β2 nicotinic acetylcholine receptor density in the hippocampal CA3, dentate gyrus and perirhinal cortex in both saline- and methamphetamine-treated rats. Conclusions: Overall, these findings suggest that nicotine-induced increases in α4β2 nicotinic acetylcholine receptors in the hippocampus and perirhinal cortex might be one mechanism by which novel object recognition deficits are attenuated by nicotine in methamphetamine-treated rats. PMID:26164716

BDNF Expression in Perirhinal Cortex is Associated with Exercise-Induced Improvement in Object Recognition Memory

PubMed Central

Hopkins, Michael E.; Bucci, David J.

2010-01-01

Physical exercise induces widespread neurobiological adaptations and improves learning and memory. Most research in this field has focused on hippocampus-based spatial tasks and changes in brain-derived neurotrophic factor (BDNF) as a putative substrate underlying exercise-induced cognitive improvements. Chronic exercise can also be anxiolytic and causes adaptive changes in stress reactivity. The present study employed a perirhinal cortex-dependent object recognition task as well as the elevated plus maze to directly test for interactions between the cognitive and anxiolytic effects of exercise in male Long Evans rats. Hippocampal and perirhinal cortex tissue was collected to determine whether the relationship between BDNF and cognitive performance extends to this non-spatial and non-hippocampal-dependent task. We also examined whether the cognitive improvements persisted once the exercise regimen was terminated. Our data indicate that 4 weeks of voluntary exercise every-other-day improved object recognition memory. Importantly, BDNF expression in the perirhinal cortex of exercising rats was strongly correlated with object recognition memory. Exercise also decreased anxiety-like behavior, however there was no evidence to support a relationship between anxiety-like behavior and performance on the novel object recognition task. There was a trend for a negative relationship between anxiety-like behavior and hippocampal BDNF. Neither the cognitive improvements nor the relationship between cognitive function and perirhinal BDNF levels persisted after 2 weeks of inactivity. These are the first data demonstrating that region-specific changes in BDNF protein levels are correlated with exercise-induced improvements in non-spatial memory, mediated by structures outside the hippocampus and are consistent with the theory that, with regard to object recognition, the anxiolytic and cognitive effects of exercise may be mediated through separable mechanisms. PMID:20601027

Hippocampal histone acetylation regulates object recognition and the estradiol-induced enhancement of object recognition

PubMed Central

Zhao, Zaorui; Fan, Lu; Fortress, Ashley M.; Boulware, Marissa I.; Frick, Karyn M.

2012-01-01

Histone acetylation has recently been implicated in learning and memory processes, yet necessity of histone acetylation for such processes has not been demonstrated using pharmacological inhibitors of histone acetyltransferases (HATs). As such, the present study tested whether garcinol, a potent HAT inhibitor in vitro, could impair hippocampal memory consolidation and block the memory-enhancing effects of the modulatory hormone 17β-estradiol (E2). We first showed that bilateral infusion of garcinol (0.1, 1, or 10 μg/side) into the dorsal hippocampus (DH) immediately after training impaired object recognition memory consolidation in ovariectomized female mice. A behaviorally effective dose of garcinol (10 μg/side) also significantly decreased DH HAT activity. We next examined whether DH infusion of a behaviorally subeffective dose of garcinol (1 ng/side) could block the effects of DH E2 infusion on object recognition and epigenetic processes. Immediately after training, ovariectomized female mice received bilateral DH infusions of vehicle, E2 (5 μg/side), garcinol (1 ng/side), or E2 plus garcinol. Forty-eight hours later, garcinol blocked the memory-enhancing effects of E2. Garcinol also reversed the E2-induced increase in DH histone H3 acetylation, HAT activity, and levels of the de novo methyltransferase DNMT3B, as well as the E2-induced decrease in levels of the memory repressor protein histone deacetylase 2 (HDAC2). Collectively, these findings suggest that histone acetylation is critical for object recognition memory consolidation and the beneficial effects of E2 on object recognition. Importantly, this work demonstrates that the role of histone acetylation in memory processes can be studied using a HAT inhibitor. PMID:22396409

[Influence of music different in volume and style on human recognition activity].

PubMed

Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

2009-01-01

The efficiency of recognition of masked visual images (Arabic numerals) under conditions of listening to classical (intensity 62 dB) or rock music (25 dB) increased. Coherence of potential in the frontal cortical region characteristic of the masked image recognition increased under conditions of listening to music. The changes in intercenter EEG relations were correlated with the formation of "the recognition dominant" at the behavioral level. Such behavioral and EEG changes were not observed during listening to louder music (85 dB) and listening to music of other styles, however, the coherence between potentials of the temporal and motor areas of the right hemisphere increased, and the latency of hand motor reactions decreased. The results suggest that the "recognition dominant" is formed under conditions of establishment of certain relations between the levels of excitation in the corresponding centers. These findings should be taken into consideration in case if it were necessary to increase the efficiency of the recognition.

Image-based automatic recognition of larvae

NASA Astrophysics Data System (ADS)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

Biometric identification

NASA Astrophysics Data System (ADS)

Syryamkim, V. I.; Kuznetsov, D. N.; Kuznetsova, A. S.

2018-05-01

Image recognition is an information process implemented by some information converter (intelligent information channel, recognition system) having input and output. The input of the system is fed with information about the characteristics of the objects being presented. The output of the system displays information about which classes (generalized images) the recognized objects are assigned to. When creating and operating an automated system for pattern recognition, a number of problems are solved, while for different authors the formulations of these tasks, and the set itself, do not coincide, since it depends to a certain extent on the specific mathematical model on which this or that recognition system is based. This is the task of formalizing the domain, forming a training sample, learning the recognition system, reducing the dimensionality of space.

Improving Measurement Efficiency of the Inner EAR Scale with Item Response Theory.

PubMed

Jessen, Annika; Ho, Andrew D; Corrales, C Eduardo; Yueh, Bevan; Shin, Jennifer J

2018-02-01

Objectives (1) To assess the 11-item Inner Effectiveness of Auditory Rehabilitation (Inner EAR) instrument with item response theory (IRT). (2) To determine whether the underlying latent ability could also be accurately represented by a subset of the items for use in high-volume clinical scenarios. (3) To determine whether the Inner EAR instrument correlates with pure tone thresholds and word recognition scores. Design IRT evaluation of prospective cohort data. Setting Tertiary care academic ambulatory otolaryngology clinic. Subjects and Methods Modern psychometric methods, including factor analysis and IRT, were used to assess unidimensionality and item properties. Regression methods were used to assess prediction of word recognition and pure tone audiometry scores. Results The Inner EAR scale is unidimensional, and items varied in their location and information. Information parameter estimates ranged from 1.63 to 4.52, with higher values indicating more useful items. The IRT model provided a basis for identifying 2 sets of items with relatively lower information parameters. Item information functions demonstrated which items added insubstantial value over and above other items and were removed in stages, creating a 8- and 3-item Inner EAR scale for more efficient assessment. The 8-item version accurately reflected the underlying construct. All versions correlated moderately with word recognition scores and pure tone averages. Conclusion The 11-, 8-, and 3-item versions of the Inner EAR scale have strong psychometric properties, and there is correlational validity evidence for the observed scores. Modern psychometric methods can help streamline care delivery by maximizing relevant information per item administered.

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

PubMed

Zhu, Yanan; Ouyang, Qi; Mao, Youdong

2017-07-21

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

An innovative multimodal virtual platform for communication with devices in a natural way

NASA Astrophysics Data System (ADS)

Kinkar, Chhayarani R.; Golash, Richa; Upadhyay, Akhilesh R.

2012-03-01

As technology grows people are diverted and are more interested in communicating with machine or computer naturally. This will make machine more compact and portable by avoiding remote, keyboard etc. also it will help them to live in an environment free from electromagnetic waves. This thought has made 'recognition of natural modality in human computer interaction' a most appealing and promising research field. Simultaneously it has been observed that using single mode of interaction limit the complete utilization of commands as well as data flow. In this paper a multimodal platform, where out of many natural modalities like eye gaze, speech, voice, face etc. human gestures are combined with human voice is proposed which will minimize the mean square error. This will loosen the strict environment needed for accurate and robust interaction while using single mode. Gesture complement Speech, gestures are ideal for direct object manipulation and natural language is used for descriptive tasks. Human computer interaction basically requires two broad sections recognition and interpretation. Recognition and interpretation of natural modality in complex binary instruction is a tough task as it integrate real world to virtual environment. The main idea of the paper is to develop a efficient model for data fusion coming from heterogeneous sensors, camera and microphone. Through this paper we have analyzed that the efficiency is increased if heterogeneous data (image & voice) is combined at feature level using artificial intelligence. The long term goal of this paper is to design a robust system for physically not able or having less technical knowledge.

Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

PubMed

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

2007-01-01

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

Two Speed Factors of Visual Recognition Independently Correlated with Fluid Intelligence

PubMed Central

Tachibana, Ryosuke; Namba, Yuri; Noguchi, Yasuki

2014-01-01

Growing evidence indicates a moderate but significant relationship between processing speed in visuo-cognitive tasks and general intelligence. On the other hand, findings from neuroscience proposed that the primate visual system consists of two major pathways, the ventral pathway for objects recognition and the dorsal pathway for spatial processing and attentive analysis. Previous studies seeking for visuo-cognitive factors of human intelligence indicated a significant correlation between fluid intelligence and the inspection time (IT), an index for a speed of object recognition performed in the ventral pathway. We thus presently examined a possibility that neural processing speed in the dorsal pathway also represented a factor of intelligence. Specifically, we used the mental rotation (MR) task, a popular psychometric measure for mental speed of spatial processing in the dorsal pathway. We found that the speed of MR was significantly correlated with intelligence scores, while it had no correlation with one’s IT (recognition speed of visual objects). Our results support the new possibility that intelligence could be explained by two types of mental speed, one related to object recognition (IT) and another for manipulation of mental images (MR). PMID:24825574

Selective verbal recognition memory impairments are associated with atrophy of the language network in non-semantic variants of primary progressive aphasia.

PubMed

Nilakantan, Aneesha S; Voss, Joel L; Weintraub, Sandra; Mesulam, M-Marsel; Rogalski, Emily J

2017-06-01

Primary progressive aphasia (PPA) is clinically defined by an initial loss of language function and preservation of other cognitive abilities, including episodic memory. While PPA primarily affects the left-lateralized perisylvian language network, some clinical neuropsychological tests suggest concurrent initial memory loss. The goal of this study was to test recognition memory of objects and words in the visual and auditory modality to separate language-processing impairments from retentive memory in PPA. Individuals with non-semantic PPA had longer reaction times and higher false alarms for auditory word stimuli compared to visual object stimuli. Moreover, false alarms for auditory word recognition memory were related to cortical thickness within the left inferior frontal gyrus and left temporal pole, while false alarms for visual object recognition memory was related to cortical thickness within the right-temporal pole. This pattern of results suggests that specific vulnerability in processing verbal stimuli can hinder episodic memory in PPA, and provides evidence for differential contributions of the left and right temporal poles in word and object recognition memory. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hierarchical Context Modeling for Video Event Recognition.

PubMed

Wang, Xiaoyang; Ji, Qiang

2016-10-11

Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.

