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Sample records for 3-d object recognition

  1. 3D object recognition based on local descriptors

    NASA Astrophysics Data System (ADS)

    Jakab, Marek; Benesova, Wanda; Racev, Marek

    2015-01-01

    In this paper, we propose an enhanced method of 3D object description and recognition based on local descriptors using RGB image and depth information (D) acquired by Kinect sensor. Our main contribution is focused on an extension of the SIFT feature vector by the 3D information derived from the depth map (SIFT-D). We also propose a novel local depth descriptor (DD) that includes a 3D description of the key point neighborhood. Thus defined the 3D descriptor can then enter the decision-making process. Two different approaches have been proposed, tested and evaluated in this paper. First approach deals with the object recognition system using the original SIFT descriptor in combination with our novel proposed 3D descriptor, where the proposed 3D descriptor is responsible for the pre-selection of the objects. Second approach demonstrates the object recognition using an extension of the SIFT feature vector by the local depth description. In this paper, we present the results of two experiments for the evaluation of the proposed depth descriptors. The results show an improvement in accuracy of the recognition system that includes the 3D local description compared with the same system without the 3D local description. Our experimental system of object recognition is working near real-time.

  2. Detailed 3D representations for object recognition and modeling.

    PubMed

    Zia, M Zeeshan; Stark, Michael; Schiele, Bernt; Schindler, Konrad

    2013-11-01

    Geometric 3D reasoning at the level of objects has received renewed attention recently in the context of visual scene understanding. The level of geometric detail, however, is typically limited to qualitative representations or coarse boxes. This is linked to the fact that today's object class detectors are tuned toward robust 2D matching rather than accurate 3D geometry, encouraged by bounding-box-based benchmarks such as Pascal VOC. In this paper, we revisit ideas from the early days of computer vision, namely, detailed, 3D geometric object class representations for recognition. These representations can recover geometrically far more accurate object hypotheses than just bounding boxes, including continuous estimates of object pose and 3D wireframes with relative 3D positions of object parts. In combination with robust techniques for shape description and inference, we outperform state-of-the-art results in monocular 3D pose estimation. In a series of experiments, we analyze our approach in detail and demonstrate novel applications enabled by such an object class representation, such as fine-grained categorization of cars and bicycles, according to their 3D geometry, and ultrawide baseline matching.

  3. 3D object recognition in TOF data sets

    NASA Astrophysics Data System (ADS)

    Hess, Holger; Albrecht, Martin; Grothof, Markus; Hussmann, Stephan; Oikonomidis, Nikolaos; Schwarte, Rudolf

    2003-08-01

    In the last years 3D-Vision systems based on the Time-Of-Flight (TOF) principle have gained more importance than Stereo Vision (SV). TOF offers a direct depth-data acquisition, whereas SV involves a great amount of computational power for a comparable 3D data set. Due to the enormous progress in TOF-techniques, nowadays 3D cameras can be manufactured and be used for many practical applications. Hence there is a great demand for new accurate algorithms for 3D object recognition and classification. This paper presents a new strategy and algorithm designed for a fast and solid object classification. A challenging example - accurate classification of a (half-) sphere - demonstrates the performance of the developed algorithm. Finally, the transition from a general model of the system to specific applications such as Intelligent Airbag Control and Robot Assistance in Surgery are introduced. The paper concludes with the current research results in the above mentioned fields.

  4. 3-D object recognition using 2-D views.

    PubMed

    Li, Wenjing; Bebis, George; Bourbakis, Nikolaos G

    2008-11-01

    We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the Expectation-Maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood

  5. 3-D Object Recognition from Point Cloud Data

    NASA Astrophysics Data System (ADS)

    Smith, W.; Walker, A. S.; Zhang, B.

    2011-09-01

    The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs). Massively parallel processes such as graphics processing unit (GPU) computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM) and digital elevation model (DEM), so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex roofs. Several case

  6. Exploring local regularities for 3D object recognition

    NASA Astrophysics Data System (ADS)

    Tian, Huaiwen; Qin, Shengfeng

    2016-11-01

    In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces (L-MSDAF), localized minimum sum of segment magnitudes of common edges (L-MSSM), and localized minimum sum of areas of child face (L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

  7. Robust feature detection for 3D object recognition and matching

    NASA Astrophysics Data System (ADS)

    Pankanti, Sharath; Dorai, Chitra; Jain, Anil K.

    1993-06-01

    Salient surface features play a central role in tasks related to 3-D object recognition and matching. There is a large body of psychophysical evidence demonstrating the perceptual significance of surface features such as local minima of principal curvatures in the decomposition of objects into a hierarchy of parts. Many recognition strategies employed in machine vision also directly use features derived from surface properties for matching. Hence, it is important to develop techniques that detect surface features reliably. Our proposed scheme consists of (1) a preprocessing stage, (2) a feature detection stage, and (3) a feature integration stage. The preprocessing step selectively smoothes out noise in the depth data without degrading salient surface details and permits reliable local estimation of the surface features. The feature detection stage detects both edge-based and region-based features, of which many are derived from curvature estimates. The third stage is responsible for integrating the information provided by the individual feature detectors. This stage also completes the partial boundaries provided by the individual feature detectors, using proximity and continuity principles of Gestalt. All our algorithms use local support and, therefore, are inherently parallelizable. We demonstrate the efficacy and robustness of our approach by applying it to two diverse domains of applications: (1) segmentation of objects into volumetric primitives and (2) detection of salient contours on free-form surfaces. We have tested our algorithms on a number of real range images with varying degrees of noise and missing data due to self-occlusion. The preliminary results are very encouraging.

  8. A Taxonomy of 3D Occluded Objects Recognition Techniques

    NASA Astrophysics Data System (ADS)

    Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

    2016-03-01

    The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

  9. 3D Object Recognition: Symmetry and Virtual Views

    DTIC Science & Technology

    1992-12-01

    NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATIONI Artificial Intelligence Laboratory REPORT NUMBER 545 Technology Square AIM 1409 Cambridge... ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING A.I. Memo No. 1409 December 1992 C.B.C.L. Paper No. 76 3D Object...research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences, and at the Artificial

  10. New neural-networks-based 3D object recognition system

    NASA Astrophysics Data System (ADS)

    Abolmaesumi, Purang; Jahed, M.

    1997-09-01

    Three-dimensional object recognition has always been one of the challenging fields in computer vision. In recent years, Ulman and Basri (1991) have proposed that this task can be done by using a database of 2-D views of the objects. The main problem in their proposed system is that the correspondent points should be known to interpolate the views. On the other hand, their system should have a supervisor to decide which class does the represented view belong to. In this paper, we propose a new momentum-Fourier descriptor that is invariant to scale, translation, and rotation. This descriptor provides the input feature vectors to our proposed system. By using the Dystal network, we show that the objects can be classified with over 95% precision. We have used this system to classify the objects like cube, cone, sphere, torus, and cylinder. Because of the nature of the Dystal network, this system reaches to its stable point by a single representation of the view to the system. This system can also classify the similar views to a single class (e.g., for the cube, the system generated 9 different classes for 50 different input views), which can be used to select an optimum database of training views. The system is also very flexible to the noise and deformed views.

  11. A primitive-based 3D object recognition system

    NASA Technical Reports Server (NTRS)

    Dhawan, Atam P.

    1988-01-01

    An intermediate-level knowledge-based system for decomposing segmented data into three-dimensional primitives was developed to create an approximate three-dimensional description of the real world scene from a single two-dimensional perspective view. A knowledge-based approach was also developed for high-level primitive-based matching of three-dimensional objects. Both the intermediate-level decomposition and the high-level interpretation are based on the structural and relational matching; moreover, they are implemented in a frame-based environment.

  12. 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.

  13. 3-D Object Recognition Using Combined Overhead And Robot Eye-In-Hand Vision System

    NASA Astrophysics Data System (ADS)

    Luc, Ren C.; Lin, Min-Hsiung

    1987-10-01

    A new approach for recognizing 3-D objects using a combined overhead and eye-in-hand vision system is presented. A novel eye-in-hand vision system using a fiber-optic image array is described. The significance of this approach is the fast and accurate recognition of 3-D object information compared to traditional stereo image processing. For the recognition of 3-D objects, the over-head vision system will take 2-D top view image and the eye-in-hand vision system will take side view images orthogonal to the top view image plane. We have developed and demonstrated a unique approach to integrate this 2-D information into a 3-D representation based on a new approach called "3-D Volumetric Descrip-tion from 2-D Orthogonal Projections". The Unimate PUMA 560 and TRAPIX 5500 real-time image processor have been used to test the success of the entire system.

  14. 3D video analysis of the novel object recognition test in rats.

    PubMed

    Matsumoto, Jumpei; Uehara, Takashi; Urakawa, Susumu; Takamura, Yusaku; Sumiyoshi, Tomiki; Suzuki, Michio; Ono, Taketoshi; Nishijo, Hisao

    2014-10-01

    The novel object recognition (NOR) test has been widely used to test memory function. We developed a 3D computerized video analysis system that estimates nose contact with an object in Long Evans rats to analyze object exploration during NOR tests. The results indicate that the 3D system reproducibly and accurately scores the NOR test. Furthermore, the 3D system captures a 3D trajectory of the nose during object exploration, enabling detailed analyses of spatiotemporal patterns of object exploration. The 3D trajectory analysis revealed a specific pattern of object exploration in the sample phase of the NOR test: normal rats first explored the lower parts of objects and then gradually explored the upper parts. A systematic injection of MK-801 suppressed changes in these exploration patterns. The results, along with those of previous studies, suggest that the changes in the exploration patterns reflect neophobia to a novel object and/or changes from spatial learning to object learning. These results demonstrate that the 3D tracking system is useful not only for detailed scoring of animal behaviors but also for investigation of characteristic spatiotemporal patterns of object exploration. The system has the potential to facilitate future investigation of neural mechanisms underlying object exploration that result from dynamic and complex brain activity.

  15. Recognition of 3D objects for autonomous mobile robot's navigation in automated shipbuilding

    NASA Astrophysics Data System (ADS)

    Lee, Hyunki; Cho, Hyungsuck

    2007-10-01

    Nowadays many parts of shipbuilding process are automated, but the painting process is not, because of the difficulty of automated on-line painting quality measurement, harsh painting environment and the difficulty of robot navigation. However, the painting automation is necessary, because it can provide consistent performance of painting film thickness. Furthermore, autonomous mobile robots are strongly required for flexible painting work. However, the main problem of autonomous mobile robot's navigation is that there are many obstacles which are not expressed in the CAD data. To overcome this problem, obstacle detection and recognition are necessary to avoid obstacles and painting work effectively. Until now many object recognition algorithms have been studied, especially 2D object recognition methods using intensity image have been widely studied. However, in our case environmental illumination does not exist, so these methods cannot be used. To overcome this, to use 3D range data must be used, but the problem of using 3D range data is high computational cost and long estimation time of recognition due to huge data base. In this paper, we propose a 3D object recognition algorithm based on PCA (Principle Component Analysis) and NN (Neural Network). In the algorithm, the novelty is that the measured 3D range data is transformed into intensity information, and then adopts the PCA and NN algorithm for transformed intensity information to reduce the processing time and make the data easy to handle which are disadvantages of previous researches of 3D object recognition. A set of experimental results are shown to verify the effectiveness of the proposed algorithm.

  16. 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

  17. Statistical and neural network classifiers in model-based 3-D object recognition

    NASA Astrophysics Data System (ADS)

    Newton, Scott C.; Nutter, Brian S.; Mitra, Sunanda

    1991-02-01

    For autonomous machines equipped with vision capabilities and in a controlled environment 3-D model-based object identification methodologies will in general solve rigid body recognition problems. In an uncontrolled environment however several factors pose difficulties for correct identification. We have addressed the problem of 3-D object recognition using a number of methods including neural network classifiers and a Bayesian-like classifier for matching image data with model projection-derived data [1 21. Neural network classifiers used began operation as simple feature vector classifiers. However unmodelled signal behavior was learned with additional samples yielding great improvement in classification rates. The model analysis drastically shortened training time of both classification systems. In an environment where signal behavior is not accurately modelled two separate forms of learning give the systems the ability to update estimates of this behavior. Required of course are sufficient samples to learn this new information. Given sufficient information and a well-controlled environment identification of 3-D objects from a limited number of classes is indeed possible. 1.

  18. Performance of a neural-network-based 3-D object recognition system

    NASA Astrophysics Data System (ADS)

    Rak, Steven J.; Kolodzy, Paul J.

    1991-08-01

    Object recognition in laser radar sensor imagery is a challenging application of neural networks. The task involves recognition of objects at a variety of distances and aspects with significant levels of sensor noise. These variables are related to sensor parameters such as sensor signal strength and angular resolution, as well as object range and viewing aspect. The effect of these parameters on a fixed recognition system based on log-polar mapped features and an unsupervised neural network classifier are investigated. This work is an attempt to quantify the design parameters of a laser radar measurement system with respect to classifying and/or identifying objects by the shape of their silhouettes. Experiments with vehicle silhouettes rotated through 90 deg-of-view angle from broadside to head-on ('out-of-plane' rotation) have been used to quantify the performance of a log-polar map/neural-network based 3-D object recognition system. These experiments investigated several key issues such as category stability, category memory compression, image fidelity, and viewing aspect. Initial results indicate a compression from 720 possible categories (8 vehicles X 90 out-of-plane rotations) to a classifier memory with approximately 30 stable recognition categories. These results parallel the human experience of studying an object from several viewing angles yet recognizing it through a wide range of viewing angles. Results are presented illustrating category formation for an eight vehicle dataset as a function of several sensor parameters. These include: (1) sensor noise, as a function of carrier-to-noise ratio; (2) pixels on the vehicle, related to angular resolution and target range; and (3) viewing aspect, as related to sensor-to-platform depression angle. This work contributes to the formation of a three- dimensional object recognition system.

  19. Artificial neural networks and model-based recognition of 3-D objects from 2-D images

    NASA Astrophysics Data System (ADS)

    Chao, Chih-Ho; Dhawan, Atam P.

    1992-09-01

    A computer vision system is developed for 3-D object recognition using artificial neural networks and a knowledge-based top-down feedback analysis system. This computer vision system can adequately analyze an incomplete edge map provided by a low-level processor for 3-D representation and recognition using key features. The key features are selected using a priority assignment and then used in an artificial neural network for matching with model key features. The result of such matching is utilized in generating the model-driven top-down feedback analysis. From the incomplete edge map we try to pick a candidate pattern utilizing the key feature priority assignment. The highest priority is given for the most connected node and associated features. The features are space invariant structures and sets of orientation for edge primitives. These features are now mapped into real numbers. A Hopfield network is then applied with two levels of matching to reduce the search time. The first match is to choose the class of possible model, the second match is then to find the model closest to the data patterns. This model is then rotated in 3-D to find the best match with the incomplete edge patterns and to provide the additional features in 3-D. In the case of multiple objects, a dynamically interconnected search strategy is designed to recognize objects using one pattern at a time. This strategy is also useful in recognizing occluded objects. The experimental results presented show the capability and effectiveness of this system.

  20. Combining depth and gray images for fast 3D object recognition

    NASA Astrophysics Data System (ADS)

    Pan, Wang; Zhu, Feng; Hao, Yingming

    2016-10-01

    Reliable and stable visual perception systems are needed for humanoid robotic assistants to perform complex grasping and manipulation tasks. The recognition of the object and its precise 6D pose are required. This paper addresses the challenge of detecting and positioning a textureless known object, by estimating its complete 6D pose in cluttered scenes. A 3D perception system is proposed in this paper, which can robustly recognize CAD models in cluttered scenes for the purpose of grasping with a mobile manipulator. Our approach uses a powerful combination of two different camera technologies, Time-Of-Flight (TOF) and RGB, to segment the scene and extract objects. Combining the depth image and gray image to recognize instances of a 3D object in the world and estimate their 3D poses. The full pose estimation process is based on depth images segmentation and an efficient shape-based matching. At first, the depth image is used to separate the supporting plane of objects from the cluttered background. Thus, cluttered backgrounds are circumvented and the search space is extremely reduced. And a hierarchical model based on the geometry information of a priori CAD model of the object is generated in the offline stage. Then using the hierarchical model we perform a shape-based matching in 2D gray images. Finally, we validate the proposed method in a number of experiments. The results show that utilizing depth and gray images together can reach the demand of a time-critical application and reduce the error rate of object recognition significantly.

  1. Combining scale-space and similarity-based aspect graphs for fast 3D object recognition.

    PubMed

    Ulrich, Markus; Wiedemann, Christian; Steger, Carsten

    2012-10-01

    This paper describes an approach for recognizing instances of a 3D object in a single camera image and for determining their 3D poses. A hierarchical model is generated solely based on the geometry information of a 3D CAD model of the object. The approach does not rely on texture or reflectance information of the object's surface, making it useful for a wide range of industrial and robotic applications, e.g., bin-picking. A hierarchical view-based approach that addresses typical problems of previous methods is applied: It handles true perspective, is robust to noise, occlusions, and clutter to an extent that is sufficient for many practical applications, and is invariant to contrast changes. For the generation of this hierarchical model, a new model image generation technique by which scale-space effects can be taken into account is presented. The necessary object views are derived using a similarity-based aspect graph. The high robustness of an exhaustive search is combined with an efficient hierarchical search. The 3D pose is refined by using a least-squares adjustment that minimizes geometric distances in the image, yielding a position accuracy of up to 0.12 percent with respect to the object distance, and an orientation accuracy of up to 0.35 degree in our tests. The recognition time is largely independent of the complexity of the object, but depends mainly on the range of poses within which the object may appear in front of the camera. For efficiency reasons, the approach allows the restriction of the pose range depending on the application. Typical runtimes are in the range of a few hundred ms.

  2. Fast and flexible 3D object recognition solutions for machine vision applications

    NASA Astrophysics Data System (ADS)

    Effenberger, Ira; Kühnle, Jens; Verl, Alexander

    2013-03-01

    In automation and handling engineering, supplying work pieces between different stages along the production process chain is of special interest. Often the parts are stored unordered in bins or lattice boxes and hence have to be separated and ordered for feeding purposes. An alternative to complex and spacious mechanical systems such as bowl feeders or conveyor belts, which are typically adapted to the parts' geometry, is using a robot to grip the work pieces out of a bin or from a belt. Such applications are in need of reliable and precise computer-aided object detection and localization systems. For a restricted range of parts, there exists a variety of 2D image processing algorithms that solve the recognition problem. However, these methods are often not well suited for the localization of randomly stored parts. In this paper we present a fast and flexible 3D object recognizer that localizes objects by identifying primitive features within the objects. Since technical work pieces typically consist to a substantial degree of geometric primitives such as planes, cylinders and cones, such features usually carry enough information in order to determine the position of the entire object. Our algorithms use 3D best-fitting combined with an intelligent data pre-processing step. The capability and performance of this approach is shown by applying the algorithms to real data sets of different industrial test parts in a prototypical bin picking demonstration system.

  3. Characteristics of eye movements in 3-D object learning: comparison between within-modal and cross-modal object recognition.

    PubMed

    Ueda, Yoshiyuki; Saiki, Jun

    2012-01-01

    Recent studies have indicated that the object representation acquired during visual learning depends on the encoding modality during the test phase. However, the nature of the differences between within-modal learning (eg visual learning-visual recognition) and cross-modal learning (eg visual learning-haptic recognition) remains unknown. To address this issue, we utilised eye movement data and investigated object learning strategies during the learning phase of a cross-modal object recognition experiment. Observers informed of the test modality studied an unfamiliar visually presented 3-D object. Quantitative analyses showed that recognition performance was consistent regardless of rotation in the cross-modal condition, but was reduced when objects were rotated in the within-modal condition. In addition, eye movements during learning significantly differed between within-modal and cross-modal learning. Fixations were more diffused for cross-modal learning than in within-modal learning. Moreover, over the course of the trial, fixation durations became longer in cross-modal learning than in within-modal learning. These results suggest that the object learning strategies employed during the learning phase differ according to the modality of the test phase, and that this difference leads to different recognition performances.

  4. An optimal sensing strategy for recognition and localization of 3-D natural quadric objects

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan; Hahn, Hernsoo

    1991-01-01

    An optimal sensing strategy for an optical proximity sensor system engaged in the recognition and localization of 3-D natural quadric objects is presented. The optimal sensing strategy consists of the selection of an optimal beam orientation and the determination of an optimal probing plane that compose an optimal data collection operation known as an optimal probing. The decision of an optimal probing is based on the measure of discrimination power of a cluster of surfaces on a multiple interpretation image (MII), where the measure of discrimination power is defined in terms of a utility function computing the expected number of interpretations that can be pruned out by a probing. An object representation suitable for active sensing based on a surface description vector (SDV) distribution graph and hierarchical tables is presented. Experimental results are shown.

  5. 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.

  6. 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

  7. Recognition by Humans and Pigeons of Novel Views of 3-D Objects and Their Photographs

    ERIC Educational Resources Information Center

    Friedman, Alinda; Spetch, Marcia L.; Ferrey, Anne

    2005-01-01

    Humans and pigeons were trained to discriminate between 2 views of actual 3-D objects or their photographs. They were tested on novel views that were either within the closest rotational distance between the training views (interpolated) or outside of that range (extrapolated). When training views were 60? apart, pigeons, but not humans,…

  8. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes

    PubMed Central

    Yebes, J. Javier; Bergasa, Luis M.; García-Garrido, Miguel Ángel

    2015-01-01

    Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM. PMID:25903553

  9. Recognition of 3-D symmetric objects from range images in automated assembly tasks

    NASA Technical Reports Server (NTRS)

    Alvertos, Nicolas; Dcunha, Ivan

    1990-01-01

    A new technique is presented for the three dimensional recognition of symmetric objects from range images. Beginning from the implicit representation of quadrics, a set of ten coefficients is determined for symmetric objects like spheres, cones, cylinders, ellipsoids, and parallelepipeds. Instead of using these ten coefficients trying to fit them to smooth surfaces (patches) based on the traditional way of determining curvatures, a new approach based on two dimensional geometry is used. For each symmetric object, a unique set of two dimensional curves is obtained from the various angles at which the object is intersected with a plane. Using the same ten coefficients obtained earlier and based on the discriminant method, each of these curves is classified as a parabola, circle, ellipse, or hyperbola. Each symmetric object is found to possess a unique set of these two dimensional curves whereby it can be differentiated from the others. It is shown that instead of using the three dimensional discriminant which involves evaluation of the rank of its matrix, it is sufficient to use the two dimensional discriminant which only requires three arithmetic operations.

  10. Spherical blurred shape model for 3-D object and pose recognition: quantitative analysis and HCI applications in smart environments.

    PubMed

    Lopes, Oscar; Reyes, Miguel; Escalera, Sergio; Gonzàlez, Jordi

    2014-12-01

    The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios.

  11. A fast 3-D object recognition algorithm for the vision system of a special-purpose dexterous manipulator

    NASA Technical Reports Server (NTRS)

    Hung, Stephen H. Y.

    1989-01-01

    A fast 3-D object recognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

  12. A roadmap to global illumination in 3D scenes: solutions for GPU object recognition applications

    NASA Astrophysics Data System (ADS)

    Picos, Kenia; Díaz-Ramírez, Victor H.; Tapia, Juan J.

    2014-09-01

    Light interactions with matter is of remarkable complexity. An adequate modeling of global illumination is a vastly studied topic since the beginning of computer graphics, and still is an unsolved problem. The rendering equation for global illumination is based of refraction and reflection of light in interaction with matter within an environment. This physical process possesses a high computational complexity when implemented in a digital computer. The appearance of an object depends on light interactions with the surface of the material, such as emission, scattering, and absorption. Several image-synthesis methods have been used to realistically render the appearance of light incidence on an object. Recent global illumination algorithms employ mathematical models and computational strategies that improve the efficiency of the simulation solution. This work presents a review the state of the art of global illumination algorithms and focuses on the efficiency of the solution in a computational implementation in a graphics processing unit. A reliable system is developed to simulate realistics scenes in the context of real-time object recognition under different lighting conditions. Computer simulations results are presented and discussed in terms of discrimination capability, and robustness to additive noise, when considering several lighting model reflections and multiple light sources.

  13. Neural network system for 3-D object recognition and pose estimation from a single arbitrary 2-D view

    NASA Astrophysics Data System (ADS)

    Khotanzad, Alireza R.; Liou, James H.

    1992-09-01

    In this paper, a robust, and fast system for recognition as well as pose estimation of a 3-D object from a single 2-D perspective of it taken from an arbitrary viewpoint is developed. The approach is invariant to location, orientation, and scale of the object in the perspective. The silhouette of the object in the 2-D perspective is first normalized with respect to location and scale. A set of rotation invariant features derived from complex and orthogonal pseudo- Zernike moments of the image are then extracted. The next stage includes a bank of multilayer feed-forward neural networks (NN) each of which classifies the extracted features. The training set for these nets consists of perspective views of each object taken from several different viewing angles. The NNs in the bank differ in the size of their hidden layer nodes as well as their initial conditions but receive the same input. The classification decisions of all the nets are combined through a majority voting scheme. It is shown that this collective decision making yields better results compared to a single NN operating alone. After the object is classified, two of its pose parameters, namely elevation and aspect angles, are estimated by another module of NNs in a two-stage process. The first stage identifies the likely region of the space that the object is being viewed from. In the second stage, an NN estimator for the identified region is used to compute the pose angles. Extensive experimental studies involving clean and noisy images of seven military ground vehicles are carried out. The performance is compared to two other traditional methods, namely a nearest neighbor rule and a binary decision tree classifier and it is shown that our approach has major advantages over them.

  14. Recognizing 3D Object Using Photometric Invariant.

    DTIC Science & Technology

    1995-02-01

    model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. Tests are given to show the stability and...positions in the model and the data space coordinates, using centroid invariance of corresponding groups of feature positions. Tests are given to show the...ognizing 3D objects. In our testing , it took only 0.2 seconds to derive corresponding positions in the model and the image for natural pictures. 2

  15. Watermarking 3D Objects for Verification

    DTIC Science & Technology

    1999-01-01

    signal ( audio /image/video) pro- cessing and steganography fields, and even newer to the computer graphics community. Inherently, digital watermarking of...Many view digital watermarking as a potential solution for copyright protection of valuable digital materials like CD-quality audio , publication...watermark. The object can be an image, an audio clip, a video clip, or a 3D model. Some papers discuss watermarking other forms of multime- dia data

  16. Representation and classification of 3-D objects.

    PubMed

    Csakany, P; Wallace, A M

    2003-01-01

    This paper addresses the problem of generic object classification from three-dimensional depth or meshed data. First, surface patches are segmented on the basis of differential geometry and quadratic surface fitting. These are represented by a modified Gaussian image that includes the well-known shape index. Learning is an interactive process in which a human teacher indicates corresponding patches, but the formation of generic classes is unaided. Classification of unknown objects is based on the measurement of similarities between feature sets of the objects and the generic classes. The process is demonstrated on a group of three-dimensional (3-D) objects built from both CAD and laser-scanned depth data.

  17. 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

  18. 3D palmprint data fast acquisition and recognition

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoxu; Huang, Shujun; Gao, Nan; Zhang, Zonghua

    2014-11-01

    This paper presents a fast 3D (Three-Dimension) palmprint capturing system and develops an efficient 3D palmprint feature extraction and recognition method. In order to fast acquire accurate 3D shape and texture of palmprint, a DLP projector triggers a CCD camera to realize synchronization. By generating and projecting green fringe pattern images onto the measured palm surface, 3D palmprint data are calculated from the fringe pattern images. The periodic feature vector can be derived from the calculated 3D palmprint data, so undistorted 3D biometrics is obtained. Using the obtained 3D palmprint data, feature matching test have been carried out by Gabor filter, competition rules and the mean curvature. Experimental results on capturing 3D palmprint show that the proposed acquisition method can fast get 3D shape information of palmprint. Some initial experiments on recognition show the proposed method is efficient by using 3D palmprint data.

  19. 3D face database for human pattern recognition

    NASA Astrophysics Data System (ADS)

    Song, LiMei; Lu, Lu

    2008-10-01

    Face recognition is an essential work to ensure human safety. It is also an important task in biomedical engineering. 2D image is not enough for precision face recognition. 3D face data includes more exact information, such as the precision size of eyes, mouth, etc. 3D face database is an important part in human pattern recognition. There is a lot of method to get 3D data, such as 3D laser scan system, 3D phase measurement, shape from shading, shape from motion, etc. This paper will introduce a non-orbit, non-contact, non-laser 3D measurement system. The main idea is from shape from stereo technique. Two cameras are used in different angle. A sequence of light will project on the face. Human face, human head, human tooth, human body can all be measured by the system. The visualization data of each person can form to a large 3D face database, which can be used in human recognition. The 3D data can provide a vivid copy of a face, so the recognition exactness can be reached to 100%. Although the 3D data is larger than 2D image, it can be used in the occasion where only few people include, such as the recognition of a family, a small company, etc.

  20. 3D fast wavelet network model-assisted 3D face recognition

    NASA Astrophysics Data System (ADS)

    Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2015-12-01

    In last years, the emergence of 3D shape in face recognition is due to its robustness to pose and illumination changes. These attractive benefits are not all the challenges to achieve satisfactory recognition rate. Other challenges such as facial expressions and computing time of matching algorithms remain to be explored. In this context, we propose our 3D face recognition approach using 3D wavelet networks. Our approach contains two stages: learning stage and recognition stage. For the training we propose a novel algorithm based on 3D fast wavelet transform. From 3D coordinates of the face (x,y,z), we proceed to voxelization to get a 3D volume which will be decomposed by 3D fast wavelet transform and modeled after that with a wavelet network, then their associated weights are considered as vector features to represent each training face . For the recognition stage, an unknown identity face is projected on all the training WN to obtain a new vector features after every projection. A similarity score is computed between the old and the obtained vector features. To show the efficiency of our approach, experimental results were performed on all the FRGC v.2 benchmark.

  1. Photon counting passive 3D image sensing for automatic target recognition.

    PubMed

    Yeom, Seokwon; Javidi, Bahram; Watson, Edward

    2005-11-14

    In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic target recognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.

  2. Lateralized Effects of Categorical and Coordinate Spatial Processing of Component Parts on the Recognition of 3D Non-Nameable Objects

    ERIC Educational Resources Information Center

    Saneyoshi, Ayako; Michimata, Chikashi

    2009-01-01

    Participants performed two object-matching tasks for novel, non-nameable objects consisting of geons. For each original stimulus, two transformations were applied to create comparison stimuli. In the categorical transformation, a geon connected to geon A was moved to geon B. In the coordinate transformation, a geon connected to geon A was moved to…

  3. Automatic object recognition

    NASA Technical Reports Server (NTRS)

    Ranganath, H. S.; Mcingvale, Pat; Sage, Heinz

    1988-01-01

    Geometric and intensity features are very useful in object recognition. An intensity feature is a measure of contrast between object pixels and background pixels. Geometric features provide shape and size information. A model based approach is presented for computing geometric features. Knowledge about objects and imaging system is used to estimate orientation of objects with respect to the line of sight.

  4. Optimal-tradeoff circular harmonic function filters for 3D target recognition

    NASA Astrophysics Data System (ADS)

    Vijaya Kumar, Bhagavatula V. K.; Xie, Chunyan; Mahalanobis, Abhijit

    2003-09-01

    3D target recognition is of significant interest because representing the object in 3D space couuld essentially provide a solution to pose variation and self-occlusion problems that are big challenges in 2D pattern recognition. Correlation filers have been used in a variety of 2D pattern matching applications and many correlation filter designs have been developed to handle problems such as rotations. Correlation filters also offer other benefits such as shift-invariance, graceful degradation and closed-form solutions. The 3D extension of correlation filter is a natural extension to handle 3D pattern recognition problem. In this paper, we propose a 3D correlation filter design method based on cylindrical circular harmonic function (CCHF) and use LADAR imagery to illustrate the good performance of CCHF filters.

  5. 3D face recognition by projection-based methods

    NASA Astrophysics Data System (ADS)

    Dutagaci, Helin; Sankur, Bülent; Yemez, Yücel

    2006-02-01

    In this paper, we investigate recognition performances of various projection-based features applied on registered 3D scans of faces. Some features are data driven, such as ICA-based features or NNMF-based features. Other features are obtained using DFT or DCT-based schemes. We apply the feature extraction techniques to three different representations of registered faces, namely, 3D point clouds, 2D depth images and 3D voxel. We consider both global and local features. Global features are extracted from the whole face data, whereas local features are computed over the blocks partitioned from 2D depth images. The block-based local features are fused both at feature level and at decision level. The resulting feature vectors are matched using Linear Discriminant Analysis. Experiments using different combinations of representation types and feature vectors are conducted on the 3D-RMA dataset.

  6. Objective and subjective quality assessment of geometry compression of reconstructed 3D humans in a 3D virtual room

    NASA Astrophysics Data System (ADS)

    Mekuria, Rufael; Cesar, Pablo; Doumanis, Ioannis; Frisiello, Antonella

    2015-09-01

    Compression of 3D object based video is relevant for 3D Immersive applications. Nevertheless, the perceptual aspects of the degradation introduced by codecs for meshes and point clouds are not well understood. In this paper we evaluate the subjective and objective degradations introduced by such codecs in a state of art 3D immersive virtual room. In the 3D immersive virtual room, users are captured with multiple cameras, and their surfaces are reconstructed as photorealistic colored/textured 3D meshes or point clouds. To test the perceptual effect of compression and transmission, we render degraded versions with different frame rates in different contexts (near/far) in the scene. A quantitative subjective study with 16 users shows that negligible distortion of decoded surfaces compared to the original reconstructions can be achieved in the 3D virtual room. In addition, a qualitative task based analysis in a full prototype field trial shows increased presence, emotion, user and state recognition of the reconstructed 3D Human representation compared to animated computer avatars.

  7. Optical 3D imaging and visualization of concealed objects

    NASA Astrophysics Data System (ADS)

    Berginc, G.; Bellet, J.-B.; Berechet, I.; Berechet, S.

    2016-09-01

    This paper gives new insights on optical 3D imagery. In this paper we explore the advantages of laser imagery to form a three-dimensional image of the scene. 3D laser imaging can be used for three-dimensional medical imaging and surveillance because of ability to identify tumors or concealed objects. We consider the problem of 3D reconstruction based upon 2D angle-dependent laser images. The objective of this new 3D laser imaging is to provide users a complete 3D reconstruction of objects from available 2D data limited in number. The 2D laser data used in this paper come from simulations that are based on the calculation of the laser interactions with the different meshed objects of the scene of interest or from experimental 2D laser images. We show that combining the Radom transform on 2D laser images with the Maximum Intensity Projection can generate 3D views of the considered scene from which we can extract the 3D concealed object in real time. With different original numerical or experimental examples, we investigate the effects of the input contrasts. We show the robustness and the stability of the method. We have developed a new patented method of 3D laser imaging based on three-dimensional reflective tomographic reconstruction algorithms and an associated visualization method. In this paper we present the global 3D reconstruction and visualization procedures.

  8. 3D Multi-Spectrum Sensor System with Face Recognition

    PubMed Central

    Kim, Joongrock; Yu, Sunjin; Kim, Ig-Jae; Lee, Sangyoun

    2013-01-01

    This paper presents a novel three-dimensional (3D) multi-spectrum sensor system, which combines a 3D depth sensor and multiple optical sensors for different wavelengths. Various image sensors, such as visible, infrared (IR) and 3D sensors, have been introduced into the commercial market. Since each sensor has its own advantages under various environmental conditions, the performance of an application depends highly on selecting the correct sensor or combination of sensors. In this paper, a sensor system, which we will refer to as a 3D multi-spectrum sensor system, which comprises three types of sensors, visible, thermal-IR and time-of-flight (ToF), is proposed. Since the proposed system integrates information from each sensor into one calibrated framework, the optimal sensor combination for an application can be easily selected, taking into account all combinations of sensors information. To demonstrate the effectiveness of the proposed system, a face recognition system with light and pose variation is designed. With the proposed sensor system, the optimal sensor combination, which provides new effectively fused features for a face recognition system, is obtained. PMID:24072025

  9. Appearance-based color face recognition with 3D model

    NASA Astrophysics Data System (ADS)

    Wang, Chengzhang; Bai, Xiaoming

    2013-03-01

    Appearance-based face recognition approaches explore color cues of face images, i.e. grey or color information for recognition task. They first encode color face images, and then extract facial features for classification. Similar to conventional singular value decomposition, hypercomplex matrix also exists singular value decomposition on hypercomplex field. In this paper, a novel color face recognition approach based on hypercomplex singular value decomposition is proposed. The approach employs hypercomplex to encode color face information of different channels simultaneously. Hypercomplex singular value decomposition is utilized then to compute the basis vectors of the color face subspace. To improve learning efficiency of the algorithm, 3D active deformable model is exploited to generate virtual face images. Color face samples are projected onto the subspace and projection coefficients are utilized as facial features. Experimental results on CMU PIE face database verify the effectiveness of the proposed approach.

  10. Phase Sensitive Cueing for 3D Objects in Overhead Images

    SciTech Connect

    Paglieroni, D

    2005-02-04

    Locating specific 3D objects in overhead images is an important problem in many remote sensing applications. 3D objects may contain either one connected component or multiple disconnected components. Solutions must accommodate images acquired with diverse sensors at various times of the day, in various seasons of the year, or under various weather conditions. Moreover, the physical manifestation of a 3D object with fixed physical dimensions in an overhead image is highly dependent on object physical dimensions, object position/orientation, image spatial resolution, and imaging geometry (e.g., obliqueness). This paper describes a two-stage computer-assisted approach for locating 3D objects in overhead images. In the matching stage, the computer matches models of 3D objects to overhead images. The strongest degree of match over all object orientations is computed at each pixel. Unambiguous local maxima in the degree of match as a function of pixel location are then found. In the cueing stage, the computer sorts image thumbnails in descending order of figure-of-merit and presents them to human analysts for visual inspection and interpretation. The figure-of-merit associated with an image thumbnail is computed from the degrees of match to a 3D object model associated with unambiguous local maxima that lie within the thumbnail. This form of computer assistance is invaluable when most of the relevant thumbnails are highly ranked, and the amount of inspection time needed is much less for the highly ranked thumbnails than for images as a whole.

  11. 3-D object-oriented image analysis of geophysical data

    NASA Astrophysics Data System (ADS)

    Fadel, I.; Kerle, N.; van der Meijde, M.

    2014-07-01

    Geophysical data are the main source of information about the subsurface. Geophysical techniques are, however, highly non-unique in determining specific physical parameters and boundaries of subsurface objects. To obtain actual physical information, an inversion process is often applied, in which measurements at or above the Earth surface are inverted into a 2- or 3-D subsurface spatial distribution of the physical property. Interpreting these models into structural objects, related to physical processes, requires a priori knowledge and expert analysis which is susceptible to subjective choices and is therefore often non-repeatable. In this research, we implemented a recently introduced object-based approach to interpret the 3-D inversion results of a single geophysical technique using the available a priori information and the physical and geometrical characteristics of the interpreted objects. The introduced methodology is semi-automatic and repeatable, and allows the extraction of subsurface structures using 3-D object-oriented image analysis (3-D OOA) in an objective knowledge-based classification scheme. The approach allows for a semi-objective setting of thresholds that can be tested and, if necessary, changed in a very fast and efficient way. These changes require only changing the thresholds used in a so-called ruleset, which is composed of algorithms that extract objects from a 3-D data cube. The approach is tested on a synthetic model, which is based on a priori knowledge on objects present in the study area (Tanzania). Object characteristics and thresholds were well defined in a 3-D histogram of velocity versus depth, and objects were fully retrieved. The real model results showed how 3-D OOA can deal with realistic 3-D subsurface conditions in which the boundaries become fuzzy, the object extensions become unclear and the model characteristics vary with depth due to the different physical conditions. As expected, the 3-D histogram of the real data was

  12. An Evaluative Review of Simulated Dynamic Smart 3d Objects

    NASA Astrophysics Data System (ADS)

    Romeijn, H.; Sheth, F.; Pettit, C. J.

    2012-07-01

    Three-dimensional (3D) modelling of plants can be an asset for creating agricultural based visualisation products. The continuum of 3D plants models ranges from static to dynamic objects, also known as smart 3D objects. There is an increasing requirement for smarter simulated 3D objects that are attributed mathematically and/or from biological inputs. A systematic approach to plant simulation offers significant advantages to applications in agricultural research, particularly in simulating plant behaviour and the influences of external environmental factors. This approach of 3D plant object visualisation is primarily evident from the visualisation of plants using photographed billboarded images, to more advanced procedural models that come closer to simulating realistic virtual plants. However, few programs model physical reactions of plants to external factors and even fewer are able to grow plants based on mathematical and/or biological parameters. In this paper, we undertake an evaluation of plant-based object simulation programs currently available, with a focus upon the components and techniques involved in producing these objects. Through an analytical review process we consider the strengths and weaknesses of several program packages, the features and use of these programs and the possible opportunities in deploying these for creating smart 3D plant-based objects to support agricultural research and natural resource management. In creating smart 3D objects the model needs to be informed by both plant physiology and phenology. Expert knowledge will frame the parameters and procedures that will attribute the object and allow the simulation of dynamic virtual plants. Ultimately, biologically smart 3D virtual plants that react to changes within an environment could be an effective medium to visually represent landscapes and communicate land management scenarios and practices to planners and decision-makers.

  13. Aesthetic preference recognition of 3D shapes using EEG.

    PubMed

    Chew, Lin Hou; Teo, Jason; Mountstephens, James

    2016-04-01

    Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.

  14. 3D object hiding using three-dimensional ptychography

    NASA Astrophysics Data System (ADS)

    Zhang, Jun; Wang, Zhibo; Li, Tuo; Pan, An; Wang, Yali; Shi, Yishi

    2016-09-01

    We present a novel technique for 3D object hiding by applying three-dimensional ptychography. Compared with 3D information hiding based on holography, the proposed ptychography-based hiding technique is easier to implement, because the reference beam and high-precision interferometric optical setup are not required. The acquisition of the 3D object and the ptychographic encoding process are performed optically. Owing to the introduction of probe keys, the security of the ptychography-based hiding system is significantly enhanced. A series of experiments and simulations demonstrate the feasibility and imperceptibility of the proposed method.

  15. 3D laser imaging for concealed object identification

    NASA Astrophysics Data System (ADS)

    Berechet, Ion; Berginc, Gérard; Berechet, Stefan

    2014-09-01

    This paper deals with new optical non-conventional 3D laser imaging. Optical non-conventional imaging explores the advantages of laser imaging to form a three-dimensional image of the scene. 3D laser imaging can be used for threedimensional medical imaging, topography, surveillance, robotic vision because of ability to detect and recognize objects. In this paper, we present a 3D laser imaging for concealed object identification. The objective of this new 3D laser imaging is to provide the user a complete 3D reconstruction of the concealed object from available 2D data limited in number and with low representativeness. The 2D laser data used in this paper come from simulations that are based on the calculation of the laser interactions with the different interfaces of the scene of interest and from experimental results. We show the global 3D reconstruction procedures capable to separate objects from foliage and reconstruct a threedimensional image of the considered object. In this paper, we present examples of reconstruction and completion of three-dimensional images and we analyse the different parameters of the identification process such as resolution, the scenario of camouflage, noise impact and lacunarity degree.

  16. Action and gait recognition from recovered 3-D human joints.

    PubMed

    Gu, Junxia; Ding, Xiaoqing; Wang, Shengjin; Wu, Youshou

    2010-08-01

    A common viewpoint-free framework that fuses pose recovery and classification for action and gait recognition is presented in this paper. First, a markerless pose recovery method is adopted to automatically capture the 3-D human joint and pose parameter sequences from volume data. Second, multiple configuration features (combination of joints) and movement features (position, orientation, and height of the body) are extracted from the recovered 3-D human joint and pose parameter sequences. A hidden Markov model (HMM) and an exemplar-based HMM are then used to model the movement features and configuration features, respectively. Finally, actions are classified by a hierarchical classifier that fuses the movement features and the configuration features, and persons are recognized from their gait sequences with the configuration features. The effectiveness of the proposed approach is demonstrated with experiments on the Institut National de Recherche en Informatique et Automatique Xmas Motion Acquisition Sequences data set.

  17. 3D dimeron as a stable topological object

    NASA Astrophysics Data System (ADS)

    Yang, Shijie; Liu, Yongkai

    2015-03-01

    Searching for novel topological objects is always an intriguing task for scientists in various fields. We study a new three-dimensional (3D) topological structure called 3D dimeron in the trapped two-component Bose-Einstein condensates. The 3D dimeron differs to the conventional 3D skyrmion for the condensates hosting two interlocked vortex-rings. We demonstrate that the vortex-rings are connected by a singular string and the complexity constitutes a vortex-molecule. The stability is investigated through numerically evolving the Gross-Pitaevskii equations, giving a coherent Rabi coupling between the two components. Alternatively, we find that the stable 3D dimeron can be naturally generated from a vortex-free Gaussian wave packet via incorporating a synthetic non-Abelian gauge potential into the condensates. This work is supported by the NSF of China under Grant No. 11374036 and the National 973 program under Grant No. 2012CB821403.

  18. Embedding objects during 3D printing to add new functionalities.

    PubMed

    Yuen, Po Ki

    2016-07-01

    A novel method for integrating and embedding objects to add new functionalities during 3D printing based on fused deposition modeling (FDM) (also known as fused filament fabrication or molten polymer deposition) is presented. Unlike typical 3D printing, FDM-based 3D printing could allow objects to be integrated and embedded during 3D printing and the FDM-based 3D printed devices do not typically require any post-processing and finishing. Thus, various fluidic devices with integrated glass cover slips or polystyrene films with and without an embedded porous membrane, and optical devices with embedded Corning(®) Fibrance™ Light-Diffusing Fiber were 3D printed to demonstrate the versatility of the FDM-based 3D printing and embedding method. Fluid perfusion flow experiments with a blue colored food dye solution were used to visually confirm fluid flow and/or fluid perfusion through the embedded porous membrane in the 3D printed fluidic devices. Similar to typical 3D printed devices, FDM-based 3D printed devices are translucent at best unless post-polishing is performed and optical transparency is highly desirable in any fluidic devices; integrated glass cover slips or polystyrene films would provide a perfect optical transparent window for observation and visualization. In addition, they also provide a compatible flat smooth surface for biological or biomolecular applications. The 3D printed fluidic devices with an embedded porous membrane are applicable to biological or chemical applications such as continuous perfusion cell culture or biocatalytic synthesis but without the need for any post-device assembly and finishing. The 3D printed devices with embedded Corning(®) Fibrance™ Light-Diffusing Fiber would have applications in display, illumination, or optical applications. Furthermore, the FDM-based 3D printing and embedding method could also be utilized to print casting molds with an integrated glass bottom for polydimethylsiloxane (PDMS) device replication

  19. A 2D range Hausdorff approach for 3D face recognition.

    SciTech Connect

    Koch, Mark William; Russ, Trina Denise; Little, Charles Quentin

    2005-04-01

    This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.

  20. A 2D range Hausdorff approach to 3D facial recognition.

    SciTech Connect

    Koch, Mark William; Russ, Trina Denise; Little, Charles Quentin

    2004-11-01

    This paper presents a 3D facial recognition algorithm based on the Hausdorff distance metric. The standard 3D formulation of the Hausdorff matching algorithm has been modified to operate on a 2D range image, enabling a reduction in computation from O(N2) to O(N) without large storage requirements. The Hausdorff distance is known for its robustness to data outliers and inconsistent data between two data sets, making it a suitable choice for dealing with the inherent problems in many 3D datasets due to sensor noise and object self-occlusion. For optimal performance, the algorithm assumes a good initial alignment between probe and template datasets. However, to minimize the error between two faces, the alignment can be iteratively refined. Results from the algorithm are presented using 3D face images from the Face Recognition Grand Challenge database version 1.0.

  1. Measuring the Visual Salience of 3D Printed Objects.

    PubMed

    Wang, Xi; Lindlbauer, David; Lessig, Christian; Maertens, Marianne; Alexa, Marc

    2016-01-01

    To investigate human viewing behavior on physical realizations of 3D objects, the authors use an eye tracker with scene camera and fiducial markers on 3D objects to gather fixations on the presented stimuli. They use this data to validate assumptions regarding visual saliency that so far have experimentally only been analyzed for flat stimuli. They provide a way to compare fixation sequences from different subjects and developed a model for generating test sequences of fixations unrelated to the stimuli. Their results suggest that human observers agree in their fixations for the same object under similar viewing conditions. They also developed a simple procedure to validate computational models for visual saliency of 3D objects and found that popular models of mesh saliency based on center surround patterns fail to predict fixations.

  2. Conformal geometry and its applications on 3D shape matching, recognition, and stitching.

    PubMed

    Wang, Sen; Wang, Yang; Jin, Miao; Gu, Xianfeng David; Samaras, Dimitris

    2007-07-01

    Three-dimensional shape matching is a fundamental issue in computer vision with many applications such as shape registration, 3D object recognition, and classification. However, shape matching with noise, occlusion, and clutter is a challenging problem. In this paper, we analyze a family of quasi-conformal maps including harmonic maps, conformal maps, and least-squares conformal maps with regards to 3D shape matching. As a result, we propose a novel and computationally efficient shape matching framework by using least-squares conformal maps. According to conformal geometry theory, each 3D surface with disk topology can be mapped to a 2D domain through a global optimization and the resulting map is a diffeomorphism, i.e., one-to-one and onto. This allows us to simplify the 3D shape-matching problem to a 2D image-matching problem, by comparing the resulting 2D parametric maps, which are stable, insensitive to resolution changes and robust to occlusion, and noise. Therefore, highly accurate and efficient 3D shape matching algorithms can be achieved by using the above three parametric maps. Finally, the robustness of least-squares conformal maps is evaluated and analyzed comprehensively in 3D shape matching with occlusion, noise, and resolution variation. In order to further demonstrate the performance of our proposed method, we also conduct a series of experiments on two computer vision applications, i.e., 3D face recognition and 3D nonrigid surface alignment and stitching.

  3. A 3-D measurement system using object-oriented FORTH

    SciTech Connect

    Butterfield, K.B.

    1989-01-01

    Discussed is a system for storing 3-D measurements of points that relates the coordinate system of the measurement device to the global coordinate system. The program described here used object-oriented FORTH to store the measured points as sons of the measuring device location. Conversion of local coordinates to absolute coordinates is performed by passing messages to the point objects. Modifications to the object-oriented FORTH system are also described. 1 ref.

  4. Algorithms for Haptic Rendering of 3D Objects

    NASA Technical Reports Server (NTRS)

    Basdogan, Cagatay; Ho, Chih-Hao; Srinavasan, Mandayam

    2003-01-01

    Algorithms have been developed to provide haptic rendering of three-dimensional (3D) objects in virtual (that is, computationally simulated) environments. The goal of haptic rendering is to generate tactual displays of the shapes, hardnesses, surface textures, and frictional properties of 3D objects in real time. Haptic rendering is a major element of the emerging field of computer haptics, which invites comparison with computer graphics. We have already seen various applications of computer haptics in the areas of medicine (surgical simulation, telemedicine, haptic user interfaces for blind people, and rehabilitation of patients with neurological disorders), entertainment (3D painting, character animation, morphing, and sculpting), mechanical design (path planning and assembly sequencing), and scientific visualization (geophysical data analysis and molecular manipulation).

