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Sample records for fish recognition based

  1. Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

    PubMed

    Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

    2015-12-01

    The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types. PMID:26723348

  2. Facial Recognition in a Group-Living Cichlid Fish.

    PubMed

    Kohda, Masanori; Jordan, Lyndon Alexander; Hotta, Takashi; Kosaka, Naoya; Karino, Kenji; Tanaka, Hirokazu; Taniyama, Masami; Takeyama, Tomohiro

    2015-01-01

    The theoretical underpinnings of the mechanisms of sociality, e.g. territoriality, hierarchy, and reciprocity, are based on assumptions of individual recognition. While behavioural evidence suggests individual recognition is widespread, the cues that animals use to recognise individuals are established in only a handful of systems. Here, we use digital models to demonstrate that facial features are the visual cue used for individual recognition in the social fish Neolamprologus pulcher. Focal fish were exposed to digital images showing four different combinations of familiar and unfamiliar face and body colorations. Focal fish attended to digital models with unfamiliar faces longer and from a further distance to the model than to models with familiar faces. These results strongly suggest that fish can distinguish individuals accurately using facial colour patterns. Our observations also suggest that fish are able to rapidly (≤ 0.5 sec) discriminate between familiar and unfamiliar individuals, a speed of recognition comparable to primates including humans. PMID:26605789

  3. Speech recognition based on pattern recognition techniques

    NASA Astrophysics Data System (ADS)

    Rabiner, Lawrence R.

    1990-05-01

    Algorithms for speech recognition can be characterized broadly as pattern recognition approaches and acoustic phonetic approaches. To date, the greatest degree of success in speech recognition has been obtained using pattern recognition paradigms. The use of pattern recognition techniques were applied to the problems of isolated word (or discrete utterance) recognition, connected word recognition, and continuous speech recognition. It is shown that understanding (and consequently the resulting recognizer performance) is best to the simplest recognition tasks and is considerably less well developed for large scale recognition systems.

  4. Context based gait recognition

    NASA Astrophysics Data System (ADS)

    Bazazian, Shermin; Gavrilova, Marina

    2012-06-01

    Gait recognition has recently become a popular topic in the field of biometrics. However, the main hurdle is the insufficient recognition rate in the presence of low quality samples. The main focus of this paper is to investigate how the performance of a gait recognition system can be improved using additional information about behavioral patterns of users and the context in which samples have been taken. The obtained results show combining the context information with biometric data improves the performance of the system at a very low cost. The amount of improvement depends on the distinctiveness of the behavioral patterns and the quality of the gait samples. Using the appropriate distinctive behavioral models it is possible to achieve a 100% recognition rate.

  5. Scents and scents-ability: pollution disrupts chemical social recognition and shoaling in fish

    PubMed Central

    Ward, Ashley J.W; Duff, Alison J; Horsfall, Jennifer S; Currie, Suzanne

    2007-01-01

    Chemical cues are of enormous importance in mediating the behaviour of animals, enabling them to navigate throughout their habitats, to detect the presence of predators or prey and for social recognition—identifying and discriminating between conspecifics. In many species of freshwater fish, social recognition is known to be based primarily on chemical cues. Such recognition mechanisms are vulnerable to disruption by the presence of anthropogenic contaminants in the aquatic environment. Here we show that acute exposure to low, environmentally relevant dosages of the ubiquitous contaminant, 4-nonylphenol, can seriously affect social recognition and ultimately social organization in fishes. A 1 hour 0.5 μg l−1 dose was sufficient to alter the response of members of a shoaling fish species (juvenile banded killifish, Fundulus diaphanus) to conspecific chemical cues. Dosages of 1–2 μg l−1 caused killifish to orient away from dosed conspecifics, in both a flow channel and an arena. Given the overall importance of shoaling as an adaptive strategy against predators and for locating food, it is likely that its disruption by anthropogenic contaminants would have serious implications for fishes' fitness. PMID:17956844

  6. Flexible Parts-based Sketch Recognition

    NASA Astrophysics Data System (ADS)

    van de Panne, Michiel; Sharon, Dana

    Drawings and sketches are a natural way for people to communicate ideas, but it remains challenging to develop automated systems that can robustly recognize and interpret what is drawn. Most commonly, a drawing is first processed to obtain a low-level representation of that drawing in terms of lines or strokes, and this representation is then searched for matches to known object templates. In this chapter we propose two template-based methods for sketch recognition. A novel feature of these methods is that they both employ a hierarchy-of-parts template model that provides explicit support for templates with optional parts. This captures significant parts-based variation which would otherwise require a multitude of fixed-structure templates to model. The first method is developed for recognition in drawings consisting of sets of connected strokes and is applied as an interface for creating 3D models of airplanes, mugs, and fish. The second method allows for the recognition of more unstructured objects such as faces, plants, and sailboats in drawings that may also contain disjoint strokes. Neither method relies on the timing information of the input strokes, which may not be available for photographed or scanned drawings.

  7. Kin Recognition in a Clonal Fish, Poecilia formosa.

    PubMed

    Makowicz, Amber M; Tiedemann, Ralph; Steele, Rachel N; Schlupp, Ingo

    2016-01-01

    Relatedness strongly influences social behaviors in a wide variety of species. For most species, the highest typical degree of relatedness is between full siblings with 50% shared genes. However, this is poorly understood in species with unusually high relatedness between individuals: clonal organisms. Although there has been some investigation into clonal invertebrates and yeast, nothing is known about kin selection in clonal vertebrates. We show that a clonal fish, the Amazon molly (Poecilia formosa), can distinguish between different clonal lineages, associating with genetically identical, sister clones, and use multiple sensory modalities. Also, they scale their aggressive behaviors according to the relatedness to other females: they are more aggressive to non-related clones. Our results demonstrate that even in species with very small genetic differences between individuals, kin recognition can be adaptive. Their discriminatory abilities and regulation of costly behaviors provides a powerful example of natural selection in species with limited genetic diversity. PMID:27483372

  8. Kin Recognition in a Clonal Fish, Poecilia formosa

    PubMed Central

    Makowicz, Amber M.; Tiedemann, Ralph; Schlupp, Ingo

    2016-01-01

    Relatedness strongly influences social behaviors in a wide variety of species. For most species, the highest typical degree of relatedness is between full siblings with 50% shared genes. However, this is poorly understood in species with unusually high relatedness between individuals: clonal organisms. Although there has been some investigation into clonal invertebrates and yeast, nothing is known about kin selection in clonal vertebrates. We show that a clonal fish, the Amazon molly (Poecilia formosa), can distinguish between different clonal lineages, associating with genetically identical, sister clones, and use multiple sensory modalities. Also, they scale their aggressive behaviors according to the relatedness to other females: they are more aggressive to non-related clones. Our results demonstrate that even in species with very small genetic differences between individuals, kin recognition can be adaptive. Their discriminatory abilities and regulation of costly behaviors provides a powerful example of natural selection in species with limited genetic diversity. PMID:27483372

  9. Cross-modal object recognition and dynamic weighting of sensory inputs in a fish.

    PubMed

    Schumacher, Sarah; Burt de Perera, Theresa; Thenert, Johanna; von der Emde, Gerhard

    2016-07-01

    Most animals use multiple sensory modalities to obtain information about objects in their environment. There is a clear adaptive advantage to being able to recognize objects cross-modally and spontaneously (without prior training with the sense being tested) as this increases the flexibility of a multisensory system, allowing an animal to perceive its world more accurately and react to environmental changes more rapidly. So far, spontaneous cross-modal object recognition has only been shown in a few mammalian species, raising the question as to whether such a high-level function may be associated with complex mammalian brain structures, and therefore absent in animals lacking a cerebral cortex. Here we use an object-discrimination paradigm based on operant conditioning to show, for the first time to our knowledge, that a nonmammalian vertebrate, the weakly electric fish Gnathonemus petersii, is capable of performing spontaneous cross-modal object recognition and that the sensory inputs are weighted dynamically during this task. We found that fish trained to discriminate between two objects with either vision or the active electric sense, were subsequently able to accomplish the task using only the untrained sense. Furthermore we show that cross-modal object recognition is influenced by a dynamic weighting of the sensory inputs. The fish weight object-related sensory inputs according to their reliability, to minimize uncertainty and to enable an optimal integration of the senses. Our results show that spontaneous cross-modal object recognition and dynamic weighting of sensory inputs are present in a nonmammalian vertebrate. PMID:27313211

  10. Intelligent word-based text recognition

    NASA Astrophysics Data System (ADS)

    Hoenes, Frank; Bleisinger, Rainer; Dengel, Andreas R.

    1991-02-01

    STRACT The need for making information within paper documents available for computers increases steadily. In this paper we present a system which is capable to read and to simply understand address blocks of business letters. It is based on optical word recognition (OWR) techniques uses feature recognition methods based on word shapes and is largly independent from different fonts and sizes. Even uncertainly recognized words can be identified using a dictionary and a specific verification algorithm. Additionally recognition accuracy is improved considering different knowledge layers like address syntax and logical dictionaries. Keywords: Text recognition document layout classification text analysis pattern recognition intelligent interfaces

  11. Modal-Power-Based Haptic Motion Recognition

    NASA Astrophysics Data System (ADS)

    Kasahara, Yusuke; Shimono, Tomoyuki; Kuwahara, Hiroaki; Sato, Masataka; Ohnishi, Kouhei

    Motion recognition based on sensory information is important for providing assistance to human using robots. Several studies have been carried out on motion recognition based on image information. However, in the motion of humans contact with an object can not be evaluated precisely by image-based recognition. This is because the considering force information is very important for describing contact motion. In this paper, a modal-power-based haptic motion recognition is proposed; modal power is considered to reveal information on both position and force. Modal power is considered to be one of the defining features of human motion. A motion recognition algorithm based on linear discriminant analysis is proposed to distinguish between similar motions. Haptic information is extracted using a bilateral master-slave system. Then, the observed motion is decomposed in terms of primitive functions in a modal space. The experimental results show the effectiveness of the proposed method.

  12. Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models

    PubMed Central

    Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

    2016-01-01

    A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner’s faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals. PMID:27191162

  13. 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

  14. Hand gesture recognition based on surface electromyography.

    PubMed

    Samadani, Ali-Akbar; Kulic, Dana

    2014-01-01

    Human hands are the most dexterous of human limbs and hand gestures play an important role in non-verbal communication. Underlying electromyograms associated with hand gestures provide a wealth of information based on which varying hand gestures can be recognized. This paper develops an inter-individual hand gesture recognition model based on Hidden Markov models that receives surface electromyography (sEMG) signals as inputs and predicts a corresponding hand gesture. The developed recognition model is tested with a dataset of 10 various hand gestures performed by 25 subjects in a leave-one-subject-out cross validation and an inter-individual recognition rate of 79% was achieved. The promising recognition rate demonstrates the efficacy of the proposed approach for discriminating between gesture-specific sEMG signals and could inform the design of sEMG-controlled prostheses and assistive devices. PMID:25570917

  15. Practical vision based degraded text recognition system

    NASA Astrophysics Data System (ADS)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

    Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published

  16. Gender recognition based on face geometric features

    NASA Astrophysics Data System (ADS)

    Xiao, Jie; Guo, Zhaoli; Cai, Chao

    2013-10-01

    Automatic gender recognition based on face images plays an important role in computer vision and machine vision. In this paper, a novel and simple gender recognition method based on face geometric features is proposed. The method is divided in three steps. Firstly, Pre-processing step provides standard face images for feature extraction. Secondly, Active Shape Model (ASM) is used to extract geometric features in frontal face images. Thirdly, Adaboost classifier is chosen to separate the two classes (male and female). We tested it on 2570 pictures (1420 males and 1150 females) downloaded from the internet, and encouraging results were acquired. The comparison of the proposed geometric feature based method and the full facial image based method demonstrats its superiority.

  17. Average Gait Differential Image Based Human Recognition

    PubMed Central

    Chen, Jinyan; Liu, Jiansheng

    2014-01-01

    The difference between adjacent frames of human walking contains useful information for human gait identification. Based on the previous idea a silhouettes difference based human gait recognition method named as average gait differential image (AGDI) is proposed in this paper. The AGDI is generated by the accumulation of the silhouettes difference between adjacent frames. The advantage of this method lies in that as a feature image it can preserve both the kinetic and static information of walking. Comparing to gait energy image (GEI), AGDI is more fit to representation the variation of silhouettes during walking. Two-dimensional principal component analysis (2DPCA) is used to extract features from the AGDI. Experiments on CASIA dataset show that AGDI has better identification and verification performance than GEI. Comparing to PCA, 2DPCA is a more efficient and less memory storage consumption feature extraction method in gait based recognition. PMID:24895648

  18. Gait recognition based on Kinect sensor

    NASA Astrophysics Data System (ADS)

    Ahmed, Mohammed; Al-Jawad, Naseer; Sabir, Azhin T.

    2014-05-01

    This paper presents gait recognition based on human skeleton and trajectory of joint points captured by Microsoft Kinect sensor. In this paper Two sets of dynamic features are extracted during one gait cycle: the first is Horizontal Distance Features (HDF) that is based on the distances between (Ankles, knees, hands, shoulders), the second set is the Vertical Distance Features (VDF) that provide significant information of human gait extracted from the height to the ground of (hand, shoulder, and ankles) during one gait cycle. Extracting these two sets of feature are difficult and not accurate based on using traditional camera, therefore the Kinect sensor is used in this paper to determine the precise measurements. The two sets of feature are separately tested and then fused to create one feature vector. A database has been created in house to perform our experiments. This database consists of sixteen males and four females. For each individual, 10 videos have been recorded, each record includes in average two gait cycles. The Kinect sensor is used here to extract all the skeleton points, and these points are used to build up the feature vectors mentioned above. K-nearest neighbor is used as the classification method based on Cityblock distance function. Based on the experimental result the proposed method provides 56% as a recognition rate using HDF, while VDF provided 83.5% recognition accuracy. When fusing both of the HDF and VDF as one feature vector, the recognition rate increased to 92%, the experimental result shows that our method provides significant result compared to the existence methods.

  19. Laptop Computer - Based Facial Recognition System Assessment

    SciTech Connect

    R. A. Cain; G. B. Singleton

    2001-03-01

    The objective of this project was to assess the performance of the leading commercial-off-the-shelf (COTS) facial recognition software package when used as a laptop application. We performed the assessment to determine the system's usefulness for enrolling facial images in a database from remote locations and conducting real-time searches against a database of previously enrolled images. The assessment involved creating a database of 40 images and conducting 2 series of tests to determine the product's ability to recognize and match subject faces under varying conditions. This report describes the test results and includes a description of the factors affecting the results. After an extensive market survey, we selected Visionics' FaceIt{reg_sign} software package for evaluation and a review of the Facial Recognition Vendor Test 2000 (FRVT 2000). This test was co-sponsored by the US Department of Defense (DOD) Counterdrug Technology Development Program Office, the National Institute of Justice, and the Defense Advanced Research Projects Agency (DARPA). Administered in May-June 2000, the FRVT 2000 assessed the capabilities of facial recognition systems that were currently available for purchase on the US market. Our selection of this Visionics product does not indicate that it is the ''best'' facial recognition software package for all uses. It was the most appropriate package based on the specific applications and requirements for this specific application. In this assessment, the system configuration was evaluated for effectiveness in identifying individuals by searching for facial images captured from video displays against those stored in a facial image database. An additional criterion was that the system be capable of operating discretely. For this application, an operational facial recognition system would consist of one central computer hosting the master image database with multiple standalone systems configured with duplicates of the master operating in

  20. Automatic Speech Recognition Based on Electromyographic Biosignals

    NASA Astrophysics Data System (ADS)

    Jou, Szu-Chen Stan; Schultz, Tanja

    This paper presents our studies of automatic speech recognition based on electromyographic biosignals captured from the articulatory muscles in the face using surface electrodes. We develop a phone-based speech recognizer and describe how the performance of this recognizer improves by carefully designing and tailoring the extraction of relevant speech feature toward electromyographic signals. Our experimental design includes the collection of audibly spoken speech simultaneously recorded as acoustic data using a close-speaking microphone and as electromyographic signals using electrodes. Our experiments indicate that electromyographic signals precede the acoustic signal by about 0.05-0.06 seconds. Furthermore, we introduce articulatory feature classifiers, which had recently shown to improved classical speech recognition significantly. We describe that the classification accuracy of articulatory features clearly benefits from the tailored feature extraction. Finally, these classifiers are integrated into the overall decoding framework applying a stream architecture. Our final system achieves a word error rate of 29.9% on a 100-word recognition task.

  1. Towards behaviour-recognition-based video surveillance

    NASA Astrophysics Data System (ADS)

    Gong, Shaogang

    2004-12-01

    We present the latest results on learnable stochastic temporal models for automatic event and behaviour recognition in CCTV surveillance video. We introduce a novel approach to modelling and recognising complex activities involving simultaneous movement of multiple objects. Our approach differs from most previous work in that the visual understanding of activity is based on visual event detection and reasoning instead of object tracking and trajectory matching. Dynamic probabilistic graph models are exploited for modelling the temporal relationships among a set of different object temporal events. Typical applications of this technology include automatic semantic video content analysis, profiling and indexing of salient event and behaviour captured in CCTV video, and the early recognition of atypical behaviour in scenes where such behaviour could lead to a threat to safety.