Size-Sensitive Perceptual Representations Underlie Visual and Haptic Object Recognition

PubMed Central

Craddock, Matt; Lawson, Rebecca

2009-01-01

A variety of similarities between visual and haptic object recognition suggests that the two modalities may share common representations. However, it is unclear whether such common representations preserve low-level perceptual features or whether transfer between vision and haptics is mediated by high-level, abstract representations. Two experiments used a sequential shape-matching task to examine the effects of size changes on unimodal and crossmodal visual and haptic object recognition. Participants felt or saw 3D plastic models of familiar objects. The two objects presented on a trial were either the same size or different sizes and were the same shape or different but similar shapes. Participants were told to ignore size changes and to match on shape alone. In Experiment 1, size changes on same-shape trials impaired performance similarly for both visual-to-visual and haptic-to-haptic shape matching. In Experiment 2, size changes impaired performance on both visual-to-haptic and haptic-to-visual shape matching and there was no interaction between the cost of size changes and direction of transfer. Together the unimodal and crossmodal matching results suggest that the same, size-specific perceptual representations underlie both visual and haptic object recognition, and indicate that crossmodal memory for objects must be at least partly based on common perceptual representations. PMID:19956685

Pattern recognition of the targets with help of polarization properties of the signal

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; de Rivera, Luis N.; Castellanos, Aldo B.; Popov, Anatoly V.

1999-10-01

We proposed to use the possibility of recognition of the targets on background of the scattering from the surface, weather objects with the help of polarimetric 3-cm radar. It has been investigated such polarization characteristics: the amplitudes of the polarization matrix elements; an anisotropy coefficient; depolarization coefficient; asymmetry coefficient; the energy section was less than 1 dB at ranges up to 15 km and less than 1.5 dB at ranges up to 100 km. During the experiments urban objects and 6 various ships of small displacement having the closest values of the backscattering cross-section were used. The analysis has shown: the factor of the polarization selection for anisotropy objects and weather objects had the values about 0.02-0.08 Isotropy had the values of polarimetric correlation factor for hydrometers about 0.7-0.8, for earth surface about 0.8-0.9, for sea surface - from 0.33 to 0.7. The results of the work of recognition algorithm of a class 'concrete objects', and 'metal objects' are submitted as example in the paper. The result of experiments have shown that the probability of correct recognition of the identified objects was in the limits from 0.93 to 0.97.

The Functional Architecture of Visual Object Recognition

DTIC Science & Technology

1991-07-01

different forms of agnosia can provide clues to the representations underlying normal object recognition (Farah, 1990). For example, the pair-wise...patterns of deficit and sparing occur. In a review of 99 published cases of agnosia , the observed patterns of co- occurrence implicated two underlying

Real-Time pedestrian detection : layered object recognition system for pedestrian collision sensing.

DOT National Transportation Integrated Search

2010-01-01

In 2005 alone, 64,000 pedestrians were injured and 4,882 were killed in the United States, with pedestrians accounting for 11 percent of all traffic fatalities and 2 percent of injuries. The focus of "Layered Object Recognition System for Pedestrian ...

Evaluating structural pattern recognition for handwritten math via primitive label graphs

NASA Astrophysics Data System (ADS)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

(abstract) A High Throughput 3-D Inner Product Processor

NASA Technical Reports Server (NTRS)

Daud, Tuan

1996-01-01

A particularily challenging image processing application is the real time scene acquisition and object discrimination. It requires spatio-temporal recognition of point and resolved objects at high speeds with parallel processing algorithms. Neural network paradigms provide fine grain parallism and, when implemented in hardware, offer orders of magnitude speed up. However, neural networks implemented on a VLSI chip are planer architectures capable of efficient processing of linear vector signals rather than 2-D images. Therefore, for processing of images, a 3-D stack of neural-net ICs receiving planar inputs and consuming minimal power are required. Details of the circuits with chip architectures will be described with need to develop ultralow-power electronics. Further, use of the architecture in a system for high-speed processing will be illustrated.

Influence of music with different volumes and styles on recognition activity in humans.

PubMed

Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

2010-10-01

The efficiency of the recognition of masked visual images (Arabic numerals) increased when accompanied by classical (62 dB) and rock music (25 dB). These changes were accompanied by increases in the coherence of potentials in the frontal areas seen on recognition without music. Changes in intercenter EEG relationships correlated with the formation a dominant at the behavioral level. When loud music (85 dB) and music of other styles was used, these changes in behavior and the EEG were not seen; however, the coherence of potentials in the temporal and motor cortex of the right hemisphere increased and the latent periods of motor reactions of the hands decreased. These results provide evidence that the "recognition" dominant is formed when there are particular ratios of the levels of excitation in the corresponding centers, which should be considered when there is a need to increase the efficiency of recognition activity in humans.

Visual working memory is more tolerant than visual long-term memory.

PubMed

Schurgin, Mark W; Flombaum, Jonathan I

2018-05-07

Human visual memory is tolerant, meaning that it supports object recognition despite variability across encounters at the image level. Tolerant object recognition remains one capacity in which artificial intelligence trails humans. Typically, tolerance is described as a property of human visual long-term memory (VLTM). In contrast, visual working memory (VWM) is not usually ascribed a role in tolerant recognition, with tests of that system usually demanding discriminatory power-identifying changes, not sameness. There are good reasons to expect that VLTM is more tolerant; functionally, recognition over the long-term must accommodate the fact that objects will not be viewed under identical conditions; and practically, the passive and massive nature of VLTM may impose relatively permissive criteria for thinking that two inputs are the same. But empirically, tolerance has never been compared across working and long-term visual memory. We therefore developed a novel paradigm for equating encoding and test across different memory types. In each experiment trial, participants saw two objects, memory for one tested immediately (VWM) and later for the other (VLTM). VWM performance was better than VLTM and remained robust despite the introduction of image and object variability. In contrast, VLTM performance suffered linearly as more variability was introduced into test stimuli. Additional experiments excluded interference effects as causes for the observed differences. These results suggest the possibility of a previously unidentified role for VWM in the acquisition of tolerant representations for object recognition. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Environmental enrichment improves novel object recognition and enhances agonistic behavior in male mice.

PubMed

Mesa-Gresa, Patricia; Pérez-Martinez, Asunción; Redolat, Rosa

2013-01-01

Environmental enrichment (EE) is an experimental paradigm in which rodents are housed in complex environments containing objects that provide stimulation, the effects of which are expected to improve the welfare of these subjects. EE has been shown to considerably improve learning and memory in rodents. However, knowledge about the effects of EE on social interaction is generally limited and rather controversial. Thus, our aim was to evaluate both novel object recognition and agonistic behavior in NMRI mice receiving EE, hypothesizing enhanced cognition and slightly enhanced agonistic interaction upon EE rearing. During a 4-week period half the mice (n = 16) were exposed to EE and the other half (n = 16) remained in a standard environment (SE). On PND 56-57, animals performed the object recognition test, in which recognition memory was measured using a discrimination index. The social interaction test consisted of an encounter between an experimental animal and a standard opponent. Results indicated that EE mice explored the new object for longer periods than SE animals (P < .05). During social encounters, EE mice devoted more time to sociability and agonistic behavior (P < .05) than their non-EE counterparts. In conclusion, EE has been shown to improve object recognition and increase agonistic behavior in adolescent/early adulthood mice. In the future we intend to extend this study on a longitudinal basis in order to assess in more depth the effect of EE and the consistency of the above-mentioned observations in NMRI mice. Copyright © 2013 Wiley Periodicals, Inc.

Estrous cycle, pregnancy, and parity enhance performance of rats in object recognition or object placement tasks

PubMed Central

Paris, Jason J; Frye, Cheryl A

2008-01-01

Ovarian hormone elevations are associated with enhanced learning/memory. During behavioral estrus or pregnancy, progestins, such as progesterone (P4) and its metabolite 5α-pregnan-3α-ol-20-one (3α,5α-THP), are elevated due, in part, to corpora luteal and placental secretion. During ‘pseudopregnancy’, the induction of corpora luteal functioning results in a hormonal milieu analogous to pregnancy, which ceases after about 12 days, due to the lack of placental formation. Multiparity is also associated with enhanced learning/memory, perhaps due to prior steroid exposure during pregnancy. Given evidence that progestins and/or parity may influence cognition, we investigated how natural alterations in the progestin milieu influence cognitive performance. In Experiment 1, virgin rats (nulliparous) or rats with two prior pregnancies (multiparous) were assessed on the object placement and recognition tasks, when in high-estrogen/P4 (behavioral estrus) or low-estrogen/P4 (diestrus) phases of the estrous cycle. In Experiment 2, primiparous or multiparous rats were tested in the object placement and recognition tasks when not pregnant, pseudopregnant, or pregnant (between gestational days (GDs) 6 and 12). In Experiment 3, pregnant primiparous or multiparous rats were assessed daily in the object placement or recognition tasks. Females in natural states associated with higher endogenous progestins (behavioral estrus, pregnancy, multiparity) outperformed rats in low progestin states (diestrus, non-pregnancy, nulliparity) on the object placement and recognition tasks. In earlier pregnancy, multiparous, compared with primiparous, rats had a lower corticosterone, but higher estrogen levels, concomitant with better object placement performance. From GD 13 until post partum, primiparous rats had higher 3α,5α-THP levels and improved object placement performance compared with multiparous rats. PMID:18390689

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

A new method of edge detection for object recognition

USGS Publications Warehouse

Maddox, Brian G.; Rhew, Benjamin

2004-01-01

Traditional edge detection systems function by returning every edge in an input image. This can result in a large amount of clutter and make certain vectorization algorithms less accurate. Accuracy problems can then have a large impact on automated object recognition systems that depend on edge information. A new method of directed edge detection can be used to limit the number of edges returned based on a particular feature. This results in a cleaner image that is easier for vectorization. Vectorized edges from this process could then feed an object recognition system where the edge data would also contain information as to what type of feature it bordered.

Object Recognition in Flight: How Do Bees Distinguish between 3D Shapes?

PubMed Central

Werner, Annette; Stürzl, Wolfgang; Zanker, Johannes

2016-01-01

Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees’ flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots. PMID:26886006

Object Recognition in Flight: How Do Bees Distinguish between 3D Shapes?

PubMed

Werner, Annette; Stürzl, Wolfgang; Zanker, Johannes

2016-01-01

Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees' flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots.

Spatiotemporal dynamics underlying object completion in human ventral visual cortex.

PubMed

Tang, Hanlin; Buia, Calin; Madhavan, Radhika; Crone, Nathan E; Madsen, Joseph R; Anderson, William S; Kreiman, Gabriel

2014-08-06

Natural vision often involves recognizing objects from partial information. Recognition of objects from parts presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. Here we recorded intracranial field potentials of 113 visually selective electrodes from epilepsy patients in response to whole and partial objects. Responses along the ventral visual stream, particularly the inferior occipital and fusiform gyri, remained selective despite showing only 9%-25% of the object areas. However, these visually selective signals emerged ∼100 ms later for partial versus whole objects. These processing delays were particularly pronounced in higher visual areas within the ventral stream. This latency difference persisted when controlling for changes in contrast, signal amplitude, and the strength of selectivity. These results argue against a purely feedforward explanation of recognition from partial information, and provide spatiotemporal constraints on theories of object recognition that involve recurrent processing. Copyright © 2014 Elsevier Inc. All rights reserved.