  5. Segmentation of 3D objects using live wire

    NASA Astrophysics Data System (ADS)

    Falcao, Alexandre X.; Udupa, Jayaram K.

    1997-04-01

    We have been developing user-steered image segmentation methods for situations which require considerable user assistance in object definition. In such situations, our segmentation methods aim (1) to provide effective control to the user on the segmentation process while it is being executed and (2) to minimize the total user's time required in the process. In the past, we have presented two paradigms, referred to as live wire and live lane, for segmenting 3D/4D object boundaries in a slice-by-slice fashion. In this paper, we introduce a 3D extension of the live wire approach which can further reduce the time spent by the user in the segmentation process. In 2D live wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment (as a set of oriented pixel edges) is the minimum-cost path between the two points. This segment is found via dynamic programming in real time as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary in this slice is identified as a set of consecutive boundary segments forming a 'closed,' 'connected,' 'oriented' contour. The strategy of the 3D extension is that, first, users specify contours via live- wiring on a few orthogonal slices. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to do live-wiring automatically on all axial slices of the 3D scene. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live wire is statistically significantly (p less than 0.0001) more repeatable and 2 - 6 times faster (p less than 0.01) than the 2D live wire method and 3 - 15 times faster than manual tracing.

  6. Pose detection of a 3D object using template matched filtering

    NASA Astrophysics Data System (ADS)

    Picos, Kenia; Díaz-Ramírez, Víctor H.

    2016-09-01

    The problem of 3D pose recognition of a rigid object is difficult to solve because the pose in a 3D space can vary with multiple degrees of freedom. In this work, we propose an accurate method for 3D pose estimation based on template matched filtering. The proposed method utilizes a bank of space-variant filters which take into account different pose states of the target and local statistical properties of the input scene. The state parameters of location coordinates, orientation angles, and scaling parameters of the target are estimated with high accuracy in the input scene. Experimental tests are performed for real and synthetic scenes. The proposed system yields good performance for 3D pose recognition in terms of detection efficiency, location and orientation errors.

  7. Pose invariant face recognition: 3D model from single photo

    NASA Astrophysics Data System (ADS)

    Napoléon, Thibault; Alfalou, Ayman

    2017-02-01

    Face recognition is widely studied in the literature for its possibilities in surveillance and security. In this paper, we report a novel algorithm for the identification task. This technique is based on an optimized 3D modeling allowing to reconstruct faces in different poses from a limited number of references (i.e. one image by class/person). Particularly, we propose to use an active shape model to detect a set of keypoints on the face necessary to deform our synthetic model with our optimized finite element method. Indeed, in order to improve our deformation, we propose a regularization by distances on graph. To perform the identification we use the VanderLugt correlator well know to effectively address this task. On the other hand we add a difference of Gaussian filtering step to highlight the edges and a description step based on the local binary patterns. The experiments are performed on the PHPID database enhanced with our 3D reconstructed faces of each person with an azimuth and an elevation ranging from -30° to +30°. The obtained results prove the robustness of our new method with 88.76% of good identification when the classic 2D approach (based on the VLC) obtains just 44.97%.

  8. 3-d interpolation in object perception: evidence from an objective performance paradigm.

    PubMed

    Kellman, Philip J; Garrigan, Patrick; Shipley, Thomas F; Yin, Carol; Machado, Liana

    2005-06-01

    Object perception requires interpolation processes that connect visible regions despite spatial gaps. Some research has suggested that interpolation may be a 3-D process, but objective performance data and evidence about the conditions leading to interpolation are needed. The authors developed an objective performance paradigm for testing 3-D interpolation and tested a new theory of 3-D contour interpolation, termed 3-D relatability. The theory indicates for a given edge which orientations and positions of other edges in space may be connected to it by interpolation. Results of 5 experiments showed that processing of orientation relations in 3-D relatable displays was superior to processing in 3-D nonrelatable displays and that these effects depended on object formation. 3-D interpolation and 3-D relatabilty are discussed in terms of their implications for computational and neural models of object perception, which have typically been based on 2-D-orientation-sensitive units.

  9. Face recognition based on matching of local features on 3D dynamic range sequences

    NASA Astrophysics Data System (ADS)

    Echeagaray-Patrón, B. A.; Kober, Vitaly

    2016-09-01

    3D face recognition has attracted attention in the last decade due to improvement of technology of 3D image acquisition and its wide range of applications such as access control, surveillance, human-computer interaction and biometric identification systems. Most research on 3D face recognition has focused on analysis of 3D still data. In this work, a new method for face recognition using dynamic 3D range sequences is proposed. Experimental results are presented and discussed using 3D sequences in the presence of pose variation. The performance of the proposed method is compared with that of conventional face recognition algorithms based on descriptors.

  10. Objective breast symmetry evaluation using 3-D surface imaging.

    PubMed

    Eder, Maximilian; Waldenfels, Fee V; Swobodnik, Alexandra; Klöppel, Markus; Pape, Ann-Kathrin; Schuster, Tibor; Raith, Stefan; Kitzler, Elena; Papadopulos, Nikolaos A; Machens, Hans-Günther; Kovacs, Laszlo

    2012-04-01

    This study develops an objective breast symmetry evaluation using 3-D surface imaging (Konica-Minolta V910(®) scanner) by superimposing the mirrored left breast over the right and objectively determining the mean 3-D contour difference between the 2 breast surfaces. 3 observers analyzed the evaluation protocol precision using 2 dummy models (n = 60), 10 test subjects (n = 300), clinically tested it on 30 patients (n = 900) and compared it to established 2-D measurements on 23 breast reconstructive patients using the BCCT.core software (n = 690). Mean 3-D evaluation precision, expressed as the coefficient of variation (VC), was 3.54 ± 0.18 for all human subjects without significant intra- and inter-observer differences (p > 0.05). The 3-D breast symmetry evaluation is observer independent, significantly more precise (p < 0.001) than the BCCT.core software (VC = 6.92 ± 0.88) and may play a part in an objective surgical outcome analysis after incorporation into clinical practice.

  11. Augmented Reality vs Virtual Reality for 3D Object Manipulation.

    PubMed

    Krichenbauer, Max; Yamamoto, Goshiro; Taketomi, Takafumi; Sandor, Christian; Kato, Hirokazu

    2017-01-25

    Virtual Reality (VR) Head-Mounted Displays (HMDs) are on the verge of becoming commodity hardware available to the average user and feasible to use as a tool for 3D work. Some HMDs include front-facing cameras, enabling Augmented Reality (AR) functionality. Apart from avoiding collisions with the environment, interaction with virtual objects may also be affected by seeing the real environment. However, whether these effects are positive or negative has not yet been studied extensively. For most tasks it is unknown whether AR has any advantage over VR. In this work we present the results of a user study in which we compared user performance measured in task completion time on a 9 degrees of freedom object selection and transformation task performed either in AR or VR, both with a 3D input device and a mouse. Our results show faster task completion time in AR over VR. When using a 3D input device, a purely VR environment increased task completion time by 22.5% on average compared to AR (p < 0:024). Surprisingly, a similar effect occurred when using a mouse: users were about 17.3% slower in VR than in AR (p < 0:04). Mouse and 3D input device produced similar task completion times in each condition (AR or VR) respectively. We further found no differences in reported comfort.

  12. Laser embedding electronics on 3D printed objects

    NASA Astrophysics Data System (ADS)

    Kirleis, Matthew A.; Simonson, Duane; Charipar, Nicholas A.; Kim, Heungsoo; Charipar, Kristin M.; Auyeung, Ray C. Y.; Mathews, Scott A.; Piqué, Alberto

    2014-03-01

    Additive manufacturing techniques such as 3D printing are able to generate reproductions of a part in free space without the use of molds; however, the objects produced lack electrical functionality from an applications perspective. At the same time, techniques such as inkjet and laser direct-write (LDW) can be used to print electronic components and connections onto already existing objects, but are not capable of generating a full object on their own. The approach missing to date is the combination of 3D printing processes with direct-write of electronic circuits. Among the numerous direct write techniques available, LDW offers unique advantages and capabilities given its compatibility with a wide range of materials, surface chemistries and surface morphologies. The Naval Research Laboratory (NRL) has developed various LDW processes ranging from the non-phase transformative direct printing of complex suspensions or inks to lase-and-place for embedding entire semiconductor devices. These processes have been demonstrated in digital manufacturing of a wide variety of microelectronic elements ranging from circuit components such as electrical interconnects and passives to antennas, sensors, actuators and power sources. At NRL we are investigating the combination of LDW with 3D printing to demonstrate the digital fabrication of functional parts, such as 3D circuits. Merging these techniques will make possible the development of a new generation of structures capable of detecting, processing, communicating and interacting with their surroundings in ways never imagined before. This paper shows the latest results achieved at NRL in this area, describing the various approaches developed for generating 3D printed electronics with LDW.

  13. OB3D, a new set of 3D objects available for research: a web-based study

    PubMed Central

    Buffat, Stéphane; Chastres, Véronique; Bichot, Alain; Rider, Delphine; Benmussa, Frédéric; Lorenceau, Jean

    2014-01-01

    Studying object recognition is central to fundamental and clinical research on cognitive functions but suffers from the limitations of the available sets that cannot always be modified and adapted to meet the specific goals of each study. We here present a new set of 3D scans of real objects available on-line as ASCII files, OB3D. These files are lists of dots, each defined by a triplet of spatial coordinates and their normal that allow simple and highly versatile transformations and adaptations. We performed a web-based experiment to evaluate the minimal number of dots required for the denomination and categorization of these objects, thus providing a reference threshold. We further analyze several other variables derived from this data set, such as the correlations with object complexity. This new stimulus set, which was found to activate the Lower Occipital Complex (LOC) in another study, may be of interest for studies of cognitive functions in healthy participants and patients with cognitive impairments, including visual perception, language, memory, etc. PMID:25339920

  14. The Visual Priming of Motion-Defined 3D Objects

    PubMed Central

    Jiang, Xiong; Jiang, Yang

    2015-01-01

    The perception of a stimulus can be influenced by previous perceptual experience, a phenomenon known as perceptual priming. However, there has been limited investigation on perceptual priming of shape perception of three-dimensional object structures defined by moving dots. Here we examined the perceptual priming of a 3D object shape defined purely by motion-in-depth cues (i.e., Shape-From-Motion, SFM) using a classic prime-target paradigm. The results from the first two experiments revealed a significant increase in accuracy when a “cloudy” SFM stimulus (whose object structure was difficult to recognize due to the presence of strong noise) was preceded by an unambiguous SFM that clearly defined the same transparent 3D shape. In contrast, results from Experiment 3 revealed no change in accuracy when a “cloudy” SFM stimulus was preceded by a static shape or a semantic word that defined the same object shape. Instead, there was a significant decrease in accuracy when preceded by a static shape or a semantic word that defined a different object shape. These results suggested that the perception of a noisy SFM stimulus can be facilitated by a preceding unambiguous SFM stimulus—but not a static image or a semantic stimulus—that defined the same shape. The potential neural and computational mechanisms underlying the difference in priming are discussed. PMID:26658496

  15. Divided attention limits perception of 3-D object shapes.

    PubMed

    Scharff, Alec; Palmer, John; Moore, Cathleen M

    2013-02-12

    Can one perceive multiple object shapes at once? We tested two benchmark models of object shape perception under divided attention: an unlimited-capacity and a fixed-capacity model. Under unlimited-capacity models, shapes are analyzed independently and in parallel. Under fixed-capacity models, shapes are processed at a fixed rate (as in a serial model). To distinguish these models, we compared conditions in which observers were presented with simultaneous or sequential presentations of a fixed number of objects (The extended simultaneous-sequential method: Scharff, Palmer, & Moore, 2011a, 2011b). We used novel physical objects as stimuli, minimizing the role of semantic categorization in the task. Observers searched for a specific object among similar objects. We ensured that non-shape stimulus properties such as color and texture could not be used to complete the task. Unpredictable viewing angles were used to preclude image-matching strategies. The results rejected unlimited-capacity models for object shape perception and were consistent with the predictions of a fixed-capacity model. In contrast, a task that required observers to recognize 2-D shapes with predictable viewing angles yielded an unlimited capacity result. Further experiments ruled out alternative explanations for the capacity limit, leading us to conclude that there is a fixed-capacity limit on the ability to perceive 3-D object shapes.

  16. Prediction models from CAD models of 3D objects

    NASA Astrophysics Data System (ADS)

    Camps, Octavia I.

    1992-11-01

    In this paper we present a probabilistic prediction based approach for CAD-based object recognition. Given a CAD model of an object, the PREMIO system combines techniques of analytic graphics and physical models of lights and sensors to predict how features of the object will appear in images. In nearly 4,000 experiments on analytically-generated and real images, we show that in a semi-controlled environment, predicting the detectability of features of the image can successfully guide a search procedure to make informed choices of model and image features in its search for correspondences that can be used to hypothesize the pose of the object. Furthermore, we provide a rigorous experimental protocol that can be used to determine the optimal number of correspondences to seek so that the probability of failing to find a pose and of finding an inaccurate pose are minimized.

  17. Image segmentation to inspect 3-D object sizes

    NASA Astrophysics Data System (ADS)

    Hsu, Jui-Pin; Fuh, Chiou-Shann

    1996-01-01

    Object size inspection is an important task and has various applications in computer vision. For example, the automatic control of stone-breaking machines, which perform better if the sizes of the stones to be broken can be predicted. An algorithm is proposed for image segmentation in size inspection for almost round stones with high or low texture. Although our experiments are focused on stones, the algorithm can be applied to other 3-D objects. We use one fixed camera and four light sources at four different positions one at a time, to take four images. Then we compute the image differences and binarize them to extract edges. We explain, step by step, the photographing, the edge extraction, the noise removal, and the edge gap filling. Experimental results are presented.

  18. Fully automatic 3D digitization of unknown objects

    NASA Astrophysics Data System (ADS)

    Rozenwald, Gabriel F.; Seulin, Ralph; Fougerolle, Yohan D.

    2010-01-01

    This paper presents a complete system for 3D digitization of objects assuming no prior knowledge on its shape. The proposed methodology is applied to a digitization cell composed of a fringe projection scanner head, a robotic arm with 6 degrees of freedom (DoF), and a turntable. A two-step approach is used to automatically guide the scanning process. The first step uses the concept of Mass Vector Chains (MVC) to perform an initial scanning. The second step directs the scanner to remaining holes of the model. Post-processing of the data is also addressed. Tests with real objects were performed and results of digitization length in time and number of views are provided along with estimated surface coverage.

  19. Phase Sensitive Cueing for 3D Objects in Overhead Images

    SciTech Connect

    Paglieroni, D W; Eppler, W G; Poland, D N

    2005-02-18

    A 3D solid model-aided object cueing method that matches phase angles of directional derivative vectors at image pixels to phase angles of vectors normal to projected model edges is described. It is intended for finding specific types of objects at arbitrary position and orientation in overhead images, independent of spatial resolution, obliqueness, acquisition conditions, and type of imaging sensor. It is shown that the phase similarity measure can be efficiently evaluated over all combinations of model position and orientation using the FFT. The highest degree of similarity over all model orientations is captured in a match surface of similarity values vs. model position. Unambiguous peaks in this surface are sorted in descending order of similarity value, and the small image thumbnails that contain them are presented to human analysts for inspection in sorted order.

  20. Recognizing Objects in 3D Point Clouds with Multi-Scale Local Features

    PubMed Central

    Lu, Min; Guo, Yulan; Zhang, Jun; Ma, Yanxin; Lei, Yinjie

    2014-01-01

    Recognizing 3D objects from point clouds in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present a coarse-to-fine 3D object recognition algorithm. During the phase of offline training, each model is represented with a set of multi-scale local surface features. During the phase of online recognition, a set of keypoints are first detected from each scene. The local surfaces around these keypoints are further encoded with multi-scale feature descriptors. These scene features are then matched against all model features to generate recognition hypotheses, which include model hypotheses and pose hypotheses. Finally, these hypotheses are verified to produce recognition results. The proposed algorithm was tested on two standard datasets, with rigorous comparisons to the state-of-the-art algorithms. Experimental results show that our algorithm was fully automatic and highly effective. It was also very robust to occlusion and clutter. It achieved the best recognition performance on all of these datasets, showing its superiority compared to existing algorithms. PMID:25517694

  1. Recognizing objects in 3D point clouds with multi-scale local features.

    PubMed

    Lu, Min; Guo, Yulan; Zhang, Jun; Ma, Yanxin; Lei, Yinjie

    2014-12-15

    Recognizing 3D objects from point clouds in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present a coarse-to-fine 3D object recognition algorithm. During the phase of offline training, each model is represented with a set of multi-scale local surface features. During the phase of online recognition, a set of keypoints are first detected from each scene. The local surfaces around these keypoints are further encoded with multi-scale feature descriptors. These scene features are then matched against all model features to generate recognition hypotheses, which include model hypotheses and pose hypotheses. Finally, these hypotheses are verified to produce recognition results. The proposed algorithm was tested on two standard datasets, with rigorous comparisons to the state-of-the-art algorithms. Experimental results show that our algorithm was fully automatic and highly effective. It was also very robust to occlusion and clutter. It achieved the best recognition performance on all of these datasets, showing its superiority compared to existing algorithms.

  2. Large distance 3D imaging of hidden objects

    NASA Astrophysics Data System (ADS)

    Rozban, Daniel; Aharon Akram, Avihai; Kopeika, N. S.; Abramovich, A.; Levanon, Assaf

    2014-06-01

    Imaging systems in millimeter waves are required for applications in medicine, communications, homeland security, and space technology. This is because there is no known ionization hazard for biological tissue, and atmospheric attenuation in this range of the spectrum is low compared to that of infrared and optical rays. The lack of an inexpensive room temperature detector makes it difficult to give a suitable real time implement for the above applications. A 3D MMW imaging system based on chirp radar was studied previously using a scanning imaging system of a single detector. The system presented here proposes to employ a chirp radar method with Glow Discharge Detector (GDD) Focal Plane Array (FPA of plasma based detectors) using heterodyne detection. The intensity at each pixel in the GDD FPA yields the usual 2D image. The value of the I-F frequency yields the range information at each pixel. This will enable 3D MMW imaging. In this work we experimentally demonstrate the feasibility of implementing an imaging system based on radar principles and FPA of inexpensive detectors. This imaging system is shown to be capable of imaging objects from distances of at least 10 meters.

  3. Probabilistic view clustering in object recognition

    NASA Astrophysics Data System (ADS)

    Camps, Octavia I.; Christoffel, Douglas W.; Pathak, Anjali

    1992-11-01

    To recognize objects and to determine their poses in a scene we need to find correspondences between the features extracted from the image and those of the object models. Models are commonly represented by describing a few characteristic views of the object representing groups of views with similar properties. Most feature-based matching schemes assume that all the features that are potentially visible in a view will appear with equal probability, and the resulting matching algorithms have to allow for 'errors' without really understanding what they mean. PREMIO is an object recognition system that uses CAD models of 3D objects and knowledge of surface reflectance properties, light sources, sensor characteristics, and feature detector algorithms to estimate the probability of the features being detectable and correctly matched. The purpose of this paper is to describe the predictions generated by PREMIO, how they are combined into a single probabilistic model, and illustrative examples showing its use in object recognition.

  4. Additive manufacturing. Continuous liquid interface production of 3D objects.

    PubMed

    Tumbleston, John R; Shirvanyants, David; Ermoshkin, Nikita; Janusziewicz, Rima; Johnson, Ashley R; Kelly, David; Chen, Kai; Pinschmidt, Robert; Rolland, Jason P; Ermoshkin, Alexander; Samulski, Edward T; DeSimone, Joseph M

    2015-03-20

    Additive manufacturing processes such as 3D printing use time-consuming, stepwise layer-by-layer approaches to object fabrication. We demonstrate the continuous generation of monolithic polymeric parts up to tens of centimeters in size with feature resolution below 100 micrometers. Continuous liquid interface production is achieved with an oxygen-permeable window below the ultraviolet image projection plane, which creates a "dead zone" (persistent liquid interface) where photopolymerization is inhibited between the window and the polymerizing part. We delineate critical control parameters and show that complex solid parts can be drawn out of the resin at rates of hundreds of millimeters per hour. These print speeds allow parts to be produced in minutes instead of hours.

  5. 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…

  6. VIEWNET: a neural architecture for learning to recognize 3D objects from multiple 2D views

    NASA Astrophysics Data System (ADS)

    Grossberg, Stephen; Bradski, Gary

    1994-10-01

    A self-organizing neural network is developed for recognition of 3-D objects from sequences of their 2-D views. Called VIEWNET because it uses view information encoded with networks, the model processes 2-D views of 3-D objects using the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and removes noise from the images. A log-polar transform is taken with respect to the centroid of the resulting figure and then re-centered to achieve 2-D scale and rotation invariance. The invariant images are coarse coded to further reduce noise, reduce foreshortening effects, and increase generalization. These compressed codes are input into a supervised learning system based on the Fuzzy ARTMAP algorithm which learns 2-D view categories. Evidence from sequences of 2-D view categories is stored in a working memory. Voting based on the unordered set of stored categories determines object recognition. Recognition is studied with noisy and clean images using slow and fast learning. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view category and of up to 98.5% correct with three 2-D view categories.

  7. Optical 3D sensor for large objects in industrial application

    NASA Astrophysics Data System (ADS)

    Kuhmstedt, Peter; Heinze, Matthias; Himmelreich, Michael; Brauer-Burchardt, Christian; Brakhage, Peter; Notni, Gunther

    2005-06-01

    A new self calibrating optical 3D measurement system using fringe projection technique named "kolibri 1500" is presented. It can be utilised to acquire the all around shape of large objects. The basic measuring principle is the phasogrammetric approach introduced by the authors /1, 2/. The "kolibri 1500" consists of a stationary system with a translation unit for handling of objects. Automatic whole body measurement is achieved by using sensor head rotation and changeable object position, which can be done completely computer controlled. Multi-view measurement is realised by using the concept of virtual reference points. In this way no matching procedures or markers are necessary for the registration of the different images. This makes the system very flexible to realise different measurement tasks. Furthermore, due to self calibrating principle mechanical alterations are compensated. Typical parameters of the system are: the measurement volume extends from 400 mm up to 1500 mm max. length, the measurement time is between 2 min for 12 images up to 20 min for 36 images and the measurement accuracy is below 50μm.The flexibility makes the measurement system useful for a wide range of applications such as quality control, rapid prototyping, design and CAD/CAM which will be shown in the paper.

  8. Identification of superficial defects in reconstructed 3D objects using phase-shifting fringe projection

    NASA Astrophysics Data System (ADS)

    Madrigal, Carlos A.; Restrepo, Alejandro; Branch, John W.

    2016-09-01

    3D reconstruction of small objects is used in applications of surface analysis, forensic analysis and tissue reconstruction in medicine. In this paper, we propose a strategy for the 3D reconstruction of small objects and the identification of some superficial defects. We applied a technique of projection of structured light patterns, specifically sinusoidal fringes and an algorithm of phase unwrapping. A CMOS camera was used to capture images and a DLP digital light projector for synchronous projection of the sinusoidal pattern onto the objects. We implemented a technique based on a 2D flat pattern as calibration process, so the intrinsic and extrinsic parameters of the camera and the DLP were defined. Experimental tests were performed in samples of artificial teeth, coal particles, welding defects and surfaces tested with Vickers indentation. Areas less than 5cm were studied. The objects were reconstructed in 3D with densities of about one million points per sample. In addition, the steps of 3D description, identification of primitive, training and classification were implemented to recognize defects, such as: holes, cracks, roughness textures and bumps. We found that pattern recognition strategies are useful, when quality supervision of surfaces has enough quantities of points to evaluate the defective region, because the identification of defects in small objects is a demanding activity of the visual inspection.

  9. 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.

  10. Object recognition memory in zebrafish.

    PubMed

    May, Zacnicte; Morrill, Adam; Holcombe, Adam; Johnston, Travis; Gallup, Joshua; Fouad, Karim; Schalomon, Melike; Hamilton, Trevor James

    2016-01-01

    The novel object recognition, or novel-object preference (NOP) test is employed to assess recognition memory in a variety of organisms. The subject is exposed to two identical objects, then after a delay, it is placed back in the original environment containing one of the original objects and a novel object. If the subject spends more time exploring one object, this can be interpreted as memory retention. To date, this test has not been fully explored in zebrafish (Danio rerio). Zebrafish possess recognition memory for simple 2- and 3-dimensional geometrical shapes, yet it is unknown if this translates to complex 3-dimensional objects. In this study we evaluated recognition memory in zebrafish using complex objects of different sizes. Contrary to rodents, zebrafish preferentially explored familiar over novel objects. Familiarity preference disappeared after delays of 5 mins. Leopard danios, another strain of D. rerio, also preferred the familiar object after a 1 min delay. Object preference could be re-established in zebra danios by administration of nicotine tartrate salt (50mg/L) prior to stimuli presentation, suggesting a memory-enhancing effect of nicotine. Additionally, exploration biases were present only when the objects were of intermediate size (2 × 5 cm). Our results demonstrate zebra and leopard danios have recognition memory, and that low nicotine doses can improve this memory type in zebra danios. However, exploration biases, from which memory is inferred, depend on object size. These findings suggest zebrafish ecology might influence object preference, as zebrafish neophobia could reflect natural anti-predatory behaviour.

  11. The Role of Active Exploration of 3D Face Stimuli on Recognition Memory of Facial Information

    ERIC Educational Resources Information Center

    Liu, Chang Hong; Ward, James; Markall, Helena

    2007-01-01

    Research on face recognition has mainly relied on methods in which observers are relatively passive viewers of face stimuli. This study investigated whether active exploration of three-dimensional (3D) face stimuli could facilitate recognition memory. A standard recognition task and a sequential matching task were employed in a yoked design.…

  12. 3-D Interpolation in Object Perception: Evidence from an Objective Performance Paradigm

    ERIC Educational Resources Information Center

    Kellman, Philip J.; Garrigan, Patrick; Shipley, Thomas F.; Yin, Carol; Machado, Liana

    2005-01-01

    Object perception requires interpolation processes that connect visible regions despite spatial gaps. Some research has suggested that interpolation may be a 3-D process, but objective performance data and evidence about the conditions leading to interpolation are needed. The authors developed an objective performance paradigm for testing 3-D…

  13. Object Segmentation and Ground Truth in 3D Embryonic Imaging

    PubMed Central

    Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C.

    2016-01-01

    Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets. PMID:27332860

  14. Object Segmentation and Ground Truth in 3D Embryonic Imaging.

    PubMed

    Rajasekaran, Bhavna; Uriu, Koichiro; Valentin, Guillaume; Tinevez, Jean-Yves; Oates, Andrew C

    2016-01-01

    Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.

  15. Identification and Detection of Simple 3D Objects with Severely Blurred Vision

    PubMed Central

    Kallie, Christopher S.; Legge, Gordon E.; Yu, Deyue

    2012-01-01

    Purpose. Detecting and recognizing three-dimensional (3D) objects is an important component of the visual accessibility of public spaces for people with impaired vision. The present study investigated the impact of environmental factors and object properties on the recognition of objects by subjects who viewed physical objects with severely reduced acuity. Methods. The experiment was conducted in an indoor testing space. We examined detection and identification of simple convex objects by normally sighted subjects wearing diffusing goggles that reduced effective acuity to 20/900. We used psychophysical methods to examine the effect on performance of important environmental variables: viewing distance (from 10–24 feet, or 3.05–7.32 m) and illumination (overhead fluorescent and artificial window), and object variables: shape (boxes and cylinders), size (heights from 2–6 feet, or 0.61–1.83 m), and color (gray and white). Results. Object identification was significantly affected by distance, color, height, and shape, as well as interactions between illumination, color, and shape. A stepwise regression analysis showed that 64% of the variability in identification could be explained by object contrast values (58%) and object visual angle (6%). Conclusions. When acuity is severely limited, illumination, distance, color, height, and shape influence the identification and detection of simple 3D objects. These effects can be explained in large part by the impact of these variables on object contrast and visual angle. Basic design principles for improving object visibility are discussed. PMID:23111613

  16. Reconstruction and 3D visualisation based on objective real 3D based documentation.

    PubMed

    Bolliger, Michael J; Buck, Ursula; Thali, Michael J; Bolliger, Stephan A

    2012-09-01

    Reconstructions based directly upon forensic evidence alone are called primary information. Historically this consists of documentation of findings by verbal protocols, photographs and other visual means. Currently modern imaging techniques such as 3D surface scanning and radiological methods (computer tomography, magnetic resonance imaging) are also applied. Secondary interpretation is based on facts and the examiner's experience. Usually such reconstructive expertises are given in written form, and are often enhanced by sketches. However, narrative interpretations can, especially in complex courses of action, be difficult to present and can be misunderstood. In this report we demonstrate the use of graphic reconstruction of secondary interpretation with supporting pictorial evidence, applying digital visualisation (using 'Poser') or scientific animation (using '3D Studio Max', 'Maya') and present methods of clearly distinguishing between factual documentation and examiners' interpretation based on three cases. The first case involved a pedestrian who was initially struck by a car on a motorway and was then run over by a second car. The second case involved a suicidal gunshot to the head with a rifle, in which the trigger was pushed with a rod. The third case dealt with a collision between two motorcycles. Pictorial reconstruction of the secondary interpretation of these cases has several advantages. The images enable an immediate overview, give rise to enhanced clarity, and compel the examiner to look at all details if he or she is to create a complete image.

  17. Automatic pole-like object modeling via 3D part-based analysis of point cloud

    NASA Astrophysics Data System (ADS)

    He, Liu; Yang, Haoxiang; Huang, Yuchun

    2016-10-01

    Pole-like objects, including trees, lampposts and traffic signs, are indispensable part of urban infrastructure. With the advance of vehicle-based laser scanning (VLS), massive point cloud of roadside urban areas becomes applied in 3D digital city modeling. Based on the property that different pole-like objects have various canopy parts and similar trunk parts, this paper proposed the 3D part-based shape analysis to robustly extract, identify and model the pole-like objects. The proposed method includes: 3D clustering and recognition of trunks, voxel growing and part-based 3D modeling. After preprocessing, the trunk center is identified as the point that has local density peak and the largest minimum inter-cluster distance. Starting from the trunk centers, the remaining points are iteratively clustered to the same centers of their nearest point with higher density. To eliminate the noisy points, cluster border is refined by trimming boundary outliers. Then, candidate trunks are extracted based on the clustering results in three orthogonal planes by shape analysis. Voxel growing obtains the completed pole-like objects regardless of overlaying. Finally, entire trunk, branch and crown part are analyzed to obtain seven feature parameters. These parameters are utilized to model three parts respectively and get signal part-assembled 3D model. The proposed method is tested using the VLS-based point cloud of Wuhan University, China. The point cloud includes many kinds of trees, lampposts and other pole-like posters under different occlusions and overlaying. Experimental results show that the proposed method can extract the exact attributes and model the roadside pole-like objects efficiently.

  18. Implicit Shape Models for Object Detection in 3d Point Clouds

    NASA Astrophysics Data System (ADS)

    Velizhev, A.; Shapovalov, R.; Schindler, K.

    2012-07-01

    We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m2 of urban area in total.

  19. 3D genome structure modeling by Lorentzian objective function.

    PubMed

    Trieu, Tuan; Cheng, Jianlin

    2016-11-29

    The 3D structure of the genome plays a vital role in biological processes such as gene interaction, gene regulation, DNA replication and genome methylation. Advanced chromosomal conformation capture techniques, such as Hi-C and tethered conformation capture, can generate chromosomal contact data that can be used to computationally reconstruct 3D structures of the genome. We developed a novel restraint-based method that is capable of reconstructing 3D genome structures utilizing both intra-and inter-chromosomal contact data. Our method was robust to noise and performed well in comparison with a panel of existing methods on a controlled simulated data set. On a real Hi-C data set of the human genome, our method produced chromosome and genome structures that are consistent with 3D FISH data and known knowledge about the human chromosome and genome, such as, chromosome territories and the cluster of small chromosomes in the nucleus center with the exception of the chromosome 18. The tool and experimental data are available at https://missouri.box.com/v/LorDG.

  20. A framework for the recognition of 3D faces and expressions

    NASA Astrophysics Data System (ADS)

    Li, Chao; Barreto, Armando

    2006-04-01

    Face recognition technology has been a focus both in academia and industry for the last couple of years because of its wide potential applications and its importance to meet the security needs of today's world. Most of the systems developed are based on 2D face recognition technology, which uses pictures for data processing. With the development of 3D imaging technology, 3D face recognition emerges as an alternative to overcome the difficulties inherent with 2D face recognition, i.e. sensitivity to illumination conditions and orientation positioning of the subject. But 3D face recognition still needs to tackle the problem of deformation of facial geometry that results from the expression changes of a subject. To deal with this issue, a 3D face recognition framework is proposed in this paper. It is composed of three subsystems: an expression recognition system, a system for the identification of faces with expression, and neutral face recognition system. A system for the recognition of faces with one type of expression (happiness) and neutral faces was implemented and tested on a database of 30 subjects. The results proved the feasibility of this framework.

  1. Use of 3D faces facilitates facial expression recognition in children

    PubMed Central

    Wang, Lamei; Chen, Wenfeng; Li, Hong

    2017-01-01

    This study assessed whether presenting 3D face stimuli could facilitate children’s facial expression recognition. Seventy-one children aged between 3 and 6 participated in the study. Their task was to judge whether a face presented in each trial showed a happy or fearful expression. Half of the face stimuli were shown with 3D representations, whereas the other half of the images were shown as 2D pictures. We compared expression recognition under these conditions. The results showed that the use of 3D faces improved the speed of facial expression recognition in both boys and girls. Moreover, 3D faces improved boys’ recognition accuracy for fearful expressions. Since fear is the most difficult facial expression for children to recognize, the facilitation effect of 3D faces has important practical implications for children with difficulties in facial expression recognition. The potential benefits of 3D representation for other expressions also have implications for developing more realistic assessments of children’s expression recognition. PMID:28368008

  2. Viewpoint Invariant Gesture Recognition and 3D Hand Pose Estimation Using RGB-D

    ERIC Educational Resources Information Center

    Doliotis, Paul

    2013-01-01

    The broad application domain of the work presented in this thesis is pattern classification with a focus on gesture recognition and 3D hand pose estimation. One of the main contributions of the proposed thesis is a novel method for 3D hand pose estimation using RGB-D. Hand pose estimation is formulated as a database retrieval problem. The proposed…

  3. Learning the 3-D structure of objects from 2-D views depends on shape, not format

    PubMed Central

    Tian, Moqian; Yamins, Daniel; Grill-Spector, Kalanit

    2016-01-01

    Humans can learn to recognize new objects just from observing example views. However, it is unknown what structural information enables this learning. To address this question, we manipulated the amount of structural information given to subjects during unsupervised learning by varying the format of the trained views. We then tested how format affected participants' ability to discriminate similar objects across views that were rotated 90° apart. We found that, after training, participants' performance increased and generalized to new views in the same format. Surprisingly, the improvement was similar across line drawings, shape from shading, and shape from shading + stereo even though the latter two formats provide richer depth information compared to line drawings. In contrast, participants' improvement was significantly lower when training used silhouettes, suggesting that silhouettes do not have enough information to generate a robust 3-D structure. To test whether the learned object representations were format-specific or format-invariant, we examined if learning novel objects from example views transfers across formats. We found that learning objects from example line drawings transferred to shape from shading and vice versa. These results have important implications for theories of object recognition because they suggest that (a) learning the 3-D structure of objects does not require rich structural cues during training as long as shape information of internal and external features is provided and (b) learning generates shape-based object representations independent of the training format. PMID:27153196

  4. 3D face recognition under expressions, occlusions, and pose variations.

    PubMed

    Drira, Hassen; Ben Amor, Boulbaba; Srivastava, Anuj; Daoudi, Mohamed; Slama, Rim

    2013-09-01

    We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. This representation, along with the elastic Riemannian metric, seems natural for measuring facial deformations and is robust to challenges such as large facial expressions (especially those with open mouths), large pose variations, missing parts, and partial occlusions due to glasses, hair, and so on. This framework is shown to be promising from both--empirical and theoretical--perspectives. In terms of the empirical evaluation, our results match or improve upon the state-of-the-art methods on three prominent databases: FRGCv2, GavabDB, and Bosphorus, each posing a different type of challenge. From a theoretical perspective, this framework allows for formal statistical inferences, such as the estimation of missing facial parts using PCA on tangent spaces and computing average shapes.

  5. 3D CARS image reconstruction and pattern recognition on SHG images

    NASA Astrophysics Data System (ADS)

    Medyukhina, Anna; Vogler, Nadine; Latka, Ines; Dietzek, Benjamin; Cicchi, Riccardo; Pavone, Francesco S.; Popp, Jürgen

    2012-06-01

    Nonlinear optical imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or second-harmonic generation (SHG) show great potential for in-vivo investigations of tissue. While the microspectroscopic imaging tools are established, automized data evaluation, i.e. image pattern recognition and automized image classification, of nonlinear optical images still bares great possibilities for future developments towards an objective clinical diagnosis. This contribution details the capability of nonlinear microscopy for both 3D visualization of human tissues and automated discrimination between healthy and diseased patterns using ex-vivo human skin samples. By means of CARS image alignment we show how to obtain a quasi-3D model of a skin biopsy, which allows us to trace the tissue structure in different projections. Furthermore, the potential of automated pattern and organization recognition to distinguish between healthy and keloidal skin tissue is discussed. A first classification algorithm employs the intrinsic geometrical features of collagen, which can be efficiently visualized by SHG microscopy. The shape of the collagen pattern allows conclusions about the physiological state of the skin, as the typical wavy collagen structure of healthy skin is disturbed e.g. in keloid formation. Based on the different collagen patterns a quantitative score characterizing the collagen waviness - and hence reflecting the physiological state of the tissue - is obtained. Further, two additional scoring methods for collagen organization, respectively based on a statistical analysis of the mutual organization of fibers and on FFT, are presented.

  6. Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

    PubMed

    Ming, Yue; Wang, Guangchao; Fan, Chunxiao

    2015-01-01

    With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

  7. Extension of RCC Topological Relations for 3d Complex Objects Components Extracted from 3d LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Xing, Xu-Feng; Abolfazl Mostafavia, Mir; Wang, Chen

    2016-06-01

    Topological relations are fundamental for qualitative description, querying and analysis of a 3D scene. Although topological relations for 2D objects have been extensively studied and implemented in GIS applications, their direct extension to 3D is very challenging and they cannot be directly applied to represent relations between components of complex 3D objects represented by 3D B-Rep models in R3. Herein we present an extended Region Connection Calculus (RCC) model to express and formalize topological relations between planar regions for creating 3D model represented by Boundary Representation model in R3. We proposed a new dimension extended 9-Intersection model to represent the basic relations among components of a complex object, including disjoint, meet and intersect. The last element in 3*3 matrix records the details of connection through the common parts of two regions and the intersecting line of two planes. Additionally, this model can deal with the case of planar regions with holes. Finally, the geometric information is transformed into a list of strings consisting of topological relations between two planar regions and detailed connection information. The experiments show that the proposed approach helps to identify topological relations of planar segments of point cloud automatically.

  8. 3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation

    PubMed Central

    Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

    2014-01-01

    Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm. PMID:24940876

  9. 3D face recognition based on multiple keypoint descriptors and sparse representation.

    PubMed

    Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei

    2014-01-01

    Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.

  10. Face recognition using 3D facial shape and color map information: comparison and combination

    NASA Astrophysics Data System (ADS)

    Godil, Afzal; Ressler, Sandy; Grother, Patrick

    2004-08-01

    In this paper, we investigate the use of 3D surface geometry for face recognition and compare it to one based on color map information. The 3D surface and color map data are from the CAESAR anthropometric database. We find that the recognition performance is not very different between 3D surface and color map information using a principal component analysis algorithm. We also discuss the different techniques for the combination of the 3D surface and color map information for multi-modal recognition by using different fusion approaches and show that there is significant improvement in results. The effectiveness of various techniques is compared and evaluated on a dataset with 200 subjects in two different positions.

  11. Real-time automated 3D sensing, detection, and recognition of dynamic biological micro-organic events

    NASA Astrophysics Data System (ADS)

    Javidi, Bahram; Yeom, Seokwon; Moon, Inkyu; Daneshpanah, Mehdi

    2006-05-01

    In this paper, we present an overview of three-dimensional (3D) optical imaging techniques for real-time automated sensing, visualization, and recognition of dynamic biological microorganisms. Real time sensing and 3D reconstruction of the dynamic biological microscopic objects can be performed by single-exposure on-line (SEOL) digital holographic microscopy. A coherent 3D microscope-based interferometer is constructed to record digital holograms of dynamic micro biological events. Complex amplitude 3D images of the biological microorganisms are computationally reconstructed at different depths by digital signal processing. Bayesian segmentation algorithms are applied to identify regions of interest for further processing. A number of pattern recognition approaches are addressed to identify and recognize the microorganisms. One uses 3D morphology of the microorganisms by analyzing 3D geometrical shapes which is composed of magnitude and phase. Segmentation, feature extraction, graph matching, feature selection, and training and decision rules are used to recognize the biological microorganisms. In a different approach, 3D technique is used that are tolerant to the varying shapes of the non-rigid biological microorganisms. After segmentation, a number of sampling patches are arbitrarily extracted from the complex amplitudes of the reconstructed 3D biological microorganism. These patches are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Also, we discuss the possibility of employing computational integral imaging for 3D sensing, visualization, and recognition of biological microorganisms illuminated under incoherent light. Experimental results with several biological microorganisms are presented to illustrate detection, segmentation, and identification of micro biological events.

  12. Proposal for the development of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

    SciTech Connect

    Deptuch, Gregory; Hoff, Jim; Kwan, Simon; Lipton, Ron; Liu, Ted; Ramberg, Erik; Todri, Aida; Yarema, Ray; Demarteua, Marcel,; Drake, Gary; Weerts, Harry; /Argonne /Chicago U. /Padua U. /INFN, Padua

    2010-10-01

    Future particle physics experiments looking for rare processes will have no choice but to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare process. The authors propose to develop a 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) chip for HEP applications, to advance the state-of-the-art for pattern recognition and track reconstruction for fast triggering.

  13. Holographic imaging of 3D objects on dichromated polymer systems

    NASA Astrophysics Data System (ADS)

    Lemelin, Guylain; Jourdain, Anne; Manivannan, Gurusamy; Lessard, Roger A.

    1996-01-01

    Conventional volume transmission holograms of a 3D scene were recorded on dichromated poly(acrylic acid) (DCPAA) films under 488 nm light. The holographic characterization and quality of reconstruction have been studied by varying the influencing parameters such as concentration of dichromate and electron donor, and the molecular weight of the polymer matrix. Ammonium and potassium dichromate have been employed to sensitize the poly(acrylic) matrix. the recorded hologram can be efficiently reconstructed either with red light or with low energy in the blue region without any post thermal or chemical processing.

  14. 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.

  15. Object-oriented urban 3D spatial data model organization method

    NASA Astrophysics Data System (ADS)

    Li, Jing-wen; Li, Wen-qing; Lv, Nan; Su, Tao

    2015-12-01

    This paper combined the 3d data model with object-oriented organization method, put forward the model of 3d data based on object-oriented method, implemented the city 3d model to quickly build logical semantic expression and model, solved the city 3d spatial information representation problem of the same location with multiple property and the same property with multiple locations, designed the space object structure of point, line, polygon, body for city of 3d spatial database, and provided a new thought and method for the city 3d GIS model and organization management.

  16. TDSIFT: a new descriptor for 2D and 3D ear recognition

    NASA Astrophysics Data System (ADS)

    Chen, Long; Mu, Zhichun; Nan, Bingfei; Zhang, Yi; Yang, Ruyin

    2017-02-01

    Descriptor is the key of any image-based recognition algorithm. For ear recognition, conventional descriptors are either based on 2D data or 3D data. 2D images provide rich texture information and human ear is a 3D surface that could offer shape information. It also inspires us that 2D data is more robust against occlusion while 3D data shows more robustness against illumination variation and pose variation. In this paper, we introduce a novel Texture and Depth Scale Invariant Feature Transform (TDSIFT) descriptor to encode 2D and 3D local features for ear recognition. Compared to the original Scale Invariant Feature Transform (SIFT) descriptor, the proposed TDSIFT shows its superiority by fusing 2D local information and 3D local information. Firstly, keypoints are detected and described on texture images. Then, 3D information of the keypoints located on the corresponding depth images is added to form the TDSIFT descriptor. Finally, a local feature based classification algorithm is adopted to identify ear samples by TDSIFT. Experimental results on a benchmark dataset demonstrate the feasibility and effectiveness of our proposed descriptor. The rank-1 recognition rate achieved on a gallery of 415 persons is 95.9% and the time involved in the computation is satisfactory compared to state-of-the-art methods.

  17. 3D passive photon counting automatic target recognition using advanced correlation filters.

    PubMed

    Cho, Myungjin; Mahalanobis, Abhijit; Javidi, Bahram

    2011-03-15

    In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

  18. 3-D Object Pose Determination Using Complex EGI

    DTIC Science & Technology

    1990-10-01

    IKEG1 ji = 0. . .. 12 4.1 Tesselated pentakis dodecahedron ..... ....................... 19 4.2 First composite object used for testing... dodecahedron (tesselated pentakis dodecahedron ) as shown in Fig. 4.1. The normal direction space is discretized into 240 cells as well. The CEGI weights are...deviation of the error distribution.) 18 Figure 4. 1: Tesselated pentakis dodecahedron Figure 4.2: First composite object used for testing 19 Figure

  19. Speckle size of light scattered from 3D rough objects.

    PubMed

    Zhang, Geng; Wu, Zhensen; Li, Yanhui

    2012-02-13

    From scalar Helmholtz integral relation and by coordinate system transformation, this paper begins with a derivation of the far-zone speckle field in the observation plane perpendicular to the scattering direction from an arbitrarily shaped conducting rough object illuminated by a plane wave illumination, followed by the spatial correlation function of the speckle intensity to obtain the speckle size from the objects. Especially, the specific expressions for the speckle sizes of light backscattered from spheres, cylinders and cones are obtained in detail showing that the speckle size along one direction in the observation plane is proportional to the incident wavelength and the distance between the object and the observation plane, and is inverse proportional to the maximal illuminated dimension of the object parallel to the direction. In addition, the shapes of the speckle of the rough objects with different shapes are different. The investigation on the speckle size in this paper will be useful for the statistical properties of speckle from complicated rough objects and the speckle imaging to target detection and identification.

  20. Interim results from a neural network 3-D automatic target recognition program

    NASA Astrophysics Data System (ADS)

    Thoet, William; Rainey, Timothy G.; Slutz, Lee A.; Weingard, Fred

    1992-09-01

    Recent results from the Artificial Neural VIsion Learning (ANVIL) program are presented. The focus of the ANVIL program is to apply neural network technologies to the air-to-surface 3D automatic target recognition (ATR) problem. The 3D Multiple Object Detection and Location System (MODALS) neural network was developed under the ANVIL program to simultaneously detect, locate, segment, and identify multiple targets. The performance results show a very high identification accuracy, a high detection rate, and low false alarm rate, even for areas with high clutter and shadowing. The results are shown as detection/false alarm curves and identification/false alarm curves. In addition, positional detection accuracy is shown for various scale sizes. To provide data for the program, visible terrain board imagery was collected under a variety of background and lighting conditions. Tests were made on over 500 targets of five types and two classes. These targets varied in scale by up to -25%, varied in azimuth by up to 120 degrees, and varied in elevation by up to 10 degrees. The performance results are shown for targets with resolution ranging from 9 to 700 pixels on target. This work is being performed under contract to Wright Laboratory AAAT-1.

  1. Multi-view indoor human behavior recognition based on 3D skeleton

    NASA Astrophysics Data System (ADS)

    Peng, Ling; Lu, Tongwei; Min, Feng

    2015-12-01

    For the problems caused by viewpoint changes in activity recognition, a multi-view interior human behavior recognition method based on 3D framework is presented. First, Microsoft's Kinect device is used to obtain body motion video in the positive perspective, the oblique angle and the side perspective. Second, it extracts bone joints and get global human features and the local features of arms and legs at the same time to form 3D skeletal features set. Third, online dictionary learning on feature set is used to reduce the dimension of feature. Finally, linear support vector machine (LSVM) is used to obtain the results of behavior recognition. The experimental results show that this method has better recognition rate.

  2. Recognizing 3-D Objects Using 2-D Images

    DTIC Science & Technology

    1993-05-01

    N00014-91-J-4038, Army contract number DACA76-85-C-0010, and under Office of Naval Research contract N00014-85-K-0124. 4 Contents 1 Introduction 9 1.1...Features ...... ..................... 89 3.3 Conclusions ......... ................................ 90 5 6 CONTENTS 4 Building a Practical Indexing...should be considered joint work between the author and David Clemens. CONTENTS T 8 Conclusions 251 8. 1 Ge eral Object Recogiiitioin

  3. On Integral Invariants for Effective 3-D Motion Trajectory Matching and Recognition.

    PubMed

    Shao, Zhanpeng; Li, Youfu

    2016-02-01

    Motion trajectories tracked from the motions of human, robots, and moving objects can provide an important clue for motion analysis, classification, and recognition. This paper defines some new integral invariants for a 3-D motion trajectory. Based on two typical kernel functions, we design two integral invariants, the distance and area integral invariants. The area integral invariants are estimated based on the blurred segment of noisy discrete curve to avoid the computation of high-order derivatives. Such integral invariants for a motion trajectory enjoy some desirable properties, such as computational locality, uniqueness of representation, and noise insensitivity. Moreover, our formulation allows the analysis of motion trajectories at a range of scales by varying the scale of kernel function. The features of motion trajectories can thus be perceived at multiscale levels in a coarse-to-fine manner. Finally, we define a distance function to measure the trajectory similarity to find similar trajectories. Through the experiments, we examine the robustness and effectiveness of the proposed integral invariants and find that they can capture the motion cues in trajectory matching and sign recognition satisfactorily.

  4. Recognition of complex human behaviours using 3D imaging for intelligent surveillance applications

    NASA Astrophysics Data System (ADS)

    Yao, Bo; Lepley, Jason J.; Peall, Robert; Butler, Michael; Hagras, Hani

    2016-10-01

    We introduce a system that exploits 3-D imaging technology as an enabler for the robust recognition of the human form. We combine this with pose and feature recognition capabilities from which we can recognise high-level human behaviours. We propose a hierarchical methodology for the recognition of complex human behaviours, based on the identification of a set of atomic behaviours, individual and sequential poses (e.g. standing, sitting, walking, drinking and eating) that provides a framework from which we adopt time-based machine learning techniques to recognise complex behaviour patterns.

  5. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    NASA Astrophysics Data System (ADS)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  6. Recurrent Processing during Object Recognition

    PubMed Central

    O’Reilly, Randall C.; Wyatte, Dean; Herd, Seth; Mingus, Brian; Jilk, David J.