  2. Photoswitchable gel assembly based on molecular recognition.

    PubMed

    Yamaguchi, Hiroyasu; Kobayashi, Yuichiro; Kobayashi, Ryosuke; Takashima, Yoshinori; Hashidzume, Akihito; Harada, Akira

    2012-01-01

    The formation of effective and precise linkages in bottom-up or top-down processes is important for the development of self-assembled materials. Self-assembly through molecular recognition events is a powerful tool for producing functionalized materials. Photoresponsive molecular recognition systems can permit the creation of photoregulated self-assembled macroscopic objects. Here we demonstrate that macroscopic gel assembly can be highly regulated through photoisomerization of an azobenzene moiety that interacts differently with two host molecules. A photoregulated gel assembly system is developed using polyacrylamide-based hydrogels functionalized with azobenzene (guest) or cyclodextrin (host) moieties. Reversible adhesion and dissociation of the host gel from the guest gel may be controlled by photoirradiation. The differential affinities of α-cyclodextrin or β-cyclodextrin for the trans-azobenzene and cis-azobenzene are employed in the construction of a photoswitchable gel assembly system. PMID:22215078

  3. Photoswitchable gel assembly based on molecular recognition

    PubMed Central

    Yamaguchi, Hiroyasu; Kobayashi, Yuichiro; Kobayashi, Ryosuke; Takashima, Yoshinori; Hashidzume, Akihito; Harada, Akira

    2012-01-01

    The formation of effective and precise linkages in bottom-up or top-down processes is important for the development of self-assembled materials. Self-assembly through molecular recognition events is a powerful tool for producing functionalized materials. Photoresponsive molecular recognition systems can permit the creation of photoregulated self-assembled macroscopic objects. Here we demonstrate that macroscopic gel assembly can be highly regulated through photoisomerization of an azobenzene moiety that interacts differently with two host molecules. A photoregulated gel assembly system is developed using polyacrylamide-based hydrogels functionalized with azobenzene (guest) or cyclodextrin (host) moieties. Reversible adhesion and dissociation of the host gel from the guest gel may be controlled by photoirradiation. The differential affinities of α-cyclodextrin or β-cyclodextrin for the trans-azobenzene and cis-azobenzene are employed in the construction of a photoswitchable gel assembly system. PMID:22215078

  4. Biometric watermarking based on face recognition

    NASA Astrophysics Data System (ADS)

    Satonaka, Takami

    2002-04-01

    We describe biometric watermarking procedure based on object recognition for accurate facial signature authentication. An adaptive metric learning algorithm incorporating watermark and facial signatures is introduced to separate an arbitrary pattern of unknown intruder classes from that of known true-user ones. The verification rule of multiple signatures is formulated to map a facial signature pattern in the overlapping classes to a separable disjoint one. The watermark signature, which is uniquely assigned to each face image, reduces the uncertainty of modeling missing facial signature patterns of the unknown intruder classes. The adaptive metric learning algorithm proposed improves a recognition error rate from 2.4% to 0.07% using the ORL database, which is better than previously reported numbers using the Karhunen-Loeve transform, convolution network and the hidden Marcov model. The face recognition facilitates generation and distribution of the watermark key. The watermarking approach focuses on using salient facial features to make watermark signatures robust to various attacks and transformation. The coarse-to-fine approach is presented to integrate pyramidal face detection, geometry analysis and face segmentation for watermarking. We conclude with an assessment of the strength and weakness of the chosen approach as well as possible improvements of the biometric watermarking system.

  5. Human body contour data based activity recognition.

    PubMed

    Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

    2013-01-01

    This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate. PMID:24111015

  6. Invariant object recognition based on extended fragments.

    PubMed

    Bart, Evgeniy; Hegdé, Jay

    2012-01-01

    Visual appearance of natural objects is profoundly affected by viewing conditions such as viewpoint and illumination. Human subjects can nevertheless compensate well for variations in these viewing conditions. The strategies that the visual system uses to accomplish this are largely unclear. Previous computational studies have suggested that in principle, certain types of object fragments (rather than whole objects) can be used for invariant recognition. However, whether the human visual system is actually capable of using this strategy remains unknown. Here, we show that human observers can achieve illumination invariance by using object fragments that carry the relevant information. To determine this, we have used novel, but naturalistic, 3-D visual objects called "digital embryos." Using novel instances of whole embryos, not fragments, we trained subjects to recognize individual embryos across illuminations. We then tested the illumination-invariant object recognition performance of subjects using fragments. We found that the performance was strongly correlated with the mutual information (MI) of the fragments, provided that MI value took variations in illumination into consideration. This correlation was not attributable to any systematic differences in task difficulty between different fragments. These results reveal two important principles of invariant object recognition. First, the subjects can achieve invariance at least in part by compensating for the changes in the appearance of small local features, rather than of whole objects. Second, the subjects do not always rely on generic or pre-existing invariance of features (i.e., features whose appearance remains largely unchanged by variations in illumination), and are capable of using learning to compensate for appearance changes when necessary. These psychophysical results closely fit the predictions of earlier computational studies of fragment-based invariant object recognition. PMID:22936910

  7. Wavelet based recognition for pulsar signals

    NASA Astrophysics Data System (ADS)

    Shan, H.; Wang, X.; Chen, X.; Yuan, J.; Nie, J.; Zhang, H.; Liu, N.; Wang, N.

    2015-06-01

    A signal from a pulsar can be decomposed into a set of features. This set is a unique signature for a given pulsar. It can be used to decide whether a pulsar is newly discovered or not. Features can be constructed from coefficients of a wavelet decomposition. Two types of wavelet based pulsar features are proposed. The energy based features reflect the multiscale distribution of the energy of coefficients. The singularity based features first classify the signals into a class with one peak and a class with two peaks by exploring the number of the straight wavelet modulus maxima lines perpendicular to the abscissa, and then implement further classification according to the features of skewness and kurtosis. Experimental results show that the wavelet based features can gain comparatively better performance over the shape parameter based features not only in the clustering and classification, but also in the error rates of the recognition tasks.

  8. Feature quality-based multimodal unconstrained eye recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Zhi; Du, Eliza Y.; Lin, Yong; Thomas, N. Luke; Belcher, Craig; Delp, Edward J.

    2013-05-01

    Iris recognition has been tested to the most accurate biometrics using high resolution near infrared images. However, it does not work well under visible wavelength illumination. Sclera recognition, however, has been shown to achieve reasonable recognition accuracy under visible wavelengths. Combining iris and sclera recognition together can achieve better recognition accuracy. However, image quality can significantly affect the recognition accuracy. Moreover, in unconstrained situations, the acquired eye images may not be frontally facing. In this research, we proposed a feature quality-based multimodal unconstrained eye recognition method that combine the respective strengths of iris recognition and sclera recognition for human identification and can work with frontal and off-angle eye images. The research results show that the proposed method is very promising.

  9. Behavioral assessment of acoustic parameters relevant to signal recognition and preference in a vocal fish.

    PubMed

    McKibben, J R; Bass, A H

    1998-12-01

    Acoustic signal recognition depends on the receiver's processing of the physical attributes of a sound. This study takes advantage of the simple communication sounds produced by plainfin midshipman fish to examine effects of signal variation on call recognition and preference. Nesting male midshipman generate both long duration (> 1 min) sinusoidal-like "hums" and short duration "grunts." The hums of neighboring males often overlap, creating beat waveforms. Presentation of humlike, single tone stimuli, but not grunts or noise, elicited robust attraction (phonotaxis) by gravid females. In two-choice tests, females differentiated and chose between acoustic signals that differed in duration, frequency, amplitude, and fine temporal content. Frequency preferences were temperature dependent, in accord with the known temperature dependence of hum fundamental frequency. Concurrent hums were simulated with two-tone beat stimuli, either presented from a single speaker or produced more naturally by interference between adjacent sources. Whereas certain single-source beats reduced stimulus attractiveness, beats which resolved into unmodulated tones at their sources did not affect preference. These results demonstrate that phonotactic assessment of stimulus relevance can be applied in a teleost fish, and that multiple signal parameters can affect receiver response in a vertebrate with relatively simple communication signals. PMID:9857511

  10. Mutual information-based facial expression recognition

    NASA Astrophysics Data System (ADS)

    Hazar, Mliki; Hammami, Mohamed; Hanêne, Ben-Abdallah

    2013-12-01

    This paper introduces a novel low-computation discriminative regions representation for expression analysis task. The proposed approach relies on interesting studies in psychology which show that most of the descriptive and responsible regions for facial expression are located around some face parts. The contributions of this work lie in the proposition of new approach which supports automatic facial expression recognition based on automatic regions selection. The regions selection step aims to select the descriptive regions responsible or facial expression and was performed using Mutual Information (MI) technique. For facial feature extraction, we have applied Local Binary Patterns Pattern (LBP) on Gradient image to encode salient micro-patterns of facial expressions. Experimental studies have shown that using discriminative regions provide better results than using the whole face regions whilst reducing features vector dimension.

  11. LBP and SIFT based facial expression recognition

    NASA Astrophysics Data System (ADS)

    Sumer, Omer; Gunes, Ece O.

    2015-02-01

    This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

  12. High-resolution (13)C nuclear magnetic resonance spectroscopy pattern recognition of fish oil capsules.

    PubMed

    Aursand, Marit; Standal, Inger B; Axelson, David E

    2007-01-10

    13C NMR (nuclear magnetic resonance) spectroscopy, in conjunction with multivariate analysis of commercial fish oil-related health food products, have been used to provide discrimination concerning the nature, composition, refinement, and/or adulteration or authentication of the products. Supervised (probabilistic neural networks, PNN) and unsupervised (principal component analysis, PCA; Kohonen neural networks; generative topographic mapping, GTM) pattern recognition techniques were used to visualize and classify samples. Simple PCA score plots demonstrated excellent, but not totally unambiguous, class distinctions, whereas Kohonen and GTM visualization provided better results. Quantitative class predictions with accuracies >95% were achieved with PNN analysis. Trout, salmon, and cod oils were completely and correctly classified. Samples reported to be salmon oils and cod liver oils did not cluster with true salmon and cod liver oil samples, indicating mislabeling or adulteration. PMID:17199311

  13. Retinal recognition using compression-based joint transform correlator

    NASA Astrophysics Data System (ADS)

    Suripon, Ubon; Widjaja, Joewono

    2013-06-01

    Retinal recognition by using compression-based joint transform correlator (JTC) is proposed. Recognition performance is quantitatively measured by taking into account effect of imbalanced illuminations and noise presence. The simulation results show that the compression-based JTC has reliable recognition performance for high-contrast retina target. Besides acceleration of image transfer time, the compression of the noise-corrupted retina target images can improve the correlator robustness to noise.

  14. Effects of temporal envelope modulation on acoustic signal recognition in a vocal fish, the plainfin midshipman.

    PubMed

    McKibben, J R; Bass, A H

    2001-06-01

    Amplitude modulation is an important parameter defining vertebrate acoustic communication signals. Nesting male plainfin midshipman fish, Porichthys notatus, emit simple, long duration hums in which modulation is strikingly absent. Envelope modulation is, however, introduced when the hums of adjacent males overlap to produce acoustic beats. Hums attract gravid females and can be mimicked with continuous tones at the fundamental frequency. While individual hums have flat envelopes, other midshipman signals are amplitude modulated. This study used one-choice playback tests with gravid females to examine the role of envelope modulation in hum recognition. Various pulse train and two-tone beat stimuli resembling natural communication signals were presented individually, and the responses compared to those for continuous pure tones. The effectiveness of pulse trains was graded and depended upon both pulse duration and the ratio of pulse to gap length. Midshipman were sensitive to beat modulations from 0.5 to 10 Hz, with fewer fish approaching the beat than the pure tone. Reducing the degree of modulation increased the effectiveness of beat stimuli. Hence, the lack of modulation in the midshipman's advertisement call corresponds to the importance of envelope modulation for the categorization of communication signals even in this relatively simple system. PMID:11425135

  15. Predator odor recognition and antipredatory response in fish: does the prey know the predator diel rhythm?

    NASA Astrophysics Data System (ADS)

    Ylönen, Hannu; Kortet, Raine; Myntti, Janne; Vainikka, Anssi

    2007-01-01

    We studied in a laboratory experiment using stream tanks if two percid prey fish, the perch ( Perca fluviatilis) and the ruffe ( Gymnocephalus cernuus), can recognize and respond to increased predation risk using odors of two piscivores, the pike ( Esox lucius) and the burbot ( Lota lota). Burbot is night-active most of the year but pike hunts predominantly visually whenever there is enough light. Perch is a common day-active prey of pike and dark-active ruffe that of burbot. We predicted that besides recognizing the predator odors, the prey species would respond more strongly to odors of the predator which share the same activity pattern. Both perch and ruffe clearly responded to both predator fish odors. They decreased movements and erected the spiny dorsal fins. Fin erection showed clearly the black warning ornamentation in the fin and thus erected fin may function besides as mechanical defense also as warning ornament for an approaching predator. No rapid escape movements were generally observed. Both perch and ruffe responded more strongly to pike odor than to burbot. There were no clear differences in response between day and night. In conclusion, we were able to verify clear predator odor recognition by both prey fish. Both perch and ruffe responded to both predator odors and it seemed that pike forms a stronger threat for both prey species. Despite of diel activity differences both perch and ruffe used the same antipredatory strategies, but the day-active perch seemed to have a more flexible antipredatory behavior by responding more strongly to burbot threat during the night when burbot is active.

  16. Image-based automatic recognition of larvae

    NASA Astrophysics Data System (ADS)

    Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

    2010-08-01

    As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

  17. Random-Profiles-Based 3D Face Recognition System

    PubMed Central

    Joongrock, Kim; Sunjin, Yu; Sangyoun, Lee

    2014-01-01

    In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognition systems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognition systems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation. PMID:24691101

  18. The neuronal substrates of human olfactory based kin recognition.

    PubMed

    Lundström, Johan N; Boyle, Julie A; Zatorre, Robert J; Jones-Gotman, Marilyn

    2009-08-01

    Kin recognition, an evolutionary phenomenon ubiquitous among phyla, is thought to promote an individual's genes by facilitating nepotism and avoidance of inbreeding. Whereas isolating and studying kin recognition mechanisms in humans using auditory and visual stimuli is problematic because of the high degree of conscious recognition of the individual involved, kin recognition based on body odors is done predominantly without conscious recognition. Using this, we mapped the neural substrates of human kin recognition by acquiring measures of regional cerebral blood flow from women smelling the body odors of either their sister or their same-sex friend. The initial behavioral experiment demonstrated that accurate identification of kin is performed with a low conscious recognition. The subsequent neuroimaging experiment demonstrated that olfactory based kin recognition in women recruited the frontal-temporal junction, the insula, and the dorsomedial prefrontal cortex; the latter area is implicated in the coding of self-referent processing and kin recognition. We further show that the neuronal response is seemingly independent of conscious identification of the individual source, demonstrating that humans have an odor based kin detection system akin to what has been shown for other mammals. PMID:19067327

  19. Recognition mechanisms for schema-based knowledge representations

    SciTech Connect

    Havens, W.S.

    1983-01-01

    The author considers generalizing formal recognition methods from parsing theory to schemata knowledge representations. Within artificial intelligence, recognition tasks include aspects of natural language understanding, computer vision, episode understanding, speech recognition, and others. The notion of schemata as a suitable knowledge representation for these tasks is discussed. A number of problems with current schemata-based recognition systems are presented. To gain insight into alternative approaches, the formal context-free parsing method of earley is examined. It is shown to suggest a useful control structure model for integrating top-down and bottom-up search in schemata representations. 46 references.

  20. Image preprocessing study on KPCA-based face recognition

    NASA Astrophysics Data System (ADS)

    Li, Xuan; Li, Dehua

    2015-12-01

    Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

  1. Robust facial expression recognition algorithm based on local metric learning

    NASA Astrophysics Data System (ADS)

    Jiang, Bin; Jia, Kebin

    2016-01-01

    In facial expression recognition tasks, different facial expressions are often confused with each other. Motivated by the fact that a learned metric can significantly improve the accuracy of classification, a facial expression recognition algorithm based on local metric learning is proposed. First, k-nearest neighbors of the given testing sample are determined from the total training data. Second, chunklets are selected from the k-nearest neighbors. Finally, the optimal transformation matrix is computed by maximizing the total variance between different chunklets and minimizing the total variance of instances in the same chunklet. The proposed algorithm can find the suitable distance metric for every testing sample and improve the performance on facial expression recognition. Furthermore, the proposed algorithm can be used for vector-based and matrix-based facial expression recognition. Experimental results demonstrate that the proposed algorithm could achieve higher recognition rates and be more robust than baseline algorithms on the JAFFE, CK, and RaFD databases.