3D visual mechinism by neural networkings

NASA Astrophysics Data System (ADS)

Sugiyama, Shigeki

2007-04-01

There are some computer vision systems that are available on a market but those are quite far from a real usage of our daily life in a sense of security guard or in a sense of a usage of recognition of a target object behaviour. Because those surroundings' sensing might need to recognize a detail description of an object, like "the distance to an object" and "an object detail figure" and "its figure of edging", which are not possible to have a clear picture of the mechanisms of them with the present recognition system. So for doing this, here studies on mechanisms of how a pair of human eyes can recognize a distance apart, an object edging, and an object in order to get basic essences of vision mechanisms. And those basic mechanisms of object recognition are simplified and are extended logically for applying to a computer vision system. Some of the results of these studies are introduced on this paper.

Comparing object recognition from binary and bipolar edge images for visual prostheses.

PubMed

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2016-11-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition.

Warmth of familiarity and chill of error: affective consequences of recognition decisions.

PubMed

Chetverikov, Andrey

2014-04-01

The present research aimed to assess the effect of recognition decision on subsequent affective evaluations of recognised and non-recognised objects. Consistent with the proposed account of post-decisional preferences, results showed that the effect of recognition on preferences depends upon objective familiarity. If stimuli are recognised, liking ratings are positively associated with exposure frequency; if stimuli are not recognised, this link is either absent (Experiment 1) or negative (Experiments 2 and 3). This interaction between familiarity and recognition exists even when recognition accuracy is at chance level and the "mere exposure" effect is absent. Finally, data obtained from repeated measurements of preferences and using manipulations of task order confirm that recognition decisions have a causal influence on preferences. The findings suggest that affective evaluation can provide fine-grained access to the efficacy of cognitive processing even in simple cognitive tasks.

Aniracetam restores object recognition impaired by age, scopolamine, and nucleus basalis lesions.

PubMed

Bartolini, L; Casamenti, F; Pepeu, G

1996-02-01

Object recognition was investigated in adult and aging male rats in a two-trials, unrewarded, test that assessed a form of working-episodic memory. Exploration time in the first trial, in which two copies of the same object were presented, was recorded. In the second trial, in which one of the familiar objects and a new object were presented, the time spent exploring the two objects was separately recorded and a discrimination index was calculated. Adult rats explored the new object longer than the familiar object when the intertrial time ranged from 1 to 60 min. Rats older than 20 months of age did not discriminate between familiar and new objects. Object discrimination was lost in adult rats after scopolamine (0.2 mg/kg SC) administration and with lesions of the nucleus basalis, resulting in a 40% decrease in cortical ChAT activity. Both aniracetam (25, 50, 100 mg/kg os) and oxiracetam (50 mg/kg os) restored object recognition in aging rats, in rats treated with scopolamine, and with lesions of the nucleus basalis. In the rat, object discrimination appears to depend on the integrity of the cholinergic system, and nootropic drugs can correct its disruption.

Agnosic vision is like peripheral vision, which is limited by crowding.

PubMed

Strappini, Francesca; Pelli, Denis G; Di Pace, Enrico; Martelli, Marialuisa

2017-04-01

Visual agnosia is a neuropsychological impairment of visual object recognition despite near-normal acuity and visual fields. A century of research has provided only a rudimentary account of the functional damage underlying this deficit. We find that the object-recognition ability of agnosic patients viewing an object directly is like that of normally-sighted observers viewing it indirectly, with peripheral vision. Thus, agnosic vision is like peripheral vision. We obtained 14 visual-object-recognition tests that are commonly used for diagnosis of visual agnosia. Our "standard" normal observer took these tests at various eccentricities in his periphery. Analyzing the published data of 32 apperceptive agnosia patients and a group of 14 posterior cortical atrophy (PCA) patients on these tests, we find that each patient's pattern of object recognition deficits is well characterized by one number, the equivalent eccentricity at which our standard observer's peripheral vision is like the central vision of the agnosic patient. In other words, each agnosic patient's equivalent eccentricity is conserved across tests. Across patients, equivalent eccentricity ranges from 4 to 40 deg, which rates severity of the visual deficit. In normal peripheral vision, the required size to perceive a simple image (e.g., an isolated letter) is limited by acuity, and that for a complex image (e.g., a face or a word) is limited by crowding. In crowding, adjacent simple objects appear unrecognizably jumbled unless their spacing exceeds the crowding distance, which grows linearly with eccentricity. Besides conservation of equivalent eccentricity across object-recognition tests, we also find conservation, from eccentricity to agnosia, of the relative susceptibility of recognition of ten visual tests. These findings show that agnosic vision is like eccentric vision. Whence crowding? Peripheral vision, strabismic amblyopia, and possibly apperceptive agnosia are all limited by crowding, making it urgent to know what drives crowding. Acuity does not (Song et al., 2014), but neural density might: neurons per deg 2 in the crowding-relevant cortical area. Copyright © 2017 Elsevier Ltd. All rights reserved.

One-Reason Decision Making Unveiled: A Measurement Model of the Recognition Heuristic

ERIC Educational Resources Information Center

Hilbig, Benjamin E.; Erdfelder, Edgar; Pohl, Rudiger F.

2010-01-01

The fast-and-frugal recognition heuristic (RH) theory provides a precise process description of comparative judgments. It claims that, in suitable domains, judgments between pairs of objects are based on recognition alone, whereas further knowledge is ignored. However, due to the confound between recognition and further knowledge, previous…

Efficiency of printed materials in worksite health promotion.

PubMed

Kishchuk, N; Anbar, F; O'Loughlin, J; Masson, P; Sacks-Silver, G

1991-01-01

Printed health promotion materials are widely believed to be an efficient means of achieving basic health promotion objectives, such as increasing knowledge of risk factors. This study examined the efficiency of cardiovascular health promotion leaflets in reaching employees in a heterogeneous sample of worksites. Two types of distribution were used: copies of the leaflets were either made available centrally or distributed to each individual employee. Interviews were conducted with 272 employees in six worksites. Respondents were asked whether they recognized, had read, and had learned something from the leaflets. Only one-quarter of respondents recognized the leaflets and only 14% stated that they had learned something. The efficiency of the leaflets was therefore much lower than expected. Z-tests for proportions showed that recognition, reading, and learning were significantly greater among those employees who had been given individual copies of the material. Among those who had been given individual copies, 45% reported recognizing the leaflet, 36% reading it, and 23% learning something from it. Among those who had only central access, the respective scores were 11%, 7% and 6%. These results suggest that the potential cost-effectiveness of printed materials such as leaflets and brochures should be weighed against alternative forms of intervention, given specific program objectives and characteristics of the target population. They also suggest that the cost and effort required in organizing the distribution of individual copies may be recouped in greater penetration.

Voice Recognition: A New Assessment Tool?

ERIC Educational Resources Information Center

Jones, Darla

2005-01-01

This article presents the results of a study conducted in Anchorage, Alaska, that evaluated the accuracy and efficiency of using voice recognition (VR) technology to collect oral reading fluency data for classroom-based assessments. The primary research question was as follows: Is voice recognition technology a valid and reliable alternative to…

Utilizing a State Level Volunteer Recognition Program at the County Level

ERIC Educational Resources Information Center

McCall, Fran Korthaus; Culp, Ken, III

2013-01-01

Volunteer recognition is an important component of Extension programs. Most land-grant universities have implemented a state volunteer recognition program. Extension professionals, however, are too overburdened with meetings, programs, and activities to effectively recognize volunteers locally. Utilizing a state model is an efficient means of…

Development of Encoding and Decision Processes in Visual Recognition.

ERIC Educational Resources Information Center

Newcombe, Nora; MacKenzie, Doris L.

This experiment examined two processes which might account for developmental increases in accuracy in visual recognition tasks: age-related increases in efficiency of scanning during inspection, and age-related increases in the ability to make decisions systematically during test. Critical details necessary for recognition were highlighted as…

Adult Word Recognition and Visual Sequential Memory

ERIC Educational Resources Information Center

Holmes, V. M.

2012-01-01

Two experiments were conducted investigating the role of visual sequential memory skill in the word recognition efficiency of undergraduate university students. Word recognition was assessed in a lexical decision task using regularly and strangely spelt words, and nonwords that were either standard orthographically legal strings or items made from…

Implementation study of wearable sensors for activity recognition systems.

PubMed

Rezaie, Hamed; Ghassemian, Mona

2015-08-01

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely 'stream-based', 'feature-based' and 'threshold-based' scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency.

Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition.

PubMed

Galbally, Javier; Marcel, Sébastien; Fierrez, Julian

2014-02-01

To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.

The memory state heuristic: A formal model based on repeated recognition judgments.

PubMed

Castela, Marta; Erdfelder, Edgar

2017-02-01

The recognition heuristic (RH) theory predicts that, in comparative judgment tasks, if one object is recognized and the other is not, the recognized one is chosen. The memory-state heuristic (MSH) extends the RH by assuming that choices are not affected by recognition judgments per se, but by the memory states underlying these judgments (i.e., recognition certainty, uncertainty, or rejection certainty). Specifically, the larger the discrepancy between memory states, the larger the probability of choosing the object in the higher state. The typical RH paradigm does not allow estimation of the underlying memory states because it is unknown whether the objects were previously experienced or not. Therefore, we extended the paradigm by repeating the recognition task twice. In line with high threshold models of recognition, we assumed that inconsistent recognition judgments result from uncertainty whereas consistent judgments most likely result from memory certainty. In Experiment 1, we fitted 2 nested multinomial models to the data: an MSH model that formalizes the relation between memory states and binary choices explicitly and an approximate model that ignores the (unlikely) possibility of consistent guesses. Both models provided converging results. As predicted, reliance on recognition increased with the discrepancy in the underlying memory states. In Experiment 2, we replicated these results and found support for choice consistency predictions of the MSH. Additionally, recognition and choice latencies were in agreement with the MSH in both experiments. Finally, we validated critical parameters of our MSH model through a cross-validation method and a third experiment. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Scene recognition following locomotion around a scene.

PubMed

Motes, Michael A; Finlay, Cory A; Kozhevnikov, Maria

2006-01-01

Effects of locomotion on scene-recognition reaction time (RT) and accuracy were studied. In experiment 1, observers memorized an 11-object scene and made scene-recognition judgments on subsequently presented scenes from the encoded view or different views (ie scenes were rotated or observers moved around the scene, both from 40 degrees to 360 degrees). In experiment 2, observers viewed different 5-object scenes on each trial and made scene-recognition judgments from the encoded view or after moving around the scene, from 36 degrees to 180 degrees. Across experiments, scene-recognition RT increased (in experiment 2 accuracy decreased) with angular distance between encoded and judged views, regardless of how the viewpoint changes occurred. The findings raise questions about conditions in which locomotion produces spatially updated representations of scenes.

Human-inspired sound environment recognition system for assistive vehicles.

PubMed

Vidal, Eduardo González; Zarricueta, Ernesto Fredes; Cheein, Fernando Auat

2015-02-01

The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. The proposed sound-based system is very efficient at providing general descriptions of the environment. Such descriptions are focused on vulnerable situations described by the ICF. The volunteers answered a questionnaire regarding the importance of constraining the vehicle velocities in risky environments, showing that all the volunteers felt comfortable with the system and its performance.

Rotation And Scale Invariant Object Recognition Using A Distributed Associative Memory

NASA Astrophysics Data System (ADS)

Wechsler, Harry; Zimmerman, George Lee

1988-04-01

This paper describes an approach to 2-dimensional object recognition. Complex-log conformal mapping is combined with a distributed associative memory to create a system which recognizes objects regardless of changes in rotation or scale. Recalled information from the memorized database is used to classify an object, reconstruct the memorized version of the object, and estimate the magnitude of changes in scale or rotation. The system response is resistant to moderate amounts of noise and occlusion. Several experiments, using real, gray scale images, are presented to show the feasibility of our approach.