    2013-01-01

    How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of naturally occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain’s visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time. PMID:23554596

  7. The impact of specular highlights on 3D-2D face recognition

    NASA Astrophysics Data System (ADS)

    Christlein, Vincent; Riess, Christian; Angelopoulou, Elli; Evangelopoulos, Georgios; Kakadiaris, Ioannis

    2013-05-01

    One of the most popular form of biometrics is face recognition. Face recognition techniques typically assume that a face exhibits Lambertian reectance. However, a face often exhibits prominent specularities, especially in outdoor environments. These specular highlights can compromise an identity authentication. In this work, we analyze the impact of such highlights on a 3D-2D face recognition system. First, we investigate three different specularity removal methods as preprocessing steps for face recognition. Then, we explicitly model facial specularities within the face detection system with the Cook-Torrance reflectance model. In our experiments, specularity removal increases the recognition rate on an outdoor face database by about 5% at a false alarm rate of 10-3. The integration of the Cook-Torrance model further improves these results, increasing the verification rate by 19% at a FAR of 10-3.

  8. Reducing Non-Uniqueness in Satellite Gravity Inversion using 3D Object Oriented Image Analysis Techniques

    NASA Astrophysics Data System (ADS)

    Fadel, I.; van der Meijde, M.; Kerle, N.

    2013-12-01

    Non-uniqueness of satellite gravity interpretation has been usually reduced by using a priori information from various sources, e.g. seismic tomography models. The reduction in non-uniqueness has been based on velocity-density conversion formulas or user interpretation for 3D subsurface structures (objects) in seismic tomography models. However, these processes introduce additional uncertainty through the conversion relations due to the dependency on the other physical parameters such as temperature and pressure, or through the bias in the interpretation due to user choices and experience. In this research, a new methodology is introduced to extract the 3D subsurface structures from 3D geophysical data using a state-of-art 3D Object Oriented Image Analysis (OOA) technique. 3D OOA is tested using a set of synthetic models that simulate the real situation in the study area of this research. Then, 3D OOA is used to extract 3D subsurface objects from a real 3D seismic tomography model. The extracted 3D objects are used to reconstruct a forward model and its response is compared with the measured satellite gravity. Finally, the result of the forward modelling, based on the extracted 3D objects, is used to constrain the inversion process of satellite gravity data. Through this work, a new object-based approach is introduced to interpret and extract the 3D subsurface objects from 3D geophysical data. This can be used to constrain modelling and inversion of potential field data using the extracted 3D subsurface structures from other methods. In summary, a new approach is introduced to constrain inversion of satellite gravity measurements and enhance interpretation capabilities.

  9. 2D Feature Recognition And 3d Reconstruction In Solar Euv Images

    NASA Astrophysics Data System (ADS)

    Aschwanden, Markus J.

    2005-05-01

    EUV images show the solar corona in a typical temperature range of T >rsim 1 MK, which encompasses the most common coronal structures: loops, filaments, and other magnetic structures in active regions, the quiet Sun, and coronal holes. Quantitative analysis increasingly demands automated 2D feature recognition and 3D reconstruction, in order to localize, track, and monitor the evolution of such coronal structures. We discuss numerical tools that “fingerprint” curvi-linear 1D features (e.g., loops and filaments). We discuss existing finger-printing algorithms, such as the brightness-gradient method, the oriented-connectivity method, stereoscopic methods, time-differencing, and space time feature recognition. We discuss improved 2D feature recognition and 3D reconstruction techniques that make use of additional a priori constraints, using guidance from magnetic field extrapolations, curvature radii constraints, and acceleration and velocity constraints in time-dependent image sequences. Applications of these algorithms aid the analysis of SOHO/EIT, TRACE, and STEREO/SECCHI data, such as disentangling, 3D reconstruction, and hydrodynamic modeling of coronal loops, postflare loops, filaments, prominences, and 3D reconstruction of the coronal magnetic field in general.

  10. Relations among Early Object Recognition Skills: Objects and Letters

    ERIC Educational Resources Information Center

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

    2015-01-01

    Human visual object recognition is multifaceted and comprised of several domains of expertise. Developmental relations between young children's letter recognition and their 3-dimensional object recognition abilities are implicated on several grounds but have received little research attention. Here, we ask how preschoolers' success in recognizing…

  11. Whole versus Part Presentations of the Interactive 3D Graphics Learning Objects

    ERIC Educational Resources Information Center

    Azmy, Nabil Gad; Ismaeel, Dina Ahmed

    2010-01-01

    The purpose of this study is to present an analysis of how the structure and design of the Interactive 3D Graphics Learning Objects can be effective and efficient in terms of Performance, Time on task, and Learning Efficiency. The study explored two treatments, namely whole versus Part Presentations of the Interactive 3D Graphics Learning Objects,…

  12. A new method of 3D scene recognition from still images

    NASA Astrophysics Data System (ADS)

    Zheng, Li-ming; Wang, Xing-song

    2014-04-01

    Most methods of monocular visual three dimensional (3D) scene recognition involve supervised machine learning. However, these methods often rely on prior knowledge. Specifically, they learn the image scene as part of a training dataset. For this reason, when the sampling equipment or scene is changed, monocular visual 3D scene recognition may fail. To cope with this problem, a new method of unsupervised learning for monocular visual 3D scene recognition is here proposed. First, the image is made using superpixel segmentation based on the CIELAB color space values L, a, and b and on the coordinate values x and y of pixels, forming a superpixel image with a specific density. Second, a spectral clustering algorithm based on the superpixels' color characteristics and neighboring relationships was used to reduce the dimensions of the superpixel image. Third, the fuzzy distribution density functions representing sky, ground, and façade are multiplied with the segment pixels, where the expectations of these segments are obtained. A preliminary classification of sky, ground, and façade is generated in this way. Fourth, the most accurate classification images of sky, ground, and façade were extracted through the tier-1 wavelet sampling and Manhattan direction feature. Finally, a depth perception map is generated based on the pinhole imaging model and the linear perspective information of ground surface. Here, 400 images of Make3D Image data from the Cornell University website were used to test the algorithm. The experimental results showed that this unsupervised learning method provides a more effective monocular visual 3D scene recognition model than other methods.

  13. 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

  14. 3D objects enlargement technique using an optical system and multiple SLMs for electronic holography.

    PubMed

    Yamamoto, Kenji; Ichihashi, Yasuyuki; Senoh, Takanori; Oi, Ryutaro; Kurita, Taiichiro

    2012-09-10

    One problem in electronic holography, which is caused by the display performance of spatial light modulators (SLM), is that the size of reconstructed 3D objects is small. Although methods for increasing the size using multiple SLMs have been considered, they typically had the problem that some parts of 3D objects were missing as a result of the gap between adjacent SLMs or 3D objects lost the vertical parallax. This paper proposes a method of resolving this problem by locating an optical system containing a lens array and other components in front of multiple SLMs. We used an optical system and 9 SLMs to construct a device equivalent to an SLM with approximately 74,600,000 pixels and used this to reconstruct 3D objects in both the horizontal and vertical parallax with an image size of 63 mm without losing any part of 3D objects.

  15. A method for 3D scene recognition using shadow information and a single fixed viewpoint

    NASA Astrophysics Data System (ADS)

    Bamber, David C.; Rogers, Jeremy D.; Page, Scott F.

    2012-05-01

    The ability to passively reconstruct a scene in 3D provides significant benefit to Situational Awareness systems employed in security and surveillance applications. Traditionally, passive 3D scene modelling techniques, such as Shape from Silhouette, require images from multiple sensor viewpoints, acquired either through the motion of a single sensor or from multiple sensors. As a result, the application of these techniques often attracts high costs, and presents numerous practical challenges. This paper presents a 3D scene reconstruction approach based on exploiting scene shadows, which only requires information from a single static sensor. This paper demonstrates that a large amount of 3D information about a scene can be interpreted from shadows; shadows reveal the shape of objects as viewed from a solar perspective and additional perspectives are gained as the sun arcs across the sky. The approach has been tested on synthetic and real data and is shown to be capable of reconstructing 3D scene objects where traditional 3D imaging methods fail. Providing the shadows within a scene are discernible, the proposed technique is able to reconstruct 3D objects that are camouflaged, obscured or even outside of the sensor's Field of View. The proposed approach can be applied in a range of applications, for example urban surveillance, checkpoint and border control, critical infrastructure protection and for identifying concealed or suspicious objects or persons which would normally be hidden from the sensor viewpoint.

  16. Image-based object recognition in man, monkey and machine.

    PubMed

    Tarr, M J; Bülthoff, H H

    1998-07-01

    Theories of visual object recognition must solve the problem of recognizing 3D objects given that perceivers only receive 2D patterns of light on their retinae. Recent findings from human psychophysics, neurophysiology and machine vision provide converging evidence for 'image-based' models in which objects are represented as collections of viewpoint-specific local features. This approach is contrasted with 'structural-description' models in which objects are represented as configurations of 3D volumes or parts. We then review recent behavioral results that address the biological plausibility of both approaches, a well as some of their computational advantages and limitations. We conclude that, although the image-based approach holds great promise, it has potential pitfalls that may be best overcome by including structural information. Thus, the most viable model of object recognition may be one that incorporates the most appealing aspects of both image-based and structural description theories.

  17. 3D Imaging for hand gesture recognition: Exploring the software-hardware interaction of current technologies

    NASA Astrophysics Data System (ADS)

    Periverzov, Frol; Ilieş, Horea T.

    2012-09-01

    Interaction with 3D information is one of the fundamental and most familiar tasks in virtually all areas of engineering and science. Several recent technological advances pave the way for developing hand gesture recognition capabilities available to all, which will lead to more intuitive and efficient 3D user interfaces (3DUI). These developments can unlock new levels of expression and productivity in all activities concerned with the creation and manipulation of virtual 3D shapes and, specifically, in engineering design. Building fully automated systems for tracking and interpreting hand gestures requires robust and efficient 3D imaging techniques as well as potent shape classifiers. We survey and explore current and emerging 3D imaging technologies, and focus, in particular, on those that can be used to build interfaces between the users' hands and the machine. The purpose of this paper is to categorize and highlight the relevant differences between these existing 3D imaging approaches in terms of the nature of the information provided, output data format, as well as the specific conditions under which these approaches yield reliable data. Furthermore we explore the impact of each of these approaches on the computational cost and reliability of the required image processing algorithms. Finally we highlight the main challenges and opportunities in developing natural user interfaces based on hand gestures, and conclude with some promising directions for future research. [Figure not available: see fulltext.

  18. 3D face recognition based on the hierarchical score-level fusion classifiers

    NASA Astrophysics Data System (ADS)

    Mráček, Štěpán.; Váša, Jan; Lankašová, Karolína; Drahanský, Martin; Doležel, Michal

    2014-05-01

    This paper describes the 3D face recognition algorithm that is based on the hierarchical score-level fusion clas-sifiers. In a simple (unimodal) biometric pipeline, the feature vector is extracted from the input data and subsequently compared with the template stored in the database. In our approachm, we utilize several feature extraction algorithms. We use 6 different image representations of the input 3D face data. Moreover, we are using Gabor and Gauss-Laguerre filter banks applied on the input image data that yield to 12 resulting feature vectors. Each representation is compared with corresponding counterpart from the biometric database. We also add the recognition based on the iso-geodesic curves. The final score-level fusion is performed on 13 comparison scores using the Support Vector Machine (SVM) classifier.

  19. 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.

  20. Generalized Hough transform based time invariant action recognition with 3D pose information

    NASA Astrophysics Data System (ADS)

    Muench, David; Huebner, Wolfgang; Arens, Michael

    2014-10-01

    Human action recognition has emerged as an important field in the computer vision community due to its large number of applications such as automatic video surveillance, content based video-search and human robot interaction. In order to cope with the challenges that this large variety of applications present, recent research has focused more on developing classifiers able to detect several actions in more natural and unconstrained video sequences. The invariance discrimination tradeoff in action recognition has been addressed by utilizing a Generalized Hough Transform. As a basis for action representation we transform 3D poses into a robust feature space, referred to as pose descriptors. For each action class a one-dimensional temporal voting space is constructed. Votes are generated from associating pose descriptors with their position in time relative to the end of an action sequence. Training data consists of manually segmented action sequences. In the detection phase valid human 3D poses are assumed as input, e.g. originating from 3D sensors or monocular pose reconstruction methods. The human 3D poses are normalized to gain view-independence and transformed into (i) relative limb-angle space to ensure independence of non-adjacent joints or (ii) geometric features. In (i) an action descriptor consists of the relative angles between limbs and their temporal derivatives. In (ii) the action descriptor consists of different geometric features. In order to circumvent the problem of time-warping we propose to use a codebook of prototypical 3D poses which is generated from sample sequences of 3D motion capture data. This idea is in accordance with the concept of equivalence classes in action space. Results of the codebook method are presented using the Kinect sensor and the CMU Motion Capture Database.

  1. Visual Short-Term Memory Benefit for Objects on Different 3-D Surfaces

    ERIC Educational Resources Information Center

    Xu, Yaoda; Nakayama, Ken

    2007-01-01

    Visual short-term memory (VSTM) plays an important role in visual cognition. Although objects are located on different 3-dimensional (3-D) surfaces in the real world, how VSTM capacity may be influenced by the presence of multiple 3-D surfaces has never been examined. By manipulating binocular disparities of visual displays, the authors found that…

  2. Simultaneous perimeter measurement for 3D object with a binocular stereo vision measurement system

    NASA Astrophysics Data System (ADS)

    Peng, Zhao; Guo-Qiang, Ni

    2010-04-01

    A simultaneous measurement scheme for multiple three-dimensional (3D) objects' surface boundary perimeters is proposed. This scheme consists of three steps. First, a binocular stereo vision measurement system with two CCD cameras is devised to obtain the two images of the detected objects' 3D surface boundaries. Second, two geodesic active contours are applied to converge to the objects' contour edges simultaneously in the two CCD images to perform the stereo matching. Finally, the multiple spatial contours are reconstructed using the cubic B-spline curve interpolation. The true contour length of every spatial contour is computed as the true boundary perimeter of every 3D object. An experiment on the bent surface's perimeter measurement for the four 3D objects indicates that this scheme's measurement repetition error decreases to 0.7 mm.

  3. Electro-holography display using computer generated hologram of 3D objects based on projection spectra

    NASA Astrophysics Data System (ADS)

    Huang, Sujuan; Wang, Duocheng; He, Chao

    2012-11-01

    A new method of synthesizing computer-generated hologram of three-dimensional (3D) objects is proposed from their projection images. A series of projection images of 3D objects are recorded with one-dimensional azimuth scanning. According to the principles of paraboloid of revolution in 3D Fourier space and 3D central slice theorem, spectra information of 3D objects can be gathered from their projection images. Considering quantization error of horizontal and vertical directions, the spectrum information from each projection image is efficiently extracted in double circle and four circles shape, to enhance the utilization of projection spectra. Then spectra information of 3D objects from all projection images is encoded into computer-generated hologram based on Fourier transform using conjugate-symmetric extension. The hologram includes 3D information of objects. Experimental results for numerical reconstruction of the CGH at different distance validate the proposed methods and show its good performance. Electro-holographic reconstruction can be realized by using an electronic addressing reflective liquid-crystal display (LCD) spatial light modulator. The CGH from the computer is loaded onto the LCD. By illuminating a reference light from a laser source to the LCD, the amplitude and phase information included in the CGH will be reconstructed due to the diffraction of the light modulated by the LCD.

  4. Efficient view based 3-D object retrieval using Hidden Markov Model

    NASA Astrophysics Data System (ADS)

    Jain, Yogendra Kumar; Singh, Roshan Kumar

    2013-12-01

    Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

  5. Synthesis and display of dynamic holographic 3D scenes with real-world objects.

    PubMed

    Paturzo, Melania; Memmolo, Pasquale; Finizio, Andrea; Näsänen, Risto; Naughton, Thomas J; Ferraro, Pietro

    2010-04-26

    A 3D scene is synthesized combining multiple optically recorded digital holograms of different objects. The novel idea consists of compositing moving 3D objects in a dynamic 3D scene using a process that is analogous to stop-motion video. However in this case the movie has the exciting attribute that it can be displayed and observed in 3D. We show that 3D dynamic scenes can be projected as an alternative to complicated and heavy computations needed to generate realistic-looking computer generated holograms. The key tool for creating the dynamic action is based on a new concept that consists of a spatial, adaptive transformation of digital holograms of real-world objects allowing full control in the manipulation of the object's position and size in a 3D volume with very high depth-of-focus. A pilot experiment to evaluate how viewers perceive depth in a conventional single-view display of the dynamic 3D scene has been performed.

  6. The 3D scanner prototype utilize object profile imaging using line laser and octave software

    NASA Astrophysics Data System (ADS)

    Nurdini, Mugi; Manunggal, Trikarsa Tirtadwipa; Samsi, Agus

    2016-11-01

    Three-dimensional scanner or 3D Scanner is a device to reconstruct the real object into digital form on a computer. 3D Scanner is a technology that is being developed, especially in developed countries, where the current 3D Scanner devices is the advanced version with a very expensive prices. This study is basically a simple prototype of 3D Scanner with a very low investment costs. 3D Scanner prototype device consists of a webcam, a rotating desk system controlled by a stepper motor and Arduino UNO, and a line laser. Objects that limit the research is the object with same radius from its center point (object pivot). Scanning is performed by using object profile imaging by line laser which is then captured by the camera and processed by a computer (image processing) using Octave software. On each image acquisition, the scanned object on a rotating desk rotated by a certain degree, so for one full turn multiple images of a number of existing side are finally obtained. Then, the profile of the entire images is extracted in order to obtain digital object dimension. Digital dimension is calibrated by length standard, called gage block. Overall dimensions are then digitally reconstructed into a three-dimensional object. Validation of the scanned object reconstruction of the original object dimensions expressed as a percentage error. Based on the results of data validation, horizontal dimension error is about 5% to 23% and vertical dimension error is about +/- 3%.

  7. Electrophysiological evidence of separate pathways for the perception of depth and 3D objects.

    PubMed

    Gao, Feng; Cao, Bihua; Cao, Yunfei; Li, Fuhong; Li, Hong

    2015-05-01

    Previous studies have investigated the neural mechanism of 3D perception, but the neural distinction between 3D-objects and depth processing remains unclear. In the present study, participants viewed three types of graphics (planar graphics, perspective drawings, and 3D objects) while event-related potentials (ERP) were recorded. The ERP results revealed the following: (1) 3D objects elicited a larger and delayed N1 component than the other two types of stimuli; (2) during the P2 time window, significant differences between 3D objects and the perspective drawings were found mainly over a group of electrode sites in the left lateral occipital region; and (3) during the N2 complex, differences between planar graphics and perspective drawings were found over a group of electrode sites in the right hemisphere, whereas differences between perspective drawings and 3D objects were observed at another group of electrode sites in the left hemisphere. These findings support the claim that depth processing and object identification might be processed by separate pathways and at different latencies.

  8. Occluded human recognition for a leader following system using 3D range and image data in forest environment

    NASA Astrophysics Data System (ADS)

    Cho, Kuk; Ilyas, Muhammad; Baeg, Seung-Ho; Park, Sangdeok

    2014-06-01

    This paper describes the occluded target recognition and tracking method for a leader-following system by fusing 3D range and image data acquired from 3D light detection and ranging (LIDAR) and a color camera installed on an autonomous vehicle in forest environment. During 3D data processing, the distance-based clustering method has an instinctive problem in close encounters. In the tracking phase, we divide an object tracking process into three phases based on occlusion scenario; before an occlusion (BO) phase, a partially or fully occlusion phase and after an occlusion (AO) phase. To improve the data association performance, we use camera's rich information to find correspondence among objects during above mentioned three phases of occlusion. In this paper, we solve a correspondence problem using the color features of human objects with the sum of squared distance (SSD) and the normalized cross correlation (NCC). The features are integrated with derived windows from Harris corner. The experimental results for a leader following on an autonomous vehicle are shown with LIDAR and camera for improving a data association problem in a multiple object tracking system.

  9. Depth representation of moving 3-D objects in apparent-motion path.

    PubMed

    Hidaka, Souta; Kawachi, Yousuke; Gyoba, Jiro

    2008-01-01

    Apparent motion is perceived when two objects are presented alternately at different positions. The internal representations of apparently moving objects are formed in an apparent-motion path which lacks physical inputs. We investigated the depth information contained in the representation of 3-D moving objects in an apparent-motion path. We examined how probe objects-briefly placed in the motion path-affected the perceived smoothness of apparent motion. The probe objects comprised 3-D objects which were defined by being shaded or by disparity (convex/concave) or 2-D (flat) objects, while the moving objects were convex/concave objects. We found that flat probe objects induced a significantly smoother motion perception than concave probe objects only in the case of the convex moving objects. However, convex probe objects did not lead to smoother motion as the flat objects did, although the convex probe objects contained the same depth information for the moving objects. Moreover, the difference between probe objects was reduced when the moving objects were concave. These counterintuitive results were consistent in conditions when both depth cues were used. The results suggest that internal representations contain incomplete depth information that is intermediate between that of 2-D and 3-D objects.

  10. 3D facial expression recognition using maximum relevance minimum redundancy geometrical features

    NASA Astrophysics Data System (ADS)

    Rabiu, Habibu; Saripan, M. Iqbal; Mashohor, Syamsiah; Marhaban, Mohd Hamiruce

    2012-12-01

    In recent years, facial expression recognition (FER) has become an attractive research area, which besides the fundamental challenges, it poses, finds application in areas, such as human-computer interaction, clinical psychology, lie detection, pain assessment, and neurology. Generally the approaches to FER consist of three main steps: face detection, feature extraction and expression recognition. The recognition accuracy of FER hinges immensely on the relevance of the selected features in representing the target expressions. In this article, we present a person and gender independent 3D facial expression recognition method, using maximum relevance minimum redundancy geometrical features. The aim is to detect a compact set of features that sufficiently represents the most discriminative features between the target classes. Multi-class one-against-one SVM classifier was employed to recognize the seven facial expressions; neutral, happy, sad, angry, fear, disgust, and surprise. The average recognition accuracy of 92.2% was recorded. Furthermore, inter database homogeneity was investigated between two independent databases the BU-3DFE and UPM-3DFE the results showed a strong homogeneity between the two databases.

  11. Breaking Object Correspondence Across Saccadic Eye Movements Deteriorates Object Recognition.

    PubMed

    Poth, Christian H; Herwig, Arvid; Schneider, Werner X

    2015-01-01

    Visual perception is based on information processing during periods of eye fixations that are interrupted by fast saccadic eye movements. The ability to sample and relate information on task-relevant objects across fixations implies that correspondence between presaccadic and postsaccadic objects is established. Postsaccadic object information usually updates and overwrites information on the corresponding presaccadic object. The presaccadic object representation is then lost. In contrast, the presaccadic object is conserved when object correspondence is broken. This helps transsaccadic memory but it may impose attentional costs on object recognition. Therefore, we investigated how breaking object correspondence across the saccade affects postsaccadic object recognition. In Experiment 1, object correspondence was broken by a brief postsaccadic blank screen. Observers made a saccade to a peripheral object which was displaced during the saccade. This object reappeared either immediately after the saccade or after the blank screen. Within the postsaccadic object, a letter was briefly presented (terminated by a mask). Observers reported displacement direction and letter identity in different blocks. Breaking object correspondence by blanking improved displacement identification but deteriorated postsaccadic letter recognition. In Experiment 2, object correspondence was broken by changing the object's contrast-polarity. There were no object displacements and observers only reported letter identity. Again, breaking object correspondence deteriorated postsaccadic letter recognition. These findings identify transsaccadic object correspondence as a key determinant of object recognition across the saccade. This is in line with the recent hypothesis that breaking object correspondence results in separate representations of presaccadic and postsaccadic objects which then compete for limited attentional processing resources (Schneider, 2013). Postsaccadic object recognition is

  12. Plane-based optimization for 3D object reconstruction from single line drawings.

    PubMed

    Liu, Jianzhuang; Cao, Liangliang; Li, Zhenguo; Tang, Xiaoou

    2008-02-01

    In previous optimization-based methods of 3D planar-faced object reconstruction from single 2D line drawings, the missing depths of the vertices of a line drawing (and other parameters in some methods) are used as the variables of the objective functions. A 3D object with planar faces is derived by finding values for these variables that minimize the objective functions. These methods work well for simple objects with a small number N of variables. As N grows, however, it is very difficult for them to find expected objects. This is because with the nonlinear objective functions in a space of large dimension N, the search for optimal solutions can easily get trapped into local minima. In this paper, we use the parameters of the planes that pass through the planar faces of an object as the variables of the objective function. This leads to a set of linear constraints on the planes of the object, resulting in a much lower dimensional nullspace where optimization is easier to achieve. We prove that the dimension of this nullspace is exactly equal to the minimum number of vertex depths which define the 3D object. Since a practical line drawing is usually not an exact projection of a 3D object, we expand the nullspace to a larger space based on the singular value decomposition of the projection matrix of the line drawing. In this space, robust 3D reconstruction can be achieved. Compared with two most related methods, our method not only can reconstruct more complex 3D objects from 2D line drawings, but also is computationally more efficient.

  13. Visual object recognition and tracking

    NASA Technical Reports Server (NTRS)

    Chang, Chu-Yin (Inventor); English, James D. (Inventor); Tardella, Neil M. (Inventor)

    2010-01-01

    This invention describes a method for identifying and tracking an object from two-dimensional data pictorially representing said object by an object-tracking system through processing said two-dimensional data using at least one tracker-identifier belonging to the object-tracking system for providing an output signal containing: a) a type of the object, and/or b) a position or an orientation of the object in three-dimensions, and/or c) an articulation or a shape change of said object in said three dimensions.

  14. Recognition memory impairments caused by false recognition of novel objects.

    PubMed

    Yeung, Lok-Kin; Ryan, Jennifer D; Cowell, Rosemary A; Barense, Morgan D

    2013-11-01

    A fundamental assumption underlying most current theories of amnesia is that memory impairments arise because previously studied information either is lost rapidly or is made inaccessible (i.e., the old information appears to be new). Recent studies in rodents have challenged this view, suggesting instead that under conditions of high interference, recognition memory impairments following medial temporal lobe damage arise because novel information appears as though it has been previously seen. Here, we developed a new object recognition memory paradigm that distinguished whether object recognition memory impairments were driven by previously viewed objects being treated as if they were novel or by novel objects falsely recognized as though they were previously seen. In this indirect, eyetracking-based passive viewing task, older adults at risk for mild cognitive impairment showed false recognition to high-interference novel items (with a significant degree of feature overlap with previously studied items) but normal novelty responses to low-interference novel items (with a lower degree of feature overlap). The indirect nature of the task minimized the effects of response bias and other memory-based decision processes, suggesting that these factors cannot solely account for false recognition. These findings support the counterintuitive notion that recognition memory impairments in this memory-impaired population are not characterized by forgetting but rather are driven by the failure to differentiate perceptually similar objects, leading to the false recognition of novel objects as having been seen before.

  15. Template protection and its implementation in 3D face recognition systems

    NASA Astrophysics Data System (ADS)

    Zhou, Xuebing

    2007-04-01

    As biometric recognition systems are widely applied in various application areas, security and privacy risks have recently attracted the attention of the biometric community. Template protection techniques prevent stored reference data from revealing private biometric information and enhance the security of biometrics systems against attacks such as identity theft and cross matching. This paper concentrates on a template protection algorithm that merges methods from cryptography, error correction coding and biometrics. The key component of the algorithm is to convert biometric templates into binary vectors. It is shown that the binary vectors should be robust, uniformly distributed, statistically independent and collision-free so that authentication performance can be optimized and information leakage can be avoided. Depending on statistical character of the biometric template, different approaches for transforming biometric templates into compact binary vectors are presented. The proposed methods are integrated into a 3D face recognition system and tested on the 3D facial images of the FRGC database. It is shown that the resulting binary vectors provide an authentication performance that is similar to the original 3D face templates. A high security level is achieved with reasonable false acceptance and false rejection rates of the system, based on an efficient statistical analysis. The algorithm estimates the statistical character of biometric templates from a number of biometric samples in the enrollment database. For the FRGC 3D face database, the small distinction of robustness and discriminative power between the classification results under the assumption of uniquely distributed templates and the ones under the assumption of Gaussian distributed templates is shown in our tests.

  16. Programming self assembly by designing the 3D shape of floating objects

    NASA Astrophysics Data System (ADS)

    Poty, Martin; Lagubeau, Guillaume; Lumay, Geoffroy; Vandewalle, Nicolas

    2014-11-01

    Self-assembly of floating particles driven by capillary forces at some liquid-air interface leads to the formation of two-dimensionnal structures. Using a 3d printer, milimeter scale objets are produced. Their 3d shape is chosen in order to create capillary multipoles. The capillary interactions between these components can be either attractive or repulsive depending on the interface local deformations along the liquid-air interface. In order to understand how the shape of an object deforms the interface, we developed an original profilometry method. The measurements show that specific structures can be programmed by selecting the 3d branched shapes.

  17. Computing 3-D structure of rigid objects using stereo and motion

    NASA Technical Reports Server (NTRS)

    Nguyen, Thinh V.

    1987-01-01

    Work performed as a step toward an intelligent automatic machine vision system for 3-D imaging is discussed. The problem considered is the quantitative 3-D reconstruction of rigid objects. Motion and stereo are the two clues considered in this system. The system basically consists of three processes: the low level process to extract image features, the middle level process to establish the correspondence in the stereo (spatial) and motion (temporal) modalities, and the high level process to compute the 3-D coordinates of the corner points by integrating the spatial and temporal correspondences.

  18. Optimal Local Searching for Fast and Robust Textureless 3D Object Tracking in Highly Cluttered Backgrounds.

    PubMed

    Seo, Byung-Kuk; Park, Jong-Il; Hinterstoisser, Stefan; Ilic, Slobodan

    2013-06-13

    Edge-based tracking is a fast and plausible approach for textureless 3D object tracking, but its robustness is still very challenging in highly cluttered backgrounds due to numerous local minima. To overcome this problem, we propose a novel method for fast and robust textureless 3D object tracking in highly cluttered backgrounds. The proposed method is based on optimal local searching of 3D-2D correspondences between a known 3D object model and 2D scene edges in an image with heavy background clutter. In our searching scheme, searching regions are partitioned into three levels (interior, contour, and exterior) with respect to the previous object region, and confident searching directions are determined by evaluating candidates of correspondences on their region levels; thus, the correspondences are searched among likely candidates in only the confident directions instead of searching through all candidates. To ensure the confident searching direction, we also adopt the region appearance, which is efficiently modeled on a newly defined local space (called a searching bundle). Experimental results and performance evaluations demonstrate that our method fully supports fast and robust textureless 3D object tracking even in highly cluttered backgrounds.

  19. Optimal local searching for fast and robust textureless 3D object tracking in highly cluttered backgrounds.

    PubMed

    Seo, Byung-Kuk; Park, Hanhoon; Park, Jong-Il; Hinterstoisser, Stefan; Ilic, Slobodan

    2014-01-01

    Edge-based tracking is a fast and plausible approach for textureless 3D object tracking, but its robustness is still very challenging in highly cluttered backgrounds due to numerous local minima. To overcome this problem, we propose a novel method for fast and robust textureless 3D object tracking in highly cluttered backgrounds. The proposed method is based on optimal local searching of 3D-2D correspondences between a known 3D object model and 2D scene edges in an image with heavy background clutter. In our searching scheme, searching regions are partitioned into three levels (interior, contour, and exterior) with respect to the previous object region, and confident searching directions are determined by evaluating candidates of correspondences on their region levels; thus, the correspondences are searched among likely candidates in only the confident directions instead of searching through all candidates. To ensure the confident searching direction, we also adopt the region appearance, which is efficiently modeled on a newly defined local space (called a searching bundle). Experimental results and performance evaluations demonstrate that our method fully supports fast and robust textureless 3D object tracking even in highly cluttered backgrounds.

  20. 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…

  1. BDNF controls object recognition memory reconsolidation.

    PubMed

    Radiske, Andressa; Rossato, Janine I; Gonzalez, Maria Carolina; Köhler, Cristiano A; Bevilaqua, Lia R; Cammarota, Martín

    2017-03-06

    Reconsolidation restabilizes memory after reactivation. Previously, we reported that the hippocampus is engaged in object recognition memory reconsolidation to allow incorporation of new information into the original engram. Here we show that BDNF is sufficient for this process, and that blockade of BDNF function in dorsal CA1 impairs updating of the reactivated recognition memory trace.

  2. The Role of Object Recognition in Young Infants' Object Segregation.

    ERIC Educational Resources Information Center

    Carey, Susan; Williams, Travis

    2001-01-01

    Discusses Needham's findings by asserting that they extend understanding of infant perception by showing that the memory representations infants draw upon have bound together information about shape, color, and pattern. Considers the distinction between two senses of "recognition" and asks in which sense object recognition contributes to object…

  3. Shaping functional nano-objects by 3D confined supramolecular assembly.

    PubMed

    Deng, Renhua; Liang, Fuxin; Li, Weikun; Liu, Shanqin; Liang, Ruijing; Cai, Mingle; Yang, Zhenzhong; Zhu, Jintao

    2013-12-20

    Nano-objects are generated through 3D confined supramolecular assembly, followed by a sequential disintegration by rupturing the hydrogen bonding. The shape of the nano-objects is tunable, ranging from nano-disc, nano-cup, to nano-toroid. The nano-objects are pH-responsive. Functional materials for example inorganic or metal nanoparticles are easily complexed onto the external surface, to extend both composition and microstructure of the nano-objects.

  4. A prescreener for 3D face recognition using radial symmerty and the Hausdorff fraction.

    SciTech Connect

    Koudelka, Melissa L.; Koch, Mark William; Russ, Trina Denise

    2005-04-01

    Face recognition systems require the ability to efficiently scan an existing database of faces to locate a match for a newly acquired face. The large number of faces in real world databases makes computationally intensive algorithms impractical for scanning entire databases. We propose the use of more efficient algorithms to 'prescreen' face databases, determining a limited set of likely matches that can be processed further to identify a match. We use both radial symmetry and shape to extract five features of interest on 3D range images of faces. These facial features determine a very small subset of discriminating points which serve as input to a prescreening algorithm based on a Hausdorff fraction. We show how to compute the Haudorff fraction in linear O(n) time using a range image representation. Our feature extraction and prescreening algorithms are verified using the FRGC v1.0 3D face scan data. Results show 97% of the extracted facial features are within 10 mm or less of manually marked ground truth, and the prescreener has a rank 6 recognition rate of 100%.

  5. Rule-Based Orientation Recognition Of A Moving Object

    NASA Astrophysics Data System (ADS)

    Gove, Robert J.

    1989-03-01

    This paper presents a detailed description and a comparative analysis of the algorithms used to determine the position and orientation of an object in real-time. The exemplary object, a freely moving gold-fish in an aquarium, provides "real-world" motion, with definable characteristics of motion (the fish never swims upside-down) and the complexities of a non-rigid body. For simplicity of implementation, and since a restricted and stationary viewing domain exists (fish-tank), we reduced the problem of obtaining 3D correspondence information to trivial alignment calculations by using two cameras orthogonally viewing the object. We applied symbolic processing techniques to recognize the 3-D orientation of a moving object of known identity in real-time. Assuming motion, each new frame (sensed by the two cameras) provides images of the object's profile which has most likely undergone translation, rotation, scaling and/or bending of the non-rigid object since the previous frame. We developed an expert system which uses heuristics of the object's motion behavior in the form of rules and information obtained via low-level image processing (like numerical inertial axis calculations) to dynamically estimate the object's orientation. An inference engine provides these estimates at frame rates of up to 10 per second (which is essentially real-time). The advantages of the rule-based approach to orientation recognition will be compared other pattern recognition techniques. Our results of an investigation of statistical pattern recognition, neural networks, and procedural techniques for orientation recognition will be included. We implemented the algorithms in a rapid-prototyping environment, the TI-Ezplorer, equipped with an Odyssey and custom imaging hardware. A brief overview of the workstation is included to clarify one motivation for our choice of algorithms. These algorithms exploit two facets of the prototype image processing and understanding workstation - both low

  6. Lossy to lossless object-based coding of 3-D MRI data.

    PubMed

    Menegaz, Gloria; Thiran, Jean-Philippe

    2002-01-01

    We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature.

  7. 3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes.

    PubMed

    Zhang, Xuesong; Zhuang, Yan; Hu, Huosheng; Wang, Wei

    2017-01-01

    This paper investigates the problem of multiclass and multiview 3-D object detection for service robots operating in a cluttered indoor environment. A novel 3-D object detection system using laser point clouds is proposed to deal with cluttered indoor scenes with a fewer and imbalanced training data. Raw 3-D point clouds are first transformed to 2-D bearing angle images to reduce the computational cost, and then jointly trained multiple object detectors are deployed to perform the multiclass and multiview 3-D object detection. The reclassification technique is utilized on each detected low confidence bounding box in the system to reduce false alarms in the detection. The RUS-SMOTEboost algorithm is used to train a group of independent binary classifiers with imbalanced training data. Dense histograms of oriented gradients and local binary pattern features are combined as a feature set for the reclassification task. Based on the dalian university of technology (DUT)-3-D data set taken from various office and household environments, experimental results show the validity and good performance of the proposed method.

  8. High-purity 3D nano-objects grown by focused-electron-beam induced deposition

    NASA Astrophysics Data System (ADS)

    Córdoba, Rosa; Sharma, Nidhi; Kölling, Sebastian; Koenraad, Paul M.; Koopmans, Bert

    2016-09-01

    To increase the efficiency of current electronics, a specific challenge for the next generation of memory, sensing and logic devices is to find suitable strategies to move from two- to three-dimensional (3D) architectures. However, the creation of real 3D nano-objects is not trivial. Emerging non-conventional nanofabrication tools are required for this purpose. One attractive method is focused-electron-beam induced deposition (FEBID), a direct-write process of 3D nano-objects. Here, we grow 3D iron and cobalt nanopillars by FEBID using diiron nonacarbonyl Fe2(CO)9, and dicobalt octacarbonyl Co2(CO)8, respectively, as starting materials. In addition, we systematically study the composition of these nanopillars at the sub-nanometer scale by atom probe tomography, explicitly mapping the homogeneity of the radial and longitudinal composition distributions. We show a way of fabricating high-purity 3D vertical nanostructures of ˜50 nm in diameter and a few micrometers in length. Our results suggest that the purity of such 3D nanoelements (above 90 at% Fe and above 95 at% Co) is directly linked to their growth regime, in which the selected deposition conditions are crucial for the final quality of the nanostructure. Moreover, we demonstrate that FEBID and the proposed characterization technique not only allow for growth and chemical analysis of single-element structures, but also offers a new way to directly study 3D core-shell architectures. This straightforward concept could establish a promising route to the design of 3D elements for future nano-electronic devices.

  9. Learning 3D Object Templates by Quantizing Geometry and Appearance Spaces.

    PubMed

    Hu, Wenze; Zhu, Song-Chun

    2015-06-01

    While 3D object-centered shape-based models are appealing in comparison with 2D viewer-centered appearance-based models for their lower model complexities and potentially better view generalizabilities, the learning and inference of 3D models has been much less studied in the recent literature due to two factors: i) the enormous complexities of 3D shapes in geometric space; and ii) the gap between 3D shapes and their appearances in images. This paper aims at tackling the two problems by studying an And-Or Tree (AoT) representation that consists of two parts: i) a geometry-AoT quantizing the geometry space, i.e. the possible compositions of 3D volumetric parts and 2D surfaces within the volumes; and ii) an appearance-AoT quantizing the appearance space, i.e. the appearance variations of those shapes in different views. In this AoT, an And-node decomposes an entity into constituent parts, and an Or-node represents alternative ways of decompositions. Thus it can express a combinatorial number of geometry and appearance configurations through small dictionaries of 3D shape primitives and 2D image primitives. In the quantized space, the problem of learning a 3D object template is transformed to a structure search problem which can be efficiently solved in a dynamic programming algorithm by maximizing the information gain. We focus on learning 3D car templates from the AoT and collect a new car dataset featuring more diverse views. The learned car templates integrate both the shape-based model and the appearance-based model to combine the benefits of both. In experiments, we show three aspects: 1) the AoT is more efficient than the frequently used octree method in space representation; 2) the learned 3D car template matches the state-of-the art performances on car detection and pose estimation in a public multi-view car dataset; and 3) in our new dataset, the learned 3D template solves the joint task of simultaneous object detection, pose/view estimation, and part

  10. 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.

  11. 3D-Web-GIS RFID location sensing system for construction objects.

    PubMed

    Ko, Chien-Ho

    2013-01-01

    Construction site managers could benefit from being able to visualize on-site construction objects. Radio frequency identification (RFID) technology has been shown to improve the efficiency of construction object management. The objective of this study is to develop a 3D-Web-GIS RFID location sensing system for construction objects. An RFID 3D location sensing algorithm combining Simulated Annealing (SA) and a gradient descent method is proposed to determine target object location. In the algorithm, SA is used to stabilize the search process and the gradient descent method is used to reduce errors. The locations of the analyzed objects are visualized using the 3D-Web-GIS system. A real construction site is used to validate the applicability of the proposed method, with results indicating that the proposed approach can provide faster, more accurate, and more stable 3D positioning results than other location sensing algorithms. The proposed system allows construction managers to better understand worksite status, thus enhancing managerial efficiency.

  12. 3D shape shearography with integrated structured light projection for strain inspection of curved objects

    NASA Astrophysics Data System (ADS)

    Anisimov, Andrei G.; Groves, Roger M.

    2015-05-01

    Shearography (speckle pattern shearing interferometry) is a non-destructive testing technique that provides full-field surface strain characterization. Although real-life objects especially in aerospace, transport or cultural heritage are not flat (e.g. aircraft leading edges or sculptures), their inspection with shearography is of interest for both hidden defect detection and material characterization. Accurate strain measuring of a highly curved or free form surface needs to be performed by combining inline object shape measuring and processing of shearography data in 3D. Previous research has not provided a general solution. This research is devoted to the practical questions of 3D shape shearography system development for surface strain characterization of curved objects. The complete procedure of calibration and data processing of a 3D shape shearography system with integrated structured light projector is presented. This includes an estimation of the actual shear distance and a sensitivity matrix correction within the system field of view. For the experimental part a 3D shape shearography system prototype was developed. It employs three spatially-distributed shearing cameras, with Michelson interferometers acting as the shearing devices, one illumination laser source and a structured light projector. The developed system performance was evaluated with a previously reported cylinder specimen (length 400 mm, external diameter 190 mmm) loaded by internal pressure. Further steps for the 3D shape shearography prototype and the technique development are also proposed.

  13. Where you look can influence haptic object recognition.

    PubMed

    Lawson, Rebecca; Boylan, Amy; Edwards, Lauren

    2014-02-01

    We investigated whether the relative position of objects and the body would influence haptic recognition. People felt objects on the right or left side of their body midline, using their right hand. Their head was turned towards or away from the object, and they could not see their hands or the object. People were better at naming 2-D raised line drawings and 3-D small-scale models of objects and also real, everyday objects when they looked towards them. However, this head-towards benefit was reliable only when their right hand crossed their body midline to feel objects on their left side. Thus, haptic object recognition was influenced by people's head position, although vision of their hand and the object was blocked. This benefit of turning the head towards the object being explored suggests that proprioceptive and haptic inputs are remapped into an external coordinate system and that this remapping is harder when the body is in an unusual position (with the hand crossing the body midline and the head turned away from the hand). The results indicate that haptic processes align sensory inputs from the hand and head even though either hand-centered or object-centered coordinate systems should suffice for haptic object recognition.

  14. Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition.

    PubMed

    Passalis, Georgios; Perakis, Panagiotis; Theoharis, Theoharis; Kakadiaris, Ioannis A

    2011-10-01

    The uncontrolled conditions of real-world biometric applications pose a great challenge to any face recognition approach. The unconstrained acquisition of data from uncooperative subjects may result in facial scans with significant pose variations along the yaw axis. Such pose variations can cause extensive occlusions, resulting in missing data. In this paper, a novel 3D face recognition method is proposed that uses facial symmetry to handle pose variations. It employs an automatic landmark detector that estimates pose and detects occluded areas for each facial scan. Subsequently, an Annotated Face Model is registered and fitted to the scan. During fitting, facial symmetry is used to overcome the challenges of missing data. The result is a pose invariant geometry image. Unlike existing methods that require frontal scans, the proposed method performs comparisons among interpose scans using a wavelet-based biometric signature. It is suitable for real-world applications as it only requires half of the face to be visible to the sensor. The proposed method was evaluated using databases from the University of Notre Dame and the University of Houston that, to the best of our knowledge, include the most challenging pose variations publicly available. The average rank-one recognition rate of the proposed method in these databases was 83.7 percent.

  15. Object recognition by use of polarimetric phase-shifting digital holography.

    PubMed

    Nomura, Takanori; Javidi, Bahram

    2007-08-01

    Pattern recognition by use of polarimetric phase-shifting digital holography is presented. Using holography, the amplitude distribution and phase difference distribution between two orthogonal polarizations of three-dimensional (3D) or two-dimensional phase objects are obtained. This information contains both complex amplitude and polarimetric characteristics of the object, and it can be used for improving the discrimination capability of object recognition. Experimental results are presented to demonstrate the idea. To the best of our knowledge, this is the first report on 3D polarimetric recognition of objects using digital holography.

  16. A Skeleton-Based 3D Shape Reconstruction of Free-Form Objects with Stereo Vision

    NASA Astrophysics Data System (ADS)

    Saini, Deepika; Kumar, Sanjeev

    2015-12-01

    In this paper, an efficient approach is proposed for recovering the 3D shape of a free-form object from its arbitrary pair of stereo images. In particular, the reconstruction problem is treated as the reconstruction of the skeleton and the external boundary of the object. The reconstructed skeleton is termed as the line-like representation or curve-skeleton of the 3D object. The proposed solution for object reconstruction is based on this evolved curve-skeleton. It is used as a seed for recovering shape of the 3D object, and the extracted boundary is used for terminating the growing process of the object. NURBS-skeleton is used to extract the skeleton of both views. Affine invariant property of the convex hulls is used to establish the correspondence between the skeletons and boundaries in the stereo images. In the growing process, a distance field is defined for each skeleton point as the smallest distance from that point to the boundary of the object. A sphere centered at a skeleton point of radius equal to the minimum distance to the boundary is tangential to the boundary. Filling in the spheres centered at each skeleton point reconstructs the object. Several results are presented in order to check the applicability and validity of the proposed algorithm.

  17. Structured light 3D depth map enhancement and gesture recognition using image content adaptive filtering

    NASA Astrophysics Data System (ADS)

    Ramachandra, Vikas; Nash, James; Atanassov, Kalin; Goma, Sergio

    2013-03-01

    A structured-light system for depth estimation is a type of 3D active sensor that consists of a structured-light projector that projects an illumination pattern on the scene (e.g. mask with vertical stripes) and a camera which captures the illuminated scene. Based on the received patterns, depths of different regions in the scene can be inferred. In this paper, we use side information in the form of image structure to enhance the depth map. This side information is obtained from the received light pattern image reflected by the scene itself. The processing steps run real time. This post-processing stage in the form of depth map enhancement can be used for better hand gesture recognition, as is illustrated in this paper.

  18. Analyzing the relevance of shape descriptors in automated recognition of facial gestures in 3D images

    NASA Astrophysics Data System (ADS)

    Rodriguez A., Julian S.; Prieto, Flavio

    2013-03-01

    The present document shows and explains the results from analyzing shape descriptors (DESIRE and Spherical Spin Image) for facial recognition of 3D images. DESIRE is a descriptor made of depth images, silhouettes and rays extended from a polygonal mesh; whereas the Spherical Spin Image (SSI) associated to a polygonal mesh point, is a 2D histogram built from neighboring points by using the position information that captures features of the local shape. The database used contains images of facial expressions which in average were recognized 88.16% using a neuronal network and 91.11% with a Bayesian classifier in the case of the first descriptor; in contrast, the second descriptor only recognizes in average 32% and 23,6% using the same mentioned classifiers respectively.

  19. Fusion of 3D laser scanner and depth images for obstacle recognition in mobile applications

    NASA Astrophysics Data System (ADS)

    Budzan, Sebastian; Kasprzyk, Jerzy

    2016-02-01

    The problem of obstacle detection and recognition or, generally, scene mapping is one of the most investigated problems in computer vision, especially in mobile applications. In this paper a fused optical system using depth information with color images gathered from the Microsoft Kinect sensor and 3D laser range scanner data is proposed for obstacle detection and ground estimation in real-time mobile systems. The algorithm consists of feature extraction in the laser range images, processing of the depth information from the Kinect sensor, fusion of the sensor information, and classification of the data into two separate categories: road and obstacle. Exemplary results are presented and it is shown that fusion of information gathered from different sources increases the effectiveness of the obstacle detection in different scenarios, and it can be used successfully for road surface mapping.

  20. Developement of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

    SciTech Connect

    Deputch, G.; Hoff, J.; Lipton, R.; Liu, T.; Olsen, J.; Ramberg, E.; Wu, Jin-Yuan; Yarema, R.; Shochet, M.; Tang, F.; Demarteau, M.; /Argonne /INFN, Padova

    2011-04-13

    Many next-generation physics experiments will be characterized by the collection of large quantities of data, taken in rapid succession, from which scientists will have to unravel the underlying physical processes. In most cases, large backgrounds will overwhelm the physics signal. Since the quantity of data that can be stored for later analysis is limited, real-time event selection is imperative to retain the interesting events while rejecting the background. Scaling of current technologies is unlikely to satisfy the scientific needs of future projects, so investments in transformational new technologies need to be made. For example, future particle physics experiments looking for rare processes will have to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare processes. In this proposal, we intend to develop hardware-based technology that significantly advances the state-of-the-art for fast pattern recognition within and outside HEP using the 3D vertical integration technology that has emerged recently in industry. The ultimate physics reach of the LHC experiments will crucially depend on the tracking trigger's ability to help discriminate between interesting rare events and the background. Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing pattern recognition

  1. 4Pi fluorescence detection and 3D particle localization with a single objective

    PubMed Central

    Schnitzbauer, J.; McGorty, R.; Huang, B.

    2013-01-01

    Coherent detection through two opposing objectives (4Pi configuration) improves the precision of three-dimensional (3D) single-molecule localization substantially along the axial direction, but suffers from instrument complexity and maintenance difficulty. To address these issues, we have realized 4Pi fluorescence detection by sandwiching the sample between the objective and a mirror, and create interference of direct incidence and mirror-reflected signal at the camera with a spatial light modulator. Multifocal imaging using this single-objective mirror interference scheme offers improvement in the axial localization similar to the traditional 4Pi method. We have also devised several PSF engineering schemes to enable 3D localization with a single emitter image, offering better axial precision than normal single-objective localization methods such as astigmatic imaging. PMID:24105517

  2. 3D-modeling of deformed halite hopper crystals by Object Based Image Analysis

    NASA Astrophysics Data System (ADS)

    Leitner, Christoph; Hofmann, Peter; Marschallinger, Robert

    2014-12-01

    Object Based Image Analysis (OBIA) is an established method for analyzing multiscale and multidimensional imagery in a range of disciplines. In the present study this method was used for the 3D reconstruction of halite hopper crystals in a mudrock sample, based on Computed Tomography data. To quantitatively assess the reliability of OBIA results, they were benchmarked against a corresponding "gold standard", a reference 3D model of the halite crystals that was derived by manual expert digitization of the CT images. For accuracy assessment, classical per-scene statistics were extended to per-object statistics. The strength of OBIA was to recognize all objects similar to halite hopper crystals and in particular to eliminate cracks. Using a support vector machine (SVM) classifier on top of OBIA, unsuitable objects like halite crystal clusters, polyhalite-coated crystals and spherical halite crystals were effectively dismissed, but simultaneously the number of well-shaped halites was reduced.