  2. Molecular Recognition: Detection of Colorless Compounds Based on Color Change

    ERIC Educational Resources Information Center

    Khalafi, Lida; Kashani, Samira; Karimi, Javad

    2016-01-01

    A laboratory experiment is described in which students measure the amount of cetirizine in allergy-treatment tablets based on molecular recognition. The basis of recognition is competition of cetirizine with phenolphthalein to form an inclusion complex with ß-cyclodextrin. Phenolphthalein is pinkish under basic condition, whereas it's complex form…

  3. An Evaluation of PC-Based Optical Character Recognition Systems.

    ERIC Educational Resources Information Center

    Schreier, E. M.; Uslan, M. M.

    1991-01-01

    The review examines six personal computer-based optical character recognition (OCR) systems designed for use by blind and visually impaired people. Considered are OCR components and terms, documentation, scanning and reading, command structure, conversion, unique features, accuracy of recognition, scanning time, speed, and cost. (DB)

  4. Sensitive-cell-based fish chromatophore biosensor

    NASA Astrophysics Data System (ADS)

    Plant, Thomas K.; Chaplen, Frank W.; Jovanovic, Goran; Kolodziej, Wojtek; Trempy, Janine E.; Willard, Corwin; Liburdy, James A.; Pence, Deborah V.; Paul, Brian K.

    2004-07-01

    A sensitive biosensor (cytosensor) has been developed based on color changes in the toxin-sensitive colored living cells of fish. These chromatophores are highly sensitive to the presence of many known and unknown toxins produced by microbial pathogens and undergo visible color changes in a dose-dependent manner. The chromatophores are immobilized and maintained in a viable state while potential pathogens multiply and fish cell-microbe interactions are monitored. Low power LED lighting is used to illuminate the chromatophores which are magnified using standard optical lenses and imaged onto a CCD array. Reaction to toxins is detected by observing changes is the total area of color in the cells. These fish chromatophores are quite sensitive to cholera toxin, Staphococcus alpha toxin, and Bordatella pertussis toxin. Numerous other toxic chemical and biological agents besides bacterial toxins also cause readily detectable color effects in chromatophores. The ability of the chromatophore cell-based biosensor to distinguish between different bacterial pathogens was examined. Toxin producing strains of Salmonella enteritis, Vibrio parahaemolyticus, and Bacillus cereus induced movement of pigmented organelles in the chromatophore cells and this movement was measured by changes in the optical density over time. Each bacterial pathogen elicited this measurable response in a distinctive and signature fashion. These results suggest a chromatophore cell-based biosensor assay may be applicable for the detection and identification of virulence activities associated with certain air-, food-, and water-borne bacterial pathogens.

  5. Individual recognition based on communication behaviour of male fowl.

    PubMed

    Smith, Carolynn L; Taubert, Jessica; Weldon, Kimberly; Evans, Christopher S

    2016-04-01

    Correctly directing social behaviour towards a specific individual requires an ability to discriminate between conspecifics. The mechanisms of individual recognition include phenotype matching and familiarity-based recognition. Communication-based recognition is a subset of familiarity-based recognition wherein the classification is based on behavioural or distinctive signalling properties. Male fowl (Gallus gallus) produce a visual display (tidbitting) upon finding food in the presence of a female. Females typically approach displaying males. However, males may tidbit without food. We used the distinctiveness of the visual display and the unreliability of some males to test for communication-based recognition in female fowl. We manipulated the prior experience of the hens with the males to create two classes of males: S(+) wherein the tidbitting signal was paired with a food reward to the female, and S (-) wherein the tidbitting signal occurred without food reward. We then conducted a sequential discrimination test with hens using a live video feed of a familiar male. The results of the discrimination tests revealed that hens discriminated between categories of males based on their signalling behaviour. These results suggest that fowl possess a communication-based recognition system. This is the first demonstration of live-to-video transfer of recognition in any species of bird. PMID:26915426

  6. Facial expression recognition based on improved DAGSVM

    NASA Astrophysics Data System (ADS)

    Luo, Yuan; Cui, Ye; Zhang, Yi

    2014-11-01

    For the cumulative error problem because of randomization sequence of traditional DAGSVM(Directed Acyclic Graph Support Vector Machine) classification, this paper presents an improved DAGSVM expression recognition method. The method uses the distance of class and the standard deviation as the measure of the classer, which minimize the error rate of the upper structure of the classification. At the same time, this paper uses the method which combines discrete cosine transform (Discrete Cosine Transform, DCT) with Local Binary Pattern(Local Binary Pattern - LBP) ,to extract expression feature and be the input to improve the DAGSVM classifier for recognition. Experimental results show that compared with other multi-class support vector machine method, improved DAGSVM classifier can achieve higher recognition rate. And when it's used at the platform of the intelligent wheelchair, experiments show that the method has a better robustness.

  7. Human gait recognition based on compactness

    NASA Astrophysics Data System (ADS)

    Chen, Feng; Jiang, Jie; Zhang, Guangjun

    2008-10-01

    Gait recognition is new biological identity technology and widely researched in recent years for its many advantages compared with other biological identity technology. In this paper, we propose a simple but effective feature-compactness for gait recognition. First an improved background subtraction algorithm is used to obtain the silhouettes. Then the compactness is extracted from the images in the gait sequence as the feature vector. In the step of classification, DTW algorithm is adopted to adjust the feature vectors before classifying and two classifiers (NN and ENN) are used as classifiers. Because of the simple features which we choose, it consumes little time for recognition and the results turn out to be encouraging.

  8. Gait recognition based on fusion features

    NASA Astrophysics Data System (ADS)

    Wu, Haizhen; Jiang, Jiafu; Chen, Xi

    2009-10-01

    Gait recognition and analysis is a promising biometrics technology finding applications in numerous sectors of our society. This paper proposes a new fusion algorithm where the static and dynamic features are fused to obtain optimal performance. The new fusion algorithm divides decision situations into two categories. The wavelet moment is used to describe the static features of gait sequence images, and the three widths of the body contour are used to describe the dynamic features. In addition, the Principal Component Analysis (PCA) for feature transformation of spatial templates is proposed. The experimental results demonstrate that the proposed algorithm performs an encouraging recognition rate.

  9. Aging and Fluency Based Illusions in Recognition Memory

    PubMed Central

    Thapar, Anjali; Westerman, Deanne L.

    2009-01-01

    We examined age related differences in susceptibility to fluency-based memory illusions. The results from two experiments, using two different methods to enhance the fluency of recognition test items, revealed that older and younger adults did not differ significantly in terms of their overall susceptibility to this type of memory illusion. Older and younger adults were also similar in that perceptual fluency did not influence recognition memory responses when there was a mismatch in the sensory modality of the study and test phases. Likewise, a more conceptual fluency manipulation influenced recognition memory responses in both older and younger adults regardless of the match in modality. Overall, the results indicate that older adults may not be more vulnerable than younger adults to fluency-based illusions of recognition memory. Moreover, younger and older adults appear to be comparable in their sensitivity to factors that modulate the influence of fluency on recognition decisions. PMID:19739915

  10. FishCam - A semi-automatic video-based monitoring system of fish migration

    NASA Astrophysics Data System (ADS)

    Kratzert, Frederik; Mader, Helmut

    2016-04-01

    One of the main objectives of the Water Framework Directive is to preserve and restore the continuum of river networks. Regarding vertebrate migration, fish passes are widely used measure to overcome anthropogenic constructions. Functionality of this measure needs to be verified by monitoring. In this study we propose a newly developed monitoring system, named FishCam, to observe fish migration especially in fish passes without contact and without imposing stress on fish. To avoid time and cost consuming field work for fish pass monitoring, this project aims to develop a semi-automatic monitoring system that enables a continuous observation of fish migration. The system consists of a detection tunnel and a high resolution camera, which is mainly based on the technology of security cameras. If changes in the image, e.g. by migrating fish or drifting particles, are detected by a motion sensor, the camera system starts recording and continues until no further motion is detectable. An ongoing key challenge in this project is the development of robust software, which counts, measures and classifies the passing fish. To achieve this goal, many different computer vision tasks and classification steps have to be combined. Moving objects have to be detected and separated from the static part of the image, objects have to be tracked throughout the entire video and fish have to be separated from non-fish objects (e.g. foliage and woody debris, shadows and light reflections). Subsequently, the length of all detected fish needs to be determined and fish should be classified into species. The object classification in fish and non-fish objects is realized through ensembles of state-of-the-art classifiers on a single image per object. The choice of the best image for classification is implemented through a newly developed "fish benchmark" value. This value compares the actual shape of the object with a schematic model of side-specific fish. To enable an automatization of the

  11. Human motion recognition based on features and models selected HMM

    NASA Astrophysics Data System (ADS)

    Lu, Haixiang; Zhou, Hongjun

    2015-03-01

    This paper research on the motion recognition based on HMM with Kinect. Kinect provides skeletal data consist of 3D body joints with its lower price and convenience. In this work, several methods are used to determine the optimal subset of features among Cartesian coordinates, distance to hip center, velocity, angle and angular velocity, in order to improve the recognition rate. K-means is used for vector quantization and HMM is used as recognition method. HMM is an effective signal processing method which contains time calibration, provides a learning mechanism and recognition ability. Cluster numbers of K-means, structure and state numbers of HMM are optimized as well. The proposed methods are applied to the MSR Action3D dataset. Results show that the proposed methods obtain better recognition accuracy than the state of the art methods.

  12. Sparse representation based face recognition using weighted regions

    NASA Astrophysics Data System (ADS)

    Bilgazyev, Emil; Yeniaras, E.; Uyanik, I.; Unan, Mahmut; Leiss, E. L.

    2013-12-01

    Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCfaceandMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios.

  13. Real-time color/shape-based traffic signs acquisition and recognition system

    NASA Astrophysics Data System (ADS)

    Saponara, Sergio

    2013-02-01

    A real-time system is proposed to acquire from an automotive fish-eye CMOS camera the traffic signs, and provide their automatic recognition on the vehicle network. Differently from the state-of-the-art, in this work color-detection is addressed exploiting the HSI color space which is robust to lighting changes. Hence the first stage of the processing system implements fish-eye correction and RGB to HSI transformation. After color-based detection a noise deletion step is implemented and then, for the classification, a template-based correlation method is adopted to identify potential traffic signs, of different shapes, from acquired images. Starting from a segmented-image a matching with templates of the searched signs is carried out using a distance transform. These templates are organized hierarchically to reduce the number of operations and hence easing real-time processing for several types of traffic signs. Finally, for the recognition of the specific traffic sign, a technique based on extraction of signs characteristics and thresholding is adopted. Implemented on DSP platform the system recognizes traffic signs in less than 150 ms at a distance of about 15 meters from 640x480-pixel acquired images. Tests carried out with hundreds of images show a detection and recognition rate of about 93%.

  14. Pattern recognition tool based on complex network-based approach

    NASA Astrophysics Data System (ADS)

    Casanova, Dalcimar; Backes, André Ricardo; Martinez Bruno, Odemir

    2013-02-01

    This work proposed a generalization of the method proposed by the authors: 'A complex network-based approach for boundary shape analysis'. Instead of modelling a contour into a graph and use complex networks rules to characterize it, here, we generalize the technique. This way, the work proposes a mathematical tool for characterization signals, curves and set of points. To evaluate the pattern description power of the proposal, an experiment of plat identification based on leaf veins image are conducted. Leaf vein is a taxon characteristic used to plant identification proposes, and one of its characteristics is that these structures are complex, and difficult to be represented as a signal or curves and this way to be analyzed in a classical pattern recognition approach. Here, we model the veins as a set of points and model as graphs. As features, we use the degree and joint degree measurements in a dynamic evolution. The results demonstrates that the technique has a good power of discrimination and can be used for plant identification, as well as other complex pattern recognition tasks.

  15. 3D face recognition based on matching of facial surfaces

    NASA Astrophysics Data System (ADS)

    Echeagaray-Patrón, Beatriz A.; Kober, Vitaly

    2015-09-01

    Face recognition is an important task in pattern recognition and computer vision. In this work a method for 3D face recognition in the presence of facial expression and poses variations is proposed. The method uses 3D shape data without color or texture information. A new matching algorithm based on conformal mapping of original facial surfaces onto a Riemannian manifold followed by comparison of conformal and isometric invariants computed in the manifold is suggested. Experimental results are presented using common 3D face databases that contain significant amount of expression and pose variations.

  16. Facial expression recognition with facial parts based sparse representation classifier

    NASA Astrophysics Data System (ADS)

    Zhi, Ruicong; Ruan, Qiuqi

    2009-10-01

    Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l1 -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.

  17. Retrieval Failure Contributes to Gist-Based False Recognition

    PubMed Central

    Guerin, Scott A.; Robbins, Clifford A.; Gilmore, Adrian W.; Schacter, Daniel L.

    2011-01-01

    People often falsely recognize items that are similar to previously encountered items. This robust memory error is referred to as gist-based false recognition. A widely held view is that this error occurs because the details fade rapidly from our memory. Contrary to this view, an initial experiment revealed that, following the same encoding conditions that produce high rates of gist-based false recognition, participants overwhelmingly chose the correct target rather than its related foil when given the option to do so. A second experiment showed that this result is due to increased access to stored details provided by reinstatement of the originally encoded photograph, rather than to increased attention to the details. Collectively, these results suggest that details needed for accurate recognition are, to a large extent, still stored in memory and that a critical factor determining whether false recognition will occur is whether these details can be accessed during retrieval. PMID:22125357

  18. Carotenoid-based coloration in cichlid fishes

    PubMed Central

    Sefc, Kristina M.; Brown, Alexandria C.; Clotfelter, Ethan D.

    2014-01-01

    Animal colors play important roles in communication, ecological interactions and speciation. Carotenoid pigments are responsible for many yellow, orange and red hues in animals. Whereas extensive knowledge on the proximate mechanisms underlying carotenoid coloration in birds has led to testable hypotheses on avian color evolution and signaling, much less is known about the expression of carotenoid coloration in fishes. Here, we promote cichlid fishes (Perciformes: Cichlidae) as a system in which to study the physiological and evolutionary significance of carotenoids. Cichlids include some of the best examples of adaptive radiation and color pattern diversification in vertebrates. In this paper, we examine fitness correlates of carotenoid pigmentation in cichlids and review hypotheses regarding the signal content of carotenoid-based ornaments. Carotenoid-based coloration is influenced by diet and body condition and is positively related to mating success and social dominance. Gaps in our knowledge are discussed in the last part of this review, particularly in the understanding of carotenoid metabolism pathways and the genetics of carotenoid coloration. We suggest that carotenoid metabolism and transport are important proximate mechanisms responsible for individual and population-differences in cichlid coloration that may ultimately contribute to diversification and speciation. PMID:24667558

  19. Cellular Phone Face Recognition System Based on Optical Phase Correlation

    NASA Astrophysics Data System (ADS)

    Watanabe, Eriko; Ishikawa, Sayuri; Ohta, Maiko; Kodate, Kashiko

    We propose a high security facial recognition system using a cellular phone on the mobile network. This system is composed of a face recognition engine based on optical phase correlation which uses phase information with emphasis on a Fourier domain, a control sever and the cellular phone with a compact camera for taking pictures, as a portable terminal. Compared with various correlation methods, our face recognition engine revealed the most accurate EER of less than 1%. By using the JAVA interface on this system, we implemented the stable system taking pictures, providing functions to prevent spoofing while transferring images. This recognition system was tested on 300 women students and the results proved this system effective.

  20. Active Shape Model-Based Gait Recognition Using Infrared Images

    NASA Astrophysics Data System (ADS)

    Kim, Daehee; Lee, Seungwon; Paik, Joonki

    We present a gait recognition system using infra-red (IR) images. Since an IR camera is not affected by the intensity of illumination, it is able to provide constant recognition performance regardless of the amount of illumination. Model-based object tracking algorithms enable robust tracking with partial occlusions or dynamic illumination. However, this algorithm often fails in tracking objects if strong edge exists near the object. Replacement of the input image by an IR image guarantees robust object region extraction because background edges do not affect the IR image. In conclusion, the proposed gait recognition algorithm improves accuracy in object extraction by using IR images and the improvements finally increase the recognition rate of gaits.

  1. Possibility of object recognition using Altera's model based design approach

    NASA Astrophysics Data System (ADS)

    Tickle, A. J.; Wu, F.; Harvey, P. K.; Smith, J. S.

    2009-07-01

    Object recognition is an image processing task of finding a given object in a selected image or video sequence. Object recognition can be divided into two areas: one of these is decision-theoretic and deals with patterns described by quantitative descriptors, for example such as length, area, shape and texture. With this Graphical User Interface Circuitry (GUIC) methodology employed here being relatively new for object recognition systems, the aim of this work is to identify if the developed circuitry can detect certain shapes or strings within the target image. A much smaller reference image feeds the preset data for identification, tests are conducted for both binary and greyscale and the additional mathematical morphology to highlight the area within the target image with the object(s) are located is also presented. This then provides proof that basic recognition methods are valid and would allow the progression to developing decision-theoretical and learning based approaches using GUICs for use in multidisciplinary tasks.