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

Cultural Diversity and Civic Education: Two Versions of the Fragmentation Objection

ERIC Educational Resources Information Center

Shorten, Andrew

2010-01-01

According to the "fragmentation objection" to multiculturalism, practices of cultural recognition undermine political stability, and this counts as a reason to be sceptical about the public recognition of minority cultures, as well as about multiculturalism construed more broadly as a public policy. Civic education programmes, designed to promote…

Action recognition using mined hierarchical compound features.

PubMed

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical approach outperforms all other methods reported thus far in the literature and can achieve real-time operation.

Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems.

PubMed

Gao, Lei; Bourke, A K; Nelson, John

2014-06-01

Physical activity has a positive impact on people's well-being and it had been shown to decrease the occurrence of chronic diseases in the older adult population. To date, a substantial amount of research studies exist, which focus on activity recognition using inertial sensors. Many of these studies adopt a single sensor approach and focus on proposing novel features combined with complex classifiers to improve the overall recognition accuracy. In addition, the implementation of the advanced feature extraction algorithms and the complex classifiers exceed the computing ability of most current wearable sensor platforms. This paper proposes a method to adopt multiple sensors on distributed body locations to overcome this problem. The objective of the proposed system is to achieve higher recognition accuracy with "light-weight" signal processing algorithms, which run on a distributed computing based sensor system comprised of computationally efficient nodes. For analysing and evaluating the multi-sensor system, eight subjects were recruited to perform eight normal scripted activities in different life scenarios, each repeated three times. Thus a total of 192 activities were recorded resulting in 864 separate annotated activity states. The methods for designing such a multi-sensor system required consideration of the following: signal pre-processing algorithms, sampling rate, feature selection and classifier selection. Each has been investigated and the most appropriate approach is selected to achieve a trade-off between recognition accuracy and computing execution time. A comparison of six different systems, which employ single or multiple sensors, is presented. The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier. The results demonstrate that elaborate classifiers and feature sets are not required to achieve high recognition accuracies on a multi-sensor system. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Differential Roles for "Nr4a1" and "Nr4a2" in Object Location vs. Object Recognition Long-Term Memory

ERIC Educational Resources Information Center

McNulty, Susan E.; Barrett, Ruth M.; Vogel-Ciernia, Annie; Malvaez, Melissa; Hernandez, Nicole; Davatolhagh, M. Felicia; Matheos, Dina P.; Schiffman, Aaron; Wood, Marcelo A.

2012-01-01

"Nr4a1" and "Nr4a2" are transcription factors and immediate early genes belonging to the nuclear receptor Nr4a family. In this study, we examine their role in long-term memory formation for object location and object recognition. Using siRNA to block expression of either "Nr4a1" or "Nr4a2", we found that "Nr4a2" is necessary for both long-term…

Remembering the snake in the grass: Threat enhances recognition but not source memory.

PubMed

Meyer, Miriam Magdalena; Bell, Raoul; Buchner, Axel

2015-12-01

Research on the influence of emotion on source memory has yielded inconsistent findings. The object-based framework (Mather, 2007) predicts that negatively arousing stimuli attract attention, resulting in enhanced within-object binding, and, thereby, enhanced source memory for intrinsic context features of emotional stimuli. To test this prediction, we presented pictures of threatening and harmless animals, the color of which had been experimentally manipulated. In a memory test, old-new recognition for the animals and source memory for their color was assessed. In all 3 experiments, old-new recognition was better for the more threatening material, which supports previous reports of an emotional memory enhancement. This recognition advantage was due to the emotional properties of the stimulus material, and not specific for snake stimuli. However, inconsistent with the prediction of the object-based framework, intrinsic source memory was not affected by emotion. (c) 2015 APA, all rights reserved).

Change blindness and visual memory: visual representations get rich and act poor.

PubMed

Varakin, D Alexander; Levin, Daniel T

2006-02-01

Change blindness is often taken as evidence that visual representations are impoverished, while successful recognition of specific objects is taken as evidence that they are richly detailed. In the current experiments, participants performed cover tasks that required each object in a display to be attended. Change detection trials were unexpectedly introduced and surprise recognition tests were given for nonchanging displays. For both change detection and recognition, participants had to distinguish objects from the same basic-level category, making it likely that specific visual information had to be used for successful performance. Although recognition was above chance, incidental change detection usually remained at floor. These results help reconcile demonstrations of poor change detection with demonstrations of good memory because they suggest that the capability to store visual information in memory is not reflected by the visual system's tendency to utilize these representations for purposes of detecting unexpected changes.

Kruskal-Wallis-based computationally efficient feature selection for face recognition.

PubMed

Ali Khan, Sajid; Hussain, Ayyaz; Basit, Abdul; Akram, Sheeraz

2014-01-01

Face recognition in today's technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.

The roles of scene gist and spatial dependency among objects in the semantic guidance of attention in real-world scenes.

PubMed

Wu, Chia-Chien; Wang, Hsueh-Cheng; Pomplun, Marc

2014-12-01

A previous study (Vision Research 51 (2011) 1192-1205) found evidence for semantic guidance of visual attention during the inspection of real-world scenes, i.e., an influence of semantic relationships among scene objects on overt shifts of attention. In particular, the results revealed an observer bias toward gaze transitions between semantically similar objects. However, this effect is not necessarily indicative of semantic processing of individual objects but may be mediated by knowledge of the scene gist, which does not require object recognition, or by known spatial dependency among objects. To examine the mechanisms underlying semantic guidance, in the present study, participants were asked to view a series of displays with the scene gist excluded and spatial dependency varied. Our results show that spatial dependency among objects seems to be sufficient to induce semantic guidance. Scene gist, on the other hand, does not seem to affect how observers use semantic information to guide attention while viewing natural scenes. Extracting semantic information mainly based on spatial dependency may be an efficient strategy of the visual system that only adds little cognitive load to the viewing task. Copyright © 2014 Elsevier Ltd. All rights reserved.

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex

PubMed Central

Leibo, Joel Z.; Liao, Qianli; Anselmi, Fabio; Poggio, Tomaso

2015-01-01

Is visual cortex made up of general-purpose information processing machinery, or does it consist of a collection of specialized modules? If prior knowledge, acquired from learning a set of objects is only transferable to new objects that share properties with the old, then the recognition system’s optimal organization must be one containing specialized modules for different object classes. Our analysis starts from a premise we call the invariance hypothesis: that the computational goal of the ventral stream is to compute an invariant-to-transformations and discriminative signature for recognition. The key condition enabling approximate transfer of invariance without sacrificing discriminability turns out to be that the learned and novel objects transform similarly. This implies that the optimal recognition system must contain subsystems trained only with data from similarly-transforming objects and suggests a novel interpretation of domain-specific regions like the fusiform face area (FFA). Furthermore, we can define an index of transformation-compatibility, computable from videos, that can be combined with information about the statistics of natural vision to yield predictions for which object categories ought to have domain-specific regions in agreement with the available data. The result is a unifying account linking the large literature on view-based recognition with the wealth of experimental evidence concerning domain-specific regions. PMID:26496457

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

PubMed

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

2016-01-01

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

Newborn chickens generate invariant object representations at the onset of visual object experience

PubMed Central

Wood, Justin N.

2013-01-01

To recognize objects quickly and accurately, mature visual systems build invariant object representations that generalize across a range of novel viewing conditions (e.g., changes in viewpoint). To date, however, the origins of this core cognitive ability have not yet been established. To examine how invariant object recognition develops in a newborn visual system, I raised chickens from birth for 2 weeks within controlled-rearing chambers. These chambers provided complete control over all visual object experiences. In the first week of life, subjects’ visual object experience was limited to a single virtual object rotating through a 60° viewpoint range. In the second week of life, I examined whether subjects could recognize that virtual object from novel viewpoints. Newborn chickens were able to generate viewpoint-invariant representations that supported object recognition across large, novel, and complex changes in the object’s appearance. Thus, newborn visual systems can begin building invariant object representations at the onset of visual object experience. These abstract representations can be generated from sparse data, in this case from a visual world containing a single virtual object seen from a limited range of viewpoints. This study shows that powerful, robust, and invariant object recognition machinery is an inherent feature of the newborn brain. PMID:23918372

A role for the CAMKK pathway in visual object recognition memory.

PubMed

Tinsley, Chris J; Narduzzo, Katherine E; Brown, Malcolm W; Warburton, E Clea

2012-03-01

The role of the CAMKK pathway in object recognition memory was investigated. Rats' performance in a preferential object recognition test was examined after local infusion into the perirhinal cortex of the CAMKK inhibitor STO-609. STO-609 infused either before or immediately after acquisition impaired memory tested after a 24 h but not a 20-min delay. Memory was not impaired when STO-609 was infused 20 min after acquisition. The expression of a downstream reaction product of CAMKK was measured by immunohistochemical staining for phospho-CAMKI(Thr177) at 10, 40, 70, and 100 min following the viewing of novel and familiar images of objects. Processing familiar images resulted in more pCAMKI stained neurons in the perirhinal cortex than processing novel images at the 10- and 40-min delays. Prior infusion of STO-609 caused a reduction in pCAMKI stained neurons in response to viewing either novel or familiar images, consistent with its role as an inhibitor of CAMKK. The results establish that the CAMKK pathway within the perirhinal cortex is important for the consolidation of object recognition memory. The activation of pCAMKI after acquisition is earlier than previously reported for pCAMKII. Copyright © 2011 Wiley Periodicals, Inc.

Global shape recognition is modulated by the spatial distance of local elements--evidence from simultanagnosia.

PubMed

Huberle, Elisabeth; Karnath, Hans-Otto

2006-01-01

Simultanagnosia is a rare deficit that impairs individuals in perceiving several objects at the same time. It is usually observed following bilateral parieto-occipital brain damage. Despite the restrictions in perceiving the global aspect of a scene, processing of individual objects remains unaffected. The mechanisms underlying simultanagnosia are not well understood. Previous findings indicated that the integration of multiple objects into a holistic representation of the environment is not impossible per se, but might depend on the spatial relationship between individual objects. The present study examined the influence of inter-element distances between individual objects on the recognition of global shapes in two patients with simultanagnosia. We presented Navon hierarchical letter stimuli with different inter-element distances between letters at the Local Scale. Improved recognition at the Global Scale was observed in both patients by reducing the inter-element distance. Global shape recognition in simultanagnosia thus seems to be modulated by the spatial distance of local elements and does not appear to be an all-or-nothing phenomenon depending on spatial continuity. The findings seem to argue against a deficit in visual working memory capacity as the primary deficit in simultanagnosia. However, further research is necessary to investigate alternative interpretations.

Bidirectional Modulation of Recognition Memory

PubMed Central

Ho, Jonathan W.; Poeta, Devon L.; Jacobson, Tara K.; Zolnik, Timothy A.; Neske, Garrett T.; Connors, Barry W.