  3. Blind Search of Faint Moving Objects in 3D Data Sets

    DTIC Science & Technology

    2013-09-01

    Blind Search of Faint Moving Objects in 3D Data Sets Phan Dao*, Peter Crabtree and Patrick McNicholl AFRL/RVBYC Tamar Payne Applied...using a simulated object signature superimposed on measured background, and show that the limiting magnitude can be improved by up to 6 visual...magnitudes. A quasi blind search algorithm that identifies the streak of photons, assuming no prior knowledge of orbital information, will be discussed

  4. Object recognition approach based on feature fusion

    NASA Astrophysics Data System (ADS)

    Wang, Runsheng

    2001-09-01

    Multi-sensor information fusion plays an important pole in object recognition and many other application fields. Fusion performance is tightly depended on the fusion level selected and the approach used. Feature level fusion is a potential and difficult fusion level though there might be mainly three fusion levels. Two schemes are developed for key issues of feature level fusion in this paper. In feature selecting, a normal method developed is to analyze the mutual relationship among the features that can be used, and to be applied to order features. In object recognition, a multi-level recognition scheme is developed, whose procedure can be controlled and updated by analyzing the decision result obtained in order to achieve a final reliable result. The new approach is applied to recognize work-piece objects with twelve classes in optical images and open-country objects with four classes based on infrared image sequence and MMW radar. Experimental results are satisfied.

  5. The Visual Representation of 3D Object Orientation in Parietal Cortex

    PubMed Central

    Cowan, Noah J.; Angelaki, Dora E.

    2013-01-01

    An accurate representation of three-dimensional (3D) object orientation is essential for interacting with the environment. Where and how the brain visually encodes 3D object orientation remains unknown, but prior studies suggest the caudal intraparietal area (CIP) may be involved. Here, we develop rigorous analytical methods for quantifying 3D orientation tuning curves, and use these tools to the study the neural coding of surface orientation. Specifically, we show that single neurons in area CIP of the rhesus macaque jointly encode the slant and tilt of a planar surface, and that across the population, the distribution of preferred slant-tilts is not statistically different from uniform. This suggests that all slant-tilt combinations are equally represented in area CIP. Furthermore, some CIP neurons are found to also represent the third rotational degree of freedom that determines the orientation of the image pattern on the planar surface. Together, the present results suggest that CIP is a critical neural locus for the encoding of all three rotational degrees of freedom specifying an object's 3D spatial orientation. PMID:24305830

  6. Acquiring 3-D information about thick objects from differential interference contrast images using texture extraction.

    PubMed

    Sierra, Heidy; Brooks, Dana; DiMarzio, Charles

    2010-01-01

    The extraction of 3-D morphological information about thick objects is explored in this work. We extract this information from 3-D differential interference contrast (DIC) images by applying a texture detection method. Texture extraction methods have been successfully used in different applications to study biological samples. A 3-D texture image is obtained by applying a local entropy-based texture extraction method. The use of this method to detect regions of blastocyst mouse embryos that are used in assisted reproduction techniques such as in vitro fertilization is presented as an example. Results demonstrate the potential of using texture detection methods to improve morphological analysis of thick samples, which is relevant to many biomedical and biological studies. Fluorescence and optical quadrature microscope phase images are used for validation.

  7. Acquiring 3-D information about thick objects from differential interference contrast images using texture extraction

    NASA Astrophysics Data System (ADS)

    Sierra, Heidy; Brooks, Dana; Dimarzio, Charles

    2010-07-01

    The extraction of 3-D morphological information about thick objects is explored in this work. We extract this information from 3-D differential interference contrast (DIC) images by applying a texture detection method. Texture extraction methods have been successfully used in different applications to study biological samples. A 3-D texture image is obtained by applying a local entropy-based texture extraction method. The use of this method to detect regions of blastocyst mouse embryos that are used in assisted reproduction techniques such as in vitro fertilization is presented as an example. Results demonstrate the potential of using texture detection methods to improve morphological analysis of thick samples, which is relevant to many biomedical and biological studies. Fluorescence and optical quadrature microscope phase images are used for validation.

  8. Robust Detection of Round Shaped Pits Lying on 3D Meshes: Application to Impact Crater Recognition

    NASA Astrophysics Data System (ADS)

    Schmidt, Martin-Pierre; Muscato, Jennifer; Viseur, Sophie; Jorda, Laurent; Bouley, Sylvain; Mari, Jean-Luc

    2015-04-01

    Most celestial bodies display impacts of collisions with asteroids and meteoroids. These traces are called craters. The possibility of observing and identifying these craters and their characteristics (radius, depth and morphology) is the only method available to measure the age of different units at the surface of the body, which in turn allows to constrain its conditions of formation. Interplanetary space probes always carry at least one imaging instrument on board. The visible images of the target are used to reconstruct high-resolution 3D models of its surface as a cloud of points in the case of multi-image dense stereo, or as a triangular mesh in the case of stereo and shape-from-shading. The goal of this work is to develop a methodology to automatically detect the craters lying on these 3D models. The robust extraction of feature areas on surface objects embedded in 3D, like circular pits, is a challenging problem. Classical approaches generally rely on image processing and template matching on a 2D flat projection of the 3D object (i.e.: a high-resolution photograph). In this work, we propose a full-3D method that mainly relies on curvature analysis. Mean and Gaussian curvatures are estimated on the surface. They are used to label vertices that belong to concave parts corresponding to specific pits on the surface. The surface is thus transformed into binary map distinguishing potential crater features to other types of features. Centers are located in the targeted surface regions, corresponding to potential crater features. Concentric rings are then built around the found centers. They consist in circular closed lines exclusively composed of edges of the initial mesh. The first built ring represents the nearest vertex neighborhood of the found center. The ring is then optimally expanded using a circularity constrain and the curvature values of the ring vertices. This method has been tested on a 3D model of the asteroid Lutetia observed by the ROSETTA (ESA

  9. A neuromorphic system for video object recognition

    PubMed Central

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  10. A neuromorphic system for video object recognition.

    PubMed

    Khosla, Deepak; Chen, Yang; Kim, Kyungnam

    2014-01-01

    Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing

  11. The effect of background and illumination on color identification of real, 3D objects

    PubMed Central

    Allred, Sarah R.; Olkkonen, Maria

    2013-01-01

    For the surface reflectance of an object to be a useful cue to object identity, judgments of its color should remain stable across changes in the object's environment. In 2D scenes, there is general consensus that color judgments are much more stable across illumination changes than background changes. Here we investigate whether these findings generalize to real 3D objects. Observers made color matches to cubes as we independently varied both the illumination impinging on the cube and the 3D background of the cube. As in 2D scenes, we found relatively high but imperfect stability of color judgments under an illuminant shift. In contrast to 2D scenes, we found that background had little effect on average color judgments. In addition, variability of color judgments was increased by an illuminant shift and decreased by embedding the cube within a background. Taken together, these results suggest that in real 3D scenes with ample cues to object segregation, the addition of a background may improve stability of color identification. PMID:24273521

  12. Three-dimensional object rotation-tolerant recognition for integral imaging using synthetic discriminant function

    NASA Astrophysics Data System (ADS)

    Hao, Jinbo; Wang, Xiaorui; Zhang, Jianqi; Xu, Yin

    2013-04-01

    This paper presents a novel approach of three-dimensional object rotation-tolerant recognition that combines the merits of Integral Imaging (II) and Synthetic Discriminant Function (SDF). SDF aims at filters and distortion-tolerant recognition, and we use it for three-dimensional (3-D) rotation-tolerant recognition with II system. Using high relevancy of elemental images of II, the approach can not only realize 3-D rotation-tolerant recognition, but also reduce computational complexity. The correctness has been validated by experimental results.

  13. Detection and Purging of Specular Reflective and Transparent Object Influences in 3d Range Measurements

    NASA Astrophysics Data System (ADS)

    Koch, R.; May, S.; Nüchter, A.

    2017-02-01

    3D laser scanners are favoured sensors for mapping in mobile service robotics at indoor and outdoor applications, since they deliver precise measurements at a wide scanning range. The resulting maps are detailed since they have a high resolution. Based on these maps robots navigate through rough terrain, fulfil advanced manipulation, and inspection tasks. In case of specular reflective and transparent objects, e.g., mirrors, windows, shiny metals, the laser measurements get corrupted. Based on the type of object and the incident angle of the incoming laser beam there are three results possible: a measurement point on the object plane, a measurement behind the object plane, and a measurement of a reflected object. It is important to detect such situations to be able to handle these corrupted points. This paper describes why it is difficult to distinguish between specular reflective and transparent surfaces. It presents a 3DReflection- Pre-Filter Approach to identify specular reflective and transparent objects in point clouds of a multi-echo laser scanner. Furthermore, it filters point clouds from influences of such objects and extract the object properties for further investigations. Based on an Iterative-Closest-Point-algorithm reflective objects are identified. Object surfaces and points behind surfaces are masked according to their location. Finally, the processed point cloud is forwarded to a mapping module. Furthermore, the object surface corners and the type of the surface is broadcasted. Four experiments demonstrate the usability of the 3D-Reflection-Pre-Filter. The first experiment was made in a empty room containing a mirror, the second experiment was made in a stairway containing a glass door, the third experiment was made in a empty room containing two mirrors, the fourth experiment was made in an office room containing a mirror. This paper demonstrate that for single scans the detection of specular reflective and transparent objects in 3D is possible. It

  14. Object recognition by artificial cortical maps.

    PubMed

    Plebe, Alessio; Domenella, Rosaria Grazia

    2007-09-01

    Object recognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of object recognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model.

  15. Recognition of object domain by color distribution

    NASA Technical Reports Server (NTRS)

    Mugitani, Takako; Mifune, Mitsuru; Nagata, Shigeki

    1988-01-01

    For the image processing of an object in its natural image, it is necessary to extract in advance the object to be processed from its image. To accomplish this the outer shape of an object is extracted through human instructions, which requires a great deal of time and patience. A method involving the setting of a model of color distribution on the surface of an object is described. This method automatically provides color recognition, a piece of knowledge that represents the properties of an object, from its natural image. A method for recognizing and extracting the object in the image according to the color recognized is also described.

  16. A Neural Network Object Recognition System

    DTIC Science & Technology

    1990-07-01

    useful for exploring different neural network configurations. There are three main computation phases of a model based object recognition system...segmentation, feature extraction, and object classification. This report focuses on the object classification stage. For segmentation, a neural network based...are available with the current system. Neural network based feature extraction may be added at a later date. The classification stage consists of a

  17. Cryo-EM structure of a 3D DNA-origami object

    PubMed Central

    Bai, Xiao-chen; Martin, Thomas G.; Scheres, Sjors H. W.; Dietz, Hendrik

    2012-01-01

    A key goal for nanotechnology is to design synthetic objects that may ultimately achieve functionalities known today only from natural macromolecular complexes. Molecular self-assembly with DNA has shown potential for creating user-defined 3D scaffolds, but the level of attainable positional accuracy has been unclear. Here we report the cryo-EM structure and a full pseudoatomic model of a discrete DNA object that is almost twice the size of a prokaryotic ribosome. The structure provides a variety of stable, previously undescribed DNA topologies for future use in nanotechnology and experimental evidence that discrete 3D DNA scaffolds allow the positioning of user-defined structural motifs with an accuracy that is similar to that observed in natural macromolecules. Thereby, our results indicate an attractive route to fabricate nanoscale devices that achieve complex functionalities by DNA-templated design steered by structural feedback. PMID:23169645

  18. 220GHz wideband 3D imaging radar for concealed object detection technology development and phenomenology studies

    NASA Astrophysics Data System (ADS)

    Robertson, Duncan A.; Macfarlane, David G.; Bryllert, Tomas

    2016-05-01

    We present a 220 GHz 3D imaging `Pathfinder' radar developed within the EU FP7 project CONSORTIS (Concealed Object Stand-Off Real-Time Imaging for Security) which has been built to address two objectives: (i) to de-risk the radar hardware development and (ii) to enable the collection of phenomenology data with ~1 cm3 volumetric resolution. The radar combines a DDS-based chirp generator and self-mixing multiplier technology to achieve a 30 GHz bandwidth chirp with such high linearity that the raw point response is close to ideal and only requires minor nonlinearity compensation. The single transceiver is focused with a 30 cm lens mounted on a gimbal to acquire 3D volumetric images of static test targets and materials.

  19. A novel iterative computation algorithm for Kinoform of 3D object

    NASA Astrophysics Data System (ADS)

    Jiang, Xiao-yu; Chuang, Pei; Wang, Xi; Zong, Yantao

    2012-11-01

    A novel method for computing kinoform of 3D object based on traditional iterate Fourier transform algorithm(IFTA) is proposed in this paper. Kinoform is a special kind of computer-generated holograms (CGH) which has very high diffraction efficiency since it only modulates the phase of illuminated light and doesn't have cross-interference from conjugate image. The traditional IFTA arithmetic assumes that reconstruction image is in infinity area(Fraunhofer diffraction region), and ignores the deepness of 3D object ,so it can only calculate two-dimensional kinoform. The proposed algorithm in this paper divides three-dimensional object into several object planes in deepness and treat every object plane as a target image then iterate computation is carried out between one input plane(kinoform) and multi-output planes(reconstruction images) .A space phase factor is added into iterate process to represent depth characters of 3D object, then reconstruction images is in Fresnel diffraction region. Optics reconstructed experiment of kinoform computed by this method is realized based on Liquid Crystals on Silicon (LCoS) Spatial Light Modulator(SLM). Mean Square Error(MSE) and Structure Similarity(SSIM) between original and reconstruction image is used to evaluate this method. The experimental result shows that this algorithm speed is fast and the result kinoform can reconstruct the object in different plane with high precision under the illumination of plane wave. The reconstruction images provide space sense of three-dimensional visual effect. At last, the influence of space and shelter between different object planes to reconstruction image is also discussed in the experiment.

  20. 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.

  1. 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

  2. Minimal camera networks for 3D image based modeling of cultural heritage objects.

    PubMed

    Alsadik, Bashar; Gerke, Markus; Vosselman, George; Daham, Afrah; Jasim, Luma

    2014-03-25

    3D modeling of cultural heritage objects like artifacts, statues and buildings is nowadays an important tool for virtual museums, preservation and restoration. In this paper, we introduce a method to automatically design a minimal imaging network for the 3D modeling of cultural heritage objects. This becomes important for reducing the image capture time and processing when documenting large and complex sites. Moreover, such a minimal camera network design is desirable for imaging non-digitally documented artifacts in museums and other archeological sites to avoid disturbing the visitors for a long time and/or moving delicate precious objects to complete the documentation task. The developed method is tested on the Iraqi famous statue "Lamassu". Lamassu is a human-headed winged bull of over 4.25 m in height from the era of Ashurnasirpal II (883-859 BC). Close-range photogrammetry is used for the 3D modeling task where a dense ordered imaging network of 45 high resolution images were captured around Lamassu with an object sample distance of 1 mm. These images constitute a dense network and the aim of our study was to apply our method to reduce the number of images for the 3D modeling and at the same time preserve pre-defined point accuracy. Temporary control points were fixed evenly on the body of Lamassu and measured by using a total station for the external validation and scaling purpose. Two network filtering methods are implemented and three different software packages are used to investigate the efficiency of the image orientation and modeling of the statue in the filtered (reduced) image networks. Internal and external validation results prove that minimal image networks can provide highly accurate records and efficiency in terms of visualization, completeness, processing time (>60% reduction) and the final accuracy of 1 mm.

  3. Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation.

    PubMed

    Matei, Bogdan; Shan, Ying; Sawhney, Harpreet S; Tan, Yi; Kumar, Rakesh; Huber, Daniel; Hebert, Martial

    2006-07-01

    We propose a new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques. Our approach achieves a sublinear complexity on the number of models, maintaining at the same time a high degree of performance for real 3D sensed data that is acquired in largely uncontrolled settings. The key component of our method is to first index surface descriptors computed at salient locations from the scene into the whole model database using the Locality Sensitive Hashing (LSH), a probabilistic approximate nearest neighbor method. Progressively complex geometric constraints are subsequently enforced to further prune the initial candidates and eliminate false correspondences due to inaccuracies in the surface descriptors and the errors of the LSH algorithm. The indexed models are selected based on the MAP rule using posterior probability of the models estimated in the joint 3D-signature space. Experiments with real 3D data employing a large database of vehicles, most of them very similar in shape, containing 1,000,000 features from more than 365 models demonstrate a high degree of performance in the presence of occlusion and obscuration, unmodeled vehicle interiors and part articulations, with an average processing time between 50 and 100 seconds per query.

  4. An object-oriented simulator for 3D digital breast tomosynthesis imaging system.

    PubMed

    Seyyedi, Saeed; Cengiz, Kubra; Kamasak, Mustafa; Yildirim, Isa

    2013-01-01

    Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values.

  5. Comparison of passive ranging integral imaging and active imaging digital holography for three-dimensional object recognition.

    PubMed

    Frauel, Yann; Tajahuerce, Enrique; Matoba, Osamu; Castro, Albertina; Javidi, Bahram

    2004-01-10

    We present an overview of three-dimensional (3D) object recognition techniques that use active sensing by interferometric imaging (digital holography) and passive sensing by integral imaging. We describe how each technique can be used to retrieve the depth information of a 3D scene and how this information can then be used for 3D object recognition. We explore various algorithms for 3D recognition such as nonlinear correlation and target distortion tolerance. We also provide a comparison of the advantages and disadvantages of the two techniques.

  6. The uncrowded window of object recognition

    PubMed Central

    Pelli, Denis G; Tillman, Katharine A

    2009-01-01

    It is now emerging that vision is usually limited by object spacing rather than size. The visual system recognizes an object by detecting and then combining its features. ‘Crowding’ occurs when objects are too close together and features from several objects are combined into a jumbled percept. Here, we review the explosion of studies on crowding—in grating discrimination, letter and face recognition, visual search, selective attention, and reading—and find a universal principle, the Bouma law. The critical spacing required to prevent crowding is equal for all objects, although the effect is weaker between dissimilar objects. Furthermore, critical spacing at the cortex is independent of object position, and critical spacing at the visual field is proportional to object distance from fixation. The region where object spacing exceeds critical spacing is the ‘uncrowded window’. Observers cannot recognize objects outside of this window and its size limits the speed of reading and search. PMID:18828191

  7. Blind robust watermarking schemes for copyright protection of 3D mesh objects.

    PubMed

    Zafeiriou, Stefanos; Tefas, Anastasios; Pitas, Ioannis

    2005-01-01

    In this paper, two novel methods suitable for blind 3D mesh object watermarking applications are proposed. The first method is robust against 3D rotation, translation, and uniform scaling. The second one is robust against both geometric and mesh simplification attacks. A pseudorandom watermarking signal is cast in the 3D mesh object by deforming its vertices geometrically, without altering the vertex topology. Prior to watermark embedding and detection, the object is rotated and translated so that its center of mass and its principal component coincide with the origin and the z-axis of the Cartesian coordinate system. This geometrical transformation ensures watermark robustness to translation and rotation. Robustness to uniform scaling is achieved by restricting the vertex deformations to occur only along the r coordinate of the corresponding (r, theta, phi) spherical coordinate system. In the first method, a set of vertices that correspond to specific angles theta is used for watermark embedding. In the second method, the samples of the watermark sequence are embedded in a set of vertices that correspond to a range of angles in the theta domain in order to achieve robustness against mesh simplifications. Experimental results indicate the ability of the proposed method to deal with the aforementioned attacks.

  8. Intuitive terrain reconstruction using height observation-based ground segmentation and 3D object boundary estimation.

    PubMed

    Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

    2012-12-12

    Mobile robot operators must make rapid decisions based on information about the robot's surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot's array of sensors, but some upper parts of objects are beyond the sensors' measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

  9. Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

    PubMed Central

    Song, Wei; Cho, Kyungeun; Um, Kyhyun; Won, Chee Sun; Sim, Sungdae

    2012-01-01

    Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances. PMID:23235454

  10. Determining the 3-D structure and motion of objects using a scanning laser range sensor

    NASA Astrophysics Data System (ADS)

    Nandhakumar, N.; Smith, Philip W.

    1993-12-01

    In order for the EVAHR robot to autonomously track and grasp objects, its vision system must be able to determine the 3-D structure and motion of an object from a sequence of sensory images. This task is accomplished by the use of a laser radar range sensor which provides dense range maps of the scene. Unfortunately, the currently available laser radar range cameras use a sequential scanning approach which complicates image analysis. Although many algorithms have been developed for recognizing objects from range images, none are suited for use with single beam, scanning, time-of-flight sensors because all previous algorithms assume instantaneous acquisition of the entire image. This assumption is invalid since the EVAHR robot is equipped with a sequential scanning laser range sensor. If an object is moving while being imaged by the device, the apparent structure of the object can be significantly distorted due to the significant non-zero delay time between sampling each image pixel. If an estimate of the motion of the object can be determined, this distortion can be eliminated; but, this leads to the motion-structure paradox - most existing algorithms for 3-D motion estimation use the structure of objects to parameterize their motions. The goal of this research is to design a rigid-body motion recovery technique which overcomes this limitation. The method being developed is an iterative, linear, feature-based approach which uses the non-zero image acquisition time constraint to accurately recover the motion parameters from the distorted structure of the 3-D range maps. Once the motion parameters are determined, the structural distortion in the range images is corrected.

  11. Applying Mean-Shift - Clustering for 3D object detection in remote sensing data

    NASA Astrophysics Data System (ADS)

    Simon, Jürgen-Lorenz; Diederich, Malte; Troemel, Silke

    2013-04-01

    The timely warning and forecasting of high-impact weather events is crucial for life, safety and economy. Therefore, the development and improvement of methods for detection and nowcasting / short-term forecasting of these events is an ongoing research question. A new 3D object detection and tracking algorithm is presented. Within the project "object-based analysis and seamless predictin (OASE)" we address a better understanding and forecasting of convective events based on the synergetic use of remotely sensed data and new methods for detection, nowcasting, validation and assimilation. In order to gain advanced insight into the lifecycle of convective cells, we perform an object-detection on a new high-resolution 3D radar- and satellite based composite and plan to track the detected objects over time, providing us with a model of the lifecycle. The insights in the lifecycle will be used in order to improve prediction of convective events in the nowcasting time scale, as well as a new type of data to be assimilated into numerical weather models, thus seamlessly bridging the gap between nowcasting and NWP.. The object identification (or clustering) is performed using a technique borrowed from computer vision, called mean-shift clustering. Mean-Shift clustering works without many of the parameterizations or rigid threshold schemes employed by many existing schemes (e. g. KONRAD, TITAN, Trace-3D), which limit the tracking to fully matured, convective cells of significant size and/or strength. Mean-Shift performs without such limiting definitions, providing a wider scope for studying larger classes of phenomena and providing a vehicle for research into the object definition itself. Since the mean-shift clustering technique could be applied on many types of remote-sensing and model data for object detection, it is of general interest to the remote sensing and modeling community. The focus of the presentation is the introduction of this technique and the results of its

  12. 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

  13. Object recognition difficulty in visual apperceptive agnosia.

    PubMed

    Grossman, M; Galetta, S; D'Esposito, M

    1997-04-01

    Two patients with visual apperceptive agnosia were examined on tasks assessing the appreciation of visual material. Elementary visual functioning was relatively preserved, but they had profound difficulty recognizing and naming line drawings. More detailed evaluation revealed accurate recognition of regular geometric shapes and colors, but performance deteriorated when the shapes were made more complex visually, when multiple-choice arrays contained larger numbers of simple targets and foils, and when a mental manipulation such as a rotation was required. The recognition of letters and words was similarly compromised. Naming, recognition, and anomaly judgments of colored pictures and real objects were more accurate than similar decisions involving black-and-white line drawings. Visual imagery for shapes, letters, and objects appeared to be more accurate than visual perception of the same materials. We hypothesize that object recognition difficulty in visual apperceptive agnosia is due to two related factors: the impaired appreciation of the visual perceptual features that constitute objects, and a limitation in the cognitive resources that are available for processing demanding material within the visual modality.

  14. Enhanced Visual-Attention Model for Perceptually Improved 3D Object Modeling in Virtual Environments

    NASA Astrophysics Data System (ADS)

    Chagnon-Forget, Maude; Rouhafzay, Ghazal; Cretu, Ana-Maria; Bouchard, Stéphane

    2016-12-01

    Three-dimensional object modeling and interactive virtual environment applications require accurate, but compact object models that ensure real-time rendering capabilities. In this context, the paper proposes a 3D modeling framework employing visual attention characteristics in order to obtain compact models that are more adapted to human visual capabilities. An enhanced computational visual attention model with additional saliency channels, such as curvature, symmetry, contrast and entropy, is initially employed to detect points of interest over the surface of a 3D object. The impact of the use of these supplementary channels is experimentally evaluated. The regions identified as salient by the visual attention model are preserved in a selectively-simplified model obtained using an adapted version of the QSlim algorithm. The resulting model is characterized by a higher density of points in the salient regions, therefore ensuring a higher perceived quality, while at the same time ensuring a less complex and more compact representation for the object. The quality of the resulting models is compared with the performance of other interest point detectors incorporated in a similar manner in the simplification algorithm. The proposed solution results overall in higher quality models, especially at lower resolutions. As an example of application, the selectively-densified models are included in a continuous multiple level of detail (LOD) modeling framework, in which an original neural-network solution selects the appropriate size and resolution of an object.

  15. Searching surface orientation of microscopic objects for accurate 3D shape recovery.

    PubMed

    Shim, Seong-O; Mahmood, Muhammad Tariq; Choi, Tae-Sun

    2012-05-01

    In this article, we propose a new shape from focus (SFF) method to estimate 3D shape of microscopic objects using surface orientation cue of each object patch. Most of the SFF algorithms compute the focus value of a pixel from the information of neighboring pixels lying on the same image frame based on an assumption that the small object patch corresponding to the small neighborhood of a pixel is a plane parallel to the focal plane. However, this assumption fails in the optics with limited depth of field where the neighboring pixels of an image have different degree of focus. To overcome this problem, we try to search the surface orientation of the small object patch corresponding to each pixel in the image sequence. Searching of the surface orientation is done indirectly by principal component analysis. Then, the focus value of each pixel is computed from the neighboring pixels lying on the surface perpendicular to the corresponding surface orientation. Experimental results on synthetic and real microscopic objects show that the proposed method produces more accurate 3D shape in comparison to the existing techniques.

  16. The 3D Elevation Program—Landslide recognition, hazard assessment, and mitigation support

    USGS Publications Warehouse

    Lukas, Vicki; Carswell, Jr., William J.

    2017-01-27

    The U.S. Geological Survey (USGS) Landslide Hazards Program conducts landslide hazard assessments, pursues landslide investigations and forecasts, provides technical assistance to respond to landslide emergencies, and engages in outreach. All of these activities benefit from the availability of high-resolution, three-dimensional (3D) elevation information in the form of light detection and ranging (lidar) data and interferometric synthetic aperture radar (IfSAR) data. Research on landslide processes addresses critical questions of where and when landslides are likely to occur as well as their size, speed, and effects. This understanding informs the development of methods and tools for hazard assessment and situational awareness used to guide efforts to avoid or mitigate landslide impacts. Such research is essential for the USGS to provide improved information on landslide potential associated with severe storms, earthquakes, volcanic activity, coastal wave erosion, and wildfire burn areas.Decisionmakers in government and the private sector increasingly depend on information the USGS provides before, during, and following disasters so that communities can live, work, travel, and build safely. The USGS 3D Elevation Program (3DEP) provides the programmatic infrastructure to generate and supply lidar-derived superior terrain data to address landslide applications and a wide range of other urgent needs nationwide. By providing data to users, 3DEP reduces users’ costs and risks and allows them to concentrate on their mission objectives. 3DEP includes (1) data acquisition partnerships that leverage funding, (2) contracts with experienced private mapping firms, (3) technical expertise, lidar data standards, and specifications, and (4) most important, public access to high-quality 3D elevation data.

  17. Exploiting core knowledge for visual object recognition.

    PubMed

    Schurgin, Mark W; Flombaum, Jonathan I

    2017-03-01

    Humans recognize thousands of objects, and with relative tolerance to variable retinal inputs. The acquisition of this ability is not fully understood, and it remains an area in which artificial systems have yet to surpass people. We sought to investigate the memory process that supports object recognition. Specifically, we investigated the association of inputs that co-occur over short periods of time. We tested the hypothesis that human perception exploits expectations about object kinematics to limit the scope of association to inputs that are likely to have the same token as a source. In several experiments we exposed participants to images of objects, and we then tested recognition sensitivity. Using motion, we manipulated whether successive encounters with an image took place through kinematics that implied the same or a different token as the source of those encounters. Images were injected with noise, or shown at varying orientations, and we included 2 manipulations of motion kinematics. Across all experiments, memory performance was better for images that had been previously encountered with kinematics that implied a single token. A model-based analysis similarly showed greater memory strength when images were shown via kinematics that implied a single token. These results suggest that constraints from physics are built into the mechanisms that support memory about objects. Such constraints-often characterized as 'Core Knowledge'-are known to support perception and cognition broadly, even in young infants. But they have never been considered as a mechanism for memory with respect to recognition. (PsycINFO Database Record

  18. Robust object tracking techniques for vision-based 3D motion analysis applications

    NASA Astrophysics Data System (ADS)

    Knyaz, Vladimir A.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

    2016-04-01

    Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

  19. Twin-beam real-time position estimation of micro-objects in 3D

    NASA Astrophysics Data System (ADS)

    Gurtner, Martin; Zemánek, Jiří

    2016-12-01

    Various optical methods for measuring positions of micro-objects in 3D have been reported in the literature. Nevertheless, the majority of them are not suitable for real-time operation, which is needed, for example, for feedback position control. In this paper, we present a method for real-time estimation of the position of micro-objects in 3D1; the method is based on twin-beam illumination and requires only a very simple hardware setup whose essential part is a standard image sensor without any lens. The performance of the proposed method is tested during a micro-manipulation task in which the estimated position served as feedback for the controller. The experiments show that the estimate is accurate to within  ∼3 μm in the lateral position and  ∼7 μm in the axial distance with the refresh rate of 10 Hz. Although the experiments are done using spherical objects, the presented method could be modified to handle non-spherical objects as well.

  20. Non-destructive 3D shape measurement of transparent and black objects with thermal fringes

    NASA Astrophysics Data System (ADS)

    Brahm, Anika; Rößler, Conrad; Dietrich, Patrick; Heist, Stefan; Kühmstedt, Peter; Notni, Gunther

    2016-05-01

    Fringe projection is a well-established optical method for the non-destructive contactless three-dimensional (3D) measurement of object surfaces. Typically, fringe sequences in the visible wavelength range (VIS) are projected onto the surfaces of objects to be measured and are observed by two cameras in a stereo vision setup. The reconstruction is done by finding corresponding pixels in both cameras followed by triangulation. Problems can occur if the properties of some materials disturb the measurements. If the objects are transparent, translucent, reflective, or strongly absorbing in the VIS range, the projected patterns cannot be recorded properly. To overcome these challenges, we present a new alternative approach in the infrared (IR) region of the electromagnetic spectrum. For this purpose, two long-wavelength infrared (LWIR) cameras (7.5 - 13 μm) are used to detect the emitted heat radiation from surfaces which is induced by a pattern projection unit driven by a CO2 laser (10.6 μm). Thus, materials like glass or black objects, e.g. carbon fiber materials, can be measured non-destructively without the need of any additional paintings. We will demonstrate the basic principles of this heat pattern approach and show two types of 3D systems based on a freeform mirror and a GOBO wheel (GOes Before Optics) projector unit.

  1. Object recognition with hierarchical discriminant saliency networks

    PubMed Central

    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

  2. Prototyping a Sensor Enabled 3d Citymodel on Geospatial Managed Objects

    NASA Astrophysics Data System (ADS)

    Kjems, E.; Kolář, J.

    2013-09-01

    One of the major development efforts within the GI Science domain are pointing at sensor based information and the usage of real time information coming from geographic referenced features in general. At the same time 3D City models are mostly justified as being objects for visualization purposes rather than constituting the foundation of a geographic data representation of the world. The combination of 3D city models and real time information based systems though can provide a whole new setup for data fusion within an urban environment and provide time critical information preserving our limited resources in the most sustainable way. Using 3D models with consistent object definitions give us the possibility to avoid troublesome abstractions of reality, and design even complex urban systems fusing information from various sources of data. These systems are difficult to design with the traditional software development approach based on major software packages and traditional data exchange. The data stream is varying from urban domain to urban domain and from system to system why it is almost impossible to design a complete system taking care of all thinkable instances now and in the future within one constraint software design complex. On several occasions we have been advocating for a new end advanced formulation of real world features using the concept of Geospatial Managed Objects (GMO). This paper presents the outcome of the InfraWorld project, a 4 million Euro project financed primarily by the Norwegian Research Council where the concept of GMO's have been applied in various situations on various running platforms of an urban system. The paper will be focusing on user experiences and interfaces rather then core technical and developmental issues. The project was primarily focusing on prototyping rather than realistic implementations although the results concerning applicability are quite clear.

  3. Efficient Use of Video for 3d Modelling of Cultural Heritage Objects

    NASA Astrophysics Data System (ADS)

    Alsadik, B.; Gerke, M.; Vosselman, G.

    2015-03-01

    Currently, there is a rapid development in the techniques of the automated image based modelling (IBM), especially in advanced structure-from-motion (SFM) and dense image matching methods, and camera technology. One possibility is to use video imaging to create 3D reality based models of cultural heritage architectures and monuments. Practically, video imaging is much easier to apply when compared to still image shooting in IBM techniques because the latter needs a thorough planning and proficiency. However, one is faced with mainly three problems when video image sequences are used for highly detailed modelling and dimensional survey of cultural heritage objects. These problems are: the low resolution of video images, the need to process a large number of short baseline video images and blur effects due to camera shake on a significant number of images. In this research, the feasibility of using video images for efficient 3D modelling is investigated. A method is developed to find the minimal significant number of video images in terms of object coverage and blur effect. This reduction in video images is convenient to decrease the processing time and to create a reliable textured 3D model compared with models produced by still imaging. Two experiments for modelling a building and a monument are tested using a video image resolution of 1920×1080 pixels. Internal and external validations of the produced models are applied to find out the final predicted accuracy and the model level of details. Related to the object complexity and video imaging resolution, the tests show an achievable average accuracy between 1 - 5 cm when using video imaging, which is suitable for visualization, virtual museums and low detailed documentation.

  4. Combining laser scan and photogrammetry for 3D object modeling using a single digital camera

    NASA Astrophysics Data System (ADS)

    Xiong, Hanwei; Zhang, Hong; Zhang, Xiangwei

    2009-07-01

    In the fields of industrial design, artistic design and heritage conservation, physical objects are usually digitalized by reverse engineering through some 3D scanning methods. Laser scan and photogrammetry are two main methods to be used. For laser scan, a video camera and a laser source are necessary, and for photogrammetry, a digital still camera with high resolution pixels is indispensable. In some 3D modeling tasks, two methods are often integrated to get satisfactory results. Although many research works have been done on how to combine the results of the two methods, no work has been reported to design an integrated device at low cost. In this paper, a new 3D scan system combining laser scan and photogrammetry using a single consumer digital camera is proposed. Nowadays there are many consumer digital cameras, such as Canon EOS 5D Mark II, they usually have features of more than 10M pixels still photo recording and full 1080p HD movie recording, so a integrated scan system can be designed using such a camera. A square plate glued with coded marks is used to place the 3d objects, and two straight wood rulers also glued with coded marks can be laid on the plate freely. In the photogrammetry module, the coded marks on the plate make up a world coordinate and can be used as control network to calibrate the camera, and the planes of two rulers can also be determined. The feature points of the object and the rough volume representation from the silhouettes can be obtained in this module. In the laser scan module, a hand-held line laser is used to scan the object, and the two straight rulers are used as reference planes to determine the position of the laser. The laser scan results in dense points cloud which can be aligned together automatically through calibrated camera parameters. The final complete digital model is obtained through a new a patchwise energy functional method by fusion of the feature points, rough volume and the dense points cloud. The design

  5. 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.

  6. Stratification approach for 3-D euclidean reconstruction of nonrigid objects from uncalibrated image sequences.

    PubMed

    Wang, Guanghui; Wu, Q M Jonathan

    2008-02-01

    This paper addresses the problem of 3-D reconstruction of nonrigid objects from uncalibrated image sequences. Under the assumption of affine camera and that the nonrigid object is composed of a rigid part and a deformation part, we propose a stratification approach to recover the structure of nonrigid objects by first reconstructing the structure in affine space and then upgrading it to the Euclidean space. The novelty and main features of the method lies in several aspects. First, we propose a deformation weight constraint to the problem and prove the invariability between the recovered structure and shape bases under this constraint. The constraint was not observed by previous studies. Second, we propose a constrained power factorization algorithm to recover the deformation structure in affine space. The algorithm overcomes some limitations of a previous singular-value-decomposition-based method. It can even work with missing data in the tracking matrix. Third, we propose to separate the rigid features from the deformation ones in 3-D affine space, which makes the detection more accurate and robust. The stratification matrix is estimated from the rigid features, which may relax the influence of large tracking errors in the deformation part. Extensive experiments on synthetic data and real sequences validate the proposed method and show improvements over existing solutions.

  7. Object-adaptive depth compensated inter prediction for depth video coding in 3D video system

    NASA Astrophysics Data System (ADS)

    Kang, Min-Koo; Lee, Jaejoon; Lim, Ilsoon; Ho, Yo-Sung

    2011-01-01

    Nowadays, the 3D video system using the MVD (multi-view video plus depth) data format is being actively studied. The system has many advantages with respect to virtual view synthesis such as an auto-stereoscopic functionality, but compression of huge input data remains a problem. Therefore, efficient 3D data compression is extremely important in the system, and problems of low temporal consistency and viewpoint correlation should be resolved for efficient depth video coding. In this paper, we propose an object-adaptive depth compensated inter prediction method to resolve the problems where object-adaptive mean-depth difference between a current block, to be coded, and a reference block are compensated during inter prediction. In addition, unique properties of depth video are exploited to reduce side information required for signaling decoder to conduct the same process. To evaluate the coding performance, we have implemented the proposed method into MVC (multiview video coding) reference software, JMVC 8.2. Experimental results have demonstrated that our proposed method is especially efficient for depth videos estimated by DERS (depth estimation reference software) discussed in the MPEG 3DV coding group. The coding gain was up to 11.69% bit-saving, and it was even increased when we evaluated it on synthesized views of virtual viewpoints.

  8. 3D Imaging with a Single-Aperture 3-mm Objective Lens: Concept, Fabrication and Test

    NASA Technical Reports Server (NTRS)

    Korniski, Ron; Bae, Sam Y.; Shearn, Mike; Manohara, Harish; Shahinian, Hrayr

    2011-01-01

    There are many advantages to minimally invasive surgery (MIS). An endoscope is the optical system of choice by the surgeon for MIS. The smaller the incision or opening made to perform the surgery, the smaller the optical system needed. For minimally invasive neurological and skull base surgeries the openings are typically 10-mm in diameter (dime sized) or less. The largest outside diameter (OD) endoscope used is 4mm. A significant drawback to endoscopic MIS is that it only provides a monocular view of the surgical site thereby lacking depth information for the surgeon. A stereo view would provide the surgeon instantaneous depth information of the surroundings within the field of view, a significant advantage especially during brain surgery. Providing 3D imaging in an endoscopic objective lens system presents significant challenges because of the tight packaging constraints. This paper presents a promising new technique for endoscopic 3D imaging that uses a single lens system with complementary multi-bandpass filters (CMBFs), and describes the proof-of-concept demonstrations performed to date validating the technique. These demonstrations of the technique have utilized many commercial off-the-shelf (COTS) components including the ones used in the endoscope objective.

  9. ROOT OO model to render multi-level 3-D geometrical objects via an OpenGL

    NASA Astrophysics Data System (ADS)

    Brun, Rene; Fine, Valeri; Rademakers, Fons

    2001-08-01

    This paper presents a set of C++ low-level classes to render 3D objects within ROOT-based frameworks. This allows developing a set of viewers with different properties the user can choose from to render one and the same 3D objects.

  10. Active learning in the lecture theatre using 3D printed objects

    PubMed Central

    Smith, David P.

    2016-01-01

    The ability to conceptualize 3D shapes is central to understanding biological processes. The concept that the structure of a biological molecule leads to function is a core principle of the biochemical field. Visualisation of biological molecules often involves vocal explanations or the use of two dimensional slides and video presentations. A deeper understanding of these molecules can however be obtained by the handling of objects. 3D printed biological molecules can be used as active learning tools to stimulate engagement in large group lectures. These models can be used to build upon initial core knowledge which can be delivered in either a flipped form or a more didactic manner. Within the teaching session the students are able to learn by handling, rotating and viewing the objects to gain an appreciation, for example, of an enzyme’s active site or the difference between the major and minor groove of DNA. Models and other artefacts can be handled in small groups within a lecture theatre and act as a focal point to generate conversation. Through the approach presented here core knowledge is first established and then supplemented with high level problem solving through a "Think-Pair-Share" cooperative learning strategy. The teaching delivery was adjusted based around experiential learning activities by moving the object from mental cognition and into the physical environment. This approach led to students being able to better visualise biological molecules and a positive engagement in the lecture. The use of objects in teaching allows the lecturer to create interactive sessions that both challenge and enable the student. PMID:27366318

  11. Knowledge guided object detection and identification in 3D point clouds

    NASA Astrophysics Data System (ADS)

    Karmacharya, A.; Boochs, F.; Tietz, B.

    2015-05-01

    Modern instruments like laser scanner and 3D cameras or image based techniques like structure from motion produce huge point clouds as base for further object analysis. This has considerably changed the way of data compilation away from selective manually guided processes towards automatic and computer supported strategies. However it's still a long way to achieve the quality and robustness of manual processes as data sets are mostly very complex. Looking at existing strategies 3D data processing for object detections and reconstruction rely heavily on either data driven or model driven approaches. These approaches come with their limitation on depending highly on the nature of data and inability to handle any deviation. Furthermore, the lack of capabilities to integrate other data or information in between the processing steps further exposes their limitations. This restricts the approaches to be executed with strict predefined strategy and does not allow deviations when and if new unexpected situations arise. We propose a solution that induces intelligence in the processing activities through the usage of semantics. The solution binds the objects along with other related knowledge domains to the numerical processing to facilitate the detection of geometries and then uses experts' inference rules to annotate them. The solution was tested within the prototypical application of the research project "Wissensbasierte Detektion von Objekten in Punktwolken für Anwendungen im Ingenieurbereich (WiDOP)". The flexibility of the solution is demonstrated through two entirely different USE Case scenarios: Deutsche Bahn (German Railway System) for the outdoor scenarios and Fraport (Frankfort Airport) for the indoor scenarios. Apart from the difference in their environments, they provide different conditions, which the solution needs to consider. While locations of the objects in Fraport were previously known, that of DB were not known at the beginning.

  12. Active learning in the lecture theatre using 3D printed objects.

    PubMed

    Smith, David P

    2016-01-01

    The ability to conceptualize 3D shapes is central to understanding biological processes. The concept that the structure of a biological molecule leads to function is a core principle of the biochemical field. Visualisation of biological molecules often involves vocal explanations or the use of two dimensional slides and video presentations. A deeper understanding of these molecules can however be obtained by the handling of objects. 3D printed biological molecules can be used as active learning tools to stimulate engagement in large group lectures. These models can be used to build upon initial core knowledge which can be delivered in either a flipped form or a more didactic manner. Within the teaching session the students are able to learn by handling, rotating and viewing the objects to gain an appreciation, for example, of an enzyme's active site or the difference between the major and minor groove of DNA. Models and other artefacts can be handled in small groups within a lecture theatre and act as a focal point to generate conversation. Through the approach presented here core knowledge is first established and then supplemented with high level problem solving through a "Think-Pair-Share" cooperative learning strategy. The teaching delivery was adjusted based around experiential learning activities by moving the object from mental cognition and into the physical environment. This approach led to students being able to better visualise biological molecules and a positive engagement in the lecture. The use of objects in teaching allows the lecturer to create interactive sessions that both challenge and enable the student.

  13. 3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands.

    PubMed

    Mateo, Carlos M; Gil, Pablo; Torres, Fernando

    2016-05-05

    Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object's surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand's fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

  14. Laser Scanning for 3D Object Characterization: Infrastructure for Exploration and Analysis of Vegetation Signatures

    NASA Astrophysics Data System (ADS)

    Koenig, K.; Höfle, B.

    2012-04-01

    Mapping and characterization of the three-dimensional nature of vegetation is increasingly gaining in importance. Deeper insight is required for e.g. forest management, biodiversity assessment, habitat analysis, precision agriculture, renewable energy production or the analysis of interaction between biosphere and atmosphere. However the potential of 3D vegetation characterization has not been exploited so far and new technologies are needed. Laser scanning has evolved into the state-of-the-art technology for highly accurate 3D data acquisition. By now several studies indicated a high value of 3D vegetation description by using laser data. The laser sensors provide a detailed geometric presentation (geometric information) of scanned objects as well as a full profile of laser energy that was scattered back to the sensor (radiometric information). In order to exploit the full potential of these datasets, profound knowledge on laser scanning technology for data acquisition, geoinformation technology for data analysis and object of interest (e.g. vegetation) for data interpretation have to be joined. A signature database is a collection of signatures of reference vegetation objects acquired under known conditions and sensor parameters and can be used to improve information extraction from unclassified vegetation datasets. Different vegetation elements (leaves, branches, etc.) at different heights above ground with different geometric composition contribute to the overall description (i.e. signature) of the scanned object. The developed tools allow analyzing tree objects according to single features (e.g. echo width and signal amplitude) and to any relation of features and derived statistical values (e.g. ratio of laser point attributes). For example, a single backscatter cross section value does not allow for tree species determination, whereas the average echo width per tree segment can give good estimates. Statistical values and/or distributions (e.g. Gaussian

  15. Sub-OBB based object recognition and localization algorithm using range images

    NASA Astrophysics Data System (ADS)

    Hoang, Dinh-Cuong; Chen, Liang-Chia; Nguyen, Thanh-Hung

    2017-02-01

    This paper presents a novel approach to recognize and estimate pose of the 3D objects in cluttered range images. The key technical breakthrough of the developed approach can enable robust object recognition and localization under undesirable condition such as environmental illumination variation as well as optical occlusion to viewing the object partially. First, the acquired point clouds are segmented into individual object point clouds based on the developed 3D object segmentation for randomly stacked objects. Second, an efficient shape-matching algorithm called Sub-OBB based object recognition by using the proposed oriented bounding box (OBB) regional area-based descriptor is performed to reliably recognize the object. Then, the 3D position and orientation of the object can be roughly estimated by aligning the OBB of segmented object point cloud with OBB of matched point cloud in a database generated from CAD model and 3D virtual camera. To detect accurate pose of the object, the iterative closest point (ICP) algorithm is used to match the object model with the segmented point clouds. From the feasibility test of several scenarios, the developed approach is verified to be feasible for object pose recognition and localization.

  16. Correlative Nanoscale 3D Imaging of Structure and Composition in Extended Objects

    PubMed Central

    Xu, Feng; Helfen, Lukas; Suhonen, Heikki; Elgrabli, Dan; Bayat, Sam; Reischig, Péter; Baumbach, Tilo; Cloetens, Peter

    2012-01-01

    Structure and composition at the nanoscale determine the behavior of biological systems and engineered materials. The drive to understand and control this behavior has placed strong demands on developing methods for high resolution imaging. In general, the improvement of three-dimensional (3D) resolution is accomplished by tightening constraints: reduced manageable specimen sizes, decreasing analyzable volumes, degrading contrasts, and increasing sample preparation efforts. Aiming to overcome these limitations, we present a non-destructive and multiple-contrast imaging technique, using principles of X-ray laminography, thus generalizing tomography towards laterally extended objects. We retain advantages that are usually restricted to 2D microscopic imaging, such as scanning of large areas and subsequent zooming-in towards a region of interest at the highest possible resolution. Our technique permits correlating the 3D structure and the elemental distribution yielding a high sensitivity to variations of the electron density via coherent imaging and to local trace element quantification through X-ray fluorescence. We demonstrate the method by imaging a lithographic nanostructure and an aluminum alloy. Analyzing a biological system, we visualize in lung tissue the subcellular response to toxic stress after exposure to nanotubes. We show that most of the nanotubes are trapped inside alveolar macrophages, while a small portion of the nanotubes has crossed the barrier to the cellular space of the alveolar wall. In general, our method is non-destructive and can be combined with different sample environmental or loading conditions. We therefore anticipate that correlative X-ray nano-laminography will enable a variety of in situ and in operando 3D studies. PMID:23185554

  17. 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

  18. 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.

  19. 3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

    PubMed Central

    Mateo, Carlos M.; Gil, Pablo; Torres, Fernando

    2016-01-01

    Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments. PMID

  20. A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours

    PubMed Central

    Gao, Yi; Kikinis, Ron; Bouix, Sylvain; Shenton, Martha; Tannenbaum, Allen

    2012-01-01

    Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community. In the past, much work has been dedicated only to the algorithmic development. Nevertheless, for clinical end users, a well designed algorithm with an interactive software is necessary for an algorithm to be utilized in their daily work. Furthermore, the software would better be open sourced in order to be used and validated by not only the authors but also the entire community. Therefore, the contribution of the present work is twofolds: First, we propose a new robust statistics based conformal metric and the conformal area driven multiple active contour framework, to simultaneously extract multiple targets from MR and CT medical imagery in 3D. Second, an open source graphically interactive 3D segmentation tool based on the aforementioned contour evolution is implemented and is publicly available for end users on multiple platforms. In using this software for the segmentation task, the process is initiated by the user drawn strokes (seeds) in the target region in the image. Then, the local robust statistics are used to describe the object features, and such features are learned adaptively from the seeds under a non-parametric estimation scheme. Subsequently, several active contours evolve simultaneously with their interactions being motivated by the principles of action and reaction — This not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the entire image domain, which was tacitly or explicitly assumed in many previous works. In doing so, the contours interact and converge to equilibrium at the desired positions of the desired multiple objects. Furthermore, with the aim of not only validating the algorithm and the software, but also demonstrating how the tool is to be used, we

  1. Efficient data exchange: Integrating a vector GIS with an object-oriented, 3-D visualization system

    SciTech Connect

    Kuiper, J.; Ayers, A.; Johnson, R.; Tolbert-Smith, M.