  2. Iris recognition based on robust principal component analysis

    NASA Astrophysics Data System (ADS)

    Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

    2014-11-01

    Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

  3. Supramolecular polymers constructed by crown ether-based molecular recognition.

    PubMed

    Zheng, Bo; Wang, Feng; Dong, Shengyi; Huang, Feihe

    2012-03-01

    Supramolecular polymers, polymeric systems beyond the molecule, have attracted more and more attention from scientists due to their applications in various fields, including stimuli-responsive materials, healable materials, and drug delivery. Due to their good selectivity and convenient enviro-responsiveness, crown ether-based molecular recognition motifs have been actively employed to fabricate supramolecular polymers with interesting properties and novel applications in recent years. In this tutorial review, we classify supramolecular polymers based on their differences in topology and cover recent advances in the marriage between crown ether-based molecular recognition and polymer science. PMID:22012256

  4. Finger Vein Recognition Based on Personalized Weight Maps

    PubMed Central

    Yang, Gongping; Xiao, Rongyang; Yin, Yilong; Yang, Lu

    2013-01-01

    Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition. PMID:24025556

  5. Finger Vein Recognition Based on a Personalized Best Bit Map

    PubMed Central

    Yang, Gongping; Xi, Xiaoming; Yin, Yilong

    2012-01-01

    Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition. PMID:22438735

  6. Detection and recognition of uneaten fish food pellets in aquaculture using image processing

    NASA Astrophysics Data System (ADS)

    Liu, Huanyu; Xu, Lihong; Li, Dawei

    2015-03-01

    The waste of fish food has always been a serious problem in aquaculture. On one hand, the leftover fish food spawns a big waste in the aquaculture industry because fish food accounts for a large proportion of the investment. On the other hand, the left over fish food may pollute the water and make fishes sick. In general, the reason for fish food waste is that there is no feedback about the consumption of delivered fish food after feeding. So it is extremely difficult for fish farmers to determine the amount of feedstuff that should be delivered each time and the feeding intervals. In this paper, we propose an effective method using image processing techniques to solve this problem. During feeding events, we use an underwater camera with supplementary LED lights to obtain images of uneaten fish food pellets on the tank bottom. An algorithm is then developed to figure out the number of left pellets using adaptive Otsu thresholding and a linear-time component labeling algorithm. This proposed algorithm proves to be effective in handling the non-uniform lighting and very accurate number of pellets are counted in experiments.

  7. Event Recognition Based on Deep Learning in Chinese Texts.

    PubMed

    Zhang, Yajun; Liu, Zongtian; Zhou, Wen

    2016-01-01

    Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%. PMID:27501231

  8. Event Recognition Based on Deep Learning in Chinese Texts

    PubMed Central

    Zhang, Yajun; Liu, Zongtian; Zhou, Wen

    2016-01-01

    Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%. PMID:27501231

  9. Current Sensor Based Home Appliance and State of Appliance Recognition

    NASA Astrophysics Data System (ADS)

    Saitoh, Takeshi; Osaki, Tomoyuki; Konishi, Ryosuke; Sugahara, Kazunori

    This paper presents a current sensor-based home appliance and its state recognition method for intelligent outlets. Our system has three main functions: remote control, monitoring, and power supply schedule management. This research focuses particular on the monitoring function. To recognize the appliance and the state of the appliance, we extract ten features based on a measured current signal. In the experiment, we gather a number of signals with various appliances, and find that three features Irms, Iavg, and Ipeak yield valid recognition results of 84.3%, 86.4%, and 90.3% for classifying the state of the appliance into three categories. Moreover, sufficient recognition rates of 97.4%, 97.7%, and 99.0% are obtained by consideration of three candidates.

  10. Finger vein recognition based on local directional code.

    PubMed

    Meng, Xianjing; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

    2012-01-01

    Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP. PMID:23202194

  11. Finger Vein Recognition Based on Local Directional Code

    PubMed Central

    Meng, Xianjing; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

    2012-01-01

    Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP. PMID:23202194

  12. Lexicon-based word recognition without word segmentation

    SciTech Connect

    Myers, G.K.; Chen, C.H.

    1994-12-31

    We present a word recognition approach that does not rely on explicit word segmentation. It treats the character recognition output as a continuous string of characters instead of first dividing it into words before word-level contextual knowledge is applied. This technique is useful in degraded document images, in which isolation of individual words by purely image- or character-based means is difficult or unreliable. We use a hypothesis generation and verification approach, in which word identities and their positions are hypothesized based on {open_quotes}seed features{close_quotes} (character substrings) extracted from the output of the character recognizer. Verification of the hypotheses consists of comparing the characters in the hypothesized word with candidate characters near the position of the seed feature in the text, and selecting the set of consecutive word hypotheses that are the most mutually consistent. Hence, word segmentation and word recognition are effectively performed in parallel.

  13. Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms

    NASA Astrophysics Data System (ADS)

    Nazimov, Alexey I.; Pavlov, Alexey N.; Hramov, Alexander E.; Grubov, Vadim V.; Koronovskii, Alexey A.; Sitnikova, Evgenija Y.

    2013-02-01

    The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.

  14. A Star Pattern Recognition Method Based on Decreasing Redundancy Matching

    NASA Astrophysics Data System (ADS)

    Yao, Lu; Xiao-xiang, Zhang; Rong-yu, Sun

    2016-04-01

    During the optical observation of space objects, it is difficult to enable the background stars to get matched when the telescope pointing error and tracking error are significant. Based on the idea of decreasing redundancy matching, an effective recognition method for background stars is proposed in this paper. The simulative images under different conditions and the observed images are used to verify the proposed method. The experimental results show that the proposed method has raised the rate of recognition and reduced the time consumption, it can be used to match star patterns accurately and rapidly.

  15. [Research progress on emotion recognition based on physiological signals].

    PubMed

    Zhang, Di; Wan, Baikun; Ming, Dong

    2015-02-01

    Emotion recognition will be prosperious in multifarious applications, like distance education, healthcare, and human-computer interactions, etc. Emotions can be recognized from the behavior signals such as speech, facial expressions, gestures or the physiological signals such as electroencephalogram and electrocardiogram. Contrast to other methods, the physiological signals based emotion recognition can achieve more objective and effective results because it is almost impossible to be disguised. This paper introduces recent advancements in emotion research using physiological signals, specified to its emotion model, elicitation stimuli, feature extraction and classification methods. Finally the paper also discusses some research challenges and future developments. PMID:25997298

  16. Recognition technology research based on 3D fingerprint

    NASA Astrophysics Data System (ADS)

    Tian, Qianxiao; Huang, Shujun; Zhang, Zonghua

    2014-11-01

    Fingerprint has been widely studied and applied to personal recognition in both forensics and civilian. However, the current widespread used fingerprint is identified by 2D (two-dimensional) fingerprint image and the mapping from 3D (three-dimensional) to 2D loses 1D information, which leads to low accurate and even wrong recognition. This paper presents a 3D fingerprint recognition method based on the fringe projection technique. A series of fringe patterns generated by software are projected onto a finger surface through a projecting system. From another viewpoint, the fringe patterns are deformed by the finger surface and captured by a CCD camera. The deformed fringe pattern images give the 3D shape data of the finger and the 3D fingerprint features. Through converting the 3D fingerprints to 2D space, traditional 2D fingerprint recognition method can be used to 3D fingerprints recognition. Experimental results on measuring and recognizing some 3D fingerprints show the accuracy and availability of the developed 3D fingerprint system.

  17. Retrieval Failure Contributes to Gist-Based False Recognition

    ERIC Educational Resources Information Center

    Guerin, Scott A.; Robbins, Clifford A.; Gilmore, Adrian W.; Schacter, Daniel L.

    2012-01-01

    People often falsely recognize items that are similar to previously encountered items. This robust memory error is referred to as "gist-based false recognition". A widely held view is that this error occurs because the details fade rapidly from our memory. Contrary to this view, an initial experiment revealed that, following the same encoding…

  18. Simulationist Models of Face-Based Emotion Recognition

    ERIC Educational Resources Information Center

    Goldman, Alvin I.; Sripada, Chandra Sekhar

    2005-01-01

    Recent studies of emotion mindreading reveal that for three emotions, fear, disgust, and anger, deficits in face-based recognition are paired with deficits in the production of the same emotion. What type of mindreading process would explain this pattern of paired deficits? The simulation approach and the theorizing approach are examined to…

  19. Quaternion-based discriminant analysis method for color face recognition.

    PubMed

    Xu, Yong

    2012-01-01

    Pattern recognition techniques have been used to automatically recognize the objects, personal identities, predict the function of protein, the category of the cancer, identify lesion, perform product inspection, and so on. In this paper we propose a novel quaternion-based discriminant method. This method represents and classifies color images in a simple and mathematically tractable way. The proposed method is suitable for a large variety of real-world applications such as color face recognition and classification of the ground target shown in multispectrum remote images. This method first uses the quaternion number to denote the pixel in the color image and exploits a quaternion vector to represent the color image. This method then uses the linear discriminant analysis algorithm to transform the quaternion vector into a lower-dimensional quaternion vector and classifies it in this space. The experimental results show that the proposed method can obtain a very high accuracy for color face recognition. PMID:22937054

  20. Hand vein recognition based on orientation of LBP

    NASA Astrophysics Data System (ADS)

    Bu, Wei; Wu, Xiangqian; Gao, Enying

    2012-06-01

    Vein recognition is becoming an effective method for personal recognition. Vein patterns lie under the skin surface of human body, and hence provide higher reliability than other biometric traits and hard to be damaged or faked. This paper proposes a novel vein feature representation method call orientation of local binary pattern (OLBP) which is an extension of local binary pattern (LBP). OLBP can represent the orientation information of the vein pixel which is an important characteristic of vein patterns. Moreover, the OLBP can also indicate on which side of the vein centerline the pixel locates. The OLBP feature maps are encoded by 4-bit binary values and an orientation distance is developed for efficient feature matching. Based on OLBP feature representation, we construct a hand vein recognition system employing multiple hand vein patterns include palm vein, dorsal vein, and three finger veins (index, middle, and ring finger). The experimental results on a large database demonstrate the effectiveness of the proposed approach.

  1. Human Gait Recognition Based on Multiview Gait Sequences

    NASA Astrophysics Data System (ADS)

    Huang, Xiaxi; Boulgouris, Nikolaos V.

    2008-12-01

    Most of the existing gait recognition methods rely on a single view, usually the side view, of the walking person. This paper investigates the case in which several views are available for gait recognition. It is shown that each view has unequal discrimination power and, therefore, should have unequal contribution in the recognition process. In order to exploit the availability of multiple views, several methods for the combination of the results that are obtained from the individual views are tested and evaluated. A novel approach for the combination of the results from several views is also proposed based on the relative importance of each view. The proposed approach generates superior results, compared to those obtained by using individual views or by using multiple views that are combined using other combination methods.

  2. Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

    NASA Technical Reports Server (NTRS)

    Huntsberger, Terry

    2011-01-01

    The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

  3. Supervised Filter Learning for Representation Based Face Recognition

    PubMed Central

    Bi, Chao; Zhang, Lei; Qi, Miao; Zheng, Caixia; Yi, Yugen; Wang, Jianzhong; Zhang, Baoxue

    2016-01-01

    Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm. PMID:27416030

  4. Supervised Filter Learning for Representation Based Face Recognition.

    PubMed

    Bi, Chao; Zhang, Lei; Qi, Miao; Zheng, Caixia; Yi, Yugen; Wang, Jianzhong; Zhang, Baoxue

    2016-01-01

    Representation based classification methods, such as Sparse Representation Classification (SRC) and Linear Regression Classification (LRC) have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances) in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP) features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm. PMID:27416030

  5. Inertial Sensor-Based Gait Recognition: A Review.

    PubMed

    Sprager, Sebastijan; Juric, Matjaz B

    2015-01-01

    With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability. PMID:26340634

  6. Inertial Sensor-Based Gait Recognition: A Review

    PubMed Central

    Sprager, Sebastijan; Juric, Matjaz B.

    2015-01-01

    With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability. PMID:26340634

  7. DNA barcode-based molecular identification system for fish species.

    PubMed

    Kim, Sungmin; Eo, Hae-Seok; Koo, Hyeyoung; Choi, Jun-Kil; Kim, Won

    2010-12-01

    In this study, we applied DNA barcoding to identify species using short DNA sequence analysis. We examined the utility of DNA barcoding by identifying 53 Korean freshwater fish species, 233 other freshwater fish species, and 1339 saltwater fish species. We successfully developed a web-based molecular identification system for fish (MISF) using a profile hidden Markov model. MISF facilitates efficient and reliable species identification, overcoming the limitations of conventional taxonomic approaches. MISF is freely accessible at http://bioinfosys.snu.ac.kr:8080/MISF/misf.jsp . PMID:21110132

  8. Understanding the importance of episodic acidification on fish predator-prey interactions: does weak acidification impair predator recognition?

    PubMed

    Brown, Grant E; Elvidge, Chris K; Ferrari, Maud C O; Chivers, Douglas P

    2012-11-15

    The ability of prey to recognize predators is a fundamental prerequisite to avoid being eaten. Indeed, many prey animals learn to distinguish species that pose a threat from those that do not. Once the prey has learned the identity of one predator, it may generalize this recognition to similar predators with which the prey has no experience. The ability to generalize reduces the costs associated with learning and further enhances the ability of the prey to avoid relevant threats. For many aquatic organisms, recognition of predators is based on odor signatures, consequently any anthropogenic alteration in water chemistry has the potential to impair recognition and learning of predators. Here we explored whether episodic acidification could influence the ability of juvenile rainbow trout to learn to recognize an unknown predator and then generalize this recognition to a closely related predator. Trout were conditioned to recognize the odor of pumpkinseed sunfish under circumneutral (~pH 7) conditions, and then tested for recognition of pumpkinseed or longear sunfish under both neutral or weakly acidic (~pH 6) conditions. When tested for a response to pumpkinseed odor, we found no significant effect of predator odor pH: trout responded similarly regardless of pH. Moreover, under neutral conditions, trout were able to generalize their recognition to the odor of longear sunfish. However, the trout could not generalize their recognition of the longear sunfish under acidic conditions. Given the widespread occurrence of anthropogenic acidification, acid-mediated impairment of predator recognition and generalization may be a pervasive problem for freshwater salmonid populations and other aquatic organisms. PMID:23063639

  9. Embedded wavelet-based face recognition under variable position

    NASA Astrophysics Data System (ADS)

    Cotret, Pascal; Chevobbe, Stéphane; Darouich, Mehdi

    2015-02-01

    For several years, face recognition has been a hot topic in the image processing field: this technique is applied in several domains such as CCTV, electronic devices delocking and so on. In this context, this work studies the efficiency of a wavelet-based face recognition method in terms of subject position robustness and performance on various systems. The use of wavelet transform has a limited impact on the position robustness of PCA-based face recognition. This work shows, for a well-known database (Yale face database B*), that subject position in a 3D space can vary up to 10% of the original ROI size without decreasing recognition rates. Face recognition is performed on approximation coefficients of the image wavelet transform: results are still satisfying after 3 levels of decomposition. Furthermore, face database size can be divided by a factor 64 (22K with K = 3). In the context of ultra-embedded vision systems, memory footprint is one of the key points to be addressed; that is the reason why compression techniques such as wavelet transform are interesting. Furthermore, it leads to a low-complexity face detection stage compliant with limited computation resources available on such systems. The approach described in this work is tested on three platforms from a standard x86-based computer towards nanocomputers such as RaspberryPi and SECO boards. For K = 3 and a database with 40 faces, the execution mean time for one frame is 0.64 ms on a x86-based computer, 9 ms on a SECO board and 26 ms on a RaspberryPi (B model).

  10. 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.

  11. Wavelet-based acoustic recognition of aircraft

    SciTech Connect

    Dress, W.B.; Kercel, S.W.

    1994-09-01

    We describe a wavelet-based technique for identifying aircraft from acoustic emissions during take-off and landing. Tests show that the sensor can be a single, inexpensive hearing-aid microphone placed close to the ground the paper describes data collection, analysis by various technique, methods of event classification, and extraction of certain physical parameters from wavelet subspace projections. The primary goal of this paper is to show that wavelet analysis can be used as a divide-and-conquer first step in signal processing, providing both simplification and noise filtering. The idea is to project the original signal onto the orthogonal wavelet subspaces, both details and approximations. Subsequent analysis, such as system identification, nonlinear systems analysis, and feature extraction, is then carried out on the various signal subspaces.

  12. Compound character recognition by run-number-based metric distance

    NASA Astrophysics Data System (ADS)

    Garain, Uptal; Chaudhuri, B. B.