2015-01-01

Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects. For example, animals and humans with perirhinal damage are unable to distinguish familiar from novel objects in recognition memory tasks. In the normal brain, perirhinal neurons respond to novelty and familiarity by increasing or decreasing firing rates. Recent work also implicates oscillatory activity in the low-beta and low-gamma frequency bands in sensory detection, perception, and recognition. Using optogenetic methods in a spontaneous object exploration (SOR) task, we altered recognition memory performance in rats. In the SOR task, normal rats preferentially explore novel images over familiar ones. We modulated exploratory behavior in this task by optically stimulating channelrhodopsin-expressing perirhinal neurons at various frequencies while rats looked at novel or familiar 2D images. Stimulation at 30–40 Hz during looking caused rats to treat a familiar image as if it were novel by increasing time looking at the image. Stimulation at 30–40 Hz was not effective in increasing exploration of novel images. Stimulation at 10–15 Hz caused animals to treat a novel image as familiar by decreasing time looking at the image, but did not affect looking times for images that were already familiar. We conclude that optical stimulation of PER at different frequencies can alter visual recognition memory bidirectionally. SIGNIFICANCE STATEMENT Recognition of novelty and familiarity are important for learning, memory, and decision making. Perirhinal cortex (PER) has a well established role in the familiarity-based recognition of individual items and objects, but how novelty and familiarity are encoded and transmitted in the brain is not known. Perirhinal neurons respond to novelty and familiarity by changing firing rates, but recent work suggests that brain oscillations may also be important for recognition. In this study, we showed that stimulation of the PER could increase or decrease exploration of novel and familiar images depending on the frequency of stimulation. Our findings suggest that optical stimulation of PER at specific frequencies can predictably alter recognition memory. PMID:26424881

Robust image region descriptor using local derivative ordinal binary pattern

NASA Astrophysics Data System (ADS)

Shang, Jun; Chen, Chuanbo; Pei, Xiaobing; Liang, Hu; Tang, He; Sarem, Mudar

2015-05-01

Binary image descriptors have received a lot of attention in recent years, since they provide numerous advantages, such as low memory footprint and efficient matching strategy. However, they utilize intermediate representations and are generally less discriminative than floating-point descriptors. We propose an image region descriptor, namely local derivative ordinal binary pattern, for object recognition and image categorization. In order to preserve more local contrast and edge information, we quantize the intensity differences between the central pixels and their neighbors of the detected local affine covariant regions in an adaptive way. These differences are then sorted and mapped into binary codes and histogrammed with a weight of the sum of the absolute value of the differences. Furthermore, the gray level of the central pixel is quantized to further improve the discriminative ability. Finally, we combine them to form a joint histogram to represent the features of the image. We observe that our descriptor preserves more local brightness and edge information than traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations, and other geometric transformations. We conduct extensive experiments on the standard ETHZ and Kentucky datasets for object recognition and PASCAL for image classification. The experimental results show that our descriptor outperforms existing state-of-the-art methods.

Culture modulates implicit ownership-induced self-bias in memory.

PubMed

Sparks, Samuel; Cunningham, Sheila J; Kritikos, Ada

2016-08-01

The relation of incoming stimuli to the self implicitly determines the allocation of cognitive resources. Cultural variations in the self-concept shape cognition, but the extent is unclear because the majority of studies sample only Western participants. We report cultural differences (Asian versus Western) in ownership-induced self-bias in recognition memory for objects. In two experiments, participants allocated a series of images depicting household objects to self-owned or other-owned virtual baskets based on colour cues before completing a surprise recognition memory test for the objects. The 'other' was either a stranger or a close other. In both experiments, Western participants showed greater recognition memory accuracy for self-owned compared with other-owned objects, consistent with an independent self-construal. In Experiment 1, which required minimal attention to the owned objects, Asian participants showed no such ownership-related bias in recognition accuracy. In Experiment 2, which required attention to owned objects to move them along the screen, Asian participants again showed no overall memory advantage for self-owned items and actually exhibited higher recognition accuracy for mother-owned than self-owned objects, reversing the pattern observed for Westerners. This is consistent with an interdependent self-construal which is sensitive to the particular relationship between the self and other. Overall, our results suggest that the self acts as an organising principle for allocating cognitive resources, but that the way it is constructed depends upon cultural experience. Additionally, the manifestation of these cultural differences in self-representation depends on the allocation of attentional resources to self- and other-associated stimuli. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

How landmark suitability shapes recognition memory signals for objects in the medial temporal lobes.

PubMed

Martin, Chris B; Sullivan, Jacqueline A; Wright, Jessey; Köhler, Stefan

2018-02-01

A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context. Guided by research suggesting that PhC plays a critical role in processing landmarks, we focused on three categories of objects that differ from each other in their landmark suitability as confirmed with behavioral ratings (buildings > trees > aircraft). Participants made item-based recognition-memory decisions for novel and previously studied objects from these categories, which were matched in accuracy. Multi-voxel pattern classification revealed category-specific item-recognition memory signals along the long axis of PrC and PhC, with no sharp functional boundaries between these structures. Memory signals for buildings were observed in the mid to posterior extent of PhC, signals for trees in anterior to posterior segments of PhC, and signals for aircraft in mid to posterior aspects of PrC and the anterior extent of PhC. Notably, item-based memory signals for the category with highest landmark suitability ratings were observed only in those posterior segments of PhC that also allowed for classification of landmark suitability of objects when memory status was held constant. These findings provide new evidence in support of the notion that item-based memory signals for objects are not limited to PrC, and that the organization of these signals along the longitudinal axis that crosses PrC and PhC can be captured with reference to landmark suitability. Copyright © 2017 Elsevier Inc. All rights reserved.

3D automatic anatomy segmentation based on iterative graph-cut-ASM.

PubMed

Chen, Xinjian; Bagci, Ulas

2011-08-01

This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

Implementation of a Peltier-based cooling device for localized deep cortical deactivation during in vivo object recognition testing

NASA Astrophysics Data System (ADS)

Marra, Kyle; Graham, Brett; Carouso, Samantha; Cox, David

2012-02-01

While the application of local cortical cooling has recently become a focus of neurological research, extended localized deactivation deep within brain structures is still unexplored. Using a wirelessly controlled thermoelectric (Peltier) device and water-based heat sink, we have achieved inactivating temperatures (<20 C) at greater depths (>8 mm) than previously reported. After implanting the device into Long Evans rats' basolateral amygdala (BLA), an inhibitory brain center that controls anxiety and fear, we ran an open field test during which anxiety-driven behavioral tendencies were observed to decrease during cooling, thus confirming the device's effect on behavior. Our device will next be implanted in the rats' temporal association cortex (TeA) and recordings from our signal-tracing multichannel microelectrodes will measure and compare activated and deactivated neuronal activity so as to isolate and study the TeA signals responsible for object recognition. Having already achieved a top performing computational face-recognition system, the lab will utilize this TeA activity data to generalize its computational efforts of face recognition to achieve general object recognition.

Neuropeptide Trefoil factor 3 improves learning and retention of novel object recognition memory in mice.

PubMed

Shi, Hai-Shui; Yin, Xi; Song, Li; Guo, Qing-Jun; Luo, Xiang-Heng

2012-02-01

Accumulating evidence has implicated neuropeptides in modulating recognition, learning and memory. However, to date, no study has investigated the effects of neuropeptide Trefoil factor 3 (TFF3) on the process of learning and memory. In the present study, we evaluated the acute effects of TFF3 administration (0.1 and 0.5mg/kg, i.p.) on the acquisition and retention of object recognition memory in mice. We found that TFF3 administration significantly enhanced both short-term and long-term memory during the retention test, conducted 90 min and 24h after training respectively. Remarkably, acute TFF3 administration transformed a learning event that would not normally result in long-term memory into an event retained for a long-term period and produced no effect on locomotor activity in mice. In conclusion, the present results provide an important role of TFF3 in improving object recognition memory and reserving it for a longer time, which suggests a potential therapeutic application for diseases with recognition and memory impairment. Copyright © 2011 Elsevier B.V. All rights reserved.

A knowledge-based object recognition system for applications in the space station

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.

1988-01-01

A knowledge-based three-dimensional (3D) object recognition system is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

Efficient local representations for three-dimensional palmprint recognition

NASA Astrophysics Data System (ADS)

Yang, Bing; Wang, Xiaohua; Yao, Jinliang; Yang, Xin; Zhu, Wenhua

2013-10-01

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Young pigs exhibit differential exploratory behavior during novelty preference tasks in response to age, sex, and delay.

PubMed

Fleming, Stephen A; Dilger, Ryan N

2017-03-15

Novelty preference paradigms have been widely used to study recognition memory and its neural substrates. The piglet model continues to advance the study of neurodevelopment, and as such, tasks that use novelty preference will serve especially useful due to their translatable nature to humans. However, there has been little use of this behavioral paradigm in the pig, and previous studies using the novel object recognition paradigm in piglets have yielded inconsistent results. The current study was conducted to determine if piglets were capable of displaying a novelty preference. Herein a series of experiments were conducted using novel object recognition or location in 3- and 4-week-old piglets. In the novel object recognition task, piglets were able to discriminate between novel and sample objects after delays of 2min, 1h, 1 day, and 2 days (all P<0.039) at both ages. Performance was sex-dependent, as females could perform both 1- and 2-day delays (P<0.036) and males could perform the 2-day delay (P=0.008) but not the 1-day delay (P=0.347). Furthermore, 4-week-old piglets and females tended to exhibit greater exploratory behavior compared with males. Such performance did not extend to novel location recognition tasks, as piglets were only able to discriminate between novel and sample locations after a short delay (P>0.046). In conclusion, this study determined that piglets are able to perform the novel object and location recognition tasks at 3-to-4 weeks of age, however performance was dependent on sex, age, and delay. Copyright © 2016 Elsevier B.V. All rights reserved.

Sparse aperture 3D passive image sensing and recognition

NASA Astrophysics Data System (ADS)

Daneshpanah, Mehdi

The way we perceive, capture, store, communicate and visualize the world has greatly changed in the past century Novel three dimensional (3D) imaging and display systems are being pursued both in academic and industrial settings. In many cases, these systems have revolutionized traditional approaches and/or enabled new technologies in other disciplines including medical imaging and diagnostics, industrial metrology, entertainment, robotics as well as defense and security. In this dissertation, we focus on novel aspects of sparse aperture multi-view imaging systems and their application in quantum-limited object recognition in two separate parts. In the first part, two concepts are proposed. First a solution is presented that involves a generalized framework for 3D imaging using randomly distributed sparse apertures. Second, a method is suggested to extract the profile of objects in the scene through statistical properties of the reconstructed light field. In both cases, experimental results are presented that demonstrate the feasibility of the techniques. In the second part, the application of 3D imaging systems in sensing and recognition of objects is addressed. In particular, we focus on the scenario in which only 10s of photons reach the sensor from the object of interest, as opposed to hundreds of billions of photons in normal imaging conditions. At this level, the quantum limited behavior of light will dominate and traditional object recognition practices may fail. We suggest a likelihood based object recognition framework that incorporates the physics of sensing at quantum-limited conditions. Sensor dark noise has been modeled and taken into account. This framework is applied to 3D sensing of thermal objects using visible spectrum detectors. Thermal objects as cold as 250K are shown to provide enough signature photons to be sensed and recognized within background and dark noise with mature, visible band, image forming optics and detector arrays. The results suggest that one might not need to venture into exotic and expensive detector arrays and associated optics for sensing room-temperature thermal objects in complete darkness.

Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

ERIC Educational Resources Information Center

Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-01-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

Ultra-FDst Object Recognition from Few Spikes

DTIC Science & Technology

2005-07-01

Ultra-fast Object Recognition from Few Spikes Chou Hung, Gabriel Kreiman , Tomaso Poggio & James J. DiCarlo AI Memo 2005-022 July 2005 CBCL Memo 253...authors, Chou Hung and Gabriel Kreiman , contributed equally to this work. Supplementary Material is available at http://ramonycajal.mit.edu... kreiman /resources/ultrafast/. _____________________________________________________________________________ This report describes research done at

Orientation-Invariant Object Recognition: Evidence from Repetition Blindness

ERIC Educational Resources Information Center

Harris, Irina M.; Dux, Paul E.

2005-01-01

The question of whether object recognition is orientation-invariant or orientation-dependent was investigated using a repetition blindness (RB) paradigm. In RB, the second occurrence of a repeated stimulus is less likely to be reported, compared to the occurrence of a different stimulus, if it occurs within a short time of the first presentation.…

Computing with Connections in Visual Recognition of Origami Objects.

ERIC Educational Resources Information Center

Sabbah, Daniel

1985-01-01

Summarizes an initial foray in tackling artificial intelligence problems using a connectionist approach. The task chosen is visual recognition of Origami objects, and the questions answered are how to construct a connectionist network to represent and recognize projected Origami line drawings and the advantages such an approach would have. (30…

Developmental Trajectories of Part-Based and Configural Object Recognition in Adolescence

ERIC Educational Resources Information Center

Juttner, Martin; Wakui, Elley; Petters, Dean; Kaur, Surinder; Davidoff, Jules

2013-01-01

Three experiments assessed the development of children's part and configural (part-relational) processing in object recognition during adolescence. In total, 312 school children aged 7-16 years and 80 adults were tested in 3-alternative forced choice (3-AFC) tasks. They judged the correct appearance of upright and inverted presented familiar…

View Combination: A Generalization Mechanism for Visual Recognition

ERIC Educational Resources Information Center

Friedman, Alinda; Waller, David; Thrash, Tyler; Greenauer, Nathan; Hodgson, Eric

2011-01-01

We examined whether view combination mechanisms shown to underlie object and scene recognition can integrate visual information across views that have little or no three-dimensional information at either the object or scene level. In three experiments, people learned four "views" of a two dimensional visual array derived from a three-dimensional…

Developmental Changes in Visual Object Recognition between 18 and 24 Months of Age

ERIC Educational Resources Information Center

Pereira, Alfredo F.; Smith, Linda B.

2009-01-01

Two experiments examined developmental changes in children's visual recognition of common objects during the period of 18 to 24 months. Experiment 1 examined children's ability to recognize common category instances that presented three different kinds of information: (1) richly detailed and prototypical instances that presented both local and…

Word-to-picture recognition is a function of motor components mappings at the stage of retrieval.

PubMed

Brouillet, Denis; Brouillet, Thibaut; Milhau, Audrey; Heurley, Loïc; Vagnot, Caroline; Brunel, Lionel

2016-10-01

Embodied approaches of cognition argue that retrieval involves the re-enactment of both sensory and motor components of the desired remembering. In this study, we investigated the effect of motor action performed to produce the response in a recognition task when this action is compatible with the affordance of the objects that have to be recognised. In our experiment, participants were first asked to learn a list of words referring to graspable objects, and then told to make recognition judgements on pictures. The pictures represented objects where the graspable part was either pointing to the same or to the opposite side of the "Yes" response key. Results show a robust effect of compatibility between objects affordance and response hand. Moreover, this compatibility improves participants' ability of discrimination, suggesting that motor components are relevant cue for memory judgement at the stage of retrieval in a recognition task. More broadly, our data highlight that memory judgements are a function of motor components mappings at the stage of retrieval. © 2015 International Union of Psychological Science.

Object-Place Recognition Learning Triggers Rapid Induction of Plasticity-Related Immediate Early Genes and Synaptic Proteins in the Rat Dentate Gyrus

PubMed Central

Soulé, Jonathan; Penke, Zsuzsa; Kanhema, Tambudzai; Alme, Maria Nordheim; Laroche, Serge; Bramham, Clive R.

2008-01-01

Long-term recognition memory requires protein synthesis, but little is known about the coordinate regulation of specific genes. Here, we examined expression of the plasticity-associated immediate early genes (Arc, Zif268, and Narp) in the dentate gyrus following long-term object-place recognition learning in rats. RT-PCR analysis from dentate gyrus tissue collected shortly after training did not reveal learning-specific changes in Arc mRNA expression. In situ hybridization and immunohistochemistry were therefore used to assess possible sparse effects on gene expression. Learning about objects increased the density of granule cells expressing Arc, and to a lesser extent Narp, specifically in the dorsal blade of the dentate gyrus, while Zif268 expression was elevated across both blades. Thus, object-place recognition triggers rapid, blade-specific upregulation of plasticity-associated immediate early genes. Furthermore, Western blot analysis of dentate gyrus homogenates demonstrated concomitant upregulation of three postsynaptic density proteins (Arc, PSD-95, and α-CaMKII) with key roles in long-term synaptic plasticity and long-term memory. PMID:19190776

The monocular visual imaging technology model applied in the airport surface surveillance

NASA Astrophysics Data System (ADS)

Qin, Zhe; Wang, Jian; Huang, Chao

2013-08-01

At present, the civil aviation airports use the surface surveillance radar monitoring and positioning systems to monitor the aircrafts, vehicles and the other moving objects. Surface surveillance radars can cover most of the airport scenes, but because of the terminals, covered bridges and other buildings geometry, surface surveillance radar systems inevitably have some small segment blind spots. This paper presents a monocular vision imaging technology model for airport surface surveillance, achieving the perception of scenes of moving objects such as aircrafts, vehicles and personnel location. This new model provides an important complement for airport surface surveillance, which is different from the traditional surface surveillance radar techniques. Such technique not only provides clear objects activities screen for the ATC, but also provides image recognition and positioning of moving targets in this area. Thereby it can improve the work efficiency of the airport operations and avoid the conflict between the aircrafts and vehicles. This paper first introduces the monocular visual imaging technology model applied in the airport surface surveillance and then the monocular vision measurement accuracy analysis of the model. The monocular visual imaging technology model is simple, low cost, and highly efficient. It is an advanced monitoring technique which can make up blind spot area of the surface surveillance radar monitoring and positioning systems.

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes.

PubMed

Li, Yuqi; Majumder, Aditi; Zhang, Hao; Gopi, M

2018-04-12

Multi-spectral imaging using a camera with more than three channels is an efficient method to acquire and reconstruct spectral data and is used extensively in tasks like object recognition, relighted rendering, and color constancy. Recently developed methods are used to only guide content-dependent filter selection where the set of spectral reflectances to be recovered are known a priori. We present the first content-independent spectral imaging pipeline that allows optimal selection of multiple channels. We also present algorithms for optimal placement of the channels in the color filter array yielding an efficient demosaicing order resulting in accurate spectral recovery of natural reflectance functions. These reflectance functions have the property that their power spectrum statistically exhibits a power-law behavior. Using this property, we propose power-law based error descriptors that are minimized to optimize the imaging pipeline. We extensively verify our models and optimizations using large sets of commercially available wide-band filters to demonstrate the greater accuracy and efficiency of our multi-spectral imaging pipeline over existing methods.

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes

PubMed Central

Li, Yuqi; Majumder, Aditi; Zhang, Hao; Gopi, M.

2018-01-01

Multi-spectral imaging using a camera with more than three channels is an efficient method to acquire and reconstruct spectral data and is used extensively in tasks like object recognition, relighted rendering, and color constancy. Recently developed methods are used to only guide content-dependent filter selection where the set of spectral reflectances to be recovered are known a priori. We present the first content-independent spectral imaging pipeline that allows optimal selection of multiple channels. We also present algorithms for optimal placement of the channels in the color filter array yielding an efficient demosaicing order resulting in accurate spectral recovery of natural reflectance functions. These reflectance functions have the property that their power spectrum statistically exhibits a power-law behavior. Using this property, we propose power-law based error descriptors that are minimized to optimize the imaging pipeline. We extensively verify our models and optimizations using large sets of commercially available wide-band filters to demonstrate the greater accuracy and efficiency of our multi-spectral imaging pipeline over existing methods. PMID:29649114

Long-lasting effects of prenatal dietary choline availability on object recognition memory ability in adult rats.

PubMed

Moreno, Hayarelis C; de Brugada, Isabel; Carias, Diamela; Gallo, Milagros

2013-11-01

Choline is an essential nutrient required for early development. Previous studies have shown that prenatal choline availability influences adult memory abilities depending on the medial temporal lobe integrity. The relevance of prenatal choline availability on object recognition memory was assessed in adult Wistar rats. Three groups of pregnant Wistar rats were fed from E12 to E18 with choline-deficient (0 g/kg choline chloride), standard (1.1 g/kg choline chloride), or choline-supplemented (5 g/kg choline chloride) diets. The offspring was cross-fostered to rat dams fed a standard diet during pregnancy and tested at the age of 3 months in an object recognition memory task applying retention tests 24 and 48 hours after acquisition. Although no significant differences have been found in the performance of the three groups during the first retention test, the supplemented group exhibited improved memory compared with both the standard and the deficient group in the second retention test, 48 hours after acquisition. In addition, at the second retention test the deficient group did not differ from chance. Taken together, the results support the notion of a long-lasting beneficial effect of prenatal choline supplementation on object recognition memory which is evident when the rats reach adulthood. The results are discussed in terms of their relevance for improving the understanding of the cholinergic involvement in object recognition memory and the implications of the importance of maternal diet for lifelong cognitive abilities.

Three-dimensional passive sensing photon counting for object classification

NASA Astrophysics Data System (ADS)

Yeom, Seokwon; Javidi, Bahram; Watson, Edward

2007-04-01

In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is discussed for classification of photon-limited images. We develop a compact distortion-tolerant recognition system based on the multiple-perspective imaging of II. Experimental and simulation results have shown that a low level of photons is sufficient to classify out-of-plane rotated objects.

Viewpoint-Specific Representations in Three-Dimensional Object Recognition

DTIC Science & Technology

1990-08-01

for useful suggestions and illuminating discuc- sions, and Ellen Hildreth for her comments on a draft of this repcrt. References [1] 1. Biederman ...1982. [24] I. Rock and J. DiVita. A case of viewer-centered object perception. Cognitive Psychology, 19:280-293, 1987 . [25] I. Rock, D. Wheeler, and...Raleigh, NC, 1987 . [30] S. Ullman. Aligning pictorial descriptions: an approach to object recognition. Cognition, 32:193-254, 1989. [31] S. UUman and R

Further insight into self-face recognition in schizophrenia patients: Why ambiguity matters.