    1996-03-01

    A common problem encountered in Geographic Information System (GIS) modeling is the exchange of data between different software packages to best utilize the unique features of each package. This paper describes a project to integrate two systems through efficient data exchange. The first is a widely used GIS based on a relational data model. This system has a broad set of data input, processing, and output capabilities, but lacks three-dimensional (3-D) visualization and certain modeling functions. The second system is a specialized object-oriented package designed for 3-D visualization and modeling. Although this second system is useful for subsurface modeling and hazardous waste site characterization, it does not provide many of the, capabilities of a complete GIS. The system-integration project resulted in an easy-to-use program to transfer information between the systems, making many of the more complex conversion issues transparent to the user. The strengths of both systems are accessible, allowing the scientist more time to focus on analysis. This paper details the capabilities of the two systems, explains the technical issues associated with data exchange and how they were solved, and outlines an example analysis project that used the integrated systems.

  2. Object and event recognition for stroke rehabilitation

    NASA Astrophysics Data System (ADS)

    Ghali, Ahmed; Cunningham, Andrew S.; Pridmore, Tony P.

    2003-06-01

    Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and machine vision technologies to produce innovative stroke rehabilitation methods. We describe a combined object recognition and event detection system that provides real time feedback to stroke patients performing everyday kitchen tasks necessary for independent living, e.g. making a cup of coffee. The image plane position of each object, including the patient"s hand, is monitored using histogram-based recognition methods. The relative positions of hand and objects are then reported to a task monitor that compares the patient"s actions against a model of the target task. A prototype system has been constructed and is currently undergoing technical and clinical evaluation.

  3. Multiple-View Object Recognition in Smart Camera Networks

    NASA Astrophysics Data System (ADS)

    Yang, Allen Y.; Maji, Subhransu; Christoudias, C. Mario; Darrell, Trevor; Malik, Jitendra; Sastry, S. Shankar

    We study object recognition in low-power, low-bandwidth smart camera networks. The ability to perform robust object recognition is crucial for applications such as visual surveillance to track and identify objects of interest, and overcome visual nuisances such as occlusion and pose variations between multiple camera views. To accommodate limited bandwidth between the cameras and the base-station computer, the method utilizes the available computational power on the smart sensors to locally extract SIFT-type image features to represent individual camera views. We show that between a network of cameras, high-dimensional SIFT histograms exhibit a joint sparse pattern corresponding to a set of shared features in 3-D. Such joint sparse patterns can be explicitly exploited to encode the distributed signal via random projections. At the network station, multiple decoding schemes are studied to simultaneously recover the multiple-view object features based on a distributed compressive sensing theory. The system has been implemented on the Berkeley CITRIC smart camera platform. The efficacy of the algorithm is validated through extensive simulation and experiment.

  4. Support plane method applied to ground objects recognition using modelled SAR images

    NASA Astrophysics Data System (ADS)

    Zherdev, Denis A.; Fursov, Vladimir A.

    2015-09-01

    In this study, the object recognition problem was solved using support plane method. The modelled SAR images were used as features vectors in the recognition algorithm. Radar signal backscattering of objects in different observing poses is presented in SAR images. For real time simulation, we used simple mixture model of Lambertian-specular reflectivity. To this end, an algorithm of ray tracing is extended for simulating SAR images of 3D man-made models. The suggested algorithm of support plane is very effective in objects recognition using SAR images and RCS diagrams.

  5. Haptic object recognition is view-independent in early blind but not sighted people

    PubMed Central

    Occelli, Valeria; Lacey, Simon; Stephens, Careese; John, Thomas; Sathian, K.

    2016-01-01

    Object recognition, whether visual or haptic, is impaired in sighted people when objects are rotated between learning and test, relative to an unrotated condition, i.e., recognition is view-dependent. Loss of vision early in life results in greater reliance on haptic perception for object identification compared to the sighted. Therefore, we hypothesized that early blind people may be more adept at recognizing objects despite spatial transformations. To test this hypothesis, we compared early blind and sighted control participants on a haptic object recognition task. Participants studied pairs of unfamiliar 3-D objects and performed a two-alternative forced-choice identification task, with the learned objects presented both unrotated and rotated 180° about the y-axis. Rotation impaired the recognition accuracy of sighted, but not blind, participants. We propose that, consistent with our hypothesis, haptic view-independence in the early blind reflects their greater experience with haptic object perception. PMID:26562881

  6. LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

    PubMed

    He, Wei; Liang, Zhi; Teng, MaiKun; Niu, LiWen

    2016-12-01

    Identifying conserved binding motifs is an efficient way to study protein-ligand recognition. Most 3D binding motifs only contain information from the protein side, and so motifs that combine information from both protein and ligand sides are desired. Here, we propose an algorithm called LibME (Ligand-binding Motif Extractor), which automatically extracts 3D binding motifs composed of the target ligand and surrounding conserved residues. We show that the motifs extracted by LibME for ATP and its analogs are highly similar to well-known motifs reported by previous studies. The superiority of our method to handle flexible ligands was also demonstrated using isocitric acid as an example. Finally, we show that these motifs, together with their visual exhibition, permit better investigating and understanding of protein-ligand recognition process.

  7. Performance analysis of different surface reconstruction algorithms for 3D reconstruction of outdoor objects from their digital images.

    PubMed

    Maiti, Abhik; Chakravarty, Debashish

    2016-01-01

    3D reconstruction of geo-objects from their digital images is a time-efficient and convenient way of studying the structural features of the object being modelled. This paper presents a 3D reconstruction methodology which can be used to generate photo-realistic 3D watertight surface of different irregular shaped objects, from digital image sequences of the objects. The 3D reconstruction approach described here is robust, simplistic and can be readily used in reconstructing watertight 3D surface of any object from its digital image sequence. Here, digital images of different objects are used to build sparse, followed by dense 3D point clouds of the objects. These image-obtained point clouds are then used for generation of photo-realistic 3D surfaces, using different surface reconstruction algorithms such as Poisson reconstruction and Ball-pivoting algorithm. Different control parameters of these algorithms are identified, which affect the quality and computation time of the reconstructed 3D surface. The effects of these control parameters in generation of 3D surface from point clouds of different density are studied. It is shown that the reconstructed surface quality of Poisson reconstruction depends on Samples per node (SN) significantly, greater SN values resulting in better quality surfaces. Also, the quality of the 3D surface generated using Ball-Pivoting algorithm is found to be highly depend upon Clustering radius and Angle threshold values. The results obtained from this study give the readers of the article a valuable insight into the effects of different control parameters on determining the reconstructed surface quality.

  8. Laser-assisted direct manufacturing of functionally graded 3D objects

    NASA Astrophysics Data System (ADS)

    Iakovlev, A.; Trunova, E.; Grevey, Dominique; Smurov, Igor

    2003-09-01

    Coaxial powder injection into a laser beam was applied for the laser-assisted direct manufacturing of 3D functionally graded (FG) objects. The powders of Stainless Steel 316L and Stellite grade 12 were applied. The following laser sources were used: (1) quasi-cw CO2 Rofin Sinar laser with 120 μm focal spot diameter and (2) pulsed-periodic Nd:YAG (HAAS HL 304P) with 200 μm focal spot diameter. The objects were fabricated layer-by-layer in the form of "walls", having the thickness of about 200 μm for CO2 laser and 300 μm for Nd:YAG laser. SEM analysis was applied for the FG objects fabricated by CO2 laser, yielding wall elements distribution in vertical direction. It was found that microhardness distribution is fully correlated with the components distribution. The compositional gradient can be smooth or sharp. Periodic multi-layered structures can be obtained as well. Minimal thickness of a layer with the fixed composition (for cw CO2 laser) is about 50 μm. Minimal thickness of a graded material zone, i.e. zone with composition variation from pure stainless steel to pure stellite is about 30 μm.

  9. Polarization imaging of a 3D object by use of on-axis phase-shifting digital holography.

    PubMed

    Nomura, Takanori; Javidi, Bahram; Murata, Shinji; Nitanai, Eiji; Numata, Takuhisa

    2007-03-01

    A polarimetric imaging method of a 3D object by use of on-axis phase-shifting digital holography is presented. The polarimetric image results from a combination of two kinds of holographic imaging using orthogonal polarized reference waves. Experimental demonstration of a 3D polarimetric imaging is presented.

  10. A modern approach to storing of 3D geometry of objects in machine engineering industry

    NASA Astrophysics Data System (ADS)

    Sokolova, E. A.; Aslanov, G. A.; Sokolov, A. A.

    2017-02-01

    3D graphics is a kind of computer graphics which has absorbed a lot from the vector and raster computer graphics. It is used in interior design projects, architectural projects, advertising, while creating educational computer programs, movies, visual images of parts and products in engineering, etc. 3D computer graphics allows one to create 3D scenes along with simulation of light conditions and setting up standpoints.

  11. Automatic anatomy recognition via fuzzy object models

    NASA Astrophysics Data System (ADS)

    Udupa, Jayaram K.; Odhner, Dewey; Falcão, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Matsumoto, Monica; Grevera, George J.; Saboury, Babak; Torigian, Drew A.

    2012-02-01

    To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) during radiological image reading becomes essential. As part of this larger goal, last year at this conference we presented a fuzzy strategy for building body-wide group-wise anatomic models. In the present paper, we describe the further advances made in fuzzy modeling and the algorithms and results achieved for AAR by using the fuzzy models. The proposed AAR approach consists of three distinct steps: (a) Building fuzzy object models (FOMs) for each population group G. (b) By using the FOMs to recognize the individual objects in any given patient image I under group G. (c) To delineate the recognized objects in I. This paper will focus mostly on (b). FOMs are built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. The hierarchical pose relationships from the parent to offspring are codified in the FOMs. Several approaches are being explored currently, grouped under two strategies, both being hierarchical: (ra1) those using search strategies; (ra2) those strategizing a one-shot approach by which the model pose is directly estimated without searching. Based on 32 patient CT data sets each from the thorax and abdomen and 25 objects modeled, our analysis indicates that objects do not all scale uniformly with patient size. Even the simplest among the (ra2) strategies of recognizing the root object and then placing all other descendants as per the learned parent-to-offspring pose relationship bring the models on an average within about 18 mm of the true locations.

  12. Vectorial seismic modeling for 3D objects by the classical solution

    NASA Astrophysics Data System (ADS)

    Ávila-Carrera, R.; Sánchez-Sesma, F. J.; Rodríguez-Castellanos, A.; Ortiz-Alemán, C.

    2010-09-01

    The analytic benchmark solution for the scattering and diffraction of elastic P- and S-waves by a single spherical obstacle is presented in a condensed format. Our aim is divulge to the scientific community this not widely known classical solution to construct a direct seismic model for 3D objects. Some of the benchmark papers are frequently plagued by misprints and none offers results on the transient response. The treatment of the vectorial case appears to be insipient in the literature. The classical solution is a superposition of incident and diffracted fields. Plane P- or S-waves are assumed. They are expressed as expansions of spherical wave functions which are tested against exact results. The diffracted field by the obstacle is calculated from the analytical enforcing of boundary conditions at the scatterer-matrix interface. The spherical obstacle is a cavity, an elastic inclusion or a fluid-filled body. A complete set of wave functions is used in terms of Bessel and Hankel radial functions. Legendre and trigonometric functions are used for the angular coordinates. In order to provide information to calibrate and approximate the seismic modeling for real objects, results are shown in time and frequency domains. Diffracted displacements amplitudes versus normalized frequency and radiation patterns for various scatterer-matrix properties are reported. To study propagation features that may be useful to geophysicists and engineers, synthetic seismograms for some relevant cases are computed.

  13. Laser Fabrication of Affective 3D Objects with 1/f Fluctuation

    NASA Astrophysics Data System (ADS)

    Maekawa, Katsuhiro; Nishii, Tomohiro; Hayashi, Terutake; Akabane, Hideo; Agu, Masahiro

    The present paper describes the application of Kansei Engineering to the physical design of engineering products as well as its realization by laser sintering. We have investigated the affective information that might be included in three-dimensional objects such as a ceramic bowl for the tea ceremony. First, an X-ray CT apparatus is utilized to retrieve surface data from the teabowl, and then a frequency analysis is carried out after noise has been filtered. The surface fluctuation is characterized by a power spectrum that is in inverse proportion to the wave number f in circumference. Second, we consider how to realize the surface with a 1/f fluctuation on a computer screen using a 3D CAD model. The fluctuation is applied to a reference shape assuming that the outer surface has a spiral flow line on which unevenness is superimposed. Finally, the selective laser sintering method has been applied to the fabrication of 1/f fluctuation objects. Nylon powder is sintered layer by layer using a CO2 laser to form an artificial teabowl with complicated surface contours.

  14. Detection of hidden objects using a real-time 3-D millimeter-wave imaging system

    NASA Astrophysics Data System (ADS)

    Rozban, Daniel; Aharon, Avihai; Levanon, Assaf; Abramovich, Amir; Yitzhaky, Yitzhak; Kopeika, N. S.

    2014-10-01

    Millimeter (mm)and sub-mm wavelengths or terahertz (THz) band have several properties that motivate their use in imaging for security applications such as recognition of hidden objects, dangerous materials, aerosols, imaging through walls as in hostage situations, and also in bad weather conditions. There is no known ionization hazard for biological tissue, and atmospheric degradation of THz radiation is relatively low for practical imaging distances. We recently developed a new technology for the detection of THz radiation. This technology is based on very inexpensive plasma neon indicator lamps, also known as Glow Discharge Detector (GDD), that can be used as very sensitive THz radiation detectors. Using them, we designed and constructed a Focal Plane Array (FPA) and obtained recognizable2-dimensional THz images of both dielectric and metallic objects. Using THz wave it is shown here that even concealed weapons made of dielectric material can be detected. An example is an image of a knife concealed inside a leather bag and also under heavy clothing. Three-dimensional imaging using radar methods can enhance those images since it can allow the isolation of the concealed objects from the body and environmental clutter such as nearby furniture or other people. The GDDs enable direct heterodyning between the electric field of the target signal and the reference signal eliminating the requirement for expensive mixers, sources, and Low Noise Amplifiers (LNAs).We expanded the ability of the FPA so that we are able to obtain recognizable 2-dimensional THz images in real time. We show here that the THz detection of objects in three dimensions, using FMCW principles is also applicable in real time. This imaging system is also shown here to be capable of imaging objects from distances allowing standoff detection of suspicious objects and humans from large distances.

  15. Modeling 4D Human-Object Interactions for Joint Event Segmentation, Recognition, and Object Localization.

    PubMed

    Wei, Ping; Zhao, Yibiao; Zheng, Nanning; Zhu, Song-Chun

    2016-06-01

    In this paper, we present a 4D human-object interaction (4DHOI) model for solving three vision tasks jointly: i) event segmentation from a video sequence, ii) event recognition and parsing, and iii) contextual object localization. The 4DHOI model represents the geometric, temporal, and semantic relations in daily events involving human-object interactions. In 3D space, the interactions of human poses and contextual objects are modeled by semantic co-occurrence and geometric compatibility. On the time axis, the interactions are represented as a sequence of atomic event transitions with coherent objects. The 4DHOI model is a hierarchical spatial-temporal graph representation which can be used for inferring scene functionality and object affordance. The graph structures and parameters are learned using an ordered expectation maximization algorithm which mines the spatial-temporal structures of events from RGB-D video samples. Given an input RGB-D video, the inference is performed by a dynamic programming beam search algorithm which simultaneously carries out event segmentation, recognition, and object localization. We collected and released a large multiview RGB-D event dataset which contains 3,815 video sequences and 383,036 RGB-D frames captured by three RGB-D cameras. The experimental results on three challenging datasets demonstrate the strength of the proposed method.

  16. Software for Building Models of 3D Objects via the Internet

    NASA Technical Reports Server (NTRS)

    Schramer, Tim; Jensen, Jeff

    2003-01-01

    The Virtual EDF Builder (where EDF signifies Electronic Development Fixture) is a computer program that facilitates the use of the Internet for building and displaying digital models of three-dimensional (3D) objects that ordinarily comprise assemblies of solid models created previously by use of computer-aided-design (CAD) programs. The Virtual EDF Builder resides on a Unix-based server computer. It is used in conjunction with a commercially available Web-based plug-in viewer program that runs on a client computer. The Virtual EDF Builder acts as a translator between the viewer program and a database stored on the server. The translation function includes the provision of uniform resource locator (URL) links to other Web-based computer systems and databases. The Virtual EDF builder can be used in two ways: (1) If the client computer is Unix-based, then it can assemble a model locally; the computational load is transferred from the server to the client computer. (2) Alternatively, the server can be made to build the model, in which case the server bears the computational load and the results are downloaded to the client computer or workstation upon completion.

  17. The Chinese Facial Emotion Recognition Database (CFERD): a computer-generated 3-D paradigm to measure the recognition of facial emotional expressions at different intensities.

    PubMed

    Huang, Charles Lung-Cheng; Hsiao, Sigmund; Hwu, Hai-Gwo; Howng, Shen-Long

    2012-12-30

    The Chinese Facial Emotion Recognition Database (CFERD), a computer-generated three-dimensional (3D) paradigm, was developed to measure the recognition of facial emotional expressions at different intensities. The stimuli consisted of 3D colour photographic images of six basic facial emotional expressions (happiness, sadness, disgust, fear, anger and surprise) and neutral faces of the Chinese. The purpose of the present study is to describe the development and validation of CFERD with nonclinical healthy participants (N=100; 50 men; age ranging between 18 and 50 years), and to generate normative data set. The results showed that the sensitivity index d' [d'=Z(hit rate)-Z(false alarm rate), where function Z(p), p∈[0,1

  18. 3D models automatic reconstruction of selected close range objects. (Polish Title: Automatyczna rekonstrukcja modeli 3D małych obiektów bliskiego zasiegu)

    NASA Astrophysics Data System (ADS)

    Zaweiska, D.

    2013-12-01

    Reconstruction of three-dimensional, realistic models of objects from digital images has been the topic of research in many areas of science for many years. This development is stimulated by new technologies and tools, which appeared recently, such as digital photography, laser scanners, increase in the equipment efficiency and Internet. The objective of this paper is to present results of automatic modeling of selected close range objects, with the use of digital photographs acquired by the Hasselblad H4D50 camera. The author's software tool was utilized for calculations; it performs successive stages of the 3D model creation. The modeling process was presented as the complete process which starts from acquisition of images and which is completed by creation of a photorealistic 3D model in the same software environment. Experiments were performed for selected close range objects, with appropriately arranged image geometry, creating a ring around the measured object. The Area Base Matching (CC/LSM) method, the RANSAC algorithm, with the use of tensor calculus, were utilized form automatic matching of points detected with the SUSAN algorithm. Reconstruction of the surface of model generation is one of the important stages of 3D modeling. Reconstruction of precise surfaces, performed on the basis of a non-organized cloud of points, acquired from automatic processing of digital images, is a difficult task, which has not been finally solved. Creation of poly-angular models, which may meet high requirements concerning modeling and visualization is required in many applications. The polynomial method is usually the best way to precise representation of measurement results, and, at the same time, to achieving the optimum description of the surface. Three algorithm were tested: the volumetric method (VCG), the Poisson method and the Ball pivoting method. Those methods are mostly applied to modeling of uniform grids of points. Results of experiments proved that incorrect

  19. Neural systems for recognition of emotional prosody: a 3-D lesion study.

    PubMed

    Adolphs, Ralph; Damasio, Hanna; Tranel, Daniel

    2002-03-01

    Which brain regions are associated with recognition of emotional prosody? Are these distinct from those for recognition of facial expression? These issues were investigated by mapping the overlaps of co-registered lesions from 66 brain-damaged participants as a function of their performance in rating basic emotions. It was found that recognizing emotions from prosody draws on the right frontoparietal operculum, the bilateral frontal pole, and the left frontal operculum. Recognizing emotions from prosody and facial expressions draws on the right frontoparietal cortex, which may be important in reconstructing aspects of the emotion signaled by the stimulus. Furthermore, there were regions in the left and right temporal lobes that contributed disproportionately to recognition of emotion from faces or prosody, respectively.

  20. Quantitative analysis and feature recognition in 3-D microstructural data sets

    NASA Astrophysics Data System (ADS)

    Lewis, A. C.; Suh, C.; Stukowski, M.; Geltmacher, A. B.; Spanos, G.; Rajan, K.

    2006-12-01

    A three-dimensional (3-D) reconstruction of an austenitic stainless-steel microstructure was used as input for an image-based finite-element model to simulate the anisotropic elastic mechanical response of the microstructure. The quantitative data-mining and data-warehousing techniques used to correlate regions of high stress with critical microstructural features are discussed. Initial analysis of elastic stresses near grain boundaries due to mechanical loading revealed low overall correlation with their location in the microstructure. However, the use of data-mining and feature-tracking techniques to identify high-stress outliers revealed that many of these high-stress points are generated near grain boundaries and grain edges (triple junctions). These techniques also allowed for the differentiation between high stresses due to boundary conditions of the finite volume reconstructed, and those due to 3-D microstructural features.

  1. 3D face recognition system using cylindrical hidden-layer neural network: spatial domain and its eigenspace domain

    NASA Astrophysics Data System (ADS)

    Kusumoputro, Benyamin; Pangabean, Martha Y.; Rachman, Leila F.

    2001-09-01

    In this paper, a 3-D face recognition system is developed using a modified neural network. This modified neural network is constructed by substituting each of neuron in its hidden layer of conventional multilayer perceptron with a circular-structure of neurons. This neural system is then called as cylindrical-structure of hidden layer neural network (CHL-NN). The neural system is then applied on a real 3-D face image database that consists of 5 Indonesian persons. The images are taken under four different expressions such as neutral, smile, laugh and free expression. The 2-D images is taken from the human face images by gradually changing visual points, which is done by successively varies the camera position from - 90 to +90 with an interval of 15 degree. The experimental result has shown that the average recognition rate of 60% could be achieved when we used the image in its spatial domain. Improvement of the system is then developed, by transforming the image in its spatial domain into its eigenspace domain. Karhunen Loeve transformation technique is used, and each image in the spatial domain is represented as a point in the eigenspace domain. Fisherface method is then utilized as a feature extraction on the eigenspace domain, and using the same database and experimental procedure, the recognition rate of the system could be increased into 84% in average.

  2. 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…

  3. Laser Transfer of Metals and Metal Alloys for Digital Microfabrication of 3D Objects.

    PubMed

    Zenou, Michael; Sa'ar, Amir; Kotler, Zvi

    2015-09-02

    3D copper logos printed on epoxy glass laminates are demonstrated. The structures are printed using laser transfer of molten metal microdroplets. The example in the image shows letters of 50 µm width, with each letter being taller than the last, from a height of 40 µm ('s') to 190 µm ('l'). The scanning microscopy image is taken at a tilt, and the topographic image was taken using interferometric 3D microscopy, to show the effective control of this technique.

  4. A new approach for semi-automatic rock mass joints recognition from 3D point clouds

    NASA Astrophysics Data System (ADS)

    Riquelme, Adrián J.; Abellán, A.; Tomás, R.; Jaboyedoff, M.

    2014-07-01

    Rock mass characterization requires a deep geometric understanding of the discontinuity sets affecting rock exposures. Recent advances in Light Detection and Ranging (LiDAR) instrumentation currently allow quick and accurate 3D data acquisition, yielding on the development of new methodologies for the automatic characterization of rock mass discontinuities. This paper presents a methodology for the identification and analysis of flat surfaces outcropping in a rocky slope using the 3D data obtained with LiDAR. This method identifies and defines the algebraic equations of the different planes of the rock slope surface by applying an analysis based on a neighbouring points coplanarity test, finding principal orientations by Kernel Density Estimation and identifying clusters by the Density-Based Scan Algorithm with Noise. Different sources of information - synthetic and 3D scanned data - were employed, performing a complete sensitivity analysis of the parameters in order to identify the optimal value of the variables of the proposed method. In addition, raw source files and obtained results are freely provided in order to allow to a more straightforward method comparison aiming to a more reproducible research.

  5. True-3D accentuating of grids and streets in urban topographic maps enhances human object location memory.

    PubMed

    Edler, Dennis; Bestgen, Anne-Kathrin; Kuchinke, Lars; Dickmann, Frank

    2015-01-01

    Cognitive representations of learned map information are subject to systematic distortion errors. Map elements that divide a map surface into regions, such as content-related linear symbols (e.g. streets, rivers, railway systems) or additional artificial layers (coordinate grids), provide an orientation pattern that can help users to reduce distortions in their mental representations. In recent years, the television industry has started to establish True-3D (autostereoscopic) displays as mass media. These modern displays make it possible to watch dynamic and static images including depth illusions without additional devices, such as 3D glasses. In these images, visual details can be distributed over different positions along the depth axis. Some empirical studies of vision research provided first evidence that 3D stereoscopic content attracts higher attention and is processed faster. So far, the impact of True-3D accentuating has not yet been explored concerning spatial memory tasks and cartography. This paper reports the results of two empirical studies that focus on investigations whether True-3D accentuating of artificial, regular overlaying line features (i.e. grids) and content-related, irregular line features (i.e. highways and main streets) in official urban topographic maps (scale 1/10,000) further improves human object location memory performance. The memory performance is measured as both the percentage of correctly recalled object locations (hit rate) and the mean distances of correctly recalled objects (spatial accuracy). It is shown that the True-3D accentuating of grids (depth offset: 5 cm) significantly enhances the spatial accuracy of recalled map object locations, whereas the True-3D emphasis of streets significantly improves the hit rate of recalled map object locations. These results show the potential of True-3D displays for an improvement of the cognitive representation of learned cartographic information.

  6. True-3D Accentuating of Grids and Streets in Urban Topographic Maps Enhances Human Object Location Memory

    PubMed Central

    Edler, Dennis; Bestgen, Anne-Kathrin; Kuchinke, Lars; Dickmann, Frank

    2015-01-01

    Cognitive representations of learned map information are subject to systematic distortion errors. Map elements that divide a map surface into regions, such as content-related linear symbols (e.g. streets, rivers, railway systems) or additional artificial layers (coordinate grids), provide an orientation pattern that can help users to reduce distortions in their mental representations. In recent years, the television industry has started to establish True-3D (autostereoscopic) displays as mass media. These modern displays make it possible to watch dynamic and static images including depth illusions without additional devices, such as 3D glasses. In these images, visual details can be distributed over different positions along the depth axis. Some empirical studies of vision research provided first evidence that 3D stereoscopic content attracts higher attention and is processed faster. So far, the impact of True-3D accentuating has not yet been explored concerning spatial memory tasks and cartography. This paper reports the results of two empirical studies that focus on investigations whether True-3D accentuating of artificial, regular overlaying line features (i.e. grids) and content-related, irregular line features (i.e. highways and main streets) in official urban topographic maps (scale 1/10,000) further improves human object location memory performance. The memory performance is measured as both the percentage of correctly recalled object locations (hit rate) and the mean distances of correctly recalled objects (spatial accuracy). It is shown that the True-3D accentuating of grids (depth offset: 5 cm) significantly enhances the spatial accuracy of recalled map object locations, whereas the True-3D emphasis of streets significantly improves the hit rate of recalled map object locations. These results show the potential of True-3D displays for an improvement of the cognitive representation of learned cartographic information. PMID:25679208

  7. Visual Object Recognition and Tracking of Tools

    NASA Technical Reports Server (NTRS)

    English, James; Chang, Chu-Yin; Tardella, Neil

    2011-01-01

    A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images

  8. Object recognition using cylindrical harmonic filter.

    PubMed

    Guerrero Bermúdez, Jáder

    2004-06-28

    We present the cylindrical harmonic filter for three-dimensional (3D) discrete correlation between range data. The filter guarantees invariance of the correlation peak intensity under target rotation around z-axis. It can be considered a harmonic decomposition, in cylindrical coordinates, of the 3D Fourier spectrum of the target. Some simulation results confirm the in-plane rotation invariance and the discrimination of the filter.

  9. Affective SSVEP BCI to effectively control 3D objects by using a prism array-based display

    NASA Astrophysics Data System (ADS)

    Mun, Sungchul; Park, Min-Chul

    2014-06-01

    3D objects with depth information can provide many benefits to users in education, surgery, and interactions. In particular, many studies have been done to enhance sense of reality in 3D interaction. Viewing and controlling stereoscopic 3D objects with crossed or uncrossed disparities, however, can cause visual fatigue due to the vergenceaccommodation conflict generally accepted in 3D research fields. In order to avoid the vergence-accommodation mismatch and provide a strong sense of presence to users, we apply a prism array-based display to presenting 3D objects. Emotional pictures were used as visual stimuli in control panels to increase information transfer rate and reduce false positives in controlling 3D objects. Involuntarily motivated selective attention by affective mechanism can enhance steady-state visually evoked potential (SSVEP) amplitude and lead to increased interaction efficiency. More attentional resources are allocated to affective pictures with high valence and arousal levels than to normal visual stimuli such as white-and-black oscillating squares and checkerboards. Among representative BCI control components (i.e., eventrelated potentials (ERP), event-related (de)synchronization (ERD/ERS), and SSVEP), SSVEP-based BCI was chosen in the following reasons. It shows high information transfer rates and takes a few minutes for users to control BCI system while few electrodes are required for obtaining reliable brainwave signals enough to capture users' intention. The proposed BCI methods are expected to enhance sense of reality in 3D space without causing critical visual fatigue to occur. In addition, people who are very susceptible to (auto) stereoscopic 3D may be able to use the affective BCI.

  10. Hippocampal histone acetylation regulates object recognition and the estradiol-induced enhancement of object recognition.

    PubMed

    Zhao, Zaorui; Fan, Lu; Fortress, Ashley M; Boulware, Marissa I; Frick, Karyn M

    2012-02-15

    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. 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.

  11. Flying triangulation--an optical 3D sensor for the motion-robust acquisition of complex objects.

    PubMed

    Ettl, Svenja; Arold, Oliver; Yang, Zheng; Häusler, Gerd

    2012-01-10

    Three-dimensional (3D) shape acquisition is difficult if an all-around measurement of an object is desired or if a relative motion between object and sensor is unavoidable. An optical sensor principle is presented-we call it "flying triangulation"-that enables a motion-robust acquisition of 3D surface topography. It combines a simple handheld sensor with sophisticated registration algorithms. An easy acquisition of complex objects is possible-just by freely hand-guiding the sensor around the object. Real-time feedback of the sequential measurement results enables a comfortable handling for the user. No tracking is necessary. In contrast to most other eligible sensors, the presented sensor generates 3D data from each single camera image.

  12. 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.

  13. Visualizing 3D Objects from 2D Cross Sectional Images Displayed "In-Situ" versus "Ex-Situ"

    ERIC Educational Resources Information Center

    Wu, Bing; Klatzky, Roberta L.; Stetten, George

    2010-01-01

    The present research investigates how mental visualization of a 3D object from 2D cross sectional images is influenced by displacing the images from the source object, as is customary in medical imaging. Three experiments were conducted to assess people's ability to integrate spatial information over a series of cross sectional images in order to…

  14. Object recognition memory: neurobiological mechanisms of encoding, consolidation and retrieval.

    PubMed

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

    2008-07-01

    Tests of object recognition memory, or the judgment of the prior occurrence of an object, have made substantial contributions to our understanding of the nature and neurobiological underpinnings of mammalian memory. Only in recent years, however, have researchers begun to elucidate the specific brain areas and neural processes involved in object recognition memory. The present review considers some of this recent research, with an emphasis on studies addressing the neural bases of perirhinal cortex-dependent object recognition memory processes. We first briefly discuss operational definitions of object recognition and the common behavioural tests used to measure it in non-human primates and rodents. We then consider research from the non-human primate and rat literature examining the anatomical basis of object recognition memory in the delayed nonmatching-to-sample (DNMS) and spontaneous object recognition (SOR) tasks, respectively. The results of these studies overwhelmingly favor the view that perirhinal cortex (PRh) is a critical region for object recognition memory. We then discuss the involvement of PRh in the different stages--encoding, consolidation, and retrieval--of object recognition memory. Specifically, recent work in rats has indicated that neural activity in PRh contributes to object memory encoding, consolidation, and retrieval processes. Finally, we consider the pharmacological, cellular, and molecular factors that might play a part in PRh-mediated object recognition memory. Recent studies in rodents have begun to indicate the remarkable complexity of the neural substrates underlying this seemingly simple aspect of declarative memory.

  15. 3D modeling of architectural objects from video data obtained with the fixed focal length lens geometry

    NASA Astrophysics Data System (ADS)

    Deliś, Paulina; Kędzierski, Michał; Fryśkowska, Anna; Wilińska, Michalina

    2013-12-01

    The article describes the process of creating 3D models of architectural objects on the basis of video images, which had been acquired by a Sony NEX-VG10E fixed focal length video camera. It was assumed, that based on video and Terrestrial Laser Scanning data it is possible to develop 3D models of architectural objects. The acquisition of video data was preceded by the calibration of video camera. The process of creating 3D models from video data involves the following steps: video frames selection for the orientation process, orientation of video frames using points with known coordinates from Terrestrial Laser Scanning (TLS), generating a TIN model using automatic matching methods. The above objects have been measured with an impulse laser scanner, Leica ScanStation 2. Created 3D models of architectural objects were compared with 3D models of the same objects for which the self-calibration bundle adjustment process was performed. In this order a PhotoModeler Software was used. In order to assess the accuracy of the developed 3D models of architectural objects, points with known coordinates from Terrestrial Laser Scanning were used. To assess the accuracy a shortest distance method was used. Analysis of the accuracy showed that 3D models generated from video images differ by about 0.06 ÷ 0.13 m compared to TLS data. Artykuł zawiera opis procesu opracowania modeli 3D obiektów architektonicznych na podstawie obrazów wideo pozyskanych kamerą wideo Sony NEX-VG10E ze stałoogniskowym obiektywem. Przyjęto założenie, że na podstawie danych wideo i danych z naziemnego skaningu laserowego (NSL) możliwe jest opracowanie modeli 3D obiektów architektonicznych. Pozyskanie danych wideo zostało poprzedzone kalibracją kamery wideo. Model matematyczny kamery był oparty na rzucie perspektywicznym. Proces opracowania modeli 3D na podstawie danych wideo składał się z następujących etapów: wybór klatek wideo do procesu orientacji, orientacja klatek wideo na

  16. The 3-D image recognition based on fuzzy neural network technology

    NASA Technical Reports Server (NTRS)

    Hirota, Kaoru; Yamauchi, Kenichi; Murakami, Jun; Tanaka, Kei

    1993-01-01

    Three dimensional stereoscopic image recognition system based on fuzzy-neural network technology was developed. The system consists of three parts; preprocessing part, feature extraction part, and matching part. Two CCD color camera image are fed to the preprocessing part, where several operations including RGB-HSV transformation are done. A multi-layer perception is used for the line detection in the feature extraction part. Then fuzzy matching technique is introduced in the matching part. The system is realized on SUN spark station and special image input hardware system. An experimental result on bottle images is also presented.

  17. 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.

  18. Genome-Wide Identification and 3D Modeling of Proteins involved in DNA Damage Recognition and Repair (Final Report)

    SciTech Connect

    Abagyan, Ruben; An, Jianghong

    2005-08-12

    DNA Damage Recognition and Repair (DDR&R) proteins play a critical role in cellular responses to low-dose radiation and are associated with cancer. We have performed a systematic, genome-wide computational analysis of genomic data for human genes involved in the DDR&R process. The significant achievements of this project include: 1) Construction of the computational pipeline for searching DDR&R genes, building and validation of 3D models of proteins involved in DDR&R; 2) Functional and structural annotation of the 3D models and generation of comprehensive lists of suggested knock-out mutations; and the development of a method to predict the effects of mutations. Large scale testing of technology to identify novel small binding pockets in protein structures leading to new DDRR inhibitor strategies 3) Improvements of macromolecular docking technology (see the CAPRI 1-3 and 4-5 results) 4) Development of a new algorithm for improved analysis of high-density oligonucleotide arrays for gene expression profiling; 5) Construction and maintenance of the DNA Damage Recognition and Repair Database; 6) Producing 15 research papers (12 published and 3 in preparation).

  19. Genome-Wide Identification and 3D Modeling of Proteins involved in DNA Damage Recognition and Repair (Final Report)

    SciTech Connect

    Ruben A. Abagyan, PhD

    2004-04-15

    OAK-B135 DNA Damage Recognition and Repair (DDR and R) proteins play a critical role in cellular responses to low-dose radiation and are associated with cancer. the authors have performed a systematic, genome-wide computational analysis of genomic data for human genes involved in the DDR and R process. The significant achievements of this project include: (1) Construction of the computational pipeline for searching DDR and R genes, building and validation of 3D models of proteins involved in DDR and R; (2) Functional and structural annotation of the 3D models and generation of comprehensive lists of suggested knock-out mutations; (3) Important improvement of macromolecular docking technology and its application to predict the DNA-Protein complex conformation; (4) Development of a new algorithm for improved analysis of high-density oligonucleotide arrays for gene expression profiling; (5) Construction and maintenance of the DNA Damage Recognition and Repair Database; and (6) Producing 14 research papers (10 published and 4 in preparation).

  20. Online 3D Ear Recognition by Combining Global and Local Features

    PubMed Central

    Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

    2016-01-01

    The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%. PMID:27935955

  1. The 3D Recognition, Generation, Fusion, Update and Refinement (RG4) Concept

    NASA Technical Reports Server (NTRS)

    Maluf, David A.; Cheeseman, Peter; Smelyanskyi, Vadim N.; Kuehnel, Frank; Morris, Robin D.; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an active (real time) recognition strategy whereby information is inferred iteratively across several viewpoints in descent imagery. We will show how we use inverse theory within the context of parametric model generation, namely height and spectral reflection functions, to generate model assertions. Using this strategy in an active context implies that, from every viewpoint, the proposed system must refine its hypotheses taking into account the image and the effect of uncertainties as well. The proposed system employs probabilistic solutions to the problem of iteratively merging information (images) from several viewpoints. This involves feeding the posterior distribution from all previous images as a prior for the next view. Novel approaches will be developed to accelerate the inversion search using novel statistic implementations and reducing the model complexity using foveated vision. Foveated vision refers to imagery where the resolution varies across the image. In this paper, we allow the model to be foveated where the highest resolution region is called the foveation region. Typically, the images will have dynamic control of the location of the foveation region. For descent imagery in the Entry, Descent, and Landing (EDL) process, it is possible to have more than one foveation region. This research initiative is directed towards descent imagery in connection with NASA's EDL applications. Three-Dimensional Model Recognition, Generation, Fusion, Update, and Refinement (RGFUR or RG4) for height and the spectral reflection characteristics are in focus for various reasons, one of which is the prospect that their interpretation will provide for real time active vision for automated EDL.

  2. 360 degree realistic 3D image display and image processing from real objects

    NASA Astrophysics Data System (ADS)

    Luo, Xin; Chen, Yue; Huang, Yong; Tan, Xiaodi; Horimai, Hideyoshi

    2016-12-01

    A 360-degree realistic 3D image display system based on direct light scanning method, so-called Holo-Table has been introduced in this paper. High-density directional continuous 3D motion images can be displayed easily with only one spatial light modulator. Using the holographic screen as the beam deflector, 360-degree full horizontal viewing angle was achieved. As an accompany part of the system, CMOS camera based image acquisition platform was built to feed the display engine, which can take a full 360-degree continuous imaging of the sample at the center. Customized image processing techniques such as scaling, rotation, format transformation were also developed and embedded into the system control software platform. In the end several samples were imaged to demonstrate the capability of our system.

  3. Sleep deprivation impairs spontaneous object-place but not novel-object recognition in rats.

    PubMed

    Ishikawa, Hiroko; Yamada, Kazuo; Pavlides, Constantine; Ichitani, Yukio

    2014-09-19

    Effects of sleep deprivation (SD) on one-trial recognition memory were investigated in rats using either a spontaneous novel-object or object-place recognition test. Rats were allowed to explore a field in which two identical objects were presented. After a delay period, they were placed again in the same field in which either: (1) one of the two objects was replaced by another object (novel-object recognition); or (2) one of the sample objects was moved to a different place (object-place recognition), and their exploration behavior to these objects was analyzed. Four hours SD immediately after the sample phase (early SD group) disrupted object-place recognition but not novel-object recognition, while SD 4-8h after the sample phase (delayed SD group) did not affect either paradigm. The results suggest that sleep selectively promotes the consolidation of hippocampal dependent memory, and that this effect is limited to within 4h after learning.

  4. Influence of limited random-phase of objects on the image quality of 3D holographic display

    NASA Astrophysics Data System (ADS)

    Ma, He; Liu, Juan; Yang, Minqiang; Li, Xin; Xue, Gaolei; Wang, Yongtian

    2017-02-01

    Limited-random-phase time average method is proposed to suppress the speckle noise of three dimensional (3D) holographic display. The initial phase and the range of the random phase are studied, as well as their influence on the optical quality of the reconstructed images, and the appropriate initial phase ranges on object surfaces are obtained. Numerical simulations and optical experiments with 2D and 3D reconstructed images are performed, where the objects with limited phase range can suppress the speckle noise in reconstructed images effectively. It is expected to achieve high-quality reconstructed images in 2D or 3D display in the future because of its effectiveness and simplicity.

  5. Simulated and Real Sheet-of-Light 3D Object Scanning Using a-Si:H Thin Film PSD Arrays.

    PubMed

    Contreras, Javier; Tornero, Josep; Ferreira, Isabel; Martins, Rodrigo; Gomes, Luis; Fortunato, Elvira

    2015-11-30

    A MATLAB/SIMULINK software simulation model (structure and component blocks) has been constructed in order to view and analyze the potential of the PSD (Position Sensitive Detector) array concept technology before it is further expanded or developed. This simulation allows changing most of its parameters, such as the number of elements in the PSD array, the direction of vision, the viewing/scanning angle, the object rotation, translation, sample/scan/simulation time, etc. In addition, results show for the first time the possibility of scanning an object in 3D when using an a-Si:H thin film 128 PSD array sensor and hardware/software system. Moreover, this sensor technology is able to perform these scans and render 3D objects at high speeds and high resolutions when using a sheet-of-light laser within a triangulation platform. As shown by the simulation, a substantial enhancement in 3D object profile image quality and realism can be achieved by increasing the number of elements of the PSD array sensor as well as by achieving an optimal position response from the sensor since clearly the definition of the 3D object profile depends on the correct and accurate position response of each detector as well as on the size of the PSD array.

  6. Optimized shape semantic graph representation for object understanding and recognition in point clouds

    NASA Astrophysics Data System (ADS)

    Ning, Xiaojuan; Wang, Yinghui; Meng, Weiliang; Zhang, Xiaopeng

    2016-10-01

    To understand and recognize the three-dimensional (3-D) objects represented as point cloud data, we use an optimized shape semantic graph (SSG) to describe 3-D objects. Based on the decomposed components of an object, the boundary surface of different components and the topology of components, the SSG gives a semantic description that is consistent with human vision perception. The similarity measurement of the SSG for different objects is effective for distinguishing the type of object and finding the most similar one. Experiments using a shape database show that the SSG is valuable for capturing the components of the objects and the corresponding relations between them. The SSG is not only suitable for an object without any loops but also appropriate for an object with loops to represent the shape and the topology. Moreover, a two-step progressive similarity measurement strategy is proposed to effectively improve the recognition rate in the shape database containing point-sample data.

  7. Stereo disparity facilitates view generalization during shape recognition for solid multipart objects.

    PubMed

    Cristino, Filipe; Davitt, Lina; Hayward, William G; Leek, E Charles

    2015-01-01

    Current theories of object recognition in human vision make different predictions about whether the recognition of complex, multipart objects should be influenced by shape information about surface depth orientation and curvature derived from stereo disparity. We examined this issue in five experiments using a recognition memory paradigm in which observers (N = 134) memorized and then discriminated sets of 3D novel objects at trained and untrained viewpoints under either mono or stereo viewing conditions. In order to explore the conditions under which stereo-defined shape information contributes to object recognition we systematically varied the difficulty of view generalization by increasing the angular disparity between trained and untrained views. In one series of experiments, objects were presented from either previously trained views or untrained views rotated (15°, 30°, or 60°) along the same plane. In separate experiments we examined whether view generalization effects interacted with the vertical or horizontal plane of object rotation across 40° viewpoint changes. The results showed robust viewpoint-dependent performance costs: Observers were more efficient in recognizing learned objects from trained than from untrained views, and recognition was worse for extrapolated than for interpolated untrained views. We also found that performance was enhanced by stereo viewing but only at larger angular disparities between trained and untrained views. These findings show that object recognition is not based solely on 2D image information but that it can be facilitated by shape information derived from stereo disparity.

  8. Geometric Aspects of Visual Object Recognition

    DTIC Science & Technology

    1992-05-01

    in the area of automated 3D model acquisition from 2D views ( Longuet - Higgins , 1981., Tomasi and Kanade, 1991, Clemens, 1991). Thus, the problems of...Hall. Longuet - Higgins H. C., 1981, A computer algorithm for reconstructing a scene from two projections, Nature, 293:133-135. Lowe D. G., 1986...take a break). Through Manfred Eigen, I made many interesting and significant scientific contacts; among them, I met Christoph von der Malsburg, whose

  9. A comparative analysis between active and passive techniques for underwater 3D reconstruction of close-range objects.

    PubMed

    Bianco, Gianfranco; Gallo, Alessandro; Bruno, Fabio; Muzzupappa, Maurizio

    2013-08-20

    In some application fields, such as underwater archaeology or marine biology, there is the need to collect three-dimensional, close-range data from objects that cannot be removed from their site. In particular, 3D imaging techniques are widely employed for close-range acquisitions in underwater environment. In this work we have compared in water two 3D imaging techniques based on active and passive approaches, respectively, and whole-field acquisition. The comparison is performed under poor visibility conditions, produced in the laboratory by suspending different quantities of clay in a water tank. For a fair comparison, a stereo configuration has been adopted for both the techniques, using the same setup, working distance, calibration, and objects. At the moment, the proposed setup is not suitable for real world applications, but it allowed us to conduct a preliminary analysis on the performances of the two techniques and to understand their capability to acquire 3D points in presence of turbidity. The performances have been evaluated in terms of accuracy and density of the acquired 3D points. Our results can be used as a reference for further comparisons in the analysis of other 3D techniques and algorithms.

  10. A Comparative Analysis between Active and Passive Techniques for Underwater 3D Reconstruction of Close-Range Objects

    PubMed Central

    Bianco, Gianfranco; Gallo, Alessandro; Bruno, Fabio; Muzzupappa, Maurizio

    2013-01-01

    In some application fields, such as underwater archaeology or marine biology, there is the need to collect three-dimensional, close-range data from objects that cannot be removed from their site. In particular, 3D imaging techniques are widely employed for close-range acquisitions in underwater environment. In this work we have compared in water two 3D imaging techniques based on active and passive approaches, respectively, and whole-field acquisition. The comparison is performed under poor visibility conditions, produced in the laboratory by suspending different quantities of clay in a water tank. For a fair comparison, a stereo configuration has been adopted for both the techniques, using the same setup, working distance, calibration, and objects. At the moment, the proposed setup is not suitable for real world applications, but it allowed us to conduct a preliminary analysis on the performances of the two techniques and to understand their capability to acquire 3D points in presence of turbidity. The performances have been evaluated in terms of accuracy and density of the acquired 3D points. Our results can be used as a reference for further comparisons in the analysis of other 3D techniques and algorithms. PMID:23966193

  11. Development of 3D interactive visual objects using the Scripps Institution of Oceanography's Visualization Center

    NASA Astrophysics Data System (ADS)

    Kilb, D.; Reif, C.; Peach, C.; Keen, C. S.; Smith, B.; Mellors, R. J.

    2003-12-01

    Within the last year scientists and educators at the Scripps Institution of Oceanography (SIO), the Birch Aquarium at Scripps and San Diego State University have collaborated with education specialists to develop 3D interactive graphic teaching modules for use in the classroom and in teacher workshops at the SIO Visualization center (http://siovizcenter.ucsd.edu). The unique aspect of the SIO Visualization center is that the center is designed around a 120 degree curved Panoram floor-to-ceiling screen (8'6" by 28'4") that immerses viewers in a virtual environment. The center is powered by an SGI 3400 Onyx computer that is more powerful, by an order of magnitude in both speed and memory, than typical base systems currently used for education and outreach presentations. This technology allows us to display multiple 3D data layers (e.g., seismicity, high resolution topography, seismic reflectivity, draped interferometric synthetic aperture radar (InSAR) images, etc.) simultaneously, render them in 3D stereo, and take a virtual flight through the data as dictated on the spot by the user. This system can also render snapshots, images and movies that are too big for other systems, and then export smaller size end-products to more commonly used computer systems. Since early 2002, we have explored various ways to provide informal education and outreach focusing on current research presented directly by the researchers doing the work. The Center currently provides a centerpiece for instruction on southern California seismology for K-12 students and teachers for various Scripps education endeavors. Future plans are in place to use the Visualization Center at Scripps for extended K-12 and college educational programs. In particular, we will be identifying K-12 curriculum needs, assisting with teacher education, developing assessments of our programs and products, producing web-accessible teaching modules and facilitating the development of appropriate teaching tools to be

  12. Time Lapse of World’s Largest 3-D Printed Object

    SciTech Connect

    2016-08-29

    Researchers at the MDF have 3D-printed a large-scale trim tool for a Boeing 777X, the world’s largest twin-engine jet airliner. The additively manufactured tool was printed on the Big Area Additive Manufacturing, or BAAM machine over a 30-hour period. The team used a thermoplastic pellet comprised of 80% ABS plastic and 20% carbon fiber from local material supplier. The tool has proven to decrease time, labor, cost and errors associated with traditional manufacturing techniques and increased energy savings in preliminary testing and will undergo further, long term testing.

  13. Infrared Time Lapse of World’s Largest 3D-Printed Object

    SciTech Connect

    2016-08-29

    Researchers at Oak Ridge National Laboratory have 3D-printed a large-scale trim tool for a Boeing 777X, the world’s largest twin-engine jet airliner. The additively manufactured tool was printed on the Big Area Additive Manufacturing, or BAAM machine over a 30-hour period. The team used a thermoplastic pellet comprised of 80% ABS plastic and 20% carbon fiber from local material supplier. The tool has proven to decrease time, labor, cost and errors associated with traditional manufacturing techniques and increased energy savings in preliminary testing and will undergo further, long term testing.

  14. 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.

  15. Flying triangulation - A motion-robust optical 3D sensor for the real-time shape acquisition of complex objects

    NASA Astrophysics Data System (ADS)

    Willomitzer, Florian; Ettl, Svenja; Arold, Oliver; Häusler, Gerd

    2013-05-01

    The three-dimensional shape acquisition of objects has become more and more important in the last years. Up to now, there are several well-established methods which already yield impressive results. However, even under quite common conditions like object movement or a complex shaping, most methods become unsatisfying. Thus, the 3D shape acquisition is still a difficult and non-trivial task. We present our measurement principle "Flying Triangulation" which enables a motion-robust 3D acquisition of complex-shaped object surfaces by a freely movable handheld sensor. Since "Flying Triangulation" is scalable, a whole sensor-zoo for different object sizes is presented. Concluding, an overview of current and future fields of investigation is given.