    1998-04-01

    This paper concerns automatic OCR of Bangla, a major Indian Language Script which is the fourth most popular script in the world. A Bangla OCR system has to recognize about 300 graphemic shapes among which 250 compound characters have quite complex stroke patterns. For recognition of such compound characters, feature based approaches are less reliable and template based approaches are less flexible to size and style variation of character font. We combine the positive aspects of feature based and template based approaches. Here we propose a run number based normalized template matching technique for compound character recognition. Run number vectors for both horizontal and vertical scanning are computed. As the number of scans may very from pattern to pattern, we normalize and abbreviate the vector. We prove that this normalized and abbreviated vector induces metric distance metric distance. Moreover, this vector is invariant to scaling, insensitive to character style variation and more effective for more complex-shaped characters than simple-shaped ones. We use this vector representation for matching within a group of compound characters. We notice that the matching is more efficient if the vector is reorganized with respect to the centroid of the pattern. We have tested our approach on a large set of segmented compounds characters at different point sizes as well as different styles. Italic characters are subject to preprocessing. The overall correct recognition rate is 99.69 percent.

  13. Wavelet-based ground vehicle recognition using acoustic signals

    NASA Astrophysics Data System (ADS)

    Choe, Howard C.; Karlsen, Robert E.; Gerhart, Grant R.; Meitzler, Thomas J.

    1996-03-01

    We present, in this paper, a wavelet-based acoustic signal analysis to remotely recognize military vehicles using their sound intercepted by acoustic sensors. Since expedited signal recognition is imperative in many military and industrial situations, we developed an algorithm that provides an automated, fast signal recognition once implemented in a real-time hardware system. This algorithm consists of wavelet preprocessing, feature extraction and compact signal representation, and a simple but effective statistical pattern matching. The current status of the algorithm does not require any training. The training is replaced by human selection of reference signals (e.g., squeak or engine exhaust sound) distinctive to each individual vehicle based on human perception. This allows a fast archiving of any new vehicle type in the database once the signal is collected. The wavelet preprocessing provides time-frequency multiresolution analysis using discrete wavelet transform (DWT). Within each resolution level, feature vectors are generated from statistical parameters and energy content of the wavelet coefficients. After applying our algorithm on the intercepted acoustic signals, the resultant feature vectors are compared with the reference vehicle feature vectors in the database using statistical pattern matching to determine the type of vehicle from where the signal originated. Certainly, statistical pattern matching can be replaced by an artificial neural network (ANN); however, the ANN would require training data sets and time to train the net. Unfortunately, this is not always possible for many real world situations, especially collecting data sets from unfriendly ground vehicles to train the ANN. Our methodology using wavelet preprocessing and statistical pattern matching provides robust acoustic signal recognition. We also present an example of vehicle recognition using acoustic signals collected from two different military ground vehicles. In this paper, we will

  14. Pattern recognition for electroencephalographic signals based on continuous neural networks.

    PubMed

    Alfaro-Ponce, M; Argüelles, A; Chairez, I

    2016-07-01

    This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results. PMID:27131469

  15. Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

    PubMed

    Zhou, Pan; Lin, Zhouchen; Zhang, Chao

    2016-05-01

    Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method. PMID:26080387

  16. Isolate Speech Recognition Based on Time-Frequency Analysis Methods

    NASA Astrophysics Data System (ADS)

    Mantilla-Caeiros, Alfredo; Nakano Miyatake, Mariko; Perez-Meana, Hector

    A feature extraction method for isolate speech recognition is proposed, which is based on a time frequency analysis using a critical band concept similar to that performed in the inner ear model; which emulates the inner ear behavior by performing signal decomposition, similar to carried out by the basilar membrane. Evaluation results show that the proposed method performs better than other previously proposed feature extraction methods when it is used to characterize normal as well as esophageal speech signal.

  17. Segment-based acoustic models for continuous speech recognition

    NASA Astrophysics Data System (ADS)

    Ostendorf, Mari; Rohlicek, J. R.

    1993-07-01

    This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.

  18. Human activity recognition based on Evolving Fuzzy Systems.

    PubMed

    Iglesias, Jose Antonio; Angelov, Plamen; Ledezma, Agapito; Sanchis, Araceli

    2010-10-01

    Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems. PMID:20945515

  19. Gesture Recognition Based on the Probability Distribution of Arm Trajectories

    NASA Astrophysics Data System (ADS)

    Wan, Khairunizam; Sawada, Hideyuki

    The use of human motions for the interaction between humans and computers is becoming an attractive alternative to verbal media, especially through the visual interpretation of the human body motion. In particular, hand gestures are used as non-verbal media for the humans to communicate with machines that pertain to the use of the human gestures to interact with them. This paper introduces a 3D motion measurement of the human upper body for the purpose of the gesture recognition, which is based on the probability distribution of arm trajectories. In this study, by examining the characteristics of the arm trajectories given by a signer, motion features are selected and classified by using a fuzzy technique. Experimental results show that the use of the features extracted from arm trajectories effectively works on the recognition of dynamic gestures of a human, and gives a good performance to classify various gesture patterns.

  20. Ontology-based improvement to human activity recognition

    NASA Astrophysics Data System (ADS)

    Tahmoush, David; Bonial, Claire

    2016-05-01

    Human activity recognition has often prioritized low-level features extracted from imagery or video over higher-level class attributes and ontologies because they have traditionally been more effective on small datasets. However, by including knowledge-driven associations between actions and attributes while recognizing the lower-level attributes with their temporal relationships, we can attempt a hybrid approach that is more easily extensible to much larger datasets. We demonstrate a combination of hard and soft features with a comparison factor that prioritizes one approach over the other with a relative weight. We then exhaustively search over the comparison factor to evaluate the performance of a hybrid human activity recognition approach in comparison to the base hard approach at 84% accuracy and the current state-of-the-art.

  1. 3D face recognition based on a modified ICP method

    NASA Astrophysics Data System (ADS)

    Zhao, Kankan; Xi, Jiangtao; Yu, Yanguang; Chicharo, Joe F.

    2011-11-01

    3D face recognition technique has gained much more attention recently, and it is widely used in security system, identification system, and access control system, etc. The core technique in 3D face recognition is to find out the corresponding points in different 3D face images. The classic partial Iterative Closest Point (ICP) method is iteratively align the two point sets based on repetitively calculate the closest points as the corresponding points in each iteration. After several iterations, the corresponding points can be obtained accurately. However, if two 3D face images with different scale are from the same person, the classic partial ICP does not work. In this paper we propose a modified partial Iterative Closest Point (ICP) method in which the scaling effect is considered to achieve 3D face recognition. We design a 3x3 diagonal matrix as the scale matrix in each iteration of the classic partial ICP. The probing face image which is multiplied by the scale matrix will keep the similar scale with the reference face image. Therefore, we can accurately determine the corresponding points even the scales of probing image and reference image are different. 3D face images in our experiments are acquired by a 3D data acquisition system based on Digital Fringe Projection Profilometry (DFPP). A 3D database consists of 30 group images, three images with the same scale, which are from the same person with different views, are included in each group. And in different groups, the scale of the 3 images may be different from other groups. The experiment results show that our proposed method can achieve 3D face recognition, especially in the case that the scales of probing image and referent image are different.

  2. A vocal-based analytical method for goose behaviour recognition.

    PubMed

    Steen, Kim Arild; Therkildsen, Ole Roland; Karstoft, Henrik; Green, Ole

    2012-01-01

    Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86-97% sensitivity, 89-98% precision) and a reasonable recognition of flushing (79-86%, 66-80%) and landing behaviour(73-91%, 79-92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system. PMID:22737037

  3. Collision recognition and direction changes for small scale fish robots by acceleration sensors

    NASA Astrophysics Data System (ADS)

    Na, Seung Y.; Shin, Daejung; Kim, Jin Y.; Lee, Bae-Ho

    2005-05-01

    Typical obstacles are walls, rocks, water plants and other nearby robots for a group of small scale fish robots and submersibles that have been constructed in our lab. Sonar sensors are not employed to make the robot structure simple enough. All of circuits, sensors and processor cards are contained in a box of 9 x 7 x 4 cm dimension except motors, fins and external covers. Therefore, image processing results are applied to avoid collisions. However, it is useful only when the obstacles are located far enough to give images processing time for detecting them. Otherwise, acceleration sensors are used to detect collision immediately after it happens. Two of 2-axes acceleration sensors are employed to measure the three components of collision angles, collision magnitudes, and the angles of robot propulsion. These data are integrated to calculate the amount of propulsion direction change. The angle of a collision incident upon an obstacle is the fundamental value to obtain a direction change needed to design a following path. But there is a significant amount of noise due to a caudal fin motor. Because caudal fin provides the main propulsion for a fish robot, there is a periodic swinging noise at the head of a robot. This noise provides a random acceleration effect on the measured acceleration data at the collision. We propose an algorithm which shows that the MEMS-type accelerometers are very effective to provide information for direction changes in spite of the intrinsic noise after the small scale fish robots have made obstacle collision.

  4. Does Angling Technique Selectively Target Fishes Based on Their Behavioural Type?

    PubMed Central

    Wilson, Alexander D. M.; Brownscombe, Jacob W.; Sullivan, Brittany; Jain-Schlaepfer, Sofia; Cooke, Steven J.

    2015-01-01

    Recently, there has been growing recognition that fish harvesting practices can have important impacts on the phenotypic distributions and diversity of natural populations through a phenomenon known as fisheries-induced evolution. Here we experimentally show that two common recreational angling techniques (active crank baits versus passive soft plastics) differentially target wild largemouth bass (Micropterus salmoides) and rock bass (Ambloplites rupestris) based on variation in their behavioural tendencies. Fish were first angled in the wild using both techniques and then brought back to the laboratory and tested for individual-level differences in common estimates of personality (refuge emergence, flight-initiation-distance, latency-to-recapture and with a net, and general activity) in an in-lake experimental arena. We found that different angling techniques appear to selectively target these species based on their boldness (as characterized by refuge emergence, a standard measure of boldness in fishes) but not other assays of personality. We also observed that body size was independently a significant predictor of personality in both species, though this varied between traits and species. Our results suggest a context-dependency for vulnerability to capture relative to behaviour in these fish species. Ascertaining the selective pressures angling practices exert on natural populations is an important area of fisheries research with significant implications for ecology, evolution, and resource management. PMID:26284779

  5. Three dimensional pattern recognition using feature-based indexing and rule-based search

    NASA Astrophysics Data System (ADS)

    Lee, Jae-Kyu

    In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells. This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene. Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage. Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size

  6. Key of Packaged Grain Quantity Recognition - - Research on Processing and Describing of "fish and Describing of "fish Scale Body"

    NASA Astrophysics Data System (ADS)

    Lin, Ying; Fang, Xinglin; Sun, Yueheng; Sun, Yanhong

    The key to identifying the packaged grain is the shape of package, and the key to identifying shape is processing and describing the boundary of package. Based on a lot of analysis and experiment, this article select the canny operator and chain code to process and describe the boundary of package. Aiming at the boundary is not absolute connectivity, the closure operation of Mathematical Morphology is introduced to do pretreatment on binary image of packaged grain. Finally the boundary is absolute connectivity. Experiments show that the proposed method enhances the anti-jamming and robustness of edge detection.

  7. Improving representation-based classification for robust face recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Hongzhi; Zhang, Zheng; Li, Zhengming; Chen, Yan; Shi, Jian

    2014-06-01

    The sparse representation classification (SRC) method proposed by Wright et al. is considered as the breakthrough of face recognition because of its good performance. Nevertheless it still cannot perfectly address the face recognition problem. The main reason for this is that variation of poses, facial expressions, and illuminations of the facial image can be rather severe and the number of available facial images are fewer than the dimensions of the facial image, so a certain linear combination of all the training samples is not able to fully represent the test sample. In this study, we proposed a novel framework to improve the representation-based classification (RBC). The framework first ran the sparse representation algorithm and determined the unavoidable deviation between the test sample and optimal linear combination of all the training samples in order to represent it. It then exploited the deviation and all the training samples to resolve the linear combination coefficients. Finally, the classification rule, the training samples, and the renewed linear combination coefficients were used to classify the test sample. Generally, the proposed framework can work for most RBC methods. From the viewpoint of regression analysis, the proposed framework has a solid theoretical soundness. Because it can, to an extent, identify the bias effect of the RBC method, it enables RBC to obtain more robust face recognition results. The experimental results on a variety of face databases demonstrated that the proposed framework can improve the collaborative representation classification, SRC, and improve the nearest neighbor classifier.

  8. Image quality-based adaptive illumination normalisation for face recognition

    NASA Astrophysics Data System (ADS)

    Sellahewa, Harin; Jassim, Sabah A.

    2009-05-01

    Automatic face recognition is a challenging task due to intra-class variations. Changes in lighting conditions during enrolment and identification stages contribute significantly to these intra-class variations. A common approach to address the effects such of varying conditions is to pre-process the biometric samples in order normalise intra-class variations. Histogram equalisation is a widely used illumination normalisation technique in face recognition. However, a recent study has shown that applying histogram equalisation on well-lit face images could lead to a decrease in recognition accuracy. This paper presents a dynamic approach to illumination normalisation, based on face image quality. The quality of a given face image is measured in terms of its luminance distortion by comparing this image against a known reference face image. Histogram equalisation is applied to a probe image if its luminance distortion is higher than a predefined threshold. We tested the proposed adaptive illumination normalisation method on the widely used Extended Yale Face Database B. Identification results demonstrate that our adaptive normalisation produces better identification accuracy compared to the conventional approach where every image is normalised, irrespective of the lighting condition they were acquired.

  9. Recognition-Based Pedagogy: Teacher Candidates' Experience of Deficit

    ERIC Educational Resources Information Center

    Parkison, Paul T.; DaoJensen, Thuy

    2014-01-01

    This study seeks to introduce what we call "recognition-based pedagogy" as a conceptual frame through which teachers and instructors can collaboratively develop educative experiences with students. Recognition-based pedagogy connects the theories of critical pedagogy, identity politics, and the politics of recognition with the educative…

  10. Emotion recognition based on physiological changes in music listening.

    PubMed

    Kim, Jonghwa; André, Elisabeth

    2008-12-01

    Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\\% and 70\\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme. PMID:18988943

  11. A national knowledge-based crop recognition in Mediterranean environment

    NASA Astrophysics Data System (ADS)

    Cohen, Yafit; Shoshany, Maxim

    2002-08-01

    Population growth, urban expansion, land degradation, civil strife and war may place plant natural resources for food and agriculture at risk. Crop and yield monitoring is basic information necessary for wise management of these resources. Satellite remote sensing techniques have proven to be cost-effective in widespread agricultural lands in Africa, America, Europe and Australia. However, they have had limited success in Mediterranean regions that are characterized by a high rate of spatio-temporal ecological heterogeneity and high fragmentation of farming lands. An integrative knowledge-based approach is needed for this purpose, which combines imagery and geographical data within the framework of an intelligent recognition system. This paper describes the development of such a crop recognition methodology and its application to an area that comprises approximately 40% of the cropland in Israel. This area contains eight crop types that represent 70% of Israeli agricultural production. Multi-date Landsat TM images representing seasonal vegetation cover variations were converted to normalized difference vegetation index (NDVI) layers. Field boundaries were delineated by merging Landsat data with SPOT-panchromatic images. Crop recognition was then achieved in two-phases, by clustering multi-temporal NDVI layers using unsupervised classification, and then applying 'split-and-merge' rules to these clusters. These rules were formalized through comprehensive learning of relationships between crop types, imagery properties (spectral and NDVI) and auxiliary data including agricultural knowledge, precipitation and soil types. Assessment of the recognition results using ground data from the Israeli Agriculture Ministry indicated an average recognition accuracy exceeding 85% which accounts for both omission and commission errors. The two-phase strategy implemented in this study is apparently successful for heterogeneous regions. This is due to the fact that it allows

  12. Gait recognition using spatio-temporal silhouette-based features

    NASA Astrophysics Data System (ADS)

    Sabir, Azhin; Al-jawad, Naseer; Jassim, Sabah

    2013-05-01

    This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider members as non-classified members.

  13. Tunnelling readout of hydrogen-bonding-based recognition.

    PubMed

    Chang, Shuai; He, Jin; Kibel, Ashley; Lee, Myeong; Sankey, Otto; Zhang, Peiming; Lindsay, Stuart

    2009-05-01

    Hydrogen bonding has a ubiquitous role in electron transport and in molecular recognition, with DNA base pairing being the best-known example. Scanning tunnelling microscope images and measurements of the decay of tunnel current as a molecular junction is pulled apart by the scanning tunnelling microscope tip are sensitive to hydrogen-bonded interactions. Here, we show that these tunnel-decay signals can be used to measure the strength of hydrogen bonding in DNA base pairs. Junctions that are held together by three hydrogen bonds per base pair (for example, guanine-cytosine interactions) are stiffer than junctions held together by two hydrogen bonds per base pair (for example, adenine-thymine interactions). Similar, but less pronounced effects are observed on the approach of the tunnelling probe, implying that attractive forces that depend on hydrogen bonds also have a role in determining the rise of current. These effects provide new mechanisms for making sensors that transduce a molecular recognition event into an electronic signal. PMID:19421214

  14. 3D ladar ATR based on recognition by parts

    NASA Astrophysics Data System (ADS)

    Sobel, Erik; Douglas, Joel; Ettinger, Gil

    2003-09-01

    LADAR imaging is unique in its potential to accurately measure the 3D surface geometry of targets. We exploit this 3D geometry to perform automatic target recognition on targets in the domain of military and civilian ground vehicles. Here we present a robust model based 3D LADAR ATR system which efficiently searches through target hypothesis space by reasoning hierarchically from vehicle parts up to identification of a whole vehicle with specific pose and articulation state. The LADAR data consists of one or more 3D point clouds generated by laser returns from ground vehicles viewed from multiple sensor locations. The key to this approach is an automated 3D registration process to precisely align and match multiple data views to model based predictions of observed LADAR data. We accomplish this registration using robust 3D surface alignment techniques which we have also used successfully in 3D medical image analysis applications. The registration routine seeks to minimize a robust 3D surface distance metric to recover the best six-degree-of-freedom pose and fit. We process the observed LADAR data by first extracting salient parts, matching these parts to model based predictions and hierarchically constructing and testing increasingly detailed hypotheses about the identity of the observed target. This cycle of prediction, extraction, and matching efficiently partitions the target hypothesis space based on the distinctive anatomy of the target models and achieves effective recognition by progressing logically from a target's constituent parts up to its complete pose and articulation state.