PubMed

Bortolon, Catherine; Capdevielle, Delphine; Salesse, Robin N; Raffard, Stephane

2016-03-01

Although some studies reported specifically self-face processing deficits in patients with schizophrenia disorder (SZ), it remains unclear whether these deficits rather reflect a more global face processing deficit. Contradictory results are probably due to the different methodologies employed and the lack of control of other confounding factors. Moreover, no study has so far evaluated possible daily life self-face recognition difficulties in SZ. Therefore, our primary objective was to investigate self-face recognition in patients suffering from SZ compared to healthy controls (HC) using an "objective measure" (reaction time and accuracy) and a "subjective measure" (self-report of daily self-face recognition difficulties). Twenty-four patients with SZ and 23 HC performed a self-face recognition task and completed a questionnaire evaluating daily difficulties in self-face recognition. Recognition task material consisted in three different faces (the own, a famous and an unknown) being morphed in steps of 20%. Results showed that SZ were overall slower than HC regardless of the face identity, but less accurate only for the faces containing 60%-40% morphing. Moreover, SZ and HC reported a similar amount of daily problems with self/other face recognition. No significant correlations were found between objective and subjective measures (p > 0.05). The small sample size and relatively mild severity of psychopathology does not allow us to generalize our results. These results suggest that: (1) patients with SZ are as capable of recognizing their own face as HC, although they are susceptible to ambiguity; (2) there are far less self recognition deficits in schizophrenia patients than previously postulated. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implementation study of wearable sensors for activity recognition systems

PubMed Central

Ghassemian, Mona

2015-01-01

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely ‘stream-based’, ‘feature-based’ and ‘threshold-based’ scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency. PMID:26609413

Effects of joint attention on long-term memory in 9-month-old infants: an event-related potentials study.

PubMed

Kopp, Franziska; Lindenberger, Ulman

2011-07-01

Joint attention develops during the first year of life but little is known about its effects on long-term memory. We investigated whether joint attention modulates long-term memory in 9-month-old infants. Infants were familiarized with visually presented objects in either of two conditions that differed in the degree of joint attention (high versus low). EEG indicators in response to old and novel objects were probed directly after the familiarization phase (immediate recognition), and following a 1-week delay (delayed recognition). In immediate recognition, the amplitude of positive slow-wave activity was modulated by joint attention. In the delayed recognition, the amplitude of the Pb component differentiated between high and low joint attention. In addition, the positive slow-wave amplitude during immediate and delayed recognition correlated with the frequency of infants' looks to the experimenter during familiarization. Under both high- and low-joint-attention conditions, the processing of unfamiliar objects was associated with an enhanced Nc component. Our results show that the degree of joint attention modulates EEG during immediate and delayed recognition. We conclude that joint attention affects long-term memory processing in 9-month-old infants by enhancing the relevance of attended items. © 2010 Blackwell Publishing Ltd.

Comparing object recognition from binary and bipolar edge images for visual prostheses

PubMed Central

Jung, Jae-Hyun; Pu, Tian; Peli, Eli

2017-01-01

Visual prostheses require an effective representation method due to the limited display condition which has only 2 or 3 levels of grayscale in low resolution. Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary (black and white) edge images have been used to represent features to convey essential information. However, in scenes with a complex cluttered background, the recognition rate of the binary edge images by human observers is limited and additional information is required. The polarity of edges and cusps (black or white features on a gray background) carries important additional information; the polarity may provide shape from shading information missing in the binary edge image. This depth information may be restored by using bipolar edges. We compared object recognition rates from 16 binary edge images and bipolar edge images by 26 subjects to determine the possible impact of bipolar filtering in visual prostheses with 3 or more levels of grayscale. Recognition rates were higher with bipolar edge images and the improvement was significant in scenes with complex backgrounds. The results also suggest that erroneous shape from shading interpretation of bipolar edges resulting from pigment rather than boundaries of shape may confound the recognition. PMID:28458481

Multiple degree of freedom object recognition using optical relational graph decision nets

NASA Technical Reports Server (NTRS)

Casasent, David P.; Lee, Andrew J.

1988-01-01

Multiple-degree-of-freedom object recognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. It is assumed that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar-log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple-degree-of-freedom pattern recognition. The design of Fisher (1936) linear discriminant functions and synthetic discriminant function for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.

Language comprehenders retain implied shape and orientation of objects.

PubMed

Pecher, Diane; van Dantzig, Saskia; Zwaan, Rolf A; Zeelenberg, René

2009-06-01

According to theories of embodied cognition, language comprehenders simulate sensorimotor experiences to represent the meaning of what they read. Previous studies have shown that picture recognition is better if the object in the picture matches the orientation or shape implied by a preceding sentence. In order to test whether strategic imagery may explain previous findings, language comprehenders first read a list of sentences in which objects were mentioned. Only once the complete list had been read was recognition memory tested with pictures. Recognition performance was better if the orientation or shape of the object matched that implied by the sentence, both immediately after reading the complete list of sentences and after a 45-min delay. These results suggest that previously found match effects were not due to strategic imagery and show that details of sensorimotor simulations are retained over longer periods.

Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis.

PubMed

Derpanis, Konstantinos G; Sizintsev, Mikhail; Cannons, Kevin J; Wildes, Richard P

2013-03-01

This paper provides a unified framework for the interrelated topics of action spotting, the spatiotemporal detection and localization of human actions in video, and action recognition, the classification of a given video into one of several predefined categories. A novel compact local descriptor of video dynamics in the context of action spotting and recognition is introduced based on visual spacetime oriented energy measurements. This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features. Importantly, the descriptor allows for the comparison of the underlying dynamics of two spacetime video segments irrespective of spatial appearance, such as differences induced by clothing, and with robustness to clutter. An associated similarity measure is introduced that admits efficient exhaustive search for an action template, derived from a single exemplar video, across candidate video sequences. The general approach presented for action spotting and recognition is amenable to efficient implementation, which is deemed critical for many important applications. For action spotting, details of a real-time GPU-based instantiation of the proposed approach are provided. Empirical evaluation of both action spotting and action recognition on challenging datasets suggests the efficacy of the proposed approach, with state-of-the-art performance documented on standard datasets.

Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

PubMed

Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

2013-11-01

Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory. Copyright © 2013 Elsevier Inc. All rights reserved.

Real-time optical multiple object recognition and tracking system and method

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Liu, Hua Kuang (Inventor)

1987-01-01

The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

Exploiting range imagery: techniques and applications

NASA Astrophysics Data System (ADS)

Armbruster, Walter

2009-07-01

Practically no applications exist for which automatic processing of 2D intensity imagery can equal human visual perception. This is not the case for range imagery. The paper gives examples of 3D laser radar applications, for which automatic data processing can exceed human visual cognition capabilities and describes basic processing techniques for attaining these results. The examples are drawn from the fields of helicopter obstacle avoidance, object detection in surveillance applications, object recognition at high range, multi-object-tracking, and object re-identification in range image sequences. Processing times and recognition performances are summarized. The techniques used exploit the bijective continuity of the imaging process as well as its independence of object reflectivity, emissivity and illumination. This allows precise formulations of the probability distributions involved in figure-ground segmentation, feature-based object classification and model based object recognition. The probabilistic approach guarantees optimal solutions for single images and enables Bayesian learning in range image sequences. Finally, due to recent results in 3D-surface completion, no prior model libraries are required for recognizing and re-identifying objects of quite general object categories, opening the way to unsupervised learning and fully autonomous cognitive systems.

New technique for real-time distortion-invariant multiobject recognition and classification

NASA Astrophysics Data System (ADS)

Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan

2001-04-01

A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

A biologically inspired neural network model to transformation invariant object recognition

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. Further, the residual critic error in DHP is generally smaller than that of HDP, and DHP achieves a 100% success rate more frequently than HDP for individual objects/subjects. On the other hand, HDP is more robust than the DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational time involved in DHP is more.

An application of viola jones method for face recognition for absence process efficiency

NASA Astrophysics Data System (ADS)

Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

2018-04-01

Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

Prevalence of Impaired Memory in Hospitalized Adults and Associations with In-Hospital Sleep Loss

PubMed Central

Calev, Hila; Spampinato, Lisa M; Press, Valerie G; Meltzer, David O; Arora, Vineet M

2015-01-01

Background Effective inpatient teaching requires intact patient memory, but studies suggest hospitalized adults may have memory deficits. Sleep loss among inpatients could contribute to memory impairment. Objective To assess memory in older hospitalized adults, and to test the association between sleep quantity, sleep quality and memory, in order to identify a possible contributor to memory deficits in these patients. Design Prospective cohort study Setting General medicine and hematology/oncology inpatient wards Patients 59 hospitalized adults at least 50 years of age with no diagnosed sleep disorder. Measurements Immediate memory and memory after a 24-hour delay were assessed using a word recall and word recognition task from the University of Southern California Repeatable Episodic Memory Test (USC-REMT). A vignette-based memory task was piloted as an alternative test more closely resembling discharge instructions. Sleep duration and efficiency overnight in the hospital were measured using actigraphy. Results Mean immediate recall was 3.8 words out of 15 (SD=2.1). Forty-nine percent of subjects had poor memory, defined as immediate recall score of 3 or lower. Median immediate recognition was 11 words out of 15 (IQR=9, 13). Median delayed recall score was 1 word and median delayed recognition was 10 words (IQR= 8–12). In-hospital sleep duration and efficiency were not significantly associated with memory. The medical vignette score was correlated with immediate recall (r=0.49, p<0.01) Conclusions About half of inpatients studied had poor memory while in the hospital, signaling that hospitalization might not be an ideal teachable moment. In-hospital sleep was not associated with memory scores. PMID:25872763

Role of fusiform and anterior temporal cortical areas in facial recognition.

PubMed

Nasr, Shahin; Tootell, Roger B H

2012-11-15

Recent fMRI studies suggest that cortical face processing extends well beyond the fusiform face area (FFA), including unspecified portions of the anterior temporal lobe. However, the exact location of such anterior temporal region(s), and their role during active face recognition, remain unclear. Here we demonstrate that (in addition to FFA) a small bilateral site in the anterior tip of the collateral sulcus ('AT'; the anterior temporal face patch) is selectively activated during recognition of faces but not houses (a non-face object). In contrast to the psychophysical prediction that inverted and contrast reversed faces are processed like other non-face objects, both FFA and AT (but not other visual areas) were also activated during recognition of inverted and contrast reversed faces. However, response accuracy was better correlated to recognition-driven activity in AT, compared to FFA. These data support a segregated, hierarchical model of face recognition processing, extending to the anterior temporal cortex. Copyright © 2012 Elsevier Inc. All rights reserved.

Automatic recognition of ship types from infrared images using superstructure moment invariants

NASA Astrophysics Data System (ADS)

Li, Heng; Wang, Xinyu

2007-11-01

Automatic object recognition is an active area of interest for military and commercial applications. In this paper, a system addressing autonomous recognition of ship types in infrared images is proposed. Firstly, an approach of segmentation based on detection of salient features of the target with subsequent shadow removing is proposed, as is the base of the subsequent object recognition. Considering the differences between the shapes of various ships mainly lie in their superstructures, we then use superstructure moment functions invariant to translation, rotation and scale differences in input patterns and develop a robust algorithm of obtaining ship superstructure. Subsequently a back-propagation neural network is used as a classifier in the recognition stage and projection images of simulated three-dimensional ship models are used as the training sets. Our recognition model was implemented and experimentally validated using both simulated three-dimensional ship model images and real images derived from video of an AN/AAS-44V Forward Looking Infrared(FLIR) sensor.

Role of Fusiform and Anterior Temporal Cortical Areas in Facial Recognition

PubMed Central

Nasr, Shahin; Tootell, Roger BH

2012-01-01

Recent FMRI studies suggest that cortical face processing extends well beyond the fusiform face area (FFA), including unspecified portions of the anterior temporal lobe. However, the exact location of such anterior temporal region(s), and their role during active face recognition, remain unclear. Here we demonstrate that (in addition to FFA) a small bilateral site in the anterior tip of the collateral sulcus (‘AT’; the anterior temporal face patch) is selectively activated during recognition of faces but not houses (a non-face object). In contrast to the psychophysical prediction that inverted and contrast reversed faces are processed like other non-face objects, both FFA and AT (but not other visual areas) were also activated during recognition of inverted and contrast reversed faces. However, response accuracy was better correlated to recognition-driven activity in AT, compared to FFA. These data support a segregated, hierarchical model of face recognition processing, extending to the anterior temporal cortex. PMID:23034518

Differentiation of perceptual and semantic subsequent memory effects using an orthographic paradigm.