  16. Tailoring bulk mechanical properties of 3D printed objects of polylactic acid varying internal micro-architecture

    NASA Astrophysics Data System (ADS)

    Malinauskas, Mangirdas; Skliutas, Edvinas; Jonušauskas, Linas; Mizeras, Deividas; Šešok, Andžela; Piskarskas, Algis

    2015-05-01

    Herein we present 3D Printing (3DP) fabrication of structures having internal microarchitecture and characterization of their mechanical properties. Depending on the material, geometry and fill factor, the manufactured objects mechanical performance can be tailored from "hard" to "soft." In this work we employ low-cost fused filament fabrication 3D printer enabling point-by-point structuring of poly(lactic acid) (PLA) with~̴400 µm feature spatial resolution. The chosen architectures are defined as woodpiles (BCC, FCC and 60 deg rotating). The period is chosen to be of 1200 µm corresponding to 800 µm pores. The produced objects structural quality is characterized using scanning electron microscope, their mechanical properties such as flexural modulus, elastic modulus and stiffness are evaluated by measured experimentally using universal TIRAtest2300 machine. Within the limitation of the carried out study we show that the mechanical properties of 3D printed objects can be tuned at least 3 times by only changing the woodpile geometry arrangement, yet keeping the same filling factor and periodicity of the logs. Additionally, we demonstrate custom 3D printed µ-fluidic elements which can serve as cheap, biocompatible and environmentally biodegradable platforms for integrated Lab-On-Chip (LOC) devices.

  17. Influence of georeference for saturated excess overland flow modelling using 3D volumetric soft geo-objects

    NASA Astrophysics Data System (ADS)

    Izham, Mohamad Yusoff; Muhamad Uznir, Ujang; Alias, Abdul Rahman; Ayob, Katimon; Wan Ruslan, Ismail

    2011-04-01

    Existing 2D data structures are often insufficient for analysing the dynamism of saturation excess overland flow (SEOF) within a basin. Moreover, all stream networks and soil surface structures in GIS must be preserved within appropriate projection plane fitting techniques known as georeferencing. Inclusion of 3D volumetric structure of the current soft geo-objects simulation model would offer a substantial effort towards representing 3D soft geo-objects of SEOF dynamically within a basin by visualising saturated flow and overland flow volume. This research attempts to visualise the influence of a georeference system towards the dynamism of overland flow coverage and total overland flow volume generated from the SEOF process using VSG data structure. The data structure is driven by Green-Ampt methods and the Topographic Wetness Index (TWI). VSGs are analysed by focusing on spatial object preservation techniques of the conformal-based Malaysian Rectified Skew Orthomorphic (MRSO) and the equidistant-based Cassini-Soldner projection plane under the existing geodetic Malaysian Revised Triangulation 1948 (MRT48) and the newly implemented Geocentric Datum for Malaysia (GDM2000) datum. The simulated result visualises deformation of SEOF coverage under different georeference systems via its projection planes, which delineate dissimilar computation of SEOF areas and overland flow volumes. The integration of Georeference, 3D GIS and the saturation excess mechanism provides unifying evidence towards successful landslide and flood disaster management through envisioning the streamflow generating process (mainly SEOF) in a 3D environment.

  18. 3D GeoWall Analysis System for Shuttle External Tank Foreign Object Debris Events

    NASA Technical Reports Server (NTRS)

    Brown, Richard; Navard, Andrew; Spruce, Joseph

    2010-01-01

    An analytical, advanced imaging method has been developed for the initial monitoring and identification of foam debris and similar anomalies that occur post-launch in reference to the space shuttle s external tank (ET). Remote sensing technologies have been used to perform image enhancement and analysis on high-resolution, true-color images collected with the DCS 760 Kodak digital camera located in the right umbilical well of the space shuttle. Improvements to the camera, using filters, have added sharpness/definition to the image sets; however, image review/analysis of the ET has been limited by the fact that the images acquired by umbilical cameras during launch are two-dimensional, and are usually nonreferenceable between frames due to rotation translation of the ET as it falls away from the space shuttle. Use of stereo pairs of these images can enable strong visual indicators that can immediately portray depth perception of damaged areas or movement of fragments between frames is not perceivable in two-dimensional images. A stereoscopic image visualization system has been developed to allow 3D depth perception of stereo-aligned image pairs taken from in-flight umbilical and handheld digital shuttle cameras. This new system has been developed to augment and optimize existing 2D monitoring capabilities. Using this system, candidate sequential image pairs are identified for transformation into stereo viewing pairs. Image orientation is corrected using control points (similar points) between frames to place the two images in proper X-Y viewing perspective. The images are then imported into the WallView stereo viewing software package. The collected control points are used to generate a transformation equation that is used to re-project one image and effectively co-register it to the other image. The co-registered, oriented image pairs are imported into a WallView image set and are used as a 3D stereo analysis slide show. Multiple sequential image pairs can be used

  19. Object oriented image analysis based on multi-agent recognition system

    NASA Astrophysics Data System (ADS)

    Tabib Mahmoudi, Fatemeh; Samadzadegan, Farhad; Reinartz, Peter

    2013-04-01

    In this paper, the capabilities of multi-agent systems are used in order to solve object recognition difficulties in complex urban areas based on the characteristics of WorldView-2 satellite imagery and digital surface model (DSM). The proposed methodology has three main steps: pre-processing of dataset, object based image analysis and multi-agent object recognition. Classified regions obtained from object based image analysis are used as input datasets in the proposed multi-agent system in order to modify/improve results. In the first operational level of the proposed multi-agent system, various kinds of object recognition agents modify initial classified regions based on their spectral, textural and 3D structural knowledge. Then, in the second operational level, 2D structural knowledge and contextual relations are used by agents for reasoning and modification. Evaluation of the capabilities of the proposed object recognition methodology is performed on WorldView-2 imagery over Rio de Janeiro (Brazil) which has been collected in January 2010. According to the obtained results of the object based image analysis process, contextual relations and structural descriptors have high potential to modify general difficulties of object recognition. Using knowledge based reasoning and cooperative capabilities of agents in the proposed multi-agent system in this paper, most of the remaining difficulties are decreased and the accuracy of object based image analysis results is improved for about three percent.

  20. Neural coding of 3D features of objects for hand action in the parietal cortex of the monkey.

    PubMed Central

    Sakata, H; Taira, M; Kusunoki, M; Murata, A; Tanaka, Y; Tsutsui, K

    1998-01-01

    In our previous studies of hand manipulation task-related neurons, we found many neurons of the parietal association cortex which responded to the sight of three-dimensional (3D) objects. Most of the task-related neurons in the AIP area (the lateral bank of the anterior intraparietal sulcus) were visually responsive and half of them responded to objects for manipulation. Most of these neurons were selective for the 3D features of the objects. More recently, we have found binocular visual neurons in the lateral bank of the caudal intraparietal sulcus (c-IPS area) that preferentially respond to a luminous bar or place at a particular orientation in space. We studied the responses of axis-orientation selective (AOS) neurons and surface-orientation selective (SOS) neurons in this area with stimuli presented on a 3D computer graphics display. The AOS neurons showed a stronger response to elongated stimuli and showed tuning to the orientation of the longitudinal axis. Many of them preferred a tilted stimulus in depth and appeared to be sensitive to orientation disparity and/or width disparity. The SOS neurons showed a stronger response to a flat than to an elongated stimulus and showed tuning to the 3D orientation of the surface. Their responses increased with the width or length of the stimulus. A considerable number of SOS neurons responded to a square in a random dot stereogram and were tuned to orientation in depth, suggesting their sensitivity to the gradient of disparity. We also found several SOS neurons that responded to a square with tilted or slanted contours, suggesting their sensitivity to orientation disparity and/or width disparity. Area c-IPS is likely to send visual signals of the 3D features of an object to area AIP for the visual guidance of hand actions. PMID:9770229

  1. 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…

  2. A stroboscopic structured illumination system used in dynamic 3D visualization of high-speed motion object

    NASA Astrophysics Data System (ADS)

    Su, Xianyu; Zhang, Qican; Li, Yong; Xiang, Liqun; Cao, Yiping; Chen, Wenjing

    2005-04-01

    A stroboscopic structured illumination system, which can be used in measurement for 3D shape and deformation of high-speed motion object, is proposed and verified by experiments. The system, present in this paper, can automatically detect the position of high-speed moving object and synchronously control the flash of LED to project a structured optical field onto surface of motion object and the shoot of imaging system to acquire an image of deformed fringe pattern, also can create a signal, set artificially through software, to synchronously control the LED and imaging system to do their job. We experiment on a civil electric fan, successful acquire a serial of instantaneous, sharp and clear images of rotation blade and reconstruct its 3D shapes in difference revolutions.

  3. Contrast- and illumination-invariant object recognition from active sensation.

    PubMed

    Rentschler, Ingo; Osman, Erol; Jüttner, Martin

    2009-01-01

    It has been suggested that the deleterious effect of contrast reversal on visual recognition is unique to faces, not objects. Here we show from priming, supervised category learning, and generalization that there is no such thing as general invariance of recognition of non-face objects against contrast reversal and, likewise, changes in direction of illumination. However, when recognition varies with rendering conditions, invariance may be restored and effects of continuous learning may be reduced by providing prior object knowledge from active sensation. Our findings suggest that the degree of contrast invariance achieved reflects functional characteristics of object representations learned in a task-dependent fashion.

  4. The case for intrinsically disordered proteins playing contributory roles in molecular recognition without a stable 3D structure

    PubMed Central

    Uversky, Vladimir N.

    2013-01-01

    The classical ‘lock-and-key’ and ‘induced-fit’ mechanisms for binding both originated in attempts to explain features of enzyme catalysis. For both of these mechanisms and for their recent refinements, enzyme catalysis requires exquisite spatial and electronic complementarity between the substrate and the catalyst. Thus, binding models derived from models originally based on catalysis will be highly biased towards mechanisms that utilize structural complementarity. If mere binding without catalysis is the endpoint, then the structural requirements for the interaction become much more relaxed. Recent observations on specific examples suggest that this relaxation can reach an extreme lack of specific 3D structure, leading to molecular recognition with biological consequences that depend not only upon structural and electrostatic complementarity between the binding partners but also upon kinetic, entropic, and generalized electrostatic effects. In addition to this discussion of binding without fixed structure, examples in which unstructured regions carry out important biological functions not involving molecular recognition will also be discussed. Finally, we discuss whether ‘intrinsically disordered protein’ (IDP) represents a useful new concept. PMID:23361308

  5. VIRO 3D: fast three-dimensional full-body scanning for humans and other living objects

    NASA Astrophysics Data System (ADS)

    Stein, Norbert; Minge, Bernhard

    1998-03-01

    The development of a family of partial and whole body scanners provides a complete technology for fully three-dimensional and contact-free scans on human bodies or other living objects within seconds. This paper gives insight into the design and the functional principles of the whole body scanner VIRO 3D operating on the basis of the laser split-beam method. The arrangement of up to 24 camera/laser combinations, thus dividing the area into different camera fields and an all- around sensor configuration travelling in vertical direction allow the complete 360-degree-scan of an object within 6 - 20 seconds. Due to a special calibration process the different sensors are matched and the measured data are combined. Up to 10 million 3D measuring points with a resolution of approximately 1 mm are processed in all coordinate axes to generate a 3D model. By means of high-performance processors in combination with real-time image processing chips the image data from almost any number of sensors can be recorded and evaluated synchronously in video real-time. VIRO 3D scanning systems have already been successfully implemented in various applications and will open up new perspectives in different other fields, ranging from industry, orthopaedic medicine, plastic surgery to art and photography.

  6. A HIGHLY COLLIMATED WATER MASER BIPOLAR OUTFLOW IN THE CEPHEUS A HW3d MASSIVE YOUNG STELLAR OBJECT

    SciTech Connect

    Chibueze, James O.; Imai, Hiroshi; Tafoya, Daniel; Omodaka, Toshihiro; Chong, Sze-Ning; Kameya, Osamu; Hirota, Tomoya; Torrelles, Jose M.

    2012-04-01

    We present the results of multi-epoch very long baseline interferometry (VLBI) water (H{sub 2}O) maser observations carried out with the VLBI Exploration of Radio Astrometry toward the Cepheus A HW3d object. We measured for the first time relative proper motions of the H{sub 2}O maser features, whose spatio-kinematics traces a compact bipolar outflow. This outflow looks highly collimated and expanding through {approx}280 AU (400 mas) at a mean velocity of {approx}21 km s{sup -1} ({approx}6 mas yr{sup -1}) without taking into account the turbulent central maser cluster. The opening angle of the outflow is estimated to be {approx}30 Degree-Sign . The dynamical timescale of the outflow is estimated to be {approx}100 years. Our results provide strong support that HW3d harbors an internal massive young star, and the observed outflow could be tracing a very early phase of star formation. We also have analyzed Very Large Array archive data of 1.3 cm continuum emission obtained in 1995 and 2006 toward Cepheus A. The comparative result of the HW3d continuum emission suggests the possibility of the existence of distinct young stellar objects in HW3d and/or strong variability in one of their radio continuum emission components.

  7. The role of nitric oxide in the object recognition memory.

    PubMed

    Pitsikas, Nikolaos

    2015-05-15

    The novel object recognition task (NORT) assesses recognition memory in animals. It is a non-rewarded paradigm that it is based on spontaneous exploratory behavior in rodents. This procedure is widely used for testing the effects of compounds on recognition memory. Recognition memory is a type of memory severely compromised in schizophrenic and Alzheimer's disease patients. Nitric oxide (NO) is sought to be an intra- and inter-cellular messenger in the central nervous system and its implication in learning and memory is well documented. Here I intended to critically review the role of NO-related compounds on different aspects of recognition memory. Current analysis shows that both NO donors and NO synthase (NOS) inhibitors are involved in object recognition memory and suggests that NO might be a promising target for cognition impairments. However, the potential neurotoxicity of NO would add a note of caution in this context.

  8. Optical Recognition And Tracking Of Objects

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Liu, Hua-Kuang

    1988-01-01

    Separate objects moving independently tracked simultaneously. System uses coherent optical techniques to obtain correlation between each object and reference image. Moving objects monitored by charge-coupled-device television camera, output fed to liquid-crystal television (LCTV) display. Acting as spatial light modulator, LCTV impresses images of moving objects on collimated laser beam. Beam spatially low-pass filtered to remove high-spatial-frequency television grid pattern.

  9. Infants' Recognition of Objects Using Canonical Color

    ERIC Educational Resources Information Center

    Kimura, Atsushi; Wada, Yuji; Yang, Jiale; Otsuka, Yumiko; Dan, Ippeita; Masuda, Tomohiro; Kanazawa, So; Yamaguchi, Masami K.

    2010-01-01

    We explored infants' ability to recognize the canonical colors of daily objects, including two color-specific objects (human face and fruit) and a non-color-specific object (flower), by using a preferential looking technique. A total of 58 infants between 5 and 8 months of age were tested with a stimulus composed of two color pictures of an object…

  10. Young Children's Self-Generated Object Views and Object Recognition

    ERIC Educational Resources Information Center

    James, Karin H.; Jones, Susan S.; Smith, Linda B.; Swain, Shelley N.

    2014-01-01

    Two important and related developments in children between 18 and 24 months of age are the rapid expansion of object name vocabularies and the emergence of an ability to recognize objects from sparse representations of their geometric shapes. In the same period, children also begin to show a preference for planar views (i.e., views of objects held…

  11. 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

  12. Mechanisms of object recognition: what we have learned from pigeons.

    PubMed

    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.

  13. Reader error, object recognition, and visual search

    NASA Astrophysics Data System (ADS)

    Kundel, Harold L.

    2004-05-01

    Small abnormalities such as hairline fractures, lung nodules and breast tumors are missed by competent radiologists with sufficient frequency to make them a matter of concern to the medical community; not only because they lead to litigation but also because they delay patient care. It is very easy to attribute misses to incompetence or inattention. To do so may be placing an unjustified stigma on the radiologists involved and may allow other radiologists to continue a false optimism that it can never happen to them. This review presents some of the fundamentals of visual system function that are relevant to understanding the search for and the recognition of small targets embedded in complicated but meaningful backgrounds like chests and mammograms. It presents a model for visual search that postulates a pre-attentive global analysis of the retinal image followed by foveal checking fixations and eventually discovery scanning. The model will be used to differentiate errors of search, recognition and decision making. The implications for computer aided diagnosis and for functional workstation design are discussed.

  14. Facial recognition software success rates for the identification of 3D surface reconstructed facial images: implications for patient privacy and security.

    PubMed

    Mazura, Jan C; Juluru, Krishna; Chen, Joseph J; Morgan, Tara A; John, Majnu; Siegel, Eliot L

    2012-06-01

    Image de-identification has focused on the removal of textual protected health information (PHI). Surface reconstructions of the face have the potential to reveal a subject's identity even when textual PHI is absent. This study assessed the ability of a computer application to match research subjects' 3D facial reconstructions with conventional photographs of their face. In a prospective study, 29 subjects underwent CT scans of the head and had frontal digital photographs of their face taken. Facial reconstructions of each CT dataset were generated on a 3D workstation. In phase 1, photographs of the 29 subjects undergoing CT scans were added to a digital directory and tested for recognition using facial recognition software. In phases 2-4, additional photographs were added in groups of 50 to increase the pool of possible matches and the test for recognition was repeated. As an internal control, photographs of all subjects were tested for recognition against an identical photograph. Of 3D reconstructions, 27.5% were matched correctly to corresponding photographs (95% upper CL, 40.1%). All study subject photographs were matched correctly to identical photographs (95% lower CL, 88.6%). Of 3D reconstructions, 96.6% were recognized simply as a face by the software (95% lower CL, 83.5%). Facial recognition software has the potential to recognize features on 3D CT surface reconstructions and match these with photographs, with implications for PHI.

  15. 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.

  16. Visualizing 3D objects from 2D cross sectional images displayed in-situ versus ex-situ.

    PubMed

    Wu, Bing; Klatzky, Roberta L; Stetten, George

    2010-03-01

    The present research investigates how mental visualization of a 3D object from 2D cross sectional images is influenced by displacing the images from the source object, as is customary in medical imaging. Three experiments were conducted to assess people's ability to integrate spatial information over a series of cross sectional images in order to visualize an object posed in 3D space. Participants used a hand-held tool to reveal a virtual rod as a sequence of cross-sectional images, which were displayed either directly in the space of exploration (in-situ) or displaced to a remote screen (ex-situ). They manipulated a response stylus to match the virtual rod's pitch (vertical slant), yaw (horizontal slant), or both. Consistent with the hypothesis that spatial colocation of image and source object facilitates mental visualization, we found that although single dimensions of slant were judged accurately with both displays, judging pitch and yaw simultaneously produced differences in systematic error between in-situ and ex-situ displays. Ex-situ imaging also exhibited errors such that the magnitude of the response was approximately correct but the direction was reversed. Regression analysis indicated that the in-situ judgments were primarily based on spatiotemporal visualization, while the ex-situ judgments relied on an ad hoc, screen-based heuristic. These findings suggest that in-situ displays may be useful in clinical practice by reducing error and facilitating the ability of radiologists to visualize 3D anatomy from cross sectional images.

  17. Quantifying the Energy Efficiency of Object Recognition and Optical Flow

    DTIC Science & Technology

    2014-03-28

    Bruce D Lucas, Takeo Kanade, et al. An Iterative Image Registration Technique with an Application to Stereo Vision. In IJCAI, volume 81, pages 674–679...board unmanned aerial vehicle (UAV) vision processing. Specifically, we focus on object recognition, object tracking, and optical flow. Given that on...6] with >1M labeled images ) for training and evaluating object recognition systems. It turns out that large datasets are a lynchpin of high-accuracy

  18. The effects of surface gloss and roughness on color constancy for real 3-D objects.

    PubMed

    Granzier, Jeroen J M; Vergne, Romain; Gegenfurtner, Karl R

    2014-02-21

    Color constancy denotes the phenomenon that the appearance of an object remains fairly stable under changes in illumination and background color. Most of what we know about color constancy comes from experiments using flat, matte surfaces placed on a single plane under diffuse illumination simulated on a computer monitor. Here we investigate whether material properties (glossiness and roughness) have an effect on color constancy for real objects. Subjects matched the color and brightness of cylinders (painted red, green, or blue) illuminated by simulated daylight (D65) or by a reddish light with a Munsell color book illuminated by a tungsten lamp. The cylinders were either glossy or matte and either smooth or rough. The object was placed in front of a black background or a colored checkerboard. We found that color constancy was significantly higher for the glossy objects compared to the matte objects, and higher for the smooth objects compared to the rough objects. This was independent of the background. We conclude that material properties like glossiness and roughness can have significant effects on color constancy.

  19. 'Breaking' position-invariant object recognition.

    PubMed

    Cox, David D; Meier, Philip; Oertelt, Nadja; DiCarlo, James J

    2005-09-01

    While it is often assumed that objects can be recognized irrespective of where they fall on the retina, little is known about the mechanisms underlying this ability. By exposing human subjects to an altered world where some objects systematically changed identity during the transient blindness that accompanies eye movements, we induced predictable object confusions across retinal positions, effectively 'breaking' position invariance. Thus, position invariance is not a rigid property of vision but is constantly adapting to the statistics of the environment.

  20. Real object use facilitates object recognition in semantic agnosia.

    PubMed

    Morady, Kamelia; Humphreys, Glyn W

    2009-01-01

    In the present paper we show that, in patients with poor semantic representations, the naming of real objects can improve when naming takes place after patients have been asked to use the objects, compared with when they name the objects either from vision or from touch alone, or together. In addition, the patients were strongly affected by action when required to name objects that were used correctly or incorrectly by the examiner. The data suggest that actions can be cued directly from sensory-motor associations, and that patients can then name on the basis of the evoked action.

  1. Automatic Recognition of Object Names in Literature

    NASA Astrophysics Data System (ADS)

    Bonnin, C.; Lesteven, S.; Derriere, S.; Oberto, A.

    2008-08-01

    SIMBAD is a database of astronomical objects that provides (among other things) their bibliographic references in a large number of journals. Currently, these references have to be entered manually by librarians who read each paper. To cope with the increasing number of papers, CDS develops a tool to assist the librarians in their work, taking advantage of the Dictionary of Nomenclature of Celestial Objects, which keeps track of object acronyms and of their origin. The program searches for object names directly in PDF documents by comparing the words with all the formats stored in the Dictionary of Nomenclature. It also searches for variable star names based on constellation names and for a large list of usual names such as Aldebaran or the Crab. Object names found in the documents often correspond to several astronomical objects. The system retrieves all possible matches, displays them with their object type given by SIMBAD, and lets the librarian make the final choice. The bibliographic reference can then be automatically added to the object identifiers in the database. Besides, the systematic usage of the Dictionary of Nomenclature, which is updated manually, permitted to automatically check it and to detect errors and inconsistencies. Last but not least, the program collects some additional information such as the position of the object names in the document (in the title, subtitle, abstract, table, figure caption...) and their number of occurrences. In the future, this will permit to calculate the 'weight' of an object in a reference and to provide SIMBAD users with an important new information, which will help them to find the most relevant papers in the object reference list.

  2. Category-Specificity in Visual Object Recognition

    ERIC Educational Resources Information Center

    Gerlach, Christian

    2009-01-01

    Are all categories of objects recognized in the same manner visually? Evidence from neuropsychology suggests they are not: some brain damaged patients are more impaired in recognizing natural objects than artefacts whereas others show the opposite impairment. Category-effects have also been demonstrated in neurologically intact subjects, but the…

  3. Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm

    NASA Astrophysics Data System (ADS)

    Skotheim, Øystein; Thielemann, Jens T.; Berge, Asbjørn; Sommerfelt, Arne

    2010-02-01

    Enabling robots to automatically locate and pick up randomly placed and oriented objects from a bin is an important challenge in factory automation, replacing tedious and heavy manual labor. A system should be able to recognize and locate objects with a predefined shape and estimate the position with the precision necessary for a gripping robot to pick it up. We describe a system that consists of a structured light instrument for capturing 3D data and a robust approach for object location and pose estimation. The method does not depend on segmentation of range images, but instead searches through pairs of 2D manifolds to localize candidates for object match. This leads to an algorithm that is not very sensitive to scene complexity or the number of objects in the scene. Furthermore, the strategy for candidate search is easily reconfigurable to arbitrary objects. Experiments reported in this paper show the utility of the method on a general random bin picking problem, in this paper exemplified by localization of car parts with random position and orientation. Full pose estimation is done in less than 380 ms per image. We believe that the method is applicable for a wide range of industrial automation problems where precise localization of 3D objects in a scene is needed.

  4. Microwave and camera sensor fusion for the shape extraction of metallic 3D space objects

    NASA Technical Reports Server (NTRS)

    Shaw, Scott W.; Defigueiredo, Rui J. P.; Krishen, Kumar

    1989-01-01

    The vacuum of space presents special problems for optical image sensors. Metallic objects in this environment can produce intense specular reflections and deep shadows. By combining the polarized RCS with an incomplete camera image, it has become possible to better determine the shape of some simple three-dimensional objects. The radar data are used in an iterative procedure that generates successive approximations to the target shape by minimizing the error between computed scattering cross-sections and the observed radar returns. Favorable results have been obtained for simulations and experiments reconstructing plates, ellipsoids, and arbitrary surfaces.

  5. Development of a common 3D pharmacophore for δ-opioid recognition from peptides and non-peptides using a novel computer program

    NASA Astrophysics Data System (ADS)

    Huang, Ping; Kim, Susan; Loew, Gilda

    1997-01-01

    A unified three-dimensional (3D) pharmacophore for recognition of the δ-opioid receptorby families of structurally diverse δ-opioid ligands, including peptides and non-peptides,has been determined. An additional structural feature required for δ-selectivity was alsocharacterized using a subset of these ligands that are highly selective for the δ-opioidreceptor. To obtain these pharmacophores, we have used a recently developed computerprogram that performs systematic and automated comparisons of molecules to determinewhether any common 3D relationships exist among candidate recognition moieties in high-affinity analogs. All the low-energy conformations of each ligand are included in thesecomparisons. The program developed should be applicable in general to molecular super-imposition problems in rational drug design and to develop both 3D recognition and activationpharmacophores for any receptor for which high- and low-affinity analogs and agonists andantagonists have been identified.

  6. An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization.

    PubMed

    Claes, Peter; Reijniers, Jonas; Shriver, Mark D; Snyders, Jonatan; Suetens, Paul; Nielandt, Joachim; De Tré, Guy; Vandermeulen, Dirk

    2015-01-01

    The human external ears, or pinnae, have an intriguing shape and, like most parts of the human external body, bilateral symmetry is observed between left and right. It is a well-known part of our auditory sensory system and mediates the spatial localization of incoming sounds in 3D from monaural cues due to its shape-specific filtering as well as binaural cues due to the paired bilateral locations of the left and right ears. Another less broadly appreciated aspect of the human pinna shape is its uniqueness from one individual to another, which is on the level of what is seen in fingerprints and facial features. This makes pinnae very useful in human identification, which is of great interest in biometrics and forensics. Anatomically, the type of symmetry observed is known as matching symmetry, with structures present as separate mirror copies on both sides of the body, and in this work we report the first such investigation of the human pinna in 3D. Within the framework of geometric morphometrics, we started by partitioning ear shape, represented in a spatially dense way, into patterns of symmetry and asymmetry, following a two-factor anova design. Matching symmetry was measured in all substructures of the pinna anatomy. However, substructures that 'stick out' such as the helix, tragus, and lobule also contained a fair degree of asymmetry. In contrast, substructures such as the conchae, antitragus, and antihelix expressed relatively stronger degrees of symmetric variation in relation to their levels of asymmetry. Insights gained from this study were injected into an accompanying identification setup exploiting matching symmetry where improved performance is demonstrated. Finally, possible implications of the results in the context of ear recognition as well as sound localization are discussed.

  7. An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization

    PubMed Central

    Claes, Peter; Reijniers, Jonas; Shriver, Mark D; Snyders, Jonatan; Suetens, Paul; Nielandt, Joachim; De Tré, Guy; Vandermeulen, Dirk

    2015-01-01

    The human external ears, or pinnae, have an intriguing shape and, like most parts of the human external body, bilateral symmetry is observed between left and right. It is a well-known part of our auditory sensory system and mediates the spatial localization of incoming sounds in 3D from monaural cues due to its shape-specific filtering as well as binaural cues due to the paired bilateral locations of the left and right ears. Another less broadly appreciated aspect of the human pinna shape is its uniqueness from one individual to another, which is on the level of what is seen in fingerprints and facial features. This makes pinnae very useful in human identification, which is of great interest in biometrics and forensics. Anatomically, the type of symmetry observed is known as matching symmetry, with structures present as separate mirror copies on both sides of the body, and in this work we report the first such investigation of the human pinna in 3D. Within the framework of geometric morphometrics, we started by partitioning ear shape, represented in a spatially dense way, into patterns of symmetry and asymmetry, following a two-factor anova design. Matching symmetry was measured in all substructures of the pinna anatomy. However, substructures that ‘stick out’ such as the helix, tragus, and lobule also contained a fair degree of asymmetry. In contrast, substructures such as the conchae, antitragus, and antihelix expressed relatively stronger degrees of symmetric variation in relation to their levels of asymmetry. Insights gained from this study were injected into an accompanying identification setup exploiting matching symmetry where improved performance is demonstrated. Finally, possible implications of the results in the context of ear recognition as well as sound localization are discussed. PMID:25382291

  8. Demonstration of an Ultrasonic Method for 3-D Visualization of Shallow Buried Underwater Objects

    DTIC Science & Technology

    2011-07-01

    with the X-Y positioning system attached. It is composed of an X-Y gantry system operated by underwater servo motors controlled by the operator’s...user interface errors there are in the software. The test was setup by placing the system over a tank of water containing know objects (Figure 4). The...Requirements Evaluation of all the user interface controls and outputs 3.4.3 Success Criteria 100% error free, all identified bugs have been

  9. The subjective experience of object recognition: comparing metacognition for object detection and object categorization.

    PubMed

    Meuwese, Julia D I; van Loon, Anouk M; Lamme, Victor A F; Fahrenfort, Johannes J

    2014-05-01

    Perceptual decisions seem to be made automatically and almost instantly. Constructing a unitary subjective conscious experience takes more time. For example, when trying to avoid a collision with a car on a foggy road you brake or steer away in a reflex, before realizing you were in a near accident. This subjective aspect of object recognition has been given little attention. We used metacognition (assessed with confidence ratings) to measure subjective experience during object detection and object categorization for degraded and masked objects, while objective performance was matched. Metacognition was equal for degraded and masked objects, but categorization led to higher metacognition than did detection. This effect turned out to be driven by a difference in metacognition for correct rejection trials, which seemed to be caused by an asymmetry of the distractor stimulus: It does not contain object-related information in the detection task, whereas it does contain such information in the categorization task. Strikingly, this asymmetry selectively impacted metacognitive ability when objective performance was matched. This finding reveals a fundamental difference in how humans reflect versus act on information: When matching the amount of information required to perform two tasks at some objective level of accuracy (acting), metacognitive ability (reflecting) is still better in tasks that rely on positive evidence (categorization) than in tasks that rely more strongly on an absence of evidence (detection).

  10. 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.

  11. Learning Distance Functions for Exemplar-Based Object Recognition

    DTIC Science & Technology

    2007-01-01

    This thesis investigates an exemplar-based approach to object recognition that learns, on an image-by-image basis, the relative importance of patch...this thesis is a method for learning a set-to-set distance function specific to each training image and demonstrating the use of these functions for...Science University of California, Berkeley Professor Jitendra Malik, Chair This thesis investigates an exemplar-based approach to object recognition that

  12. 3D profile measurements of objects by using zero order Generalized Morse Wavelet

    NASA Astrophysics Data System (ADS)

    Kocahan, Özlem; Durmuş, ćaǧla; Elmas, Merve Naz; Coşkun, Emre; Tiryaki, Erhan; Özder, Serhat

    2017-02-01

    Generalized Morse wavelets are proposed to evaluate the phase information from projected fringe pattern with the spatial carrier frequency in the x direction. The height profile of the object is determined through the phase change distribution by using the phase of the continuous wavelet transform. The phase distribution is extracted from the optical fringe pattern choosing zero order Generalized Morse Wavelet (GMW) as a mother wavelet. In this study, standard fringe projection technique is used for obtaining images. Experimental results for the GMW phase method are compared with the results of Morlet and Paul wavelet transform.

  13. 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.

  14. Comparing object recognition from binary and bipolar edge features

    PubMed Central

    Jung, Jae-Hyun; Pu, Tian; Peli, Eli

    2017-01-01

    Edges derived from abrupt luminance changes in images carry essential information for object recognition. Typical binary edge images (black edges on white background or white edges on black background) have been used to represent features (edges and cusps) in scenes. However, the polarity of cusps and edges may contain important depth information (depth from shading) which is lost in the binary edge representation. This depth information may be restored, to some degree, using bipolar edges. We compared recognition rates of 16 binary edge images, or bipolar features, by 26 subjects. Object recognition rates were higher with bipolar edges and the improvement was significant in scenes with complex backgrounds.

  15. Object Recognition and Random Image Structure Evolution

    ERIC Educational Resources Information Center

    Sadr, Jvid; Sinha, Pawan

    2004-01-01

    We present a technique called Random Image Structure Evolution (RISE) for use in experimental investigations of high-level visual perception. Potential applications of RISE include the quantitative measurement of perceptual hysteresis and priming, the study of the neural substrates of object perception, and the assessment and detection of subtle…

  16. Unposed Object Recognition using an Active Approach

    DTIC Science & Technology

    2013-02-01

    a transition between two different visual aspects V1, and V2 . The human brain stores pose in a simi- lar manner. Neurophysiological evidence sug...re- gion that is not on the table, as estimated using the Depth information provided by the Kinect . The size of the object was normalized in the same

  17. Changes in functional connectivity support conscious object recognition.

    PubMed

    Imamoglu, Fatma; Kahnt, Thorsten; Koch, Christof; Haynes, John-Dylan

    2012-12-01

    What are the brain mechanisms that mediate conscious object recognition? To investigate this question, it is essential to distinguish between brain processes that cause conscious recognition of a stimulus from other correlates of its sensory processing. Previous fMRI studies have identified large-scale brain activity ranging from striate to high-level sensory and prefrontal regions associated with conscious visual perception or recognition. However, the possible role of changes in connectivity during conscious perception between these regions has only rarely been studied. Here, we used fMRI and connectivity analyses, together with 120 custom-generated, two-tone, Mooney images to directly assess whether conscious recognition of an object is accompanied by a dynamical change in the functional coupling between extrastriate cortex and prefrontal areas. We compared recognizing an object versus not recognizing it in 19 naïve subjects using two different response modalities. We find that connectivity between the extrastriate cortex and the dorsolateral prefrontal cortex (DLPFC) increases when objects are consciously recognized. This interaction was independent of the response modality used to report conscious recognition. Furthermore, computing the difference in Granger causality between recognized and not recognized conditions reveals stronger feedforward connectivity than feedback connectivity when subjects recognized the objects. We suggest that frontal and visual brain regions are part of a functional network that supports conscious object recognition by changes in functional connectivity.

  18. Planning Multiple Observations for Object Recognition

    DTIC Science & Technology

    1992-12-09

    choosing the branch with the highest weight at each level, and backtracking when necessary. The PREMIO system of Camps, et al [51 predicts object...appearances under various conditions of lighting, viewpoint, sensor, and image processing operators. Unlike other systems, PREMIO also evaluates the utility...1988). [51 Camps, 0. 1., Shapiro, L. G., and Haralick, R. M. PREMIO : an overview. Proc. IEEE Workshop on Directions in Automated CAD-Based Vision, pp

  19. Calculations of Arctic ozone chemistry using objectively analyzed data in a 3-D CTM

    NASA Technical Reports Server (NTRS)

    Kaminski, J. W.; Mcconnell, J. C.; Sandilands, J. W.

    1994-01-01

    A three-dimensional chemical transport model (CTM) (Kaminski, 1992) has been used to study the evolution of the Arctic ozone during the winter of 1992. The continuity equation has been solved using a spectral method with Rhomboidal 15 (R15) truncation and leap-frog time stepping. Six-hourly meteorological fields from the Canadian Meteorological Center global objective analysis routines run at T79 were degraded to the model resolution. In addition, they were interpolated to the model time grid and were used to drive the model from the surface to 10 mb. In the model, processing of Cl(x) occurred over Arctic latitudes but some of the initial products were still present by mid-January. Also, the large amounts of ClO formed in the model in early January were converted to ClNO3. The results suggest that the model resolution may be insufficient to resolve the details of the Arctic transport during this time period. In particular, the wind field does not move the ClO(x) 'cloud' to the south over Europe as seen in the MLS measurements.

  20. Multiple Kernel Learning for Visual Object Recognition: A Review.

    PubMed

    Bucak, Serhat S; Rong Jin; Jain, Anil K

    2014-07-01

    Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown that MKL is a useful tool for object recognition, where each image is represented by multiple sets of features and MKL is applied to combine different feature sets. We review the state-of-the-art for MKL, including different formulations and algorithms for solving the related optimization problems, with the focus on their applications to object recognition. One dilemma faced by practitioners interested in using MKL for object recognition is that different studies often provide conflicting results about the effectiveness and efficiency of MKL. To resolve this, we conduct extensive experiments on standard datasets to evaluate various approaches to MKL for object recognition. We argue that the seemingly contradictory conclusions offered by studies are due to different experimental setups. The conclusions of our study are: (i) given a sufficient number of training examples and feature/kernel types, MKL is more effective for object recognition than simple kernel combination (e.g., choosing the best performing kernel or average of kernels); and (ii) among the various approaches proposed for MKL, the sequential minimal optimization, semi-infinite programming, and level method based ones are computationally most efficient.

  1. Leveraging Cognitive Context for Object Recognition

    DTIC Science & Technology

    2014-06-01

    when looking in the kitchen, context may suggest related concepts such as apples or lemons . Any ambiguities that might arise from other similar...small and round), it might also look sim- ilar to other known small round objects (e.g., lemon ). Therefore, while our classification decision might...T3 T4 T5 # correct apple A A A A A 5 raisins R R R R A 4 banana B B B B B 5 lemon L L L L L 5 coyote R C R C C 3 wire W W W W W 5 Table 1. Results of

  2. Induced gamma band responses predict recognition delays during object identification.

    PubMed

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

    2007-06-01

    Neural mechanisms of object recognition seem to rely on activity of distributed neural assemblies coordinated by synchronous firing in the gamma-band range (>20 Hz). In the present electroencephalogram (EEG) study, we investigated induced gamma band activity during the naming of line drawings of upright objects and objects rotated in the image plane. Such plane-rotation paradigms elicit view-dependent processing, leading to delays in recognition of disoriented objects. Our behavioral results showed reaction time delays for rotated, as opposed to upright, images. These delays were accompanied by delays in the peak latency of induced gamma band responses (GBRs), in the absence of any effects on other measures of EEG activity. The latency of the induced GBRs has thus, for the first time, been selectively modulated by an experimental manipulation that delayed recognition. This finding indicates that induced GBRs have a genuine role as neural markers of late representational processes during object recognition. In concordance with the view that object recognition is achieved through dynamic learning processes, we propose that induced gamma band activity could be one of the possible cortical markers of such dynamic object coding.

  3. 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.

  4. Segmentation of complex objects with non-spherical topologies from volumetric medical images using 3D livewire

    NASA Astrophysics Data System (ADS)

    Poon, Kelvin; Hamarneh, Ghassan; Abugharbieh, Rafeef

    2007-03-01

    Segmentation of 3D data is one of the most challenging tasks in medical image analysis. While reliable automatic methods are typically preferred, their success is often hindered by poor image quality and significant variations in anatomy. Recent years have thus seen an increasing interest in the development of semi-automated segmentation methods that combine computational tools with intuitive, minimal user interaction. In an earlier work, we introduced a highly-automated technique for medical image segmentation, where a 3D extension of the traditional 2D Livewire was proposed. In this paper, we present an enhanced and more powerful 3D Livewire-based segmentation approach with new features designed to primarily enable the handling of complex object topologies that are common in biological structures. The point ordering algorithm we proposed earlier, which automatically pairs up seedpoints in 3D, is improved in this work such that multiple sets of points are allowed to simultaneously exist. Point sets can now be automatically merged and split to accommodate for the presence of concavities, protrusions, and non-spherical topologies. The robustness of the method is further improved by extending the 'turtle algorithm', presented earlier, by using a turtle-path pruning step. Tests on both synthetic and real medical images demonstrate the efficiency, reproducibility, accuracy, and robustness of the proposed approach. Among the examples illustrated is the segmentation of the left and right ventricles from a T1-weighted MRI scan, where an average task time reduction of 84.7% was achieved when compared to a user performing 2D Livewire segmentation on every slice.

  5. Reference Frames and 3-D Shape Perception of Pictured Objects: On Verticality and Viewpoint-From-Above

    PubMed Central

    van Doorn, Andrea J.; Wagemans, Johan

    2016-01-01

    Research on the influence of reference frames has generally focused on visual phenomena such as the oblique effect, the subjective visual vertical, the perceptual upright, and ambiguous figures. Another line of research concerns mental rotation studies in which participants had to discriminate between familiar or previously seen 2-D figures or pictures of 3-D objects and their rotated versions. In the present study, we disentangled the influence of the environmental and the viewer-centered reference frame, as classically done, by comparing the performances obtained in various picture and participant orientations. However, this time, the performance is the pictorial relief: the probed 3-D shape percept of the depicted object reconstructed from the local attitude settings of the participant. Comparisons between the pictorial reliefs based on different picture and participant orientations led to two major findings. First, in general, the pictorial reliefs were highly similar if the orientation of the depicted object was vertical with regard to the environmental or the viewer-centered reference frame. Second, a viewpoint-from-above interpretation could almost completely account for the shears occurring between the pictorial reliefs. More specifically, the shears could largely be considered as combinations of slants generated from the viewpoint-from-above, which was determined by the environmental as well as by the viewer-centered reference frame. PMID:27433329

  6. Robust object recognition under partial occlusions using NMF.

    PubMed

    Soukup, Daniel; Bajla, Ivan

    2008-01-01

    In recent years, nonnegative matrix factorization (NMF) methods of a reduced image data representation attracted the attention of computer vision community. These methods are considered as a convenient part-based representation of image data for recognition tasks with occluded objects. A novel modification in NMF recognition tasks is proposed which utilizes the matrix sparseness control introduced by Hoyer. We have analyzed the influence of sparseness on recognition rates (RRs) for various dimensions of subspaces generated for two image databases, ORL face database, and USPS handwritten digit database. We have studied the behavior of four types of distances between a projected unknown image object and feature vectors in NMF subspaces generated for training data. One of these metrics also is a novelty we proposed. In the recognition phase, partial occlusions in the test images have been modeled by putting two randomly large, randomly positioned black rectangles into each test image.

  7. Parallel phase-shifting digital holography and its application to high-speed 3D imaging of dynamic object

    NASA Astrophysics Data System (ADS)

    Awatsuji, Yasuhiro; Xia, Peng; Wang, Yexin; Matoba, Osamu

    2016-03-01

    Digital holography is a technique of 3D measurement of object. The technique uses an image sensor to record the interference fringe image containing the complex amplitude of object, and numerically reconstructs the complex amplitude by computer. Parallel phase-shifting digital holography is capable of accurate 3D measurement of dynamic object. This is because this technique can reconstruct the complex amplitude of object, on which the undesired images are not superimposed, form a single hologram. The undesired images are the non-diffraction wave and the conjugate image which are associated with holography. In parallel phase-shifting digital holography, a hologram, whose phase of the reference wave is spatially and periodically shifted every other pixel, is recorded to obtain complex amplitude of object by single-shot exposure. The recorded hologram is decomposed into multiple holograms required for phase-shifting digital holography. The complex amplitude of the object is free from the undesired images is reconstructed from the multiple holograms. To validate parallel phase-shifting digital holography, a high-speed parallel phase-shifting digital holography system was constructed. The system consists of a Mach-Zehnder interferometer, a continuous-wave laser, and a high-speed polarization imaging camera. Phase motion picture of dynamic air flow sprayed from a nozzle was recorded at 180,000 frames per second (FPS) have been recorded by the system. Also phase motion picture of dynamic air induced by discharge between two electrodes has been recorded at 1,000,000 FPS, when high voltage was applied between the electrodes.

  8. Generalized Sparselet Models for Real-Time Multiclass Object Recognition.

    PubMed

    Song, Hyun Oh; Girshick, Ross; Zickler, Stefan; Geyer, Christopher; Felzenszwalb, Pedro; Darrell, Trevor

    2015-05-01

    The problem of real-time multiclass object recognition is of great practical importance in object recognition. In this paper, we describe a framework that simultaneously utilizes shared representation, reconstruction sparsity, and parallelism to enable real-time multiclass object detection with deformable part models at 5Hz on a laptop computer with almost no decrease in task performance. Our framework is trained in the standard structured output prediction formulation and is generically applicable for speeding up object recognition systems where the computational bottleneck is in multiclass, multi-convolutional inference. We experimentally demonstrate the efficiency and task performance of our method on PASCAL VOC, subset of ImageNet, Caltech101 and Caltech256 dataset.

  9. A hippocampal signature of perceptual learning in object recognition.

    PubMed

    Guggenmos, Matthias; Rothkirch, Marcus; Obermayer, Klaus; Haynes, John-Dylan; Sterzer, Philipp

    2015-04-01

    Perceptual learning is the improvement in perceptual performance through training or exposure. Here, we used fMRI before and after extensive behavioral training to investigate the effects of perceptual learning on the recognition of objects under challenging viewing conditions. Objects belonged either to trained or untrained categories. Trained categories were further subdivided into trained and untrained exemplars and were coupled with high or low monetary rewards during training. After a 3-day training, object recognition was markedly improved. Although there was a considerable transfer of learning to untrained exemplars within categories, an enhancing effect of reward reinforcement was specific to trained exemplars. fMRI showed that hippocampus responses to both trained and untrained exemplars of trained categories were enhanced by perceptual learning and correlated with the effect of reward reinforcement. Our results suggest a key role of hippocampus in object recognition after perceptual learning.

  10. Workflows and the Role of Images for Virtual 3d Reconstruction of no Longer Extant Historic Objects

    NASA Astrophysics Data System (ADS)

    Münster, S.

    2013-07-01

    3D reconstruction technologies have gained importance as tools for the research and visualization of no longer extant historic objects during the last decade. Within such reconstruction processes, visual media assumes several important roles: as the most important sources especially for a reconstruction of no longer extant objects, as a tool for communication and cooperation within the production process, as well as for a communication and visualization of results. While there are many discourses about theoretical issues of depiction as sources and as visualization outcomes of such projects, there is no systematic research about the importance of depiction during a 3D reconstruction process and based on empirical findings. Moreover, from a methodological perspective, it would be necessary to understand which role visual media plays during the production process and how it is affected by disciplinary boundaries and challenges specific to historic topics. Research includes an analysis of published work and case studies investigating reconstruction projects. This study uses methods taken from social sciences to gain a grounded view of how production processes would take place in practice and which functions and roles images would play within them. For the investigation of these topics, a content analysis of 452 conference proceedings and journal articles related to 3D reconstruction modeling in the field of humanities has been completed. Most of the projects described in those publications dealt with data acquisition and model building for existing objects. Only a small number of projects focused on structures that no longer or never existed physically. Especially that type of project seems to be interesting for a study of the importance of pictures as sources and as tools for interdisciplinary cooperation during the production process. In the course of the examination the authors of this paper applied a qualitative content analysis for a sample of 26 previously

  11. 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…

  12. Spontaneous Object Recognition Memory in Aged Rats: Complexity versus Similarity

    ERIC Educational Resources Information Center

    Gamiz, Fernando; Gallo, Milagros

    2012-01-01

    Previous work on the effect of aging on spontaneous object recognition (SOR) memory tasks in rats has yielded controversial results. Although the results at long-retention intervals are consistent, conflicting results have been reported at shorter delays. We have assessed the potential relevance of the type of object used in the performance of…

  13. 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.

  14. Towards Perceptual Interface for Visualization Navigation of Large Data Sets Using Gesture Recognition with Bezier Curves and Registered 3-D Data

    SciTech Connect

    Shin, M C; Tsap, L V; Goldgof, D B

    2003-03-20

    This paper presents a gesture recognition system for visualization navigation. Scientists are interested in developing interactive settings for exploring large data sets in an intuitive environment. The input consists of registered 3-D data. A geometric method using Bezier curves is used for the trajectory analysis and classification of gestures. The hand gesture speed is incorporated into the algorithm to enable correct recognition from trajectories with variations in hand speed. The method is robust and reliable: correct hand identification rate is 99.9% (from 1641 frames), modes of hand movements are correct 95.6% of the time, recognition rate (given the right mode) is 97.9%. An application to gesture-controlled visualization of 3D bioinformatics data is also presented.

  15. A Proposed Biologically Inspired Model for Object Recognition

    NASA Astrophysics Data System (ADS)

    Al-Absi, Hamada R. H.; Abdullah, Azween B.

    Object recognition has attracted the attention of many researchers as it is considered as one of the most important problems in computer vision. Two main approaches have been utilized to develop object recognition solutions i.e. machine and biological vision. Many algorithms have been developed in machine vision. Recently, Biology has inspired computer scientist to map the features of the human and primate's visual systems into computational models. Some of these models are based on the feed-forward mechanism of information processing in cortex; however, the performance of these models has been affected by the increase of clutter in the scene. Another mechanism of information processing in cortex is called the feedback. This mechanism has also been mapped into computational models. However, the results were also not satisfying. In this paper an object recognition model based on the integration of the feed-forward and feedback functions in the visual cortex is proposed.

  16. Object Recognition Method of Space Debris Tracking Image Sequence

    NASA Astrophysics Data System (ADS)

    Chen, Zhang; Yi-ding, Ping

    2016-07-01

    In order to strengthen the capability of space debris detection, the automated optical observation becomes more and more popular. Thus, the fully unattended automatic object recognition is urgently needed to study. As the open-loop tracking, which guides the telescope only with the historical orbital elements, is a simple and robust way to track space debris, based on the analysis on the point distribution characteristics of object's open-loop tracking image sequence in the pixel space, this paper has proposed to use the cluster identification method for the automatic space debris recognition, and made a comparison on the three kinds of different algorithms.

  17. 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.