  15. Cough Recognition Based on Mel Frequency Cepstral Coefficients and Dynamic Time Warping

    NASA Astrophysics Data System (ADS)

    Zhu, Chunmei; Liu, Baojun; Li, Ping

    Cough recognition provides important clinical information for the treatment of many respiratory diseases, but the assessment of cough frequency over a long period of time remains unsatisfied for either clinical or research purpose. In this paper, according to the advantage of dynamic time warping (DTW) and the characteristic of cough recognition, an attempt is made to adapt DTW as the recognition algorithm for cough recognition. The process of cough recognition based on mel frequency cepstral coefficients (MFCC) and DTW is introduced. Experiment results of testing samples from 3 subjects show that acceptable performances of cough recognition are obtained by DTW with a small training set.

  16. Infrared target recognition based on improved joint local ternary pattern

    NASA Astrophysics Data System (ADS)

    Sun, Junding; Wu, Xiaosheng

    2016-05-01

    This paper presents a simple, efficient, yet robust approach, named joint orthogonal combination of local ternary pattern, for automatic forward-looking infrared target recognition. It gives more advantages to describe the macroscopic textures and microscopic textures by fusing variety of scales than the traditional LBP-based methods. In addition, it can effectively reduce the feature dimensionality. Further, the rotation invariant and uniform scheme, the robust LTP, and soft concave-convex partition are introduced to enhance its discriminative power. Experimental results demonstrate that the proposed method can achieve competitive results compared with the state-of-the-art methods.

  17. Track-based event recognition in a realistic crowded environment

    NASA Astrophysics Data System (ADS)

    van Huis, Jasper R.; Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; den Hollander, Richard J. M.; Dijk, Judith; van Rest, Jeroen H.

    2014-10-01

    Automatic detection of abnormal behavior in CCTV cameras is important to improve the security in crowded environments, such as shopping malls, airports and railway stations. This behavior can be characterized at different time scales, e.g., by small-scale subtle and obvious actions or by large-scale walking patterns and interactions between people. For example, pickpocketing can be recognized by the actual snatch (small scale), when he follows the victim, or when he interacts with an accomplice before and after the incident (longer time scale). This paper focusses on event recognition by detecting large-scale track-based patterns. Our event recognition method consists of several steps: pedestrian detection, object tracking, track-based feature computation and rule-based event classification. In the experiment, we focused on single track actions (walk, run, loiter, stop, turn) and track interactions (pass, meet, merge, split). The experiment includes a controlled setup, where 10 actors perform these actions. The method is also applied to all tracks that are generated in a crowded shopping mall in a selected time frame. The results show that most of the actions can be detected reliably (on average 90%) at a low false positive rate (1.1%), and that the interactions obtain lower detection rates (70% at 0.3% FP). This method may become one of the components that assists operators to find threatening behavior and enrich the selection of videos that are to be observed.

  18. Pattern recognition via multispectral, hyperspectral, and polarization-based imaging

    NASA Astrophysics Data System (ADS)

    El-Saba, Aed; Alam, Mohammad S.; Sakla, Wesam A.

    2010-04-01

    Pattern recognition deals with the detection and identification of a specific target in an unknown input scene. Target features such as shape, color, surface dynamics, and material characteristics are common target attributes used for identification and detection purposes. Pattern recognition using multispectral (MS), hyperspectral (HS), and polarization-based spectral (PS) imaging can be effectively exploited to highlight one or more of these attributes for more efficient target identification and detection. In general, pattern recognition involves two steps: gathering target information from sensor data and identifying and detecting the desired target from sensor data in the presence of noise, clutter, and other artifacts. Multispectral and hyperspectral imaging (MSI/HSI) provide both spectral and spatial information about the target. As the reflection or emission spectral signatures depend on the elemental composition of objects residing within the scene, the polarization state of radiation is sensitive to the surface features such as relative smoothness or roughness, surface material, shapes and edges, etc. Therefore, polarization information imparted by surface reflections of the target yields unique and discriminatory signatures which could be used to augment spectral target detection techniques, through the fusion of sensor data. Sensor data fusion is currently being used to effectively recognize and detect one or more of the target attributes. However, variations between sensors and temporal changes within sensors can introduce noise in the measurements, contributing to additional target variability that hinders the detection process. This paper provides a quick overview of target identification and detection using MSI/HSI, highlighting the advantages and disadvantages of each. It then discusses the effectiveness of using polarization-based imaging in highlighting some of the target attributes at single and multiple spectral bands using polarization

  19. Prediction Method of Speech Recognition Performance Based on HMM-based Speech Synthesis Technique

    NASA Astrophysics Data System (ADS)

    Terashima, Ryuta; Yoshimura, Takayoshi; Wakita, Toshihiro; Tokuda, Keiichi; Kitamura, Tadashi

    We describe an efficient method that uses a HMM-based speech synthesis technique as a test pattern generator for evaluating the word recognition rate. The recognition rates of each word and speaker can be evaluated by the synthesized speech by using this method. The parameter generation technique can be formulated as an algorithm that can determine the speech parameter vector sequence O by maximizing P(O¦Q,λ) given the model parameter λ and the state sequence Q, under a dynamic acoustic feature constraint. We conducted recognition experiments to illustrate the validity of the method. Approximately 100 speakers were used to train the speaker dependent models for the speech synthesis used in these experiments, and the synthetic speech was generated as the test patterns for the target speech recognizer. As a result, the recognition rate of the HMM-based synthesized speech shows a good correlation with the recognition rate of the actual speech. Furthermore, we find that our method can predict the speaker recognition rate with approximately 2% error on average. Therefore the evaluation of the speaker recognition rate will be performed automatically by using the proposed method.

  20. Meat and Fish Freshness Inspection System Based on Odor Sensing

    PubMed Central

    Hasan, Najam ul; Ejaz, Naveed; Ejaz, Waleed; Kim, Hyung Seok

    2012-01-01

    We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy. PMID:23202222

  1. Fast Sampling-Based Whole-Genome Haplotype Block Recognition.

    PubMed

    Taliun, Daniel; Gamper, Johann; Leser, Ulf; Pattaro, Cristian

    2016-01-01

    Scaling linkage disequilibrium (LD) based haplotype block recognition to the entire human genome has always been a challenge. The best-known algorithm has quadratic runtime complexity and, even when sophisticated search space pruning is applied, still requires several days of computations. Here, we propose a novel sampling-based algorithm, called S-MIG (++), where the main idea is to estimate the area that most likely contains all haplotype blocks by sampling a very small number of SNP pairs. A subsequent refinement step computes the exact blocks by considering only the SNP pairs within the estimated area. This approach significantly reduces the number of computed LD statistics, making the recognition of haplotype blocks very fast. We theoretically and empirically prove that the area containing all haplotype blocks can be estimated with a very high degree of certainty. Through experiments on the 243,080 SNPs on chromosome 20 from the 1,000 Genomes Project, we compared our previous algorithm MIG (++) with the new S-MIG (++) and observed a runtime reduction from 2.8 weeks to 34.8 hours. In a parallelized version of the S-MIG (++) algorithm using 32 parallel processes, the runtime was further reduced to 5.1 hours. PMID:27045830

  2. Polygon cluster pattern recognition based on new visual distance

    NASA Astrophysics Data System (ADS)

    Shuai, Yun; Shuai, Haiyan; Ni, Lin

    2007-06-01

    The pattern recognition of polygon clusters is a most attention-getting problem in spatial data mining. The paper carries through a research on this problem, based on spatial cognition principle and visual recognition Gestalt principle combining with spatial clustering method, and creates two innovations: First, the paper carries through a great improvement to the concept---"visual distance". In the definition of this concept, not only are Euclid's Distance, orientation difference and dimension discrepancy comprehensively thought out, but also is "similarity degree of object shape" crucially considered. In the calculation of "visual distance", the distance calculation model is built using Delaunay Triangulation geometrical structure. Second, the research adopts spatial clustering analysis based on MST Tree. In the design of pruning algorithm, the study initiates data automatism delamination mechanism and introduces Simulated Annealing Optimization Algorithm. This study provides a new research thread for GIS development, namely, GIS is an intersection principle, whose research method should be open and diverse. Any mature technology of other relative principles can be introduced into the study of GIS, but, they need to be improved on technical measures according to the principles of GIS as "spatial cognition science". Only to do this, can GIS develop forward on a higher and stronger plane.

  3. Building recognition based on big template in FLIR images

    NASA Astrophysics Data System (ADS)

    Zhang, Jiangwei; Niu, Zhaodong; Liu, Songlin; Liu, Fang; Chen, Zengping

    2014-10-01

    In order to enhance the robustness of building recognition in forward-looking infrared (FLIR) images, an effective method based on big template is proposed. Big template is a set of small templates which contains a great amount of information of surface features. Its information content cannot be matched by any small template and it has advantages in conquering noise interference or incompleteness and avoiding erroneous judgments. Firstly, digital surface model (DSM) was utilized to make big template, distance transformation was operated on the big template, and region of interest (ROI) was extracted by the way of template matching between the big template and contour of real-time image. Secondly, corners were detected from the big template, response function was defined by utilizing gradients and phases of corners and their neighborhoods, a kind of similarity measure was designed based on the response function and overlap ratio, then the template and real-time image were matched accurately. Finally, a large number of image data was used to test the performance of the algorithm, and optimal parameters selection criterion was designed. Test results indicate that the target matching ratio of the algorithm can reach 95%, it has effectively solved the problem of building recognition under the conditions of noise disturbance, incompleteness or the target is not in view.

  4. Auditory analysis for speech recognition based on physiological models

    NASA Astrophysics Data System (ADS)

    Jeon, Woojay; Juang, Biing-Hwang

    2001-05-01

    To address the limitations of traditional cepstrum or LPC based front-end processing methods for automatic speech recognition, more elaborate methods based on physiological models of the human auditory system may be used to achieve more robust speech recognition in adverse environments. For this purpose, a modified version of a model of the primary auditory cortex featuring a three dimensional mapping of auditory spectra [Wang and Shamma, IEEE Trans. Speech Audio Process. 3, 382-395 (1995)] is adopted and investigated for its use as an improved front-end processing method. The study is conducted in two ways: first, by relating the model's redundant representation to traditional spectral representations and showing that the former not only encompasses information provided by the latter, but also reveals more relevant information that makes it superior in describing the identifying features of speech signals; and second, by observing the statistical features of the representation for various classes of sound to show how different identifying features manifest themselves as specific patterns on the cortical map, thereby becoming a place-coded data set on which detection theory could be applied to simulate auditory perception and cognition.

  5. Wavelet-based moment invariants for pattern recognition

    NASA Astrophysics Data System (ADS)

    Chen, Guangyi; Xie, Wenfang

    2011-07-01

    Moment invariants have received a lot of attention as features for identification and inspection of two-dimensional shapes. In this paper, two sets of novel moments are proposed by using the auto-correlation of wavelet functions and the dual-tree complex wavelet functions. It is well known that the wavelet transform lacks the property of shift invariance. A little shift in the input signal will cause very different output wavelet coefficients. The autocorrelation of wavelet functions and the dual-tree complex wavelet functions, on the other hand, are shift-invariant, which is very important in pattern recognition. Rotation invariance is the major concern in this paper, while translation invariance and scale invariance can be achieved by standard normalization techniques. The Gaussian white noise is added to the noise-free images and the noise levels vary with different signal-to-noise ratios. Experimental results conducted in this paper show that the proposed wavelet-based moments outperform Zernike's moments and the Fourier-wavelet descriptor for pattern recognition under different rotation angles and different noise levels. It can be seen that the proposed wavelet-based moments can do an excellent job even when the noise levels are very high.

  6. Spatial distorted target recognition based on improved MACH filter

    NASA Astrophysics Data System (ADS)

    Chen, Yu; Huo, Furong; Zheng, Liqin

    2014-11-01

    Joint transform correlator (JTC) can make targets recognized and located accurately, but the bottleneck technique of JTC is how to recognize spatial distorted targets in cluttered scene. This has restricted the development of the pattern recognition with JTC to a great extent. In order to solve the problem, improved maximum average correlation height (MACH) filter algorithm is presented in this paper. The MACH algorithm has powerful capability of recognition for spatial distorted targets (rotation and scale changed etc.). The controlling parameters of the synthesized filter are optimized in this paper, which makes the filter have higher distortion tolerance and can suppress cluttered noise effectively. When improved MACH filter algorithm in frequency domain is projected to space domain, the MACH reference template image can be obtained which includes various forms of distorted target image. Based on amounts of computer simulation and optical experiments, MACH reference template is proved to have the capability of sharpening the correlation peaks and expanding recognizing scope for distorted targets in cluttered scene. MATLAB software is applied to produce MACH reference image for the detected target images and conduct simulation experiments for its powerful calculation capability of matrix. In order to prove the feasibility of MACH reference in JTC and determine the recognition scope, experiments for an aircraft target in the sky are carried out. After the original image is processed by edge extraction, a MACH filter reference template is obtained in space domain from improved MACH filter in frequency domain. From simulation experiments, the improved MACH filter is proved to have the feasibility of sharpening correlation peaks for distorted targets. Optical experiments are given to verify the effectiveness further. The experiments show the angular distortion tolerance can reach up to +/-15 degrees and scale distortion tolerance can reach up to +/-23%. Within this

  7. Rapid classification of hairtail fish and pork freshness using an electronic nose based on the PCA method.

    PubMed

    Tian, Xiu-Ying; Cai, Qiang; Zhang, Yong-Ming

    2012-01-01

    We report a method for building a simple and reproducible electronic nose based on commercially available metal oxide sensors (MOS) to monitor the freshness of hairtail fish and pork stored at 15, 10, and 5 °C. After assembly in the laboratory, the proposed product was tested by a manufacturer. Sample delivery was based on the dynamic headspace method, and two features were extracted from the transient response of each sensor using an unsupervised principal component analysis (PCA) method. The compensation method and pattern recognition based on PCA are discussed in the current paper. PCA compensation can be used for all storage temperatures, however, pattern recognition differs according to storage conditions. Total volatile basic nitrogen (TVBN) and aerobic bacterial counts of the samples were measured simultaneously with the standard indicators of hairtail fish and pork freshness. The PCA models based on TVBN and aerobic bacterial counts were used to classify hairtail fish samples as "fresh" (TVBN ≤ 25 g and microbial counts ≤ 10(6) cfu/g) or "spoiled" (TVBN ≥ 25 g and microbial counts ≥ 10(6) cfu/g) and pork samples also as "fresh" (TVBN ≤ 15 g and microbial counts ≤ 10(6) cfu/g) or "spoiled" (TVBN ≥ 15 g and microbial counts ≥ 10(6) cfu/g). Good correlation coefficients between the responses of the electronic nose and the TVBN and aerobic bacterial counts of the samples were obtained. For hairtail fish, correlation coefficients were 0.97 and 0.91, and for pork, correlation coefficients were 0.81 and 0.88, respectively. Through laboratory simulation and field application, we were able to determine that the electronic nose could help ensure the shelf life of hairtail fish and pork, especially when an instrument is needed to take measurements rapidly. The results also showed that the electronic nose could analyze the process and level of spoilage for hairtail fish and pork. PMID:22368468

  8. A species of reef fish that uses ultraviolet patterns for covert face recognition.

    PubMed

    Siebeck, Ulrike E; Parker, Amira N; Sprenger, Dennis; Mäthger, Lydia M; Wallis, Guy

    2010-03-01

    The evolutionary and behavioral significance of an animal's color patterns remains poorly understood [1-4], not least, patterns that reflect ultraviolet (UV) light [5]. The current belief is that UV signals must be broad and bold to be detected because (1) they are prone to scattering in air and water, (2) when present, UV-sensitive cones are generally found in low numbers, and (3) long-wavelength-sensitive cones predominate in form vision in those species tested to date [6]. We report a study of two species of damselfish whose appearance differs only in the fine detail of UV-reflective facial patterns. We show that, contrary to expectations, the Ambon damselfish (Pomacentrus amboinensis) is able to use these patterns for species discrimination. We also reveal that the essential features of the patterns are contained in their shape rather than color. The results provide support for the hypothesis that UV is used by some fish as a high-fidelity "secret communication channel" hidden from predators [7, 8]. In more general terms, the findings help unravel the details of a language of color and pattern long since lost to our primate forebears, but which has been part of the world of many seeing organisms for millions of years. PMID:20188557

  9. Semi-supervised kernel learning based optical image recognition

    NASA Astrophysics Data System (ADS)

    Li, Jun-Bao; Yang, Zhi-Ming; Yu, Yang; Sun, Zhen

    2012-08-01

    This paper is to propose semi-supervised kernel learning based optical image recognition, called Semi-supervised Graph-based Global and Local Preserving Projection (SGGLPP) through integrating graph construction with the specific DR process into one unified framework. SGGLPP preserves not only the positive and negative constraints but also the local and global structure of the data in the low dimensional space. In SGGLPP, the intrinsic and cost graphs are constructed using the positive and negative constraints from side-information and k nearest neighbor criterion from unlabeled samples. Moreover, kernel trick is applied to extend SGGLPP called KSGGLPP by on the performance of nonlinear feature extraction. Experiments are implemented on UCI database and two real image databases to testify the feasibility and performance of the proposed algorithm.