PubMed

Kuo, Michael C C; Liu, Karen P Y; Ting, Kin Hung; Chan, Chetwyn C H

2012-11-27

This study aimed to differentiate perceptual and semantic encoding processes using subsequent memory effects (SMEs) elicited by the recognition of orthographs of single Chinese characters. Participants studied a series of Chinese characters perceptually (by inspecting orthographic components) or semantically (by determining the object making sounds), and then made studied or unstudied judgments during the recognition phase. Recognition performance in terms of d-prime measure in the semantic condition was higher, though not significant, than that of the perceptual condition. The between perceptual-semantic condition differences in SMEs at P550 and late positive component latencies (700-1000ms) were not significant in the frontal area. An additional analysis identified larger SME in the semantic condition during 600-1000ms in the frontal pole regions. These results indicate that coordination and incorporation of orthographic information into mental representation is essential to both task conditions. The differentiation was also revealed in earlier SMEs (perceptual>semantic) at N3 (240-360ms) latency, which is a novel finding. The left-distributed N3 was interpreted as more efficient processing of meaning with semantically learned characters. Frontal pole SMEs indicated strategic processing by executive functions, which would further enhance memory. Copyright © 2012 Elsevier B.V. All rights reserved.

Perceptual Learning of Object Shape

PubMed Central

Golcu, Doruk; Gilbert, Charles D.

2009-01-01

Recognition of objects is accomplished through the use of cues that depend on internal representations of familiar shapes. We used a paradigm of perceptual learning during visual search to explore what features human observers use to identify objects. Human subjects were trained to search for a target object embedded in an array of distractors, until their performance improved from near-chance levels to over 80% of trials in an object specific manner. We determined the role of specific object components in the recognition of the object as a whole by measuring the transfer of learning from the trained object to other objects sharing components with it. Depending on the geometric relationship of the trained object with untrained objects, transfer to untrained objects was observed. Novel objects that shared a component with the trained object were identified at much higher levels than those that did not, and this could be used as an indicator of which features of the object were important for recognition. Training on an object also transferred to the components of the object when these components were embedded in an array of distractors of similar complexity. These results suggest that objects are not represented in a holistic manner during learning, but that their individual components are encoded. Transfer between objects was not complete, and occurred for more than one component, regardless of how well they distinguish the object from distractors. This suggests that a joint involvement of multiple components was necessary for full performance. PMID:19864574

Perirhinal Cortex Resolves Feature Ambiguity in Configural Object Recognition and Perceptual Oddity Tasks

ERIC Educational Resources Information Center

Bartko, Susan J.; Winters, Boyer D.; Cowell, Rosemary A.; Saksida, Lisa M.; Bussey, Timothy J.

2007-01-01

The perirhinal cortex (PRh) has a well-established role in object recognition memory. More recent studies suggest that PRh is also important for two-choice visual discrimination tasks. Specifically, it has been suggested that PRh contains conjunctive representations that help resolve feature ambiguity, which occurs when a task cannot easily be…

The Fundamentals of Thermal Imaging Systems.

DTIC Science & Technology

1979-05-10

detection , recognition, or identification, of real ’coene objects aire discussed. It is hoped that the text will be useful to FLIR designers, evaluators...AND ANDERSON EXPERIMENT ........................ 205 Appendix F - BASIC SNR AND DETECTIVITY RELATIONS ................................... 209 Appendix... detection , recognition, or identification, of real scene objects are discussed. I• It is hoped that the material in the text will be useful to

Role of the Anterior Cingulate Cortex in the Retrieval of Novel Object Recognition Memory after a Long Delay

ERIC Educational Resources Information Center

Pezze, Marie A.; Marshall, Hayley J.; Fone, Kevin C. F.; Cassaday, Helen J.

2017-01-01

Previous in vivo electrophysiological studies suggest that the anterior cingulate cortex (ACgx) is an important substrate of novel object recognition (NOR) memory. However, intervention studies are needed to confirm this conclusion and permanent lesion studies cannot distinguish effects on encoding and retrieval. The interval between encoding and…

AMPA Receptor Endocytosis in Rat Perirhinal Cortex Underlies Retrieval of Object Memory

ERIC Educational Resources Information Center

Cazakoff, Brittany N.; Howland, John G.

2011-01-01

Mechanisms consistent with long-term depression in the perirhinal cortex (PRh) play a fundamental role in object recognition memory; however, whether AMPA receptor endocytosis is involved in distinct phases of recognition memory is not known. To address this question, we used local PRh infusions of the cell membrane-permeable Tat-GluA2[subscript…

Speckle-learning-based object recognition through scattering media.

PubMed

Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

2015-12-28

We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning.

The effect of scene context on episodic object recognition: parahippocampal cortex mediates memory encoding and retrieval success.

PubMed

Hayes, Scott M; Nadel, Lynn; Ryan, Lee

2007-01-01

Previous research has investigated intentional retrieval of contextual information and contextual influences on object identification and word recognition, yet few studies have investigated context effects in episodic memory for objects. To address this issue, unique objects embedded in a visually rich scene or on a white background were presented to participants. At test, objects were presented either in the original scene or on a white background. A series of behavioral studies with young adults demonstrated a context shift decrement (CSD)-decreased recognition performance when context is changed between encoding and retrieval. The CSD was not attenuated by encoding or retrieval manipulations, suggesting that binding of object and context may be automatic. A final experiment explored the neural correlates of the CSD, using functional Magnetic Resonance Imaging. Parahippocampal cortex (PHC) activation (right greater than left) during incidental encoding was associated with subsequent memory of objects in the context shift condition. Greater activity in right PHC was also observed during successful recognition of objects previously presented in a scene. Finally, a subset of regions activated during scene encoding, such as bilateral PHC, was reactivated when the object was presented on a white background at retrieval. Although participants were not required to intentionally retrieve contextual information, the results suggest that PHC may reinstate visual context to mediate successful episodic memory retrieval. The CSD is attributed to automatic and obligatory binding of object and context. The results suggest that PHC is important not only for processing of scene information, but also plays a role in successful episodic memory encoding and retrieval. These findings are consistent with the view that spatial information is stored in the hippocampal complex, one of the central tenets of Multiple Trace Theory. (c) 2007 Wiley-Liss, Inc.

A depictive neural model for the representation of motion verbs.

PubMed

Rao, Sunil; Aleksander, Igor

2011-11-01

In this paper, we present a depictive neural model for the representation of motion verb semantics in neural models of visual awareness. The problem of modelling motion verb representation is shown to be one of function application, mapping a set of given input variables defining the moving object and the path of motion to a defined output outcome in the motion recognition context. The particular function-applicative implementation and consequent recognition model design presented are seen as arising from a noun-adjective recognition model enabling the recognition of colour adjectives as applied to a set of shapes representing objects to be recognised. The presence of such a function application scheme and a separately implemented position identification and path labelling scheme are accordingly shown to be the primitives required to enable the design and construction of a composite depictive motion verb recognition scheme. Extensions to the presented design to enable the representation of transitive verbs are also discussed.

Selective attention meets spontaneous recognition memory: Evidence for effects at retrieval.

PubMed

Moen, Katherine C; Miller, Jeremy K; Lloyd, Marianne E

2017-03-01

Previous research on the effects of Divided Attention on recognition memory have shown consistent impairments during encoding but more variable effects at retrieval. The present study explored whether effects of Selective Attention at retrieval and subsequent testing were parallel to those of Divided Attention. Participants studied a list of pictures and then had a recognition memory test that included both full attention and selective attention (the to be responded to object was overlaid atop a blue outlined object) trials. All participants then completed a second recognition memory test. The results of 2 experiments suggest that subsequent tests consistently show impacts of the status of the ignored stimulus, and that having an initial test changes performance on a later test. The results are discussed in relation to effect of attention on memory more generally as well as spontaneous recognition memory research. Copyright © 2017 Elsevier Inc. All rights reserved.

Mapping parahippocampal systems for recognition and recency memory in the absence of the rat hippocampus

PubMed Central

Kinnavane, L; Amin, E; Horne, M; Aggleton, J P

2014-01-01

The present study examined immediate-early gene expression in the perirhinal cortex of rats with hippocampal lesions. The goal was to test those models of recognition memory which assume that the perirhinal cortex can function independently of the hippocampus. The c-fos gene was targeted, as its expression in the perirhinal cortex is strongly associated with recognition memory. Four groups of rats were examined. Rats with hippocampal lesions and their surgical controls were given either a recognition memory task (novel vs. familiar objects) or a relative recency task (objects with differing degrees of familiarity). Perirhinal Fos expression in the hippocampal-lesioned groups correlated with both recognition and recency performance. The hippocampal lesions, however, had no apparent effect on overall levels of perirhinal or entorhinal cortex c-fos expression in response to novel objects, with only restricted effects being seen in the recency condition. Network analyses showed that whereas the patterns of parahippocampal interactions were differentially affected by novel or familiar objects, these correlated networks were not altered by hippocampal lesions. Additional analyses in control rats revealed two modes of correlated medial temporal activation. Novel stimuli recruited the pathway from the lateral entorhinal cortex (cortical layer II or III) to hippocampal field CA3, and thence to CA1. Familiar stimuli recruited the direct pathway from the lateral entorhinal cortex (principally layer III) to CA1. The present findings not only reveal the independence from the hippocampus of some perirhinal systems associated with recognition memory, but also show how novel stimuli engage hippocampal subfields in qualitatively different ways from familiar stimuli. PMID:25264133

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

PubMed Central

Cadieu, Charles F.; Hong, Ha; Yamins, Daniel L. K.; Pinto, Nicolas; Ardila, Diego; Solomon, Ethan A.; Majaj, Najib J.; DiCarlo, James J.

2014-01-01

The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of “kernel analysis” that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds. PMID:25521294

Invariant visual object recognition and shape processing in rats

PubMed Central

Zoccolan, Davide

2015-01-01

Invariant visual object recognition is the ability to recognize visual objects despite the vastly different images that each object can project onto the retina during natural vision, depending on its position and size within the visual field, its orientation relative to the viewer, etc. Achieving invariant recognition represents such a formidable computational challenge that is often assumed to be a unique hallmark of primate vision. Historically, this has limited the invasive investigation of its neuronal underpinnings to monkey studies, in spite of the narrow range of experimental approaches that these animal models allow. Meanwhile, rodents have been largely neglected as models of object vision, because of the widespread belief that they are incapable of advanced visual processing. However, the powerful array of experimental tools that have been developed to dissect neuronal circuits in rodents has made these species very attractive to vision scientists too, promoting a new tide of studies that have started to systematically explore visual functions in rats and mice. Rats, in particular, have been the subjects of several behavioral studies, aimed at assessing how advanced object recognition and shape processing is in this species. Here, I review these recent investigations, as well as earlier studies of rat pattern vision, to provide an historical overview and a critical summary of the status of the knowledge about rat object vision. The picture emerging from this survey is very encouraging with regard to the possibility of using rats as complementary models to monkeys in the study of higher-level vision. PMID:25561421