  18. Spun-wrapped aligned nanofiber (SWAN) lithography for fabrication of micro/nano-structures on 3D objects

    NASA Astrophysics Data System (ADS)

    Ye, Zhou; Nain, Amrinder S.; Behkam, Bahareh

    2016-06-01

    Fabrication of micro/nano-structures on irregularly shaped substrates and three-dimensional (3D) objects is of significant interest in diverse technological fields. However, it remains a formidable challenge thwarted by limited adaptability of the state-of-the-art nanolithography techniques for nanofabrication on non-planar surfaces. In this work, we introduce Spun-Wrapped Aligned Nanofiber (SWAN) lithography, a versatile, scalable, and cost-effective technique for fabrication of multiscale (nano to microscale) structures on 3D objects without restriction on substrate material and geometry. SWAN lithography combines precise deposition of polymeric nanofiber masks, in aligned single or multilayer configurations, with well-controlled solvent vapor treatment and etching processes to enable high throughput (>10-7 m2 s-1) and large-area fabrication of sub-50 nm to several micron features with high pattern fidelity. Using this technique, we demonstrate whole-surface nanopatterning of bulk and thin film surfaces of cubes, cylinders, and hyperbola-shaped objects that would be difficult, if not impossible to achieve with existing methods. We demonstrate that the fabricated feature size (b) scales with the fiber mask diameter (D) as b1.5 ~ D. This scaling law is in excellent agreement with theoretical predictions using the Johnson, Kendall, and Roberts (JKR) contact theory, thus providing a rational design framework for fabrication of systems and devices that require precisely designed multiscale features.Fabrication of micro/nano-structures on irregularly shaped substrates and three-dimensional (3D) objects is of significant interest in diverse technological fields. However, it remains a formidable challenge thwarted by limited adaptability of the state-of-the-art nanolithography techniques for nanofabrication on non-planar surfaces. In this work, we introduce Spun-Wrapped Aligned Nanofiber (SWAN) lithography, a versatile, scalable, and cost-effective technique for

  19. Visual Exploration and Object Recognition by Lattice Deformation

    PubMed Central

    Melloni, Lucia; Mureşan, Raul C.

    2011-01-01

    Mechanisms of explicit object recognition are often difficult to investigate and require stimuli with controlled features whose expression can be manipulated in a precise quantitative fashion. Here, we developed a novel method (called “Dots”), for generating visual stimuli, which is based on the progressive deformation of a regular lattice of dots, driven by local contour information from images of objects. By applying progressively larger deformation to the lattice, the latter conveys progressively more information about the target object. Stimuli generated with the presented method enable a precise control of object-related information content while preserving low-level image statistics, globally, and affecting them only little, locally. We show that such stimuli are useful for investigating object recognition under a naturalistic setting – free visual exploration – enabling a clear dissociation between object detection and explicit recognition. Using the introduced stimuli, we show that top-down modulation induced by previous exposure to target objects can greatly influence perceptual decisions, lowering perceptual thresholds not only for object recognition but also for object detection (visual hysteresis). Visual hysteresis is target-specific, its expression and magnitude depending on the identity of individual objects. Relying on the particular features of dot stimuli and on eye-tracking measurements, we further demonstrate that top-down processes guide visual exploration, controlling how visual information is integrated by successive fixations. Prior knowledge about objects can guide saccades/fixations to sample locations that are supposed to be highly informative, even when the actual information is missing from those locations in the stimulus. The duration of individual fixations is modulated by the novelty and difficulty of the stimulus, likely reflecting cognitive demand. PMID:21818397

  20. Shape and motion reconstruction from 3D-to-1D orthographically projected data via object-image relations.

    PubMed

    Ferrara, Matthew; Arnold, Gregory; Stuff, Mark

    2009-10-01

    This paper describes an invariant-based shape- and motion reconstruction algorithm for 3D-to-1D orthographically projected range data taken from unknown viewpoints. The algorithm exploits the object-image relation that arises in echo-based range data and represents a simplification and unification of previous work in the literature. Unlike one proposed approach, this method does not require uniqueness constraints, which makes its algorithmic form independent of the translation removal process (centroid removal, range alignment, etc.). The new algorithm, which simultaneously incorporates every projection and does not use an initialization in the optimization process, requires fewer calculations and is more straightforward than the previous approach. Additionally, the new algorithm is shown to be the natural extension of the approach developed by Tomasi and Kanade for 3D-to-2D orthographically projected data and is applied to a realistic inverse synthetic aperture radar imaging scenario, as well as experiments with varying amounts of aperture diversity and noise.

  1. Object recognition using neural networks and high-order perspective-invariant relational descriptions

    NASA Astrophysics Data System (ADS)

    Miller, Kenyon R.; Gilmore, John F.

    1992-02-01

    The task of 3-D object recognition can be viewed as consisting of four modules: extraction of structural descriptions, hypothesis generation, pose estimation, and hypothesis verification. The recognition time is determined by the efficiency of each of the four modules, but particularly on the hypothesis generation module which determines how many pose estimates and verifications must be done to recognize the object. In this paper, a set of high-order perspective-invariant relations are defined which can be used with a neural network algorithm to obtain a high-quality set of model-image matches between a model and image of a robot workstation. Using these matches, the number of hypotheses which must be generated to find a correct pose is greatly reduced.

  2. Individual differences in involvement of the visual object recognition system during visual word recognition.

    PubMed

    Laszlo, Sarah; Sacchi, Elizabeth

    2015-01-01

    Individuals with dyslexia often evince reduced activation during reading in left hemisphere (LH) language regions. This can be observed along with increased activation in the right hemisphere (RH), especially in areas associated with object recognition - a pattern referred to as RH compensation. The mechanisms of RH compensation are relatively unclear. We hypothesize that RH compensation occurs when the RH object recognition system is called upon to supplement an underperforming LH visual word form recognition system. We tested this by collecting ERPs while participants with a range of reading abilities viewed words, objects, and word/object ambiguous items (e.g., "SMILE" shaped like a smile). Less experienced readers differentiate words, objects, and ambiguous items less strongly, especially over the RH. We suggest that this lack of differentiation may have negative consequences for dyslexic individuals demonstrating RH compensation.

  3. Single objective light-sheet microscopy for high-speed whole-cell 3D super-resolution

    PubMed Central

    Meddens, Marjolein B. M.; Liu, Sheng; Finnegan, Patrick S.; Edwards, Thayne L.; James, Conrad D.; Lidke, Keith A.

    2016-01-01

    We have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single molecule super-resolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet. PMID:27375939

  4. Acquiring multi-viewpoint image of 3D object for integral imaging using synthetic aperture phase-shifting digital holography

    NASA Astrophysics Data System (ADS)

    Jeong, Min-Ok; Kim, Nam; Park, Jae-Hyeung; Jeon, Seok-Hee; Gil, Sang-Keun

    2009-02-01

    We propose a method generating elemental images for the auto-stereoscopic three-dimensional display technique, integral imaging, using phase-shifting digital holography. Phase shifting digital holography is a way recording the digital hologram by changing phase of the reference beam and extracting the complex field of the object beam. Since all 3D information is captured by the phase-shifting digital holography, the elemental images for any specifications of the lens array can be generated from single phase-shifting digital holography. We expanded the viewing angle of the generated elemental image by using the synthetic aperture phase-shifting digital hologram. The principle of the proposed method is verified experimentally.

  5. Single objective light-sheet microscopy for high-speed whole-cell 3D super-resolution.

    PubMed

    Meddens, Marjolein B M; Liu, Sheng; Finnegan, Patrick S; Edwards, Thayne L; James, Conrad D; Lidke, Keith A

    2016-06-01

    We have developed a method for performing light-sheet microscopy with a single high numerical aperture lens by integrating reflective side walls into a microfluidic chip. These 45° side walls generate light-sheet illumination by reflecting a vertical light-sheet into the focal plane of the objective. Light-sheet illumination of cells loaded in the channels increases image quality in diffraction limited imaging via reduction of out-of-focus background light. Single molecule super-resolution is also improved by the decreased background resulting in better localization precision and decreased photo-bleaching, leading to more accepted localizations overall and higher quality images. Moreover, 2D and 3D single molecule super-resolution data can be acquired faster by taking advantage of the increased illumination intensities as compared to wide field, in the focused light-sheet.

  6. 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.…

  7. A novel multi-view object recognition in complex background

    NASA Astrophysics Data System (ADS)

    Chang, Yongxin; Yu, Huapeng; Xu, Zhiyong; Fu, Chengyu; Gao, Chunming

    2015-02-01

    Recognizing objects from arbitrary aspects is always a highly challenging problem in computer vision, and most existing algorithms mainly focus on a specific viewpoint research. Hence, in this paper we present a novel recognizing framework based on hierarchical representation, part-based method and learning in order to recognize objects from different viewpoints. The learning evaluates the model's mistakes and feeds it back the detector to avid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these shared appearance features to support recognition combining with the improved multiple view model. Compared with other recognition models, the proposed approach can efficiently tackle multi-view problem and promote the recognition versatility of our system. For an quantitative valuation The novel algorithm has been tested on several benchmark datasets such as Caltech 101 and PASCAL VOC 2010. The experimental results validate that our approach can recognize objects more precisely and the performance outperforms others single view recognition methods.

  8. 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…

  9. 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.

  10. Learning Distance Functions for Exemplar-Based Object Recognition

    DTIC Science & Technology

    2007-08-08

    NOTES 14. ABSTRACT This thesis investigates an exemplar-based approach to object recognition that learns, on an image-by-image basis, the relative...contribution of this thesis is a method for learning a set-to-set distance function specific to each training image and demonstrating the use of these...Computer Science University of California, Berkeley Professor Jitendra Malik, Chair This thesis investigates an exemplar-based approach to object

  11. 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

  12. Priming for novel object associations: Neural differences from object item priming and equivalent forms of recognition.

    PubMed

    Gomes, Carlos Alexandre; Figueiredo, Patrícia; Mayes, Andrew

    2016-04-01

    The neural substrates of associative and item priming and recognition were investigated in a functional magnetic resonance imaging study over two separate sessions. In the priming session, participants decided which object of a pair was bigger during both study and test phases. In the recognition session, participants saw different object pairs and performed the same size-judgement task followed by an associative recognition memory task. Associative priming was accompanied by reduced activity in the right middle occipital gyrus as well as in bilateral hippocampus. Object item priming was accompanied by reduced activity in extensive priming-related areas in the bilateral occipitotemporofrontal cortex, as well as in the perirhinal cortex, but not in the hippocampus. Associative recognition was characterized by activity increases in regions linked to recollection, such as the hippocampus, posterior cingulate cortex, anterior medial frontal gyrus and posterior parahippocampal cortex. Item object priming and recognition recruited broadly overlapping regions (e.g., bilateral middle occipital and prefrontal cortices, left fusiform gyrus), even though the BOLD response was in opposite directions. These regions along with the precuneus, where both item priming and recognition were accompanied by activation, have been found to respond to object familiarity. The minimal structural overlap between object associative priming and recollection-based associative recognition suggests that they depend on largely different stimulus-related information and that the different directions of the effects indicate distinct retrieval mechanisms. In contrast, item priming and familiarity-based recognition seemed mainly based on common memory information, although the extent of common processing between priming and familiarity remains unclear. Further implications of these findings are discussed.

  13. Invariant visual object recognition and shape processing in rats.

    PubMed

    Zoccolan, Davide

    2015-05-15

    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.

  14. 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

  15. See-Through Imaging of Laser-Scanned 3d Cultural Heritage Objects Based on Stochastic Rendering of Large-Scale Point Clouds

    NASA Astrophysics Data System (ADS)

    Tanaka, S.; Hasegawa, K.; Okamoto, N.; Umegaki, R.; Wang, S.; Uemura, M.; Okamoto, A.; Koyamada, K.

    2016-06-01

    We propose a method for the precise 3D see-through imaging, or transparent visualization, of the large-scale and complex point clouds acquired via the laser scanning of 3D cultural heritage objects. Our method is based on a stochastic algorithm and directly uses the 3D points, which are acquired using a laser scanner, as the rendering primitives. This method achieves the correct depth feel without requiring depth sorting of the rendering primitives along the line of sight. Eliminating this need allows us to avoid long computation times when creating natural and precise 3D see-through views of laser-scanned cultural heritage objects. The opacity of each laser-scanned object is also flexibly controllable. For a laser-scanned point cloud consisting of more than 107 or 108 3D points, the pre-processing requires only a few minutes, and the rendering can be executed at interactive frame rates. Our method enables the creation of cumulative 3D see-through images of time-series laser-scanned data. It also offers the possibility of fused visualization for observing a laser-scanned object behind a transparent high-quality photographic image placed in the 3D scene. We demonstrate the effectiveness of our method by applying it to festival floats of high cultural value. These festival floats have complex outer and inner 3D structures and are suitable for see-through imaging.

  16. Topomorphologic Separation of Fused Isointensity Objects via Multiscale Opening: Separating Arteries and Veins in 3-D Pulmonary CT

    PubMed Central

    Gao, Zhiyun; Alford, Sara K.; Sonka, Milan; Hoffman, Eric A.

    2015-01-01

    A novel multiscale topomorphologic approach for opening of two isointensity objects fused at different locations and scales is presented and applied to separating arterial and venous trees in 3-D pulmonary multidetector X-ray computed tomography (CT) images. Initialized with seeds, the two isointensity objects (arteries and veins) grow iteratively while maintaining their spatial exclusiveness and eventually form two mutually disjoint objects at convergence. The method is intended to solve the following two fundamental challenges: how to find local size of morphological operators and how to trace continuity of locally separated regions. These challenges are met by combining fuzzy distance transform (FDT), a morphologic feature with a topologic fuzzy connectivity, and a new morphological reconstruction step to iteratively open finer and finer details starting at large scales and progressing toward smaller scales. The method employs efficient user intervention at locations where local morphological separability assumption does not hold due to imaging ambiguities or any other reason. The approach has been validated on mathematically generated tubular objects and applied to clinical pulmonary noncontrast CT data for separating arteries and veins. The tradeoff between accuracy and the required user intervention for the method has been quantitatively examined by comparing with manual outlining. The experimental study, based on a blind seed selection strategy, has demonstrated that above 95% accuracy may be achieved using 25–40 seeds for each of arteries and veins. Our method is very promising for semiautomated separation of arteries and veins in pulmonary CT images even when there is no object-specific intensity variation at conjoining locations. PMID:20199919

  17. 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.

  18. Simple and efficient improvement of spin image for three-dimensional object recognition

    NASA Astrophysics Data System (ADS)

    Lu, Rongrong; Zhu, Feng; Hao, Yingming; Wu, Qingxiao

    2016-11-01

    This paper presents a highly distinctive and robust local three-dimensional (3-D) feature descriptor named longitude and latitude spin image (LLSI). The whole procedure has two modules: local reference frame (LRF) definition and LLSI feature description. We employ the same technique as Tombari to define the LRF. The LLSI feature descriptor is obtained by stitching the longitude and latitude (LL) image to the original spin image vertically, where the LL image was generated similarly with the spin image by mapping a two-tuple (θ,φ) into a discrete two-dimensional histogram. The performance of the proposed LLSI descriptor was rigorously tested on a number of popular and publicly available datasets. The results showed that our method is more robust with respect to noise and varying mesh resolution than existing techniques. Finally, we tested our LLSI-based algorithm for 3-D object recognition on two popular datasets. Our LLSI-based algorithm achieved recognition rates of 100%, 98.2%, and 96.2%, respectively, when tested on the Bologna, University of Western Australia (UWA) (up to 84% occlusion), UWA datasets (all). Moreover, our LLSI-based algorithm achieved 100% recognition rate on the whole UWA dataset when generating the LLSI descriptor with the LRF proposed by Guo.

  19. Shape Recognition Of Complex Objects By Syntactical Primitives

    NASA Astrophysics Data System (ADS)

    Lenger, D.; Cipovic, H.

    1985-04-01

    The paper describes a pattern recognition method based on syntactic image analysis applicable in autonomous systems of robot vision for the purpose of pattern detection or classification. The discrimination of syntactic elements is realized by polygonal approximation of contours employing a very fast algorithm based upon coding, local pixel logic and methods of choice instead of numerical methods. Semantic information is derived from attributes calculated from the filtered shape vector. No a priori information on image objects is required, and the choice of starting point is determined by finding the significant directions on the shape vector. The radius of recognition sphere is minimum Euclidian distance, i.e. maximum similarity between the unknown model and each individual grammar created in the learning phase. By keeping information on derivations of individual syntactic elements, an alternative of parsing recognition is left. The analysis is very flexible, and permits the recognition of highly distorted or even partially visible objects. The output from syntactic analyzer is the measure of irregularity, and the method is thus applicable in any application where sample deformation is being examined.

  20. Distortion-invariant kernel correlation filters for general object recognition

    NASA Astrophysics Data System (ADS)

    Patnaik, Rohit

    General object recognition is a specific application of pattern recognition, in which an object in a background must be classified in the presence of several distortions such as aspect-view differences, scale differences, and depression-angle differences. Since the object can be present at different locations in the test input, a classification algorithm must be applied to all possible object locations in the test input. We emphasize one type of classifier, the distortion-invariant filter (DIF), for fast object recognition, since it can be applied to all possible object locations using a fast Fourier transform (FFT) correlation. We refer to distortion-invariant correlation filters simply as DIFs. DIFs all use a combination of training-set images that are representative of the expected distortions in the test set. In this dissertation, we consider a new approach that combines DIFs and the higher-order kernel technique; these form what we refer to as "kernel DIFs." Our objective is to develop higher-order classifiers that can be applied (efficiently and fast) to all possible locations of the object in the test input. All prior kernel DIFs ignored the issue of efficient filter shifts. We detail which kernel DIF formulations are computational realistic to use and why. We discuss the proper way to synthesize DIFs and kernel DIFs for the wide area search case (i.e., when a small filter must be applied to a much larger test input) and the preferable way to perform wide area search with these filters; this is new. We use computer-aided design (CAD) simulated infrared (IR) object imagery and real IR clutter imagery to obtain test results. Our test results on IR data show that a particular kernel DIF, the kernel SDF filter and its new "preprocessed" version, is promising, in terms of both test-set performance and on-line calculations, and is emphasized in this dissertation. We examine the recognition of object variants. We also quantify the effect of different constant

  1. Top-down facilitation of visual object recognition: object-based and context-based contributions.

    PubMed

    Fenske, Mark J; Aminoff, Elissa; Gronau, Nurit; Bar, Moshe

    2006-01-01

    The neural mechanisms subserving visual recognition are traditionally described in terms of bottom-up analysis, whereby increasingly complex aspects of the visual input are processed along a hierarchical progression of cortical regions. However, the importance of top-down facilitation in successful recognition has been emphasized in recent models and research findings. Here we consider evidence for top-down facilitation of recognition that is triggered by early information about an object, as well as by contextual associations between an object and other objects with which it typically appears. The object-based mechanism is proposed to trigger top-down facilitation of visual recognition rapidly, using a partially analyzed version of the input image (i.e., a blurred image) that is projected from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC information that is back-projected as "initial guesses" to the temporal cortex where it presensitizes the most likely interpretations of the input object. In addition to this object-based facilitation, a context-based mechanism is proposed to trigger top-down facilitation through contextual associations between objects in scenes. These contextual associations activate predictive information about which objects are likely to appear together, and can influence the "initial guesses" about an object's identity. We have shown that contextual associations are analyzed by a network that includes the parahippocampal cortex and the retrosplenial complex. The integrated proposal described here is that object- and context-based top-down influences operate together, promoting efficient recognition by framing early information about an object within the constraints provided by a lifetime of experience with contextual associations.

  2. 3D shape and eccentricity measurements of fast rotating rough objects by two mutually tilted interference fringe systems

    NASA Astrophysics Data System (ADS)

    Czarske, J. W.; Kuschmierz, R.; Günther, P.

    2013-06-01

    Precise measurements of distance, eccentricity and 3D-shape of fast moving objects such as turning parts of lathes, gear shafts, magnetic bearings, camshafts, crankshafts and rotors of vacuum pumps are on the one hand important tasks. On the other hand they are big challenges, since contactless precise measurement techniques are required. Optical techniques are well suitable for distance measurements of non-moving surfaces. However, measurements of laterally fast moving surfaces are still challenging. For such tasks the laser Doppler distance sensor technique was invented by the TU Dresden some years ago. This technique has been realized by two mutually tilted interference fringe systems, where the distance is coded in the phase difference between the generated interference signals. However, due to the speckle effect different random envelopes and phase jumps of the interference signals occur. They disturb the phase difference estimation between the interference signals. In this paper, we will report on a scientific breakthrough on the measurement uncertainty budget which has been achieved recently. Via matching of the illumination and receiving optics the measurement uncertainty of the displacement and distance can be reduced by about one magnitude. For displacement measurements of a recurring rough surface a standard deviation of 110 nm were attained at lateral velocities of 5 m / s. Due to the additionally measured lateral velocity and the rotational speed, the two-dimensional shape of rotating objects is calculated. The three-dimensional shape can be conducted by employment of a line camera. Since the measurement uncertainty of the displacement, vibration, distance, eccentricity, and shape is nearly independent of the lateral surface velocity, this technique is predestined for fast-rotating objects. Especially it can be advantageously used for the quality control of workpieces inside of a lathe towards the reduction of process tolerances, installation times and

  3. Neuronal substrates characterizing two stages in visual object recognition.

    PubMed

    Taminato, Tomoya; Miura, Naoki; Sugiura, Motoaki; Kawashima, Ryuta

    2014-12-01

    Visual object recognition is classically believed to involve two stages: a perception stage in which perceptual information is integrated, and a memory stage in which perceptual information is matched with an object's representation. The transition from the perception to the memory stage can be slowed to allow for neuroanatomical segregation using a degraded visual stimuli (DVS) task in which images are first presented at low spatial resolution and then gradually sharpened. In this functional magnetic resonance imaging study, we characterized these two stages using a DVS task based on the classic model. To separate periods that are assumed to dominate the perception, memory, and post-recognition stages, subjects responded once when they could guess the identity of the object in the image and a second time when they were certain of the identity. Activation of the right medial occipitotemporal region and the posterior part of the rostral medial frontal cortex was found to be characteristic of the perception and memory stages, respectively. Although the known role of the former region in perceptual integration was consistent with the classic model, a likely role of the latter region in monitoring for confirmation of recognition suggests the advantage of recently proposed interactive models.

  4. Multisensory interactions between auditory and haptic object recognition.

    PubMed

    Kassuba, Tanja; Menz, Mareike M; Röder, Brigitte; Siebner, Hartwig R

    2013-05-01

    Object manipulation produces characteristic sounds and causes specific haptic sensations that facilitate the recognition of the manipulated object. To identify the neural correlates of audio-haptic binding of object features, healthy volunteers underwent functional magnetic resonance imaging while they matched a target object to a sample object within and across audition and touch. By introducing a delay between the presentation of sample and target stimuli, it was possible to dissociate haptic-to-auditory and auditory-to-haptic matching. We hypothesized that only semantically coherent auditory and haptic object features activate cortical regions that host unified conceptual object representations. The left fusiform gyrus (FG) and posterior superior temporal sulcus (pSTS) showed increased activation during crossmodal matching of semantically congruent but not incongruent object stimuli. In the FG, this effect was found for haptic-to-auditory and auditory-to-haptic matching, whereas the pSTS only displayed a crossmodal matching effect for congruent auditory targets. Auditory and somatosensory association cortices showed increased activity during crossmodal object matching which was, however, independent of semantic congruency. Together, the results show multisensory interactions at different hierarchical stages of auditory and haptic object processing. Object-specific crossmodal interactions culminate in the left FG, which may provide a higher order convergence zone for conceptual object knowledge.

  5. Early recurrent feedback facilitates visual object recognition under challenging conditions

    PubMed Central

    Wyatte, Dean; Jilk, David J.; O'Reilly, Randall C.

    2014-01-01

    Standard models of the visual object recognition pathway hold that a largely feedforward process from the retina through inferotemporal cortex leads to object identification. A subsequent feedback process originating in frontoparietal areas through reciprocal connections to striate cortex provides attentional support to salient or behaviorally-relevant features. Here, we review mounting evidence that feedback signals also originate within extrastriate regions and begin during the initial feedforward process. This feedback process is temporally dissociable from attention and provides important functions such as grouping, associational reinforcement, and filling-in of features. Local feedback signals operating concurrently with feedforward processing are important for object identification in noisy real-world situations, particularly when objects are partially occluded, unclear, or otherwise ambiguous. Altogether, the dissociation of early and late feedback processes presented here expands on current models of object identification, and suggests a dual role for descending feedback projections. PMID:25071647

  6. Europeana and 3D

    NASA Astrophysics Data System (ADS)

    Pletinckx, D.

    2011-09-01

    The current 3D hype creates a lot of interest in 3D. People go to 3D movies, but are we ready to use 3D in our homes, in our offices, in our communication? Are we ready to deliver real 3D to a general public and use interactive 3D in a meaningful way to enjoy, learn, communicate? The CARARE project is realising this for the moment in the domain of monuments and archaeology, so that real 3D of archaeological sites and European monuments will be available to the general public by 2012. There are several aspects to this endeavour. First of all is the technical aspect of flawlessly delivering 3D content over all platforms and operating systems, without installing software. We have currently a working solution in PDF, but HTML5 will probably be the future. Secondly, there is still little knowledge on how to create 3D learning objects, 3D tourist information or 3D scholarly communication. We are still in a prototype phase when it comes to integrate 3D objects in physical or virtual museums. Nevertheless, Europeana has a tremendous potential as a multi-facetted virtual museum. Finally, 3D has a large potential to act as a hub of information, linking to related 2D imagery, texts, video, sound. We describe how to create such rich, explorable 3D objects that can be used intuitively by the generic Europeana user and what metadata is needed to support the semantic linking.

  7. How does the brain solve visual object recognition?

    PubMed Central

    Zoccolan, Davide; Rust, Nicole C.

    2012-01-01

    Mounting evidence suggests that “core object recognition,” the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains little-understood. Here we review evidence ranging from individual neurons, to neuronal populations, to behavior, to computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical sub-networks with a common functional goal. PMID:22325196

  8. Factor G utilizes a carbohydrate-binding cleft that is conserved between horseshoe crab and bacteria for the recognition of beta-1,3-D-glucans.

    PubMed

    Ueda, Yuki; Ohwada, Shuhei; Abe, Yoshito; Shibata, Toshio; Iijima, Manabu; Yoshimitsu, Yukiko; Koshiba, Takumi; Nakata, Munehiro; Ueda, Tadashi; Kawabata, Shun-ichiro

    2009-09-15

    In the horseshoe crab, the recognition of beta-1,3-D-glucans by factor G triggers hemolymph coagulation. Factor G contains a domain of two tandem xylanase Z-like modules (Z1-Z2), each of which recognizes beta-1,3-D-glucans. To gain an insight into the recognition of beta-1,3-D-glucans from a structural view point, recombinants of Z1-Z2, the C-terminal module Z2, Z2 with a Cys to Ala substitution (Z2A), and its tandem repeat Z2A-Z2A were characterized. Z2 and Z1-Z2, but not Z2A and Z2A-Z2A, formed insoluble aggregates at higher concentrations more than approximately 30 and 3 microM, respectively. Z1-Z2 and Z2A-Z2A bound more strongly to an insoluble beta-1,3-D-glucan (curdlan) than Z2A. The affinity of Z2A for a soluble beta-1,3-D-glucan (laminarin) was equivalent to those of Z1-Z2, Z2A-Z2A, and native factor G, suggesting that the binding of a single xylanase Z-like module prevents the subsequent binding of another module to laminarin. Interestingly, Z2A as well as intact factor G exhibited fungal agglutinating activity, and fungi were specifically detected with fluorescently tagged Z2A by microscopy. The chemical shift perturbation of Z2A induced by the interaction with laminaripentaose was analyzed by nuclear magnetic resonance spectroscopy. The ligand-binding site of Z2A was located in a cleft on a beta-sheet in a predicted beta-sandwich structure, which was superimposed onto cleft B in a cellulose-binding module of endoglucanase 5A from the soil bacterium Cellvibrio mixtus. We conclude that the pattern recognition for beta-1,3-D-glucans by factor G is accomplished via a carbohydrate-binding cleft that is evolutionally conserved between horseshoe crab and bacteria.

  9. Methylphenidate restores novel object recognition in DARPP-32 knockout mice.

    PubMed

    Heyser, Charles J; McNaughton, Caitlyn H; Vishnevetsky, Donna; Fienberg, Allen A

    2013-09-15

    Previously, we have shown that Dopamine- and cAMP-regulated phosphoprotein of 32kDa (DARPP-32) knockout mice required significantly more trials to reach criterion than wild-type mice in an operant reversal-learning task. The present study was conducted to examine adult male and female DARPP-32 knockout mice and wild-type controls in a novel object recognition test. Wild-type and knockout mice exhibited comparable behavior during the initial exploration trials. As expected, wild-type mice exhibited preferential exploration of the novel object during the substitution test, demonstrating recognition memory. In contrast, knockout mice did not show preferential exploration of the novel object, instead exhibiting an increase in exploration of all objects during the test trial. Given that the removal of DARPP-32 is an intracellular manipulation, it seemed possible to pharmacologically restore some cellular activity and behavior by stimulating dopamine receptors. Therefore, a second experiment was conducted examining the effect of methylphenidate. The results show that methylphenidate increased horizontal activity in both wild-type and knockout mice, though this increase was blunted in knockout mice. Pretreatment with methylphenidate significantly impaired novel object recognition in wild-type mice. In contrast, pretreatment with methylphenidate restored the behavior of DARPP-32 knockout mice to that observed in wild-type mice given saline. These results provide additional evidence for a functional role of DARPP-32 in the mediation of processes underlying learning and memory. These results also indicate that the behavioral deficits in DARPP-32 knockout mice may be restored by the administration of methylphenidate.

  10. Artificial neural networks and model-based recognition of three-dimensional objects from two-dimensional images

    NASA Astrophysics Data System (ADS)

    Chao, Chih-Ho; Dhawan, Atam P.

    1994-01-01

    A computer vision system is developed for 3-D object recognition using artificial neural networks and a model-based top-down feedback analysis approach. This system can adequately address the problems caused by an incomplete edge map provided by a low-level processor for 3-D representation and recognition. The system uses key patterns that are selected using a priority assignment. The highest priority is given to the key pattern with the most connected node and associated features. The features are space invariant structures and sets of orientation for edge primitives. The labeled key features are provided as input to an artificial neural network for matching with model key patterns. A Hopfield-Tank network is applied to two levels of matching to increase the computational effectiveness. The first matching is to choose the class of the possible model and the second matching is to find the model closest to the candidate. The result of such matchings is utilized in generating the model-driven top-down feedback analysis. This model is then rotated in 3-D space to find the best match with the candidate and to provide the additional features in 3-D. In the case of multiple objects, a dynamic search strategy is adopted to recognize objects using one pattern at a time. This strategy is also useful in recognizing occluded objects. The experimental results are presented to show the capability and effectiveness of the system.

  11. The role of the dorsal dentate gyrus in object and object-context recognition.

    PubMed

    Dees, Richard L; Kesner, Raymond P

    2013-11-01

    The aim of this study was to determine the role of the dorsal dentate gyrus (dDG) in object recognition memory using a black box and object-context recognition memory using a clear box with available cues that define a spatial context. Based on a 10 min retention interval between the study phase and the test phase, the results indicated that dDG lesioned rats are impaired when compared to controls in the object-context recognition test in the clear box. However, there were no reliable differences between the dDG lesioned rats and the control group for the object recognition test in the black box. Even though the dDG lesioned rats were more active in object exploration, the habituation gradients did not differ. These results suggest that the dentate gyrus lesioned rats are clearly impaired when there is an important contribution of context. Furthermore, based on a 24 h retention interval in the black box the dDG lesioned rats were impaired compared to controls.

  12. Visual appearance interacts with conceptual knowledge in object recognition

    PubMed Central

    Cheung, Olivia S.; Gauthier, Isabel

    2014-01-01

    Objects contain rich visual and conceptual information, but do these two types of information interact? Here, we examine whether visual and conceptual information interact when observers see novel objects for the first time. We then address how this interaction influences the acquisition of perceptual expertise. We used two types of novel objects (Greebles), designed to resemble either animals or tools, and two lists of words, which described non-visual attributes of people or man-made objects. Participants first judged if a word was more suitable for describing people or objects while ignoring a task-irrelevant image, and showed faster responses if the words and the unfamiliar objects were congruent in terms of animacy (e.g., animal-like objects with words that described human). Participants then learned to associate objects and words that were either congruent or not in animacy, before receiving expertise training to rapidly individuate the objects. Congruent pairing of visual and conceptual information facilitated observers' ability to become a perceptual expert, as revealed in a matching task that required visual identification at the basic or subordinate levels. Taken together, these findings show that visual and conceptual information interact at multiple levels in object recognition. PMID:25120509

  13. Touching and Hearing Unseen Objects: Multisensory Effects on Scene Recognition

    PubMed Central

    van Lier, Rob

    2016-01-01

    In three experiments, we investigated the influence of object-specific sounds on haptic scene recognition without vision. Blindfolded participants had to recognize, through touch, spatial scenes comprising six objects that were placed on a round platform. Critically, in half of the trials, object-specific sounds were played when objects were touched (bimodal condition), while sounds were turned off in the other half of the trials (unimodal condition). After first exploring the scene, two objects were swapped and the task was to report, which of the objects swapped positions. In Experiment 1, geometrical objects and simple sounds were used, while in Experiment 2, the objects comprised toy animals that were matched with semantically compatible animal sounds. In Experiment 3, we replicated Experiment 1, but now a tactile-auditory object identification task preceded the experiment in which the participants learned to identify the objects based on tactile and auditory input. For each experiment, the results revealed a significant performance increase only after the switch from bimodal to unimodal. Thus, it appears that the release of bimodal identification, from audio-tactile to tactile-only produces a benefit that is not achieved when having the reversed order in which sound was added after having experience with haptic-only. We conclude that task-related factors other than mere bimodal identification cause the facilitation when switching from bimodal to unimodal conditions. PMID:27698985

  14. Selective visual attention in object recognition and scene analysis

    NASA Astrophysics Data System (ADS)

    Gonzaga, Adilson; de Almeida Neves, Evelina M.; Frere, Annie F.

    1998-10-01

    An important feature of human vision system is the ability of selective visual attention. The stimulus that reaches the primate retina is processed in two different cortical pathways; one is specialized for object vision (`What') and the other for spatial vision (`Where'). By this, the visual system is able to recognize objects independently where they appear in the visual field. There are two major theories to explain the human visual attention. According to the Object- Based theory there is a limit on the isolated objects that could be perceived simultaneously and by the Space-Based theory there is a limit on the spatial areas from which the information could be taken up. This paper deals with the Object-Based theory that states the visual world occurs in two stages. The scene is segmented into isolated objects by region growing techniques in the pre-attentive stage. Invariant features (moments) are extracted and used as input of an Artificial Neural Network giving the probable object location (`Where'). In the focal-stage, particular objects are analyzed in detail through another neural network that performs the object recognition (`What'). The number of analyzed objects is based on a top-down process doing a consistent scene interpretation. With Visual Attention is possible the development of more efficient and flexible interfaces between low sensory information and high level process.

  15. The role of color diagnosticity in object recognition and representation.

    PubMed

    Therriault, David J; Yaxley, Richard H; Zwaan, Rolf A

    2009-11-01

    The role of color diagnosticity in object recognition and representation was assessed in three Experiments. In Experiment 1a, participants named pictured objects that were strongly associated with a particular color (e.g., pumpkin and orange). Stimuli were presented in a congruent color, incongruent color, or grayscale. Results indicated that congruent color facilitated naming time, incongruent color impeded naming time, and naming times for grayscale items were situated between the congruent and incongruent conditions. Experiment 1b replicated Experiment 1a using a verification task. Experiment 2 employed a picture rebus paradigm in which participants read sentences one word at a time that included pictures of color diagnostic objects (i.e., pictures were substituted for critical nouns). Results indicated that the "reading" times of these pictures mirrored the pattern found in Experiment 1. In Experiment 3, an attempt was made to override color diagnosticity using linguistic context (e.g., a pumpkin was described as painted green). Linguistic context did not override color diagnosticity. Collectively, the results demonstrate that color information is regularly utilized in object recognition and representation for highly color diagnostic items.

  16. Long-term visual object recognition memory in aged rats.

    PubMed

    Platano, Daniela; Fattoretti, Patrizia; Balietti, Marta; Bertoni-Freddari, Carlo; Aicardi, Giorgio

    2008-04-01

    Aging is associated with memory impairments, but the neural bases of this process need to be clarified. To this end, behavioral protocols for memory testing may be applied to aged animals to compare memory performances with functional and structural characteristics of specific brain regions. Visual object recognition memory can be investigated in the rat using a behavioral task based on its spontaneous preference for exploring novel rather than familiar objects. We found that a behavioral task able to elicit long-term visual object recognition memory in adult Long-Evans rats failed in aged (25-27 months old) Wistar rats. Since no tasks effective in aged rats are reported in the literature, we changed the experimental conditions to improve consolidation processes to assess whether this form of memory can still be maintained for long term at this age: the learning trials were performed in a smaller box, identical to the home cage, and the inter-trial delays were shortened. We observed a reduction in anxiety in this box (as indicated by the lower number of fecal boli produced during habituation), and we developed a learning protocol able to elicit a visual object recognition memory that was maintained after 24 h in these aged rats. When we applied the same protocol to adult rats, we obtained similar results. This experimental approach can be useful to study functional and structural changes associated with age-related memory impairments, and may help to identify new behavioral strategies and molecular targets that can be addressed to ameliorate memory performances during aging.

  17. Category selectivity in human visual cortex: Beyond visual object recognition.

    PubMed

    Peelen, Marius V; Downing, Paul E

    2017-04-02

    Human ventral temporal cortex shows a categorical organization, with regions responding selectively to faces, bodies, tools, scenes, words, and other categories. Why is this? Traditional accounts explain category selectivity as arising within a hierarchical system dedicated to visual object recognition. For example, it has been proposed that category selectivity reflects the clustering of category-associated visual feature representations, or that it reflects category-specific computational algorithms needed to achieve view invariance. This visual object recognition framework has gained renewed interest with the success of deep neural network models trained to "recognize" objects: these hierarchical feed-forward networks show similarities to human visual cortex, including categorical separability. We argue that the object recognition framework is unlikely to fully account for category selectivity in visual cortex. Instead, we consider category selectivity in the context of other functions such as navigation, social cognition, tool use, and reading. Category-selective regions are activated during such tasks even in the absence of visual input and even in individuals with no prior visual experience. Further, they are engaged in close connections with broader domain-specific networks. Considering the diverse functions of these networks, category-selective regions likely encode their preferred stimuli in highly idiosyncratic formats; representations that are useful for navigation, social cognition, or reading are unlikely to be meaningfully similar to each other and to varying degrees may not be entirely visual. The demand for specific types of representations to support category-associated tasks may best account for category selectivity in visual cortex. This broader view invites new experimental and computational approaches.

  18. Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.

    PubMed

    Kheradpisheh, Saeed R; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call "variation level." We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

  19. Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder

    PubMed Central

    Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

    2016-01-01

    View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call “variation level.” We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research. PMID:27642281

  20. A New Approach for Investigating the Molecular Recognition of Protein: Toward Structure-Based Drug Design Based on the 3D-RISM Theory.

    PubMed

    Kiyota, Yasuomi; Yoshida, Norio; Hirata, Fumio

    2011-11-08

    A new approach to investigate a molecular recognition process of protein is presented based on the three-dimensional reference interaction site model (3D-RISM) theory, a statistical mechanics theory of molecular liquids. Numerical procedure for solving the conventional 3D-RISM equation consists of two steps. In step 1, we solve ordinary RISM (or 1D-RISM) equations for a solvent mixture including target ligands in order to obtain the density pair correlation functions (PCF) among molecules in the solution. Then, we solve the 3D-RISM equation for a solute-solvent system to find three-dimensional density distribution functions (3D-DDF) of solvent species around a protein, using PCF obtained in the first step. A key to the success of the method was to regard a target ligand as one of "solvent" species. However, the success is limited due to a difficulty of solving the 1D-RISM equation for a solvent mixture, including large ligand molecules. In the present paper, we propose a method which eases the limitation concerning solute size in the conventional method. In this approach, we solve a solute-solute 3D-RISM equations for a protein-ligand system in which both proteins and ligands are regarded as "solutes" at infinite dilution. The 3D- and 1D-RISM equations are solved for protein-solvent and ligand-solvent systems, respectively, in order to obtain the 3D- and 1D-DDF of solvent around the solutes, which are required for solving the solute-solute 3D-RISM equation. The method is applied to two practical and noteworthy examples concerning pharmaceutical design. One is an odorant binding protein in the Drosophila melanogaster , which binds an ethanol molecule. The other is phospholipase A2, which is known as a receptor of acetylsalicylic acid or aspirin. The result indicates that the method successfully reproduces the binding mode of the ligand molecules in the binding sites measured by the experiments.

  1. Canonical Wnt signaling is necessary for object recognition memory consolidation.

    PubMed

    Fortress, Ashley M; Schram, Sarah L; Tuscher, Jennifer J; Frick, Karyn M

    2013-07-31

    Wnt signaling has emerged as a potent regulator of hippocampal synaptic function, although no evidence yet supports a critical role for Wnt signaling in hippocampal memory. Here, we sought to determine whether canonical β-catenin-dependent Wnt signaling is necessary for hippocampal memory consolidation. Immediately after training in a hippocampal-dependent object recognition task, mice received a dorsal hippocampal (DH) infusion of vehicle or the canonical Wnt antagonist Dickkopf-1 (Dkk-1; 50, 100, or 200 ng/hemisphere). Twenty-four hours later, mice receiving vehicle remembered the familiar object explored during training. However, mice receiving Dkk-1 exhibited no memory for the training object, indicating that object recognition memory consolidation is dependent on canonical Wnt signaling. To determine how Dkk-1 affects canonical Wnt signaling, mice were infused with vehicle or 50 ng/hemisphere Dkk-1 and protein levels of Wnt-related proteins (Dkk-1, GSK3β, β-catenin, TCF1, LEF1, Cyclin D1, c-myc, Wnt7a, Wnt1, and PSD95) were measured in the dorsal hippocampus 5 min or 4 h later. Dkk-1 produced a rapid increase in Dkk-1 protein levels and a decrease in phosphorylated GSK3β levels, followed by a decrease in β-catenin, TCF1, LEF1, Cyclin D1, c-myc, Wnt7a, and PSD95 protein levels 4 h later. These data suggest that alterations in Wnt/GSK3β/β-catenin signaling may underlie the memory impairments induced by Dkk-1. In a subsequent experiment, object training alone rapidly increased DH GSK3β phosphorylation and levels of β-catenin and Cyclin D1. These data suggest that canonical Wnt signaling is regulated by object learning and is necessary for hippocampal memory consolidation.

  2. Determinants of novel object and location recognition during development.

    PubMed

    Jablonski, S A; Schreiber, W B; Westbrook, S R; Brennan, L E; Stanton, M E

    2013-11-01

    In the novel object recognition (OR) paradigm, rats are placed in an arena where they encounter two sample objects during a familiarization phase. A few minutes later, they are returned to the same arena and are presented with a familiar object and a novel object. The object location recognition (OL) variant involves the same familiarization procedure but during testing one of the familiar objects is placed in a novel location. Normal adult rats are able to perform both the OR and OL tasks, as indicated by enhanced exploration of the novel vs. the familiar test item. Rats with hippocampal lesions perform the OR but not OL task indicating a role of spatial memory in OL. Recently, these tasks have been used to study the ontogeny of spatial memory but the literature has yielded conflicting results. The current experiments add to this literature by: (1) behaviorally characterizing these paradigms in postnatal day (PD) 21, 26 and 31-day-old rats; (2) examining the role of NMDA systems in OR vs. OL; and (3) investigating the effects of neonatal alcohol exposure on both tasks. Results indicate that normal-developing rats are able to perform OR and OL by PD21, with greater novelty exploration in the OR task at each age. Second, memory acquisition in the OL but not OR task requires NMDA receptor function in juvenile rats [corrected]. Lastly, neonatal alcohol exposure does not disrupt performance in either task. Implications for the ontogeny of incidental spatial learning and its disruption by developmental alcohol exposure are discussed.

  3. Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration.

    PubMed

    Wang, Panqu; Gauthier, Isabel; Cottrell, Garrison

    2016-04-01

    Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al. [Gauthier, I., McGugin, R. W., Richler, J. J., Herzmann, G., Speegle, M., & VanGulick, A. E. Experience moderates overlap between object and face recognition, suggesting a common ability. Journal of Vision, 14, 7, 2014] recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing ["The Model", TM, Cottrell, G. W., & Hsiao, J. H. Neurocomputational models of face processing. In A. J. Calder, G. Rhodes, M. Johnson, & J. Haxby (Eds.), The Oxford handbook of face perception. Oxford, UK: Oxford University Press, 2011]. We model the domain general ability v as the available computational resources (number of hidden units) in the mapping from input to label and experience as the frequency of individual exemplars in an object category appearing during network training. Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces (separating them in representational space) that

  4. Orientation-invariant object recognition: evidence from repetition blindness.

    PubMed

    Harris, Irina M; Dux, Paul E

    2005-02-01

    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. This failure is usually interpreted as a difficulty in assigning two separate episodic tokens to the same visual type. Thus, RB can provide useful information about which representations are treated as the same by the visual system. Two experiments tested whether RB occurs for repeated objects that were either in identical orientations, or differed by 30, 60, 90, or 180 degrees . Significant RB was found for all orientation differences, consistent with the existence of orientation-invariant object representations. However, under some circumstances, RB was reduced or even eliminated when the repeated object was rotated by 180 degrees , suggesting easier individuation of the repeated objects in this case. A third experiment confirmed that the upside-down orientation is processed more easily than other rotated orientations. The results indicate that, although object identity can be determined independently of orientation, orientation plays an important role in establishing distinct episodic representations of a repeated object, thus enabling one to report them as separate events.

  5. Recognition memory for object form and object location: an event-related potential study.

    PubMed

    Mecklinger, A; Meinshausen, R M

    1998-09-01

    In this study, the processes associated with retrieving object forms and object locations from working memory were examined with the use of simultaneously recorded event-related potential (ERP) activity. Subjects memorized object forms and their spatial locations and made either object-based or location-based recognition judgments. In Experiment 1, recognition performance was higher for object locations than for object forms. Old responses evoked more positive-going ERP activity between 0.3 and 1.8 sec poststimulus than did new responses. The topographic distribution of these old/new effects in the P300 time interval was task specific, with object-based recognition judgments being associated with anteriorly focused effects and location-based judgments with posteriorly focused effects. Late old/new effects were dominant at right frontal recordings. Using an interference paradigm, it was shown in Experiment 2 that visual representations were used to rehearse both object forms and object locations in working memory. The results of Experiment 3 indicated that the observed differential topographic distributions of the old/new effects in the P300 time interval are unlikely to reflect differences between easy and difficult recognition judgments. More specific effects were obtained for a subgroup of subjects for which the processing characteristics during location-based judgments presumably were similar to those in Experiment 1. These data, together with those from Experiment 1, indicate that different brain areas are engaged in retrieving object forms and object locations from working memory. Further analyses support the view that retrieval of object forms relies on conceptual semantic representation, whereas retrieving object locations is based on structural representations of spatial information. The effects in the later time intervals may play a functional role in post-retrieval processing, such as recollecting information from the study episode or other processes

  6. Recognition of similar objects using simulated prosthetic vision.

    PubMed

    Hu, Jie; Xia, Peng; Gu, Chaochen; Qi, Jin; Li, Sheng; Peng, Yinghong

    2014-02-01

    Due to the limitations of existing techniques, even the most advanced visual prostheses, using several hundred electrodes to transmit signals to the visual pathway, restrict sensory function and visual information. To identify the bottlenecks and guide prosthesis designing, psychophysics simulations of a visual prosthesis in normally sighted individuals are desirable. In this study, psychophysical experiments of discriminating objects with similar profiles were used to test the effects of phosphene array parameters (spatial resolution, gray scale, distortion, and dropout rate) on visual information using simulated prosthetic vision. The results showed that the increase in spatial resolution and number of gray levels and the decrease in phosphene distortion and dropout rate improved recognition performance, and the accuracy is 78.5% under the optimum condition (resolution: 32 × 32, gray level: 8, distortion: k = 0, dropout: 0%). In combined parameter tests, significant facial recognition accuracy was achieved for all the images with k = 0.1 distortion and 10% dropout. Compared with other experiments, we find that different objects do not show specific sensitivity to the changes of parameters and visual information is not nearly enough even under the optimum condition. The results suggests that higher spatial resolution and more gray levels are required for visual prosthetic devices and further research on image processing strategies to improve prosthetic vision is necessary, especially when the wearers have to accomplish more than simple visual tasks.

  7. Neural Substrates of View-Invariant Object Recognition Developed without Experiencing Rotations of the Objects

    PubMed Central

    Okamura, Jun-ya; Yamaguchi, Reona; Honda, Kazunari; Tanaka, Keiji

    2014-01-01

    One fails to recognize an unfamiliar object across changes in viewing angle when it must be discriminated from similar distractor objects. View-invariant recognition gradually develops as the viewer repeatedly sees the objects in rotation. It is assumed that different views of each object are associated with one another while their successive appearance is experienced in rotation. However, natural experience of objects also contains ample opportunities to discriminate among objects at each of the multiple viewing angles. Our previous behavioral experiments showed that after experiencing a new set of object stimuli during a task that required only discrimination at each of four viewing angles at 30° intervals, monkeys could recognize the objects across changes in viewing angle up to 60°. By recording activities of neurons from the inferotemporal cortex after various types of preparatory experience, we here found a possible neural substrate for the monkeys' performance. For object sets that the monkeys had experienced during the task that required only discrimination at each of four viewing angles, many inferotemporal neurons showed object selectivity covering multiple views. The degree of view generalization found for these object sets was similar to that found for stimulus sets with which the monkeys had been trained to conduct view-invariant recognition. These results suggest that the experience of discriminating new objects in each of several viewing angles develops the partially view-generalized object selectivity distributed over many neurons in the inferotemporal cortex, which in turn bases the monkeys' emergent capability to discriminate the objects across changes in viewing angle. PMID:25378169

  8. Neural substrates of view-invariant object recognition developed without experiencing rotations of the objects.

    PubMed

    Okamura, Jun-Ya; Yamaguchi, Reona; Honda, Kazunari; Wang, Gang; Tanaka, Keiji

    2014-11-05

    One fails to recognize an unfamiliar object across changes in viewing angle when it must be discriminated from similar distractor objects. View-invariant recognition gradually develops as the viewer repeatedly sees the objects in rotation. It is assumed that different views of each object are associated with one another while their successive appearance is experienced in rotation. However, natural experience of objects also contains ample opportunities to discriminate among objects at each of the multiple viewing angles. Our previous behavioral experiments showed that after experiencing a new set of object stimuli during a task that required only discrimination at each of four viewing angles at 30° intervals, monkeys could recognize the objects across changes in viewing angle up to 60°. By recording activities of neurons from the inferotemporal cortex after various types of preparatory experience, we here found a possible neural substrate for the monkeys' performance. For object sets that the monkeys had experienced during the task that required only discrimination at each of four viewing angles, many inferotemporal neurons showed object selectivity covering multiple views. The degree of view generalization found for these object sets was similar to that found for stimulus sets with which the monkeys had been trained to conduct view-invariant recognition. These results suggest that the experience of discriminating new objects in each of several viewing angles develops the partially view-generalized object selectivity distributed over many neurons in the inferotemporal cortex, which in turn bases the monkeys' emergent capability to discriminate the objects across changes in viewing angle.

  9. EM modelling of arbitrary shaped anisotropic dielectric objects using an efficient 3D leapfrog scheme on unstructured meshes

    NASA Astrophysics Data System (ADS)

    Gansen, A.; Hachemi, M. El; Belouettar, S.; Hassan, O.; Morgan, K.