  10. Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information

    PubMed Central

    2014-01-01

    Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature. PMID:25521502

  11. Automatic recognition of landslides based on change detection

    NASA Astrophysics Data System (ADS)

    Li, Song; Hua, Houqiang

    2009-07-01

    After Wenchuan earthquake disaster, landslide disaster becomes a common concern, and remote sensing becomes more and more important in the application of landslide monitoring. Now, the method of interpretation and recognition for landslides using remote sensing is visual interpretation mostly. Automatic recognition of landslide is a new and difficult but significative job. For the purpose of seeking a more effective method to recognize landslide automatically, this project analyzes the current methods for the recognition of landslide disasters, and their applicability to the practice of landslide monitoring. Landslide is a phenomenon and disaster triggered by natural and artificial reasons that a part of slope comprised of rock, soil and other fragmental materials slide alone a certain weak structural surface under the gravitation. Consequently, according to the geo-science principle of landslide, there is an obvious change in the sliding region between the pre-landslide and post-landslide, and it can be described in remote sensing imagery, so we develop the new approach to identify landslides, which uses change detection based on texture analysis in multi-temporal imageries. Preprocessing the remote sensing data including the following aspects of image enhancement and filtering, smoothing and cutting, image mosaics, registration and merge, geometric correction and radiation calibration, this paper does change detection base on texture characteristics in multi-temporal images to recognize landslide automatically. After change detection of multi-temporal remote sensing images based on texture analysis, if there is no change in remote sensing image, the image detected is relatively homogeneous, the image detected shows some clustering characteristics; if there is part change in image, the image detected will show two or more clustering centers; if there is complete change in remote sensing image, the image detected will show disorderly and unsystematic. At last, this

  12. Beacon recognition and tracking based on omni-vision

    NASA Astrophysics Data System (ADS)

    Zhang, Baofeng; Liu, Yuli; Cao, Zuoliang

    2008-12-01

    Omnidirectional vision (Omni-vision) has the feature that an extremely wide view can be achieved simultaneously. The omni-image brings a highly unavoidable inherent distortion while it provides hemispherical field of views. In this paper, a method called Spherical Perspective Projection is used for correction of such distorted image. Omni-vision target recognition and tracking with fisheye lens for AGVs appears definite significant since its advantage of acquiring all vision information of the three-dimensional space once. A novel Beacon Model and Omni-vision tracker for mobile robots is described. At present, the research of target model has many different problems, such as outdoor illumination, target veiling, target losing. Specially, outdoor illumination and beacon veiling are the key problems which need an effective method to solve. The new beacon model which features particular topology shape can be recognized in the outdoors with part veiled of the object. In this paper an improved omni-vision object tracking method based on mean shift algorithm is proposed. The mean shift algorithm which is a powerful technique for tracking objects in image sequences with complex background has been proved to be successful for the fast computation and effective tracking problems. The recognition and tracking functions have been demonstrated on experimental platform.

  13. Fast recognition of musical sounds based on timbre.

    PubMed

    Agus, Trevor R; Suied, Clara; Thorpe, Simon J; Pressnitzer, Daniel

    2012-05-01

    Human listeners seem to have an impressive ability to recognize a wide variety of natural sounds. However, there is surprisingly little quantitative evidence to characterize this fundamental ability. Here the speed and accuracy of musical-sound recognition were measured psychophysically with a rich but acoustically balanced stimulus set. The set comprised recordings of notes from musical instruments and sung vowels. In a first experiment, reaction times were collected for three target categories: voice, percussion, and strings. In a go/no-go task, listeners reacted as quickly as possible to members of a target category while withholding responses to distractors (a diverse set of musical instruments). Results showed near-perfect accuracy and fast reaction times, particularly for voices. In a second experiment, voices were recognized among strings and vice-versa. Again, reaction times to voices were faster. In a third experiment, auditory chimeras were created to retain only spectral or temporal features of the voice. Chimeras were recognized accurately, but not as quickly as natural voices. Altogether, the data suggest rapid and accurate neural mechanisms for musical-sound recognition based on selectivity to complex spectro-temporal signatures of sound sources. PMID:22559384

  14. A wavelet-based approach to face verification/recognition

    NASA Astrophysics Data System (ADS)

    Jassim, Sabah; Sellahewa, Harin

    2005-10-01

    Face verification/recognition is a tough challenge in comparison to identification based on other biometrics such as iris, or fingerprints. Yet, due to its unobtrusive nature, the face is naturally suitable for security related applications. Face verification process relies on feature extraction from face images. Current schemes are either geometric-based or template-based. In the latter, the face image is statistically analysed to obtain a set of feature vectors that best describe it. Performance of a face verification system is affected by image variations due to illumination, pose, occlusion, expressions and scale. This paper extends our recent work on face verification for constrained platforms, where the feature vector of a face image is the coefficients in the wavelet transformed LL-subbands at depth 3 or more. It was demonstrated that the wavelet-only feature vector scheme has a comparable performance to sophisticated state-of-the-art when tested on two benchmark databases (ORL, and BANCA). The significance of those results stem from the fact that the size of the k-th LL- subband is 1/4k of the original image size. Here, we investigate the use of wavelet coefficients in various subbands at level 3 or 4 using various wavelet filters. We shall compare the performance of the wavelet-based scheme for different filters at different subbands with a number of state-of-the-art face verification/recognition schemes on two benchmark databases, namely ORL and the control section of BANCA. We shall demonstrate that our schemes have comparable performance to (or outperform) the best performing other schemes.

  15. Discussion Based Fish Bowl Strategy in Learning Psychology

    ERIC Educational Resources Information Center

    Singaravelu, G.

    2007-01-01

    The present study investigates the learning problems in psychology at Master of Education(M.Ed.,) in Bharathiar University and finds the effectiveness of Discussion Based Fish Bowl Strategy in learning psychology. Single group Experimental method was adopted for the study. Both qualitative and quantitative approaches were adopted for this study.…

  16. Fish farming in land-based closed-containment systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    'An International Summit on Fish Farming in Land-Based Closed-Containment Systems' was hosted by the Conservation Fund's Freshwater Institute, the Gordon and Betty Moore Foundation (GBMF), the Atlantic Salmon Federation (ASF), and Tides Canada (TC) at the National Conservation Training Center in She...

  17. An efficient frequency recognition method based on likelihood ratio test for SSVEP-based BCI.

    PubMed

    Zhang, Yangsong; Dong, Li; Zhang, Rui; Yao, Dezhong; Zhang, Yu; Xu, Peng

    2014-01-01

    An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI. PMID:25250058

  18. Infrared face recognition based on binary particle swarm optimization and SVM-wrapper model

    NASA Astrophysics Data System (ADS)

    Xie, Zhihua; Liu, Guodong

    2015-10-01

    Infrared facial imaging, being light- independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. Robust feature selection and representation is a key issue for infrared face recognition research. This paper proposes a novel infrared face recognition method based on local binary pattern (LBP). LBP can improve the robust of infrared face recognition under different environment situations. How to make full use of the discriminant ability in LBP patterns is an important problem. A search algorithm combination binary particle swarm with SVM is used to find out the best discriminative subset in LBP features. Experimental results show that the proposed method outperforms traditional LBP based infrared face recognition methods. It can significantly improve the recognition performance of infrared face recognition.

  19. Liver recognition based on statistical shape model in CT images

    NASA Astrophysics Data System (ADS)

    Xiang, Dehui; Jiang, Xueqing; Shi, Fei; Zhu, Weifang; Chen, Xinjian

    2016-03-01

    In this paper, an automatic method is proposed to recognize the liver on clinical 3D CT images. The proposed method effectively use statistical shape model of the liver. Our approach consist of three main parts: (1) model training, in which shape variability is detected using principal component analysis from the manual annotation; (2) model localization, in which a fast Euclidean distance transformation based method is able to localize the liver in CT images; (3) liver recognition, the initial mesh is locally and iteratively adapted to the liver boundary, which is constrained with the trained shape model. We validate our algorithm on a dataset which consists of 20 3D CT images obtained from different patients. The average ARVD was 8.99%, the average ASSD was 2.69mm, the average RMSD was 4.92mm, the average MSD was 28.841mm, and the average MSD was 13.31%.

  20. Business model for sensor-based fall recognition systems.

    PubMed

    Fachinger, Uwe; Schöpke, Birte

    2014-01-01

    AAL systems require, in addition to sophisticated and reliable technology, adequate business models for their launch and sustainable establishment. This paper presents the basic features of alternative business models for a sensor-based fall recognition system which was developed within the context of the "Lower Saxony Research Network Design of Environments for Ageing" (GAL). The models were developed parallel to the R&D process with successive adaptation and concretization. An overview of the basic features (i.e. nine partial models) of the business model is given and the mutual exclusive alternatives for each partial model are presented. The partial models are interconnected and the combinations of compatible alternatives lead to consistent alternative business models. However, in the current state, only initial concepts of alternative business models can be deduced. The next step will be to gather additional information to work out more detailed models. PMID:25148563

  1. Skeleton-based human action recognition using multiple sequence alignment

    NASA Astrophysics Data System (ADS)

    Ding, Wenwen; Liu, Kai; Cheng, Fei; Zhang, Jin; Li, YunSong

    2015-05-01

    Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.

  2. Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

    NASA Astrophysics Data System (ADS)

    Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

    2010-12-01

    This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.

  3. Affinity sensor based on immobilized molecular imprinted synthetic recognition elements.

    PubMed

    Lenain, Pieterjan; De Saeger, Sarah; Mattiasson, Bo; Hedström, Martin

    2015-07-15

    An affinity sensor based on capacitive transduction was developed to detect a model compound, metergoline, in a continuous flow system. This system simulates the monitoring of low-molecular weight organic compounds in natural flowing waters, i.e. rivers and streams. During operation in such scenarios, control of the experimental parameters is not possible, which poses a true analytical challenge. A two-step approach was used to produce a sensor for metergoline. Submicron spherical molecularly imprinted polymers, used as recognition elements, were obtained through emulsion polymerization and subsequently coupled to the sensor surface by electropolymerization. This way, a robust and reusable sensor was obtained that regenerated spontaneously under the natural conditions in a river. Small organic compounds could be analyzed in water without manipulating the binding or regeneration conditions, thereby offering a viable tool for on-site application. PMID:25703726

  4. A Primitive-Based 3D Object Recognition System

    NASA Astrophysics Data System (ADS)

    Dhawan, Atam P.

    1988-08-01

    A knowledge-based 3D object recognition system has been developed. The system uses the hierarchical structural, geometrical and relational knowledge in matching the 3D object models to the image data through pre-defined primitives. The primitives, we have selected, to begin with, are 3D boxes, cylinders, and spheres. These primitives as viewed from different angles covering complete 3D rotation range are stored in a "Primitive-Viewing Knowledge-Base" in form of hierarchical structural and relational graphs. The knowledge-based system then hypothesizes about the viewing angle and decomposes the segmented image data into valid primitives. A rough 3D structural and relational description is made on the basis of recognized 3D primitives. This description is now used in the detailed high-level frame-based structural and relational matching. The system has several expert and knowledge-based systems working in both stand-alone and cooperative modes to provide multi-level processing. This multi-level processing utilizes both bottom-up (data-driven) and top-down (model-driven) approaches in order to acquire sufficient knowledge to accept or reject any hypothesis for matching or recognizing the objects in the given image.

  5. Individual-based modeling of fish: Linking to physical models and water quality.

    SciTech Connect

    Rose, K.A.

    1997-08-01

    The individual-based modeling approach for the simulating fish population and community dynamics is gaining popularity. Individual-based modeling has been used in many other fields, such as forest succession and astronomy. The popularity of the individual-based approach is partly a result of the lack of success of the more aggregate modeling approaches traditionally used for simulating fish population and community dynamics. Also, recent recognition that it is often the atypical individual that survives has fostered interest in the individual-based approach. Two general types of individual-based models are distribution and configuration. Distribution models follow the probability distributions of individual characteristics, such as length and age. Configuration models explicitly simulate each individual; the sum over individuals being the population. DeAngelis et al (1992) showed that, when distribution and configuration models were formulated from the same common pool of information, both approaches generated similar predictions. The distribution approach was more compact and general, while the configuration approach was more flexible. Simple biological changes, such as making growth rate dependent on previous days growth rates, were easy to implement in the configuration version but prevented simple analytical solution of the distribution version.

  6. Habitat-based polymorphism is common in stream fishes.

    PubMed

    Senay, Caroline; Boisclair, Daniel; Peres-Neto, Pedro R

    2015-01-01

    Morphological differences (size and shape) across habitats are common in lake fish where differences relate to two dominant contrasting habitats: the pelagic and littoral habitat. Repeated occurrence of littoral and pelagic morphs across multiple populations of several lake fish species has been considered as important evidence that polymorphism is adaptive in these systems. It has been suggested that these habitat-based polymorphic differences are due to the temporal stability of the differences between littoral and pelagic habitats. Although streams are spatially heterogeneous, they are also more temporally dynamic than lakes and it is still an open question whether streams provide the environmental conditions that promote habitat-based polymorphism. We tested whether fish from riffle, run and pool habitats, respectively, differed consistently in their morphology. Our test compared patterns of morphological variation (size and shape) in 10 fish species from the three stream habitat types in 36 separate streams distributed across three watersheds. For most species, body size and shape (after controlling for body size) differed across riffle, run and pool habitats. Unlike many lake species, the nature of these differences was not consistent across species, possibly because these species use these habitat types in different ways. Our results suggest that habitat-based polymorphism is an important feature also in stream fishes despite the fact that streams are temporally variable in contrast to lake systems. Future research is required to assess whether the patterns of habitat-based polymorphism encountered in streams have a genetic basis or they are simply the result of within generation phenotypic plasticity. PMID:25041645

  7. Driving profile modeling and recognition based on soft computing approach.

    PubMed

    Wahab, Abdul; Quek, Chai; Tan, Chin Keong; Takeda, Kazuya

    2009-04-01

    Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers. PMID:19258199

  8. All-optical multibit address recognition at 20 Gb/s based on TOAD

    NASA Astrophysics Data System (ADS)

    Yan, Yumei; Wu, Jian; Lin, Jintong

    2005-04-01

    All-optical multibit address recognition at 20 Gb/s is demonstrated based on a special AND logic of terahertz optical asymmetric demultiplexer (TOAD). The semiconductor optical amplifier (SOA) used in the TOAD is biased at transparency status to accelerate the gain recovery. This is the highest bit rate that multibit address recognition is demonstrated with SOA-based interferometer. The experimental results show low pattern dependency. With this method, address recognition can be performed without separating address and payload beforehand.

  9. Gender-Based Prototype Formation in Face Recognition

    ERIC Educational Resources Information Center

    Baudouin, Jean-Yves; Brochard, Renaud

    2011-01-01

    The role of gender categories in prototype formation during face recognition was investigated in 2 experiments. The participants were asked to learn individual faces and then to recognize them. During recognition, individual faces were mixed with faces, which were blended faces of same or different genders. The results of the 2 experiments showed…

  10. Feature based sliding window technique for face recognition

    NASA Astrophysics Data System (ADS)

    Javed, Muhammad Younus; Mohsin, Syed Maajid; Anjum, Muhammad Almas

    2010-02-01

    Human beings are commonly identified by biometric schemes which are concerned with identifying individuals by their unique physical characteristics. The use of passwords and personal identification numbers for detecting humans are being used for years now. Disadvantages of these schemes are that someone else may use them or can easily be forgotten. Keeping in view of these problems, biometrics approaches such as face recognition, fingerprint, iris/retina and voice recognition have been developed which provide a far better solution when identifying individuals. A number of methods have been developed for face recognition. This paper illustrates employment of Gabor filters for extracting facial features by constructing a sliding window frame. Classification is done by assigning class label to the unknown image that has maximum features similar to the image stored in the database of that class. The proposed system gives a recognition rate of 96% which is better than many of the similar techniques being used for face recognition.