    2016-09-01

    The standard Yee algorithm is widely used in computational electromagnetics because of its simplicity and divergence free nature. A generalization of the classical Yee scheme to 3D unstructured meshes is adopted, based on the use of a Delaunay primal mesh and its high quality Voronoi dual. This allows the problem of accuracy losses, which are normally associated with the use of the standard Yee scheme and a staircased representation of curved material interfaces, to be circumvented. The 3D dual mesh leapfrog-scheme which is presented has the ability to model both electric and magnetic anisotropic lossy materials. This approach enables the modelling of problems, of current practical interest, involving structured composites and metamaterials.

  10. A method of 3D reconstruction via ISAR Sequences based on scattering centers association for space rigid object

    NASA Astrophysics Data System (ADS)

    Li, Gang; Zou, Jiangwei; Xu, Shiyou; Tian, Biao; Chen, Zengping

    2014-10-01

    In this paper the effects of orbits motion makes for scattering centers trajectory is analyzed, and introduced to scattering centers association, as a constraint. A screening method of feature points is presented to analysis the false points of reconstructed result, and the wrong association which lead these false points. The loop iteration between 3D reconstruction and association result makes the precision of final reconstructed result have a further improvement. The simulation data shows the validity of the algorithm.

  11. 3D micro-XRF for cultural heritage objects: new analysis strategies for the investigation of the Dead Sea Scrolls.

    PubMed

    Mantouvalou, Ioanna; Wolff, Timo; Hahn, Oliver; Rabin, Ira; Lühl, Lars; Pagels, Marcel; Malzer, Wolfgang; Kanngiesser, Birgit

    2011-08-15

    A combination of 3D micro X-ray fluorescence spectroscopy (3D micro-XRF) and micro-XRF was utilized for the investigation of a small collection of highly heterogeneous, partly degraded Dead Sea Scroll parchment samples from known excavation sites. The quantitative combination of the two techniques proves to be suitable for the identification of reliable marker elements which may be used for classification and provenance studies. With 3D micro-XRF, the three-dimensional nature, i.e. the depth-resolved elemental composition as well as density variations, of the samples was investigated and bromine could be identified as a suitable marker element. It is shown through a comparison of quantitative and semiquantitative values for the bromine content derived using both techniques that, for elements which are homogeneously distributed in the sample matrix, quantification with micro-XRF using a one-layer model is feasible. Thus, the possibility for routine provenance studies using portable micro-XRF instrumentation on a vast amount of samples, even on site, is obtained through this work.

  12. Short-term plasticity of visuo-haptic object recognition.

    PubMed

    Kassuba, Tanja; Klinge, Corinna; Hölig, Cordula; Röder, Brigitte; Siebner, Hartwig R

    2014-01-01

    Functional magnetic resonance imaging (fMRI) studies have provided ample evidence for the involvement of the lateral occipital cortex (LO), fusiform gyrus (FG), and intraparietal sulcus (IPS) in visuo-haptic object integration. Here we applied 30 min of sham (non-effective) or real offline 1 Hz repetitive transcranial magnetic stimulation (rTMS) to perturb neural processing in left LO immediately before subjects performed a visuo-haptic delayed-match-to-sample task during fMRI. In this task, subjects had to match sample (S1) and target (S2) objects presented sequentially within or across vision and/or haptics in both directions (visual-haptic or haptic-visual) and decide whether or not S1 and S2 were the same objects. Real rTMS transiently decreased activity at the site of stimulation and remote regions such as the right LO and bilateral FG during haptic S1 processing. Without affecting behavior, the same stimulation gave rise to relative increases in activation during S2 processing in the right LO, left FG, bilateral IPS, and other regions previously associated with object recognition. Critically, the modality of S2 determined which regions were recruited after rTMS. Relative to sham rTMS, real rTMS induced increased activations during crossmodal congruent matching in the left FG for haptic S2 and the temporal pole for visual S2. In addition, we found stronger activations for incongruent than congruent matching in the right anterior parahippocampus and middle frontal gyrus for crossmodal matching of haptic S2 and in the left FG and bilateral IPS for unimodal matching of visual S2, only after real but not sham rTMS. The results imply that a focal perturbation of the left LO triggers modality-specific interactions between the stimulated left LO and other key regions of object processing possibly to maintain unimpaired object recognition. This suggests that visual and haptic processing engage partially distinct brain networks during visuo-haptic object matching.

  13. Implicit encoding of extrinsic object properties in stored representations mediating recognition: evidence from shadow-specific repetition priming.

    PubMed

    Leek, E Charles; Davitt, Lina I; Cristino, Filipe

    2015-03-01

    This study investigated whether, and under what conditions, stored shape representations mediating recognition encode extrinsic object properties that vary according to viewing conditions. This was examined in relation to cast shadow. Observers (N = 90) first memorised a subset of 3D multi-part novel objects from a limited range of viewpoints rendered with either no shadow, object internal shadow, or both object internal and external (ground) plane shadow. During a subsequent test phase previously memorised targets were discriminated from visually similar distractors across learned and novel views following brief presentation of a same-shape masked prime. The primes contained either matching or mismatching shadow rendering from the training condition. The results showed a recognition advantage for objects memorised with object internal shadow. In addition, objects encoded with internal shadow were primed more strongly by matching internal shadow primes, than by same shape primes with either no shadow or both object internal and external (ground) shadow. This pattern of priming effects generalises to previously unseen views of targets rendered with object internal shadow. The results suggest that the object recognition system contains a level of stored representation at which shape and the extrinsic object property of cast shadow are bound. We propose that this occurs when cast shadow cannot be discounted during perception on the basis of external cues to the scene lighting model.

  14. Object recognition testing: methodological considerations on exploration and discrimination measures.

    PubMed

    Akkerman, Sven; Blokland, Arjan; Reneerkens, Olga; van Goethem, Nick P; Bollen, Eva; Gijselaers, Hieronymus J M; Lieben, Cindy K J; Steinbusch, Harry W M; Prickaerts, Jos

    2012-07-01

    The object recognition task (ORT) is a popular one-trial learning test for animals. In the current study, we investigated several methodological issues concerning the task. Data was pooled from 28 ORT studies, containing 731 male Wistar rats. We investigated the relationship between 3 common absolute- and relative discrimination measures, as well as their relation to exploratory activity. In this context, the effects of pre-experimental habituation, object familiarity, trial duration, retention interval and the amnesic drugs MK-801 and scopolamine were investigated. Our analyses showed that the ORT is very sensitive, capable of detecting subtle differences in memory (discrimination) and exploratory performance. As a consequence, it is susceptible to potential biases due to (injection) stress and side effects of drugs. Our data indicated that a minimum amount of exploration is required in the sample and test trial for stable significant discrimination performance. However, there was no relationship between the level of exploration in the sample trial and discrimination performance. In addition, the level of exploration in the test trial was positively related to the absolute discrimination measure, whereas this was not the case for relative discrimination measures, which correct for exploratory differences, making them more resistant to exploration biases. Animals appeared to remember object information over multiple test sessions. Therefore, when animals have encountered both objects in prior test sessions, the object preference observed in the test trial of 1h retention intervals is probably due to a relative difference in familiarity between the objects in the test trial, rather than true novelty per se. Taken together, our findings suggest to take into consideration pre-experimental exposure (familiarization) to objects, habituation to treatment procedures, and the use of relative discrimination measures when using the ORT.

  15. Visual object recognition for mobile tourist information systems

    NASA Astrophysics Data System (ADS)

    Paletta, Lucas; Fritz, Gerald; Seifert, Christin; Luley, Patrick; Almer, Alexander

    2005-03-01

    We describe a mobile vision system that is capable of automated object identification using images captured from a PDA or a camera phone. We present a solution for the enabling technology of outdoors vision based object recognition that will extend state-of-the-art location and context aware services towards object based awareness in urban environments. In the proposed application scenario, tourist pedestrians are equipped with GPS, W-LAN and a camera attached to a PDA or a camera phone. They are interested whether their field of view contains tourist sights that would point to more detailed information. Multimedia type data about related history, the architecture, or other related cultural context of historic or artistic relevance might be explored by a mobile user who is intending to learn within the urban environment. Learning from ambient cues is in this way achieved by pointing the device towards the urban sight, capturing an image, and consequently getting information about the object on site and within the focus of attention, i.e., the users current field of view.

  16. Preliminary study of statistical pattern recognition-based coin counterfeit detection by means of high resolution 3D scanners

    NASA Astrophysics Data System (ADS)

    Leich, Marcus; Kiltz, Stefan; Krätzer, Christian; Dittmann, Jana; Vielhauer, Claus

    2011-03-01

    According to the European Commission around 200,000 counterfeit Euro coins are removed from circulation every year. While approaches exist to automatically detect these coins, satisfying error rates are usually only reached for low quality forgeries, so-called "local classes". High-quality minted forgeries ("common classes") pose a problem for these methods as well as for trained humans. This paper presents a first approach for statistical analysis of coins based on high resolution 3D data acquired with a chromatic white light sensor. The goal of this analysis is to determine whether two coins are of common origin. The test set for these first and new investigations consists of 62 coins from not more than five different sources. The analysis is based on the assumption that, apart from markings caused by wear such as scratches and residue consisting of grease and dust, coins from equal origin have a more similar height field than coins from different mints. First results suggest that the selected approach is heavily affected by influences of wear like dents and scratches and the further research is required the eliminate this influence. A course for future work is outlined.

  17. Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats.

    PubMed

    Rosselli, Federica B; Alemi, Alireza; Ansuini, Alessio; Zoccolan, Davide

    2015-01-01

    In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness). In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant) to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: (i) smaller and more scattered; (ii) only partially preserved across object views; and (iii) only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning.

  18. Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats

    PubMed Central

    Rosselli, Federica B.; Alemi, Alireza; Ansuini, Alessio; Zoccolan, Davide

    2015-01-01

    In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness). In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant) to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: (i) smaller and more scattered; (ii) only partially preserved across object views; and (iii) only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning. PMID:25814936

  19. Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

    PubMed

    Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

    2016-10-01

    This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

  20. Anthropomorphic robot for recognition and drawing generalized object images

    NASA Astrophysics Data System (ADS)

    Ginzburg, Vera M.

    1998-10-01

    The process of recognition, for instance, understanding the text, written by different fonts, consists in the depriving of the individual attributes of the letters in the particular font. It is shown that such process, in Nature and technique, can be provided by the narrowing the spatial frequency of the object's image by its defocusing. In defocusing images remain only areas, so-called Informative Fragments (IFs), which all together form the generalized (stylized) image of many identical objects. It is shown that the variety of shapes of IFs is restricted and can be presented by `Geometrical alphabet'. The `letters' for this alphabet can be created using two basic `genetic' figures: a stripe and round spot. It is known from physiology that the special cells of visual cortex response to these particular figures. The prototype of such `genetic' alphabet has been made using Boolean algebra (Venn's diagrams). The algorithm for drawing the letter's (`genlet's') shape in this alphabet and generalized images of objects (for example, `sleeping cat'), are given. A scheme of an anthropomorphic robot is shown together with results of model computer experiment of the robot's action--`drawing' the generalized image.

  1. Optical full-depth refocusing of 3-D objects based on subdivided-elemental images and local periodic δ-functions in integral imaging.

    PubMed

    Ai, Ling-Yu; Dong, Xiao-Bin; Jang, Jae-Young; Kim, Eun-Soo

    2016-05-16

    We propose a new approach for optical refocusing of three-dimensional (3-D) objects on their real depth without a pickup-range limitation based on subdivided-elemental image arrays (sub-EIAs) and local periodic δ-function arrays (L-PDFAs). The captured EIA from the 3-D objects locating out of the pickup-range, is divided into a number of sub-EIAs depending on the object distance from the lens array. Then, by convolving these sub-EIAs with each L-PDFA whose spatial period corresponds to the specific object's depth, as well as whose size is matched to that of the sub-EIA, arrays of spatially-filtered sub-EIAs (SF-sub-EIAs) for each object depth can be uniquely extracted. From these arrays of SF-sub-EIAs, 3-D objects can be optically reconstructed to be refocused on their real depth. Operational principle of the proposed method is analyzed based on ray-optics. In addition, to confirm the feasibility of the proposed method in the practical application, experiments with test objects are carried out and the results are comparatively discussed with those of the conventional method.

  2. Infrared detection, recognition and identification of handheld objects

    NASA Astrophysics Data System (ADS)

    Adomeit, Uwe

    2012-10-01

    A main criterion for comparison and selection of thermal imagers for military applications is their nominal range performance. This nominal range performance is calculated for a defined task and standardized target and environmental conditions. The only standardization available to date is STANAG 4347. The target defined there is based on a main battle tank in front view. Because of modified military requirements, this target is no longer up-to-date. Today, different topics of interest are of interest, especially differentiation between friend and foe and identification of humans. There is no direct way to differentiate between friend and foe in asymmetric scenarios, but one clue can be that someone is carrying a weapon. This clue can be transformed in the observer tasks detection: a person is carrying or is not carrying an object, recognition: the object is a long / medium / short range weapon or civil equipment and identification: the object can be named (e. g. AK-47, M-4, G36, RPG7, Axe, Shovel etc.). These tasks can be assessed experimentally and from the results of such an assessment, a standard target for handheld objects may be derived. For a first assessment, a human carrying 13 different handheld objects in front of his chest was recorded at four different ranges with an IR-dual-band camera. From the recorded data, a perception experiment was prepared. It was conducted with 17 observers in a 13-alternative forced choice, unlimited observation time arrangement. The results of the test together with Minimum Temperature Difference Perceived measurements of the camera and temperature difference and critical dimension derived from the recorded imagery allowed defining a first standard target according to the above tasks. This standard target consist of 2.5 / 3.5 / 5 DRI line pairs on target, 0.24 m critical size and 1 K temperature difference. The values are preliminary and have to be refined in the future. Necessary are different aspect angles, different

  3. An analysis of TA-Student Interaction and the Development of Concepts in 3-d Space Through Language, Objects, and Gesture in a College-level Geoscience Laboratory

    NASA Astrophysics Data System (ADS)

    King, S. L.

    2015-12-01

    The purpose of this study is twofold: 1) to describe how a teaching assistant (TA) in an undergraduate geology laboratory employs a multimodal system in order to mediate the students' understanding of scientific knowledge and develop a contextualization of a concept in three-dimensional space and 2) to describe how a linguistic awareness of gestural patterns can be used to inform TA training assessment of students' conceptual understanding in situ. During the study the TA aided students in developing the conceptual understanding and reconstruction of a meteoric impact, which produces shatter cone formations. The concurrent use of speech, gesture, and physical manipulation of objects is employed by the TA in order to aid the conceptual understanding of this particular phenomenon. Using the methods of gestural analysis in works by Goldin-Meadow, 2000 and McNeill, 1992, this study describes the gestures of the TA and the students as well as the purpose and motivation of the meditational strategies employed by TA in order to build the geological concept in the constructed 3-dimensional space. Through a series of increasingly complex gestures, the TA assists the students to construct the forensic concept of the imagined 3-D space, which can then be applied to a larger context. As the TA becomes more familiar with the students' meditational needs, the TA adapts teaching and gestural styles to meet their respective ZPDs (Vygotsky 1978). This study shows that in the laboratory setting language, gesture, and physical manipulation of the experimental object are all integral to the learning and demonstration of scientific concepts. Recognition of the gestural patterns of the students allows the TA the ability to dynamically assess the students understanding of a concept. Using the information from this example of student-TA interaction, a brief short course has been created to assist TAs in recognizing the mediational power as well as the assessment potential of gestural

  4. Object recognition and pose estimation of planar objects from range data

    NASA Technical Reports Server (NTRS)

    Pendleton, Thomas W.; Chien, Chiun Hong; Littlefield, Mark L.; Magee, Michael

    1994-01-01

    The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for object recognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and

  5. The influence of color information on the recognition of color diagnostic and noncolor diagnostic objects.

    PubMed

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

    2011-01-01

    In the present study, the authors explore in detail the level of visual object recognition at which perceptual color information improves the recognition of color diagnostic and noncolor diagnostic objects. To address this issue, 3 object recognition tasks with different cognitive demands were designed: (a) an object verification task; (b) a category verification task; and (c) a name verification task. The authors found that perceptual color information improved color diagnostic object recognition mainly in tasks for which access to the semantic knowledge about the object was necessary to perform the task; that is, in category and name verification. In contrast, the authors found that perceptual color information facilitates noncolor diagnostic object recognition when access to the object's structural description from long-term memory was necessary--that is, object verification. In summary, the present study shows that the role of perceptual color information in object recognition is dependent on color diagnosticity.

  6. Three-Dimensional Object Recognition and Registration for Robotic Grasping Systems Using a Modified Viewpoint Feature Histogram.

    PubMed

    Chen, Chin-Sheng; Chen, Po-Chun; Hsu, Chih-Ming

    2016-11-23

    This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) descriptor for the 3D point cloud data then encodes the geometry and viewpoint, so an object can be simultaneously recognized and registered in a stable pose and the information is stored in a database. The VFH is robust to a large degree of surface noise and missing depth information so it is reliable for stereo data. However, the pose estimation for an object fails when the object is placed symmetrically to the viewpoint. To overcome this problem, this study proposes a modified viewpoint feature histogram (MVFH) descriptor that consists of two parts: a surface shape component that comprises an extended fast point feature histogram and an extended viewpoint direction component. The MVFH descriptor characterizes an object's pose and enhances the system's ability to identify objects with mirrored poses. Finally, the refined pose is further estimated using an iterative closest point when the object has been recognized and the pose roughly estimated by the MVFH descriptor and it has been registered on a database. The estimation results demonstrate that the MVFH feature descriptor allows more accurate pose estimation. The experiments also show that the proposed method can be applied in vision-guided robotic grasping systems.

  7. A hierarchical multiple-view approach to three-dimensional object recognition.

    PubMed

    Lin, W C; Liao, F Y; Tsao, C K; Lingutla, T

    1991-01-01

    A hierarchical approach is proposed for solving the surface and vertex correspondence problems in multiple-view-based 3D object-recognition systems. The proposed scheme is a coarse-to-fine search process, and a Hopfield network is used at each stage. Compared with conventional object-matching schemes, the proposed technique provides a more general and compact formulation of the problem and a solution more suitable for parallel implementation. At the coarse search stage, the surface matching scores between the input image and each object model in the database are computed through a Hopfield network and are used to select the candidates for further consideration. At the fine search stage, the object models selected from the previous stage are fed into another Hopfield network for vertex matching. The object model that has the best surface and vertex correspondences with the input image is finally singled out as the best matched model. Experimental results are reported using both synthetic and real range images to corroborate the proposed theory.

  8. Improving human object recognition performance using video enhancement techniques

    NASA Astrophysics Data System (ADS)

    Whitman, Lucy S.; Lewis, Colin; Oakley, John P.

    2004-12-01

    Atmospheric scattering causes significant degradation in the quality of video images, particularly when imaging over long distances. The principle problem is the reduction in contrast due to scattered light. It is known that when the scattering particles are not too large compared with the imaging wavelength (i.e. Mie scattering) then high spatial resolution information may be contained within a low-contrast image. Unfortunately this information is not easily perceived by a human observer, particularly when using a standard video monitor. A secondary problem is the difficulty of achieving a sharp focus since automatic focus techniques tend to fail in such conditions. Recently several commercial colour video processing systems have become available. These systems use various techniques to improve image quality in low contrast conditions whilst retaining colour content. These systems produce improvements in subjective image quality in some situations, particularly in conditions of haze and light fog. There is also some evidence that video enhancement leads to improved ATR performance when used as a pre-processing stage. Psychological literature indicates that low contrast levels generally lead to a reduction in the performance of human observers in carrying out simple visual tasks. The aim of this paper is to present the results of an empirical study on object recognition in adverse viewing conditions. The chosen visual task was vehicle number plate recognition at long ranges (500 m and beyond). Two different commercial video enhancement systems are evaluated using the same protocol. The results show an increase in effective range with some differences between the different enhancement systems.

  9. Multispectral image analysis for object recognition and classification

    NASA Astrophysics Data System (ADS)

    Viau, C. R.; Payeur, P.; Cretu, A.-M.

    2016-05-01

    Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.

  10. Three-Dimensional Object Recognition and Registration for Robotic Grasping Systems Using a Modified Viewpoint Feature Histogram

    PubMed Central

    Chen, Chin-Sheng; Chen, Po-Chun; Hsu, Chih-Ming

    2016-01-01

    This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) descriptor for the 3D point cloud data then encodes the geometry and viewpoint, so an object can be simultaneously recognized and registered in a stable pose and the information is stored in a database. The VFH is robust to a large degree of surface noise and missing depth information so it is reliable for stereo data. However, the pose estimation for an object fails when the object is placed symmetrically to the viewpoint. To overcome this problem, this study proposes a modified viewpoint feature histogram (MVFH) descriptor that consists of two parts: a surface shape component that comprises an extended fast point feature histogram and an extended viewpoint direction component. The MVFH descriptor characterizes an object’s pose and enhances the system’s ability to identify objects with mirrored poses. Finally, the refined pose is further estimated using an iterative closest point when the object has been recognized and the pose roughly estimated by the MVFH descriptor and it has been registered on a database. The estimation results demonstrate that the MVFH feature descriptor allows more accurate pose estimation. The experiments also show that the proposed method can be applied in vision-guided robotic grasping systems. PMID:27886080

  11. Biological object recognition in μ-radiography images

    NASA Astrophysics Data System (ADS)

    Prochazka, A.; Dammer, J.; Weyda, F.; Sopko, V.; Benes, J.; Zeman, J.; Jandejsek, I.

    2015-03-01

    This study presents an applicability of real-time microradiography to biological objects, namely to horse chestnut leafminer, Cameraria ohridella (Insecta: Lepidoptera, Gracillariidae) and following image processing focusing on image segmentation and object recognition. The microradiography of insects (such as horse chestnut leafminer) provides a non-invasive imaging that leaves the organisms alive. The imaging requires a high spatial resolution (micrometer scale) radiographic system. Our radiographic system consists of a micro-focus X-ray tube and two types of detectors. The first is a charge integrating detector (Hamamatsu flat panel), the second is a pixel semiconductor detector (Medipix2 detector). The latter allows detection of single quantum photon of ionizing radiation. We obtained numerous horse chestnuts leafminer pupae in several microradiography images easy recognizable in automatic mode using the image processing methods. We implemented an algorithm that is able to count a number of dead and alive pupae in images. The algorithm was based on two methods: 1) noise reduction using mathematical morphology filters, 2) Canny edge detection. The accuracy of the algorithm is higher for the Medipix2 (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.83), than for the flat panel (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.77). Therefore, we conclude that Medipix2 has lower noise and better displays contours (edges) of biological objects. Our method allows automatic selection and calculation of dead and alive chestnut leafminer pupae. It leads to faster monitoring of the population of one of the world's important insect pest.

  12. Associative recognition and the hippocampus: differential effects of hippocampal lesions on object-place, object-context and object-place-context memory.

    PubMed

    Langston, Rosamund F; Wood, Emma R

    2010-10-01

    The hippocampus is thought to be required for the associative recognition of objects together with the spatial or temporal contexts in which they occur. However, recent data showing that rats with fornix lesions perform as well as controls in an object-place task, while being impaired on an object-place-context task (Eacott and Norman (2004) J Neurosci 24:1948-1953), suggest that not all forms of context-dependent associative recognition depend on the integrity of the hippocampus. To examine the role of the hippocampus in context-dependent recognition directly, the present study tested the effects of large, selective, bilateral hippocampus lesions in rats on performance of a series of spontaneous recognition memory tasks: object recognition, object-place recognition, object-context recognition and object-place-context recognition. Consistent with the effects of fornix lesions, animals with hippocampus lesions were impaired only on the object-place-context task. These data confirm that not all forms of context-dependent associative recognition are mediated by the hippocampus. Subsequent experiments suggested that the object-place task does not require an allocentric representation of space, which could account for the lack of impairment following hippocampus lesions. Importantly, as the object-place-context task has similar spatial requirements, the selective deficit in object-place-context recognition suggests that this task requires hippocampus-dependent neural processes distinct from those required for allocentric spatial memory, or for object memory, object-place memory or object-context memory. Two possibilities are that object, place, and context information converge only in the hippocampus, or that recognition of integrated object-place-context information requires a hippocampus-dependent mode of retrieval, such as recollection.

  13. 3D tracking of single nanoparticles and quantum dots in living cells by out-of-focus imaging with diffraction pattern recognition.

    PubMed

    Gardini, Lucia; Capitanio, Marco; Pavone, Francesco S

    2015-11-03

    Live cells are three-dimensional environments where biological molecules move to find their targets and accomplish their functions. However, up to now, most single molecule investigations have been limited to bi-dimensional studies owing to the complexity of 3d-tracking techniques. Here, we present a novel method for three-dimensional localization of single nano-emitters based on automatic recognition of out-of-focus diffraction patterns. Our technique can be applied to track the movements of single molecules in living cells using a conventional epifluorescence microscope. We first demonstrate three-dimensional localization of fluorescent nanobeads over 4 microns depth with accuracy below 2 nm in vitro. Remarkably, we also establish three-dimensional tracking of Quantum Dots, overcoming their anisotropic emission, by adopting a ligation strategy that allows rotational freedom of the emitter combined with proper pattern recognition. We localize commercially available Quantum Dots in living cells with accuracy better than 7 nm over 2 microns depth. We validate our technique by tracking the three-dimensional movements of single protein-conjugated Quantum Dots in living cell. Moreover, we find that important localization errors can occur in off-focus imaging when improperly calibrated and we give indications to avoid them. Finally, we share a Matlab script that allows readily application of our technique by other laboratories.

  14. 3D and Education

    NASA Astrophysics Data System (ADS)

    Meulien Ohlmann, Odile

    2013-02-01

    Today the industry offers a chain of 3D products. Learning to "read" and to "create in 3D" becomes an issue of education of primary importance. 25 years professional experience in France, the United States and Germany, Odile Meulien set up a personal method of initiation to 3D creation that entails the spatial/temporal experience of the holographic visual. She will present some different tools and techniques used for this learning, their advantages and disadvantages, programs and issues of educational policies, constraints and expectations related to the development of new techniques for 3D imaging. Although the creation of display holograms is very much reduced compared to the creation of the 90ies, the holographic concept is spreading in all scientific, social, and artistic activities of our present time. She will also raise many questions: What means 3D? Is it communication? Is it perception? How the seeing and none seeing is interferes? What else has to be taken in consideration to communicate in 3D? How to handle the non visible relations of moving objects with subjects? Does this transform our model of exchange with others? What kind of interaction this has with our everyday life? Then come more practical questions: How to learn creating 3D visualization, to learn 3D grammar, 3D language, 3D thinking? What for? At what level? In which matter? for whom?

  15. Atypical Time Course of Object Recognition in Autism Spectrum Disorder

    PubMed Central

    Caplette, Laurent; Wicker, Bruno; Gosselin, Frédéric

    2016-01-01

    In neurotypical observers, it is widely believed that the visual system samples the world in a coarse-to-fine fashion. Past studies on Autism Spectrum Disorder (ASD) have identified atypical responses to fine visual information but did not investigate the time course of the sampling of information at different levels of granularity (i.e. Spatial Frequencies, SF). Here, we examined this question during an object recognition task in ASD and neurotypical observers using a novel experimental paradigm. Our results confirm and characterize with unprecedented precision a coarse-to-fine sampling of SF information in neurotypical observers. In ASD observers, we discovered a different pattern of SF sampling across time: in the first 80 ms, high SFs lead ASD observers to a higher accuracy than neurotypical observers, and these SFs are sampled differently across time in the two subject groups. Our results might be related to the absence of a mandatory precedence of global information, and to top-down processing abnormalities in ASD. PMID:27752088

  16. Crowding, grouping, and object recognition: A matter of appearance

    PubMed Central

    Herzog, Michael H.; Sayim, Bilge; Chicherov, Vitaly; Manassi, Mauro

    2015-01-01

    In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around the target (Bouma's law). (b) Increasing the number of flankers can only deteriorate performance. (c) Target-flanker interference is feature-specific. These characteristics are usually explained by pooling models, which are well in the spirit of classic models of object recognition. In this review, we summarize recent findings showing that crowding is not determined by the above characteristics, thus, challenging most models of crowding. We propose that the spatial configuration across the entire visual field determines crowding. Only when one understands how all elements of a visual scene group with each other, can one determine crowding strength. We put forward the hypothesis that appearance (i.e., how stimuli look) is a good predictor for crowding, because both crowding and appearance reflect the output of recurrent processing rather than interactions during the initial phase of visual processing. PMID:26024452

  17. Poka Yoke system based on image analysis and object recognition

    NASA Astrophysics Data System (ADS)

    Belu, N.; Ionescu, L. M.; Misztal, A.; Mazăre, A.

    2015-11-01

    Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with “fail-sating” or “mistake-proofing”. The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

  18. Crowding, grouping, and object recognition: A matter of appearance.

    PubMed

    Herzog, Michael H; Sayim, Bilge; Chicherov, Vitaly; Manassi, Mauro

    2015-01-01

    In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around the target (Bouma's law). (b) Increasing the number of flankers can only deteriorate performance. (c) Target-flanker interference is feature-specific. These characteristics are usually explained by pooling models, which are well in the spirit of classic models of object recognition. In this review, we summarize recent findings showing that crowding is not determined by the above characteristics, thus, challenging most models of crowding. We propose that the spatial configuration across the entire visual field determines crowding. Only when one understands how all elements of a visual scene group with each other, can one determine crowding strength. We put forward the hypothesis that appearance (i.e., how stimuli look) is a good predictor for crowding, because both crowding and appearance reflect the output of recurrent processing rather than interactions during the initial phase of visual processing.

  19. Objective 3D surface evaluation of intracranial electrophysiologic correlates of cerebral glucose metabolic abnormalities in children with focal epilepsy.

    PubMed

    Jeong, Jeong-Won; Asano, Eishi; Kumar Pilli, Vinod; Nakai, Yasuo; Chugani, Harry T; Juhász, Csaba

    2017-03-21

    To determine the spatial relationship between 2-deoxy-2[(18) F]fluoro-D-glucose (FDG) metabolic and intracranial electrophysiological abnormalities in children undergoing two-stage epilepsy surgery, statistical parametric mapping (SPM) was used to correlate hypo- and hypermetabolic cortical regions with ictal and interictal electrocorticography (ECoG) changes mapped onto the brain surface. Preoperative FDG-PET scans of 37 children with intractable epilepsy (31 with non-localizing MRI) were compared with age-matched pseudo-normal pediatric control PET data. Hypo-/hypermetabolic maps were transformed to 3D-MRI brain surface to compare the locations of metabolic changes with electrode coordinates of the ECoG-defined seizure onset zone (SOZ) and interictal spiking. While hypometabolic clusters showed a good agreement with the SOZ on the lobar level (sensitivity/specificity = 0.74/0.64), detailed surface-distance analysis demonstrated that large portions of ECoG-defined SOZ and interictal spiking area were located at least 3 cm beyond hypometabolic regions with the same statistical threshold (sensitivity/specificity = 0.18-0.25/0.94-0.90 for overlap 3-cm distance); for a lower threshold, sensitivity for SOZ at 3 cm increased to 0.39 with a modest compromise of specificity. Performance of FDG-PET SPM was slightly better in children with smaller as compared with widespread SOZ. The results demonstrate that SPM utilizing age-matched pseudocontrols can reliably detect the lobe of seizure onset. However, the spatial mismatch between metabolic and EEG epileptiform abnormalities indicates that a more complete SOZ detection could be achieved by extending intracranial electrode coverage at least 3 cm beyond the metabolic abnormality. Considering that the extent of feasible electrode coverage is limited, localization information from other modalities is particularly important to optimize grid coverage in cases of large hypometabolic cortex. Hum Brain Mapp, 2017. © 2017

  20. Optometric Measurements Predict Performance but not Comfort on a Virtual Object Placement Task with a Stereoscopic 3D Display

    DTIC Science & Technology

    2014-09-16

    environment, depth perception 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT: SAR 18. NUMBER OF PAGES 29 19a. NAME OF...virtual environment, depth perception 1 Distribution A: Approved for public release; distribution unlimited. 88ABW Cleared 9/9/2013; 88ABW...precision placement of a virtual object in depth at the same location as a target object. Subjective discomfort was assessed using the Simulator Sickness

  1. Transforming clinical imaging and 3D data for virtual reality learning objects: HTML5 and mobile devices implementation.

    PubMed

    Trelease, Robert B; Nieder, Gary L

    2013-01-01

    Web deployable anatomical simulations or "virtual reality learning objects" can easily be produced with QuickTime VR software, but their use for online and mobile learning is being limited by the declining support for web browser plug-ins for personal computers and unavailability on popular mobile devices like Apple iPad and Android tablets. This article describes complementary methods for creating comparable, multiplatform VR learning objects in the new HTML5 standard format, circumventing platform-specific limitations imposed by the QuickTime VR multimedia file format. Multiple types or "dimensions" of anatomical information can be embedded in such learning objects, supporting different kinds of online learning applications, including interactive atlases, examination questions, and complex, multi-structure presentations. Such HTML5 VR learning objects are usable on new mobile devices that do not support QuickTime VR, as well as on personal computers. Furthermore, HTML5 VR learning objects can be embedded in "ebook" document files, supporting the development of new types of electronic textbooks on mobile devices that are increasingly popular and self-adopted for mobile learning.

  2. Transforming Clinical Imaging and 3D Data for Virtual Reality Learning Objects: HTML5 and Mobile Devices Implementation

    ERIC Educational Resources Information Center

    Trelease, Robert B.; Nieder, Gary L.

    2013-01-01

    Web deployable anatomical simulations or "virtual reality learning objects" can easily be produced with QuickTime VR software, but their use for online and mobile learning is being limited by the declining support for web browser plug-ins for personal computers and unavailability on popular mobile devices like Apple iPad and Android…

  3. 3D-Modeling of deformed halite hopper crystals: Object based image analysis and support vector machine, a first evaluation

    NASA Astrophysics Data System (ADS)

    Leitner, Christoph; Hofmann, Peter; Marschallinger, Robert

    2014-05-01

    Halite hopper crystals are thought to develop by displacive growth in unconsolidated mud (Gornitz & Schreiber, 1984). The Alpine Haselgebirge, but also e.g. the salt deposits of the Rhine graben (mined at the beginning of the 20th century), comprise hopper crystals with shapes of cuboids, parallelepipeds and rhombohedrons (Görgey, 1912). Obviously, they deformed under oriented stress, which had been tried to reconstruct with respect to the sedimentary layering (Leitner et al., 2013). In the present work, deformed halite hopper crystals embedded in mudrock were automated reconstructed. Object based image analysis (OBIA) has been used successfully in remote sensing for 2D images before. The present study represents the first time that the method was used for reconstruction of three dimensional geological objects. First, manually a reference (gold standard) was created by redrawing contours of the halite crystals on each HRXCT scanning slice. Then, for OBIA, the computer program eCognition was used. For the automated reconstruction a rule set was developed. Thereby, the strength of OBIA was to recognize all objects similar to halite hopper crystals and in particular to eliminate cracks. In a second step, all the objects unsuitable for a structural deformation analysis were dismissed using a support vector machine (SVM) (clusters, polyhalite-coated crystals and spherical halites) The SVM simultaneously drastically reduced the number of halites. From 184 OBIA-objects 67 well shaped remained, which comes close to the number of pre-selected 52 objects. To assess the accuracy of the automated reconstruction, the result before and after SVM was compared to the reference, i.e. the gold standard. State-of the art per-scene statistics were extended to a per-object statistics. Görgey R (1912) Zur Kenntnis der Kalisalzlager von Wittelsheim im Ober-Elsaß. Tschermaks Mineral Petrogr Mitt 31:339-468 Gornitz VM, Schreiber BC (1981) Displacive halite hoppers from the dead sea

  4. Regulation of object recognition and object placement by ovarian sex steroid hormones.

    PubMed

    Tuscher, Jennifer J; Fortress, Ashley M; Kim, Jaekyoon; Frick, Karyn M

    2015-05-15

    The ovarian hormones 17β-estradiol (E2) and progesterone (P4) are potent modulators of hippocampal memory formation. Both hormones have been demonstrated to enhance hippocampal memory by regulating the cellular and molecular mechanisms thought to underlie memory formation. Behavioral neuroendocrinologists have increasingly used the object recognition and object placement (object location) tasks to investigate the role of E2 and P4 in regulating hippocampal memory formation in rodents. These one-trial learning tasks are ideal for studying acute effects of hormone treatments on different phases of memory because they can be administered during acquisition (pre-training), consolidation (post-training), or retrieval (pre-testing). This review synthesizes the rodent literature testing the effects of E2 and P4 on object recognition (OR) and object placement (OP), and the molecular mechanisms in the hippocampus supporting memory formation in these tasks. Some general trends emerge from the data. Among gonadally intact females, object memory tends to be best when E2 and P4 levels are elevated during the estrous cycle, pregnancy, and in middle age. In ovariectomized females, E2 given before or immediately after testing generally enhances OR and OP in young and middle-aged rats and mice, although effects are mixed in aged rodents. Effects of E2 treatment on OR and OP memory consolidation can be mediated by both classical estrogen receptors (ERα and ERβ), and depend on glutamate receptors (NMDA, mGluR1) and activation of numerous cell signaling cascades (e.g., ERK, PI3K/Akt, mTOR) and epigenetic processes (e.g., histone acetylation, DNA methylation). Acute P4 treatment given immediately after training also enhances OR and OP in young and middle-aged ovariectomized females by activating similar cell signaling pathways as E2 (e.g., ERK, mTOR). The few studies that have administered both hormones in combination suggest that treatment can enhance OR and OP, but that effects

  5. The importance of visual features in generic vs. specialized object recognition: a computational study

    PubMed Central

    Ghodrati, Masoud; Rajaei, Karim; Ebrahimpour, Reza

    2014-01-01

    It is debated whether the representation of objects in inferior temporal (IT) cortex is distributed over activities of many neurons or there are restricted islands of neurons responsive to a specific set of objects. There are lines of evidence demonstrating that fusiform face area (FFA-in human) processes information related to specialized object recognition (here we say within category object recognition such as face identification). Physiological studies have also discovered several patches in monkey ventral temporal lobe that are responsible for facial processing. Neuronal recording from these patches shows that neurons are highly selective for face images whereas for other objects we do not see such selectivity in IT. However, it is also well-supported that objects are encoded through distributed patterns of neural activities that are distinctive for each object category. It seems that visual cortex utilize different mechanisms for between category object recognition (e.g., face vs. non-face objects) vs. within category object recognition (e.g., two different faces). In this study, we address this question with computational simulations. We use two biologically inspired object recognition models and define two experiments which address these issues. The models have a hierarchical structure of several processing layers that simply simulate visual processing from V1 to aIT. We show, through computational modeling, that the difference between these two mechanisms of recognition can underlie the visual feature and extraction mechanism. It is argued that in order to perform generic and specialized object recognition, visual cortex must separate the mechanisms involved in within category from between categories object recognition. High recognition performance in within category object recognition can be guaranteed when class-specific features with intermediate size and complexity are extracted. However, generic object recognition requires a distributed universal

  6. Similarity dependency of the change in ERP component N1 accompanying with the object recognition learning.

    PubMed

    Tokudome, Wataru; Wang, Gang

    2012-01-01

    Performance during object recognition across views is largely dependent on inter-object similarity. The present study was designed to investigate the similarity dependency of object recognition learning on the changes in ERP component N1. Human subjects were asked to train themselves to recognize novel objects with different inter-object similarity by performing object recognition tasks. During the tasks, images of an object had to be discriminated from the images of other objects irrespective of the viewpoint. When objects had a high inter-object similarity, the ERP component, N1 exhibited a significant increase in both the amplitude and the latency variation across objects during the object recognition learning process, and the N1 amplitude and latency variation across the views of the same objects decreased significantly. In contrast, no significant changes were found during the learning process when using objects with low inter-object similarity. The present findings demonstrate that the changes in the variation of N1 that accompany the object recognition learning process are dependent upon the inter-object similarity and imply that there is a difference in the neuronal representation for object recognition when using objects with high and low inter-object similarity.

  7. Multispectral and hyperspectral imaging with AOTF for object recognition

    NASA Astrophysics Data System (ADS)

    Gupta, Neelam; Dahmani, Rachid

    1999-01-01

    Acousto-optic tunable-filter (AOTF) technology has been used in the design of a no-moving parts, compact, lightweight, field portable, automated, adaptive spectral imaging system when combined with a high sensitivity imaging detector array. Such a system could detect spectral signatures of targets and/or background, which contain polarization information and can be digitally processed by a variety of algorithms. At the Army Research Laboratory, we have developed and used a number of AOTF imaging systems and are also carrying out the development of such imagers at longer wavelengths. We have carried out hyperspectral and multispectral imaging using AOTF systems covering the spectral range from the visible to mid-IR. One of the imager uses a two-cascaded collinear-architecture AOTF cell in the visible-to-near-IR range with a digital Si charge-coupled device camera as the detector. The images obtained with this system showed no color blurring or image shift due to the angular deviation of different colors as a result of diffraction, and the digital images are stored and processed with great ease. The spatial resolution of the filter was evaluated by means of the lines of a target chart. We have also obtained and processed images from another noncollinear visible-to-near-IR AOTF imager with a digital camera, and used hyperspectral image processing software to enhance object recognition in cluttered background. We are presently working on a mid-IR AOTF imaging system that uses a high- performance InSb focal plane array and image acquisition and processing software. We describe our hyperspectral imaging program and present results from our imaging experiments.

  8. Role of the dentate gyrus in mediating object-spatial configuration recognition.

    PubMed

    Kesner, Raymond P; Taylor, James O; Hoge, Jennifer; Andy, Ford

    2015-02-01

    In the present study the effects of dorsal dentate gyrus (dDG) lesions in rats were tested on recognition memory tasks based on the interaction between objects, features of objects, and spatial features. The results indicated that the rats with dDG lesions did not differ from controls in recognition for a change within object feature configuration and object recognition tasks. In contrast, there was a deficit for the dDG lesioned rats relative to controls in recognition for a change within object-spatial feature configuration, complex object-place feature configuration and spatial recognition tasks. It is suggested that the dDG subregion of the hippocampus supports object-place and complex object-place feature information via a conjunctive encoding process.

  9. 3D multi-object segmentation of cardiac MSCT imaging by using a multi-agent approach.

    PubMed

    Fleureau, Julien; Garreau, Mireille; Boulmier, Dominique; Hernández, Alfredo

    2007-01-01

    We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed.

  10. 3D Multi-Object Segmentation of Cardiac MSCT Imaging by using a Multi-Agent Approach

    PubMed Central

    Fleureau, Julien; Garreau, Mireille; Boulmier, Dominique; Hernandez, Alfredo

    2007-01-01

    We propose a new technique for general purpose, semi-interactive and multi-object segmentation in N-dimensional images, applied to the extraction of cardiac structures in MultiSlice Computed Tomography (MSCT) imaging. The proposed approach makes use of a multi-agent scheme combined with a supervised classification methodology allowing the introduction of a priori information and presenting fast computing times. The multi-agent system is organised around a communicating agent which manages a population of situated agents which segment the image through cooperative and competitive interactions. The proposed technique has been tested on several patient data sets. Some typical results are finally presented and discussed. PMID:18003382

  11. Computer-aided laser-optoelectronic OPTEL 3D measurement systems of complex-shaped object geometry

    NASA Astrophysics Data System (ADS)

    Galiulin, Ravil M.; Galiulin, Rishat M.; Bakirov, J. M.; Bogdanov, D. R.; Shulupin, C. O.; Khamitov, D. H.; Khabibullin, M. G.; Pavlov, A. F.; Ryabov, M. S.; Yamaliev, K. N.

    1996-03-01

    Technical characteristics, advantages and applications of automated optoelectronic measuring systems designed at the Regional Interuniversity Optoelectronic Systems Laboratory ('OPTEL') of Ufa State Aviation Technical University are given. The suggested range of systems is the result of the long-term scientific and research experiments, work on design and introduction work. The system can be applied in industrial development and research, in the field of high precision measurement of geometrical parameters in aerospace, robotic, etc., where non-contact and fast measurements of complicated shape objects made of various materials including brittle and plastic articles are required.

  12. Research on recognition methods of aphid objects in complex backgrounds

    NASA Astrophysics Data System (ADS)

    Zhao, Hui-Yan; Zhang, Ji-Hong

    2009-07-01

    In order to improve the recognition accuracy among the kinds of aphids in the complex backgrounds, the recognition method among kinds of aphids based on Dual-Tree Complex Wavelet Transform (DT-CWT) and Support Vector Machine (Libsvm) is proposed. Firstly the image is pretreated; secondly the aphid images' texture feature of three crops are extracted by DT-CWT in order to get the training parameters of training model; finally the training model could recognize aphids among the three kinds of crops. By contrasting to Gabor wavelet transform and the traditional extracting texture's methods based on Gray-Level Co-Occurrence Matrix (GLCM), the experiment result shows that the method has a certain practicality and feasibility and provides basic for aphids' recognition between the identification among same kind aphid.

  13. 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.

  14. 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

  15. Crossmodal object recognition in rats with and without multimodal object pre-exposure: no effect of hippocampal lesions.

    PubMed

    Reid, James M; Jacklin, Derek L; Winters, Boyer D

    2012-10-01

    The neural mechanisms and brain circuitry involved in the formation, storage, and utilization of multisensory object representations are poorly understood. We have recently introduced a crossmodal object recognition (CMOR) task that enables the study of such questions in rats. Our previous research has indicated that the perirhinal and posterior parietal cortices functionally interact to mediate spontaneous (tactile-to-visual) CMOR performance in rats; however, it remains to be seen whether other brain regions, particularly those receiving polymodal sensory inputs, contribute to this cognitive function. In the current study, we assessed the potential contribution of one such polymodal region, the hippocampus (HPC), to crossmodal object recognition memory. Rats with bilateral excitotoxic HPC lesions were tested in two versions of crossmodal object recognition: (1) the original CMOR task, which requires rats to compare between a stored tactile object representation and visually-presented objects to discriminate the novel and familiar stimuli; and (2) a novel 'multimodal pre-exposure' version of the CMOR task (PE/CMOR), in which simultaneous exploration of the tactile and visual sensory features of an object 24 h prior to the sample phase enhances CMOR performance across longer retention delays. Hippocampus-lesioned rats performed normally on both crossmodal object recognition tasks, but were impaired on a radial arm maze test of spatial memory, demonstrating the functional effectiveness of the lesions. These results strongly suggest that the HPC, despite its polymodal anatomical connections, is not critically involved in tactile-to-visual crossmodal object recognition memory.

  16. Performance Evaluation of Neuromorphic-Vision Object Recognition Algorithms

    DTIC Science & Technology

    2014-08-01

    Malibu Canyon Rd, Malibu, CA 90265, USA Lior Elazary, Randolph C. Voorhies, Daniel F. Parks, Laurent Itti University of Southern California 3641...and Pattern Recognition, CVPR’07, pp. 1-8. [12] Carpenter , G., Grossberg, S. and Reynolds. J.H. (1991), “ARTMAP: Supervised real-time learning and

  17. Dual-Hierarchy Graph Method for Object Indexing and Recognition

    DTIC Science & Technology

    2014-07-01

    INDEXING AND RECOGNITION 5a. CONTRACT NUMBER FA8750-12-C-0117 5b. GRANT NUMBER N/A 5c. PROGRAM ELEMENT NUMBER 62305E 6. AUTHOR(S) Isaac Weiss, Fan...hierarchies. We then adjust the position and orientation of the node by minimizing its total energy using a simple one-step Newton method. Next we adjust

  18. Insular Cortex Is Involved in Consolidation of Object Recognition Memory

    ERIC Educational Resources Information Center

    Bermudez-Rattoni, Federico; Okuda, Shoki; Roozendaal, Benno; McGaugh, James L.

    2005-01-01

    Extensive evidence indicates that the insular cortex (IC), also termed gustatory cortex, is critically involved in conditioned taste aversion and taste recognition memory. Although most studies of the involvement of the IC in memory have investigated taste, there is some evidence that the IC is involved in memory that is not based on taste. In…

  19. Object-based 3D geomodel with multiple constraints for early Pliocene fan delta in the south of Lake Albert Basin, Uganda

    NASA Astrophysics Data System (ADS)

    Wei, Xu; Lei, Fang; Xinye, Zhang; Pengfei, Wang; Xiaoli, Yang; Xipu, Yang; Jun, Liu

    2017-01-01

    The early Pliocene fan delta complex developed in the south of Lake Albert Basin which is located at the northern end of the western branch in the East African Rift System. The stratigraphy of this succession is composed of distributary channels, overbank, mouthbar and lacustrine shales. Limited by the poor seismic quality and few wells, it is full of challenge to delineate the distribution area and patterns of reservoir sands. Sedimentary forward simulation and basin analogue were applied to analyze the spatial distribution of facies configuration and then a conceptual sedimentary model was constructed by combining with core, heavy mineral and palynology evidences. A 3D geological model of a 120 m thick stratigraphic succession was built using well logs and seismic surfaces based on the established sedimentary model. The facies modeling followed a hierarchical object-based approach conditioned to multiple trend constraints like channel intensity, channel azimuth and channel width. Lacustrine shales were modeled as background facies and then in turn eroded by distribute channels, overbank and mouthbar respectively. At the same time a body facies parameter was created to indicate the connectivity of the reservoir sands. The resultant 3D facies distributions showed that the distributary channels flowed from east bounding fault to west flank and overbank was adhered to the fringe of channels while mouthbar located at the end of channels. Furthermore, porosity and permeability were modeled using sequential Gaussian simulation (SGS) honoring core observations and petrophysical interpretation results. Despite the poor seismic is not supported to give enough information for fan delta sand distribution, creating a truly representative 3D geomodel is still able to be achieved. This paper highlights the integration of various data and comprehensive steps of building a consistent representative 3D geocellular fan delta model used for numeral simulation studies and field

  20. 3D workflow for HDR image capture of projection systems and objects for CAVE virtual environments authoring with wireless touch-sensitive devices

    NASA Astrophysics Data System (ADS)

    Prusten, Mark J.; McIntyre, Michelle; Landis, Marvin

    2006-02-01

    A 3D workflow pipeline is presented for High Dynamic Range (HDR) image capture of projected scenes or objects for presentation in CAVE virtual environments. The methods of HDR digital photography of environments vs. objects are reviewed. Samples of both types of virtual authoring being the actual CAVE environment and a sculpture are shown. A series of software tools are incorporated into a pipeline called CAVEPIPE, allowing for high-resolution objects and scenes to be composited together in natural illumination environments [1] and presented in our CAVE virtual reality environment. We also present a way to enhance the user interface for CAVE environments. The traditional methods of controlling the navigation through virtual environments include: glove, HUD's and 3D mouse devices. By integrating a wireless network that includes both WiFi (IEEE 802.11b/g) and Bluetooth (IEEE 802.15.1) protocols the non-graphical input control device can be eliminated. Therefore wireless devices can be added that would include: PDA's, Smart Phones, TabletPC's, Portable Gaming consoles, and PocketPC's.

  1. 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…

  2. 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…

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

    PubMed

    Wood, Justin N; Wood, Samantha M W

    2016-04-27

    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 rep