  11. Cross-validation of δ15N and FishBase estimates of fish trophic position in a Mediterranean lagoon: The importance of the isotopic baseline

    NASA Astrophysics Data System (ADS)

    Mancinelli, Giorgio; Vizzini, Salvatrice; Mazzola, Antonio; Maci, Stefano; Basset, Alberto

    2013-12-01

    FishBase, a relational database freely available on the Internet, is to date widely used as a source of quantitative information on the trophic position of marine fish species. Here, we compared FishBase estimates for an assemblage of 30 fish species sampled in a Mediterranean lagoon (Acquatina lagoon, SE Italy) with their trophic positions calculated using nitrogen stable isotopes.

  12. Finger vein recognition based on the hyperinformation feature

    NASA Astrophysics Data System (ADS)

    Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Yang, Lu

    2014-01-01

    The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.

  13. 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).

  14. A sensor and video based ontology for activity recognition in smart environments.

    PubMed

    Mitchell, D; Morrow, Philip J; Nugent, Chris D

    2014-01-01

    Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data. PMID:25571347

  15. The Relative Success of Recognition-Based Inference in Multichoice Decisions

    ERIC Educational Resources Information Center

    McCloy, Rachel; Beaman, C. Philip; Smith, Philip T.

    2008-01-01

    The utility of an "ecologically rational" recognition-based decision rule in multichoice decision problems is analyzed, varying the type of judgment required (greater or lesser). The maximum size and range of a counterintuitive advantage associated with recognition-based judgment (the "less-is-more effect") is identified for a range of cue…

  16. Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

    PubMed Central

    Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

    2015-01-01

    Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. PMID:25608217

  17. Development of the hidden Markov models based Lithuanian speech recognition system

    NASA Astrophysics Data System (ADS)

    Ringeliene, Z.; Lipeika, A.

    2010-09-01

    The paper presents a prototype of the speaker-independent Lithuanian isolated word recognition system. The system is based on the hidden Markov models, a powerful statistical method for modeling speech signals. The prototype system can be used for Lithuanian words recognition investigations and is a good starting point for the development of a more sophisticated recognition system. The system graphical user interface is easy to control. Visualization of the entire recognition process is useful for analyzing of the recognition results. Based on this recognizer, a system for Web browser control by voice was developed. The program, which implements control by voice commands, was integrated in the speech recognition system. The system performance was evaluated by using different sets of acoustic models and vocabularies.

  18. Continuous speech recognition based on convolutional neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Qing-qing; Liu, Yong; Pan, Jie-lin; Yan, Yong-hong

    2015-07-01

    Convolutional Neural Networks (CNNs), which showed success in achieving translation invariance for many image processing tasks, are investigated for continuous speech recognitions in the paper. Compared to Deep Neural Networks (DNNs), which have been proven to be successful in many speech recognition tasks nowadays, CNNs can reduce the NN model sizes significantly, and at the same time achieve even better recognition accuracies. Experiments on standard speech corpus TIMIT showed that CNNs outperformed DNNs in the term of the accuracy when CNNs had even smaller model size.

  19. Induction of Fish Biomarkers by Synthetic-Based Drilling Muds

    PubMed Central

    Gagnon, Marthe Monique; Bakhtyar, Sajida

    2013-01-01

    The study investigated the effects of chronic exposure of pink snapper (Pagrus auratus Forster), to synthetic based drilling muds (SBMs). Fish were exposed to three mud systems comprised of three different types of synthetic based fluids (SBFs): an ester (E), an isomerized olefin (IO) and linear alpha olefin (LAO). Condition factor (CF), liver somatic index (LSI), hepatic detoxification (EROD activity), biliary metabolites, DNA damage and stress proteins (HSP-70) were determined. Exposure to E caused biologically significant effects by increasing CF and LSI, and triggered biliary metabolite accumulation. While ester-based SBFs have a rapid biodegradation rate in the environment, they caused the most pronounced effects on fish health. IO induced EROD activity and biliary metabolites and LAO induced EROD activity and stress protein levels. The results demonstrate that while acute toxicity of SBMs is generally low, chronic exposure to weathering cutting piles has the potential to affect fish health. The study illustrates the advantages of the Western Australian government case-by-case approach to drilling fluid management, and highlights the importance of considering the receiving environment in the selection of SBMs. PMID:23894492

  20. Sunspot drawings handwritten character recognition method based on deep learning

    NASA Astrophysics Data System (ADS)

    Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

    2016-05-01

    High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

  1. Recognition of chemical entities: combining dictionary-based and grammar-based approaches

    PubMed Central

    2015-01-01

    Background The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. Results The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. Conclusions We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named

  2. Zernike moments features for shape-based gait recognition

    NASA Astrophysics Data System (ADS)

    Qin, Huanfeng; Qin, Lan; Liu, Jun; Chao, Jiang

    2011-12-01

    The paper proposes a new spatio-temporal gait representation, called cycles gait Zernike moments (CGZM), to characterize human walking properties for individual recognition. Firstly, Zernike moments as shape descriptors are used to characterize gait silhouette shape. Secondly, we generate CGZM from Zernike moments of silhouette sequences. Finally, the phase and magnitude coefficientsof CGZM are utilized to perform classification by the modified Hausdorff distance (MHD) classifier. Experimental results show that the proposed approach have an encouraging recognition performance.

  3. Text vectorization based on character recognition and character stroke modeling

    NASA Astrophysics Data System (ADS)

    Fan, Zhigang; Zhou, Bingfeng; Tse, Francis; Mu, Yadong; He, Tao

    2014-03-01

    In this paper, a text vectorization method is proposed using OCR (Optical Character Recognition) and character stroke modeling. This is based on the observation that for a particular character, its font glyphs may have different shapes, but often share same stroke structures. Like many other methods, the proposed algorithm contains two procedures, dominant point determination and data fitting. The first one partitions the outlines into segments and second one fits a curve to each segment. In the proposed method, the dominant points are classified as "major" (specifying stroke structures) and "minor" (specifying serif shapes). A set of rules (parameters) are determined offline specifying for each character the number of major and minor dominant points and for each dominant point the detection and fitting parameters (projection directions, boundary conditions and smoothness). For minor points, multiple sets of parameters could be used for different fonts. During operation, OCR is performed and the parameters associated with the recognized character are selected. Both major and minor dominant points are detected as a maximization process as specified by the parameter set. For minor points, an additional step could be performed to test the competing hypothesis and detect degenerated cases.

  4. Biomolecular recognition and detection using gold-based nanoprobes

    NASA Astrophysics Data System (ADS)

    Crew, Elizabeth

    The ability to control the biomolecular interactions is important for developing bioanalytical probes used in biomolecule and biomarker detections. This work aims at a fundamental understanding of the interactions and reactivities involving DNA, miRNA, and amino acids using gold-based nanoparticles as nanoprobes, which has implications for developing new strategies for the early detection of diseases, such as cancer, and controlled delivery of drugs. Surface modifications of the nanoprobes with DNA, miRNA, and amino acids and the nanoprobe directed biomolecular reactivities, such as complementary-strand binding, enzymatic cutting and amino acid interactions, have been investigated. Among various analytical techniques employed for the analysis of the biomolecule-nanoprobe interactions, surface enhanced Raman scattering spectroscopy (SERS) has been demonstrated to provide a powerful tool for real time monitoring of the DNA assembly and enzymatic cutting processes in solutions. This demonstration harnesses the "hot-spot" characteristic tuned by the interparticle biomolecular-regulated interactions and distances. The assembly of gold nanoparticles has also been exploited as sensing thin films on chemiresistor arrays for the detection of volatile organic compounds, including biomarker molecules associated with diabetes. Important findings of the nanoprobes in delivering miRNA to cells, detecting DNA hybridization kinetics, discerning chiral recognition with enantiomeric cysteines, and sensing biomarker molecules with the nanostructured thin films will be discussed, along with their implications to enhancing sensitivity, selectivity and limits of detection.

  5. Cooperative Search and Rescue with Artificial Fishes Based on Fish-Swarm Algorithm for Underwater Wireless Sensor Networks

    PubMed Central

    Zhao, Wei; Tang, Zhenmin; Yang, Yuwang; Wang, Lei; Lan, Shaohua

    2014-01-01

    This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes' moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties. PMID:24741341

  6. Cooperative search and rescue with artificial fishes based on fish-swarm algorithm for underwater wireless sensor networks.

    PubMed

    Zhao, Wei; Tang, Zhenmin; Yang, Yuwang; Wang, Lei; Lan, Shaohua

    2014-01-01

    This paper presents a searching control approach for cooperating mobile sensor networks. We use a density function to represent the frequency of distress signals issued by victims. The mobile nodes' moving in mission space is similar to the behaviors of fish-swarm in water. So, we take the mobile node as artificial fish node and define its operations by a probabilistic model over a limited range. A fish-swarm based algorithm is designed requiring local information at each fish node and maximizing the joint detection probabilities of distress signals. Optimization of formation is also considered for the searching control approach and is optimized by fish-swarm algorithm. Simulation results include two schemes: preset route and random walks, and it is showed that the control scheme has adaptive and effective properties. PMID:24741341

  7. Ambient temperature normalization for infrared face recognition based on the second-order polynomial model

    NASA Astrophysics Data System (ADS)

    Wang, Zhengzi

    2015-08-01

    The influence of ambient temperature is a big challenge to robust infrared face recognition. This paper proposes a new ambient temperature normalization algorithm to improve the performance of infrared face recognition under variable ambient temperatures. Based on statistical regression theory, a second order polynomial model is learned to describe the ambient temperature's impact on infrared face image. Then, infrared image was normalized to reference ambient temperature by the second order polynomial model. Finally, this normalization method is applied to infrared face recognition to verify its efficiency. The experiments demonstrate that the proposed temperature normalization method is feasible and can significantly improve the robustness of infrared face recognition.

  8. 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. PMID:17027376

  9. 38 CFR 52.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Adult Day Health Care in State Homes § 52.20 Application for recognition based on certification. To apply for recognition and certification of a State home for adult day health care, a...

  10. 38 CFR 52.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Adult Day Health Care in State Homes § 52.20 Application for recognition based on certification. To apply for recognition and certification of a State home for adult day health care, a...

  11. Effects of Bilateral Eye Movements on Gist Based False Recognition in the DRM Paradigm

    ERIC Educational Resources Information Center

    Parker, Andrew; Dagnall, Neil

    2007-01-01

    The effects of saccadic bilateral (horizontal) eye movements on gist based false recognition was investigated. Following exposure to lists of words related to a critical but non-studied word participants were asked to engage in 30s of bilateral vs. vertical vs. no eye movements. Subsequent testing of recognition memory revealed that those who…

  12. Computer-Based Voice Recognition: Characteristics, Applications, and Guidelines for Use.

    ERIC Educational Resources Information Center

    Milheim, William D.

    1993-01-01

    Describes computer-based voice recognition technology, including disadvantages; identifies vocabulary, training requirements, and ability to understand continuous speech as the basic characteristics of voice-recognition systems; describes applications in education and industry; suggests guidelines for design and implementation; and discusses…

  13. 38 CFR 51.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.20 Application for recognition based on certification. To apply for recognition and certification of a State home for nursing home care, a State must: (a) Send...

  14. 38 CFR 51.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.20 Application for recognition based on certification. To apply for recognition and certification of a State home for nursing home care, a State must: (a) Send...

  15. 38 CFR 51.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.20 Application for recognition based on certification. To apply for recognition and certification of a State home for nursing home care, a State must: (a) Send...

  16. 38 CFR 51.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.20 Application for recognition based on certification. To apply for recognition and certification of a State home for nursing home care, a State must: (a) Send...

  17. 38 CFR 51.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.20 Application for recognition based on certification. To apply for recognition and certification of a State home for nursing home care, a State must: (a) Send...

  18. 38 CFR 52.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Adult Day Health Care in State Homes § 52.20 Application for recognition based on certification. To apply for recognition and certification of a State home for adult day health care, a...

  19. Visual recognition based on discriminative and collaborative representation

    NASA Astrophysics Data System (ADS)

    Xiang, Fengtao; Wang, Zhengzhi; Liu, Hongfu

    2014-11-01

    In this paper, a low computation complexity, yet very efficient representation of image for visual recognition tasks is presented. The collaborative representation and discriminative ingredient are combined in a unified framework. The coefficients of collaborative representation of test samples are sparse and robust to occlusion or other disguises. It is known that in the recognition or classification tasks, the discriminative model is also very important. The proposed model has two-fold advantages. It can represent the test sample well using redundant representation with sparsity and robust to disguises. On the other hand, the representation coefficients are generated with more discriminative information. It is very helpful for visual recognition issues. The point is that the l 2 norm can achieve comparable performance to the l 1 norm with simple implementation. Experimental evaluations on some benchmarks indicate that the proposed method could achieve impressive performances in terms of accuracy and efficiency with other existing works.

  20. Episodic Reasoning for Vision-Based Human Action Recognition

    PubMed Central

    Martinez-del-Rincon, Jesus

    2014-01-01

    Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning. PMID:24959602

  1. FPGA-Based Vocabulary Recognition Module for Humanoid Robot

    NASA Astrophysics Data System (ADS)

    Su, Yu-Te; Hu, Chun-Yang; Li, Tzuu-Hseng S.

    This paper focuses on using FPGA board to realize the ability of vocabulary recognition for humanoid robot. At first, the system structure of the humanoid robot system is setup. The central process unit of the humanoid robot is a FPGA board, ALTERA Nios II EP2C20F324C8, which not only controls all the motors of robot but also processes the information of vision system. The vocabulary recognition method is then introduced. We apply the image segment to find the valid region, and use the encoding method to sample the word. After matching algorithm, we use a speech module, Emic TTS module, to pronounce the word. Finally the experiments verify the procedure of the proposed module and demonstrate the feasibility of the vocabulary recognition and speak out function for the humanoid robot.

  2. Arousal recognition system based on heartbeat dynamics during auditory elicitation.

    PubMed

    Nardelli, Mimma; Valenza, Gaetano; Greco, Alberto; Lanata, Antonio; Scilingo, Enzo Pasquale

    2015-08-01

    This study reports on the recognition of different arousal levels, elicited by affective sounds, performed using estimates of autonomic nervous system dynamics. Specifically, as a part of the circumplex model of affect, arousal levels were recognized by properly combining information gathered from standard and nonlinear analysis of heartbeat dynamics, which was derived from the electrocardiogram (ECG). Affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal. A group of 27 healthy volunteers underwent such elicitation while ECG signals were continuously recorded. Results showed that a quadratic discriminant classifier, as applied implementing a leave-one-subject-out procedure, achieved a recognition accuracy of 84.26%. Moreover, this study confirms the crucial role of heartbeat nonlinear dynamics for emotion recognition, hereby estimated through lagged Poincare plots. PMID:26737686

  3. A study of speech emotion recognition based on hybrid algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei

    2011-10-01

    To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

  4. Robust recognition of handwritten numerals based on dual cooperative network

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan; Choi, Yeongwoo

    1992-01-01

    An approach to robust recognition of handwritten numerals using two operating parallel networks is presented. The first network uses inputs in Cartesian coordinates, and the second network uses the same inputs transformed into polar coordinates. How the proposed approach realizes the robustness to local and global variations of input numerals by handling inputs both in Cartesian coordinates and in its transformed Polar coordinates is described. The required network structures and its learning scheme are discussed. Experimental results show that by tracking only a small number of distinctive features for each teaching numeral in each coordinate, the proposed system can provide robust recognition of handwritten numerals.

  5. Anion recognition by oligo-(thio)urea-based receptors.

    PubMed

    Jia, Chuandong; Zuo, Wei; Zhang, Dan; Yang, Xiao-Juan; Wu, Biao

    2016-07-26

    Oligo-(thio)ureas have proven to be a promising class of receptors that are widely applied in anion recognition. This article aims to present some recent progress in the construction of oligoureas and their anion coordination (recognition) chemistry. Typical examples of metal-coordination assisted and covalently connected oligo-(thio)urea receptors are summarized, with focus on geometry characteristics required for achieving complementary binding of a target anion. Special emphasis is given to ortho-phenylene-connected oligoureas in the application of anion binding and the self-assembly of important supramolecular architectures, including helicates, tetrahedral cages, and so on. PMID:27352298

  6. Subauditory Speech Recognition based on EMG/EPG Signals

    NASA Technical Reports Server (NTRS)

    Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

    2003-01-01

    Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

  7. Detection and recognition of analytes based on their crystallization patterns

    DOEpatents

    Morozov, Victor; Bailey, Charles L.; Vsevolodov, Nikolai N.; Elliott, Adam

    2008-05-06

    The invention contemplates a method for recognition of proteins and other biological molecules by imaging morphology, size and distribution of crystalline and amorphous dry residues in droplets (further referred to as "crystallization pattern") containing predetermined amount of certain crystal-forming organic compounds (reporters) to which protein to be analyzed is added. It has been shown that changes in the crystallization patterns of a number of amino-acids can be used as a "signature" of a protein added. It was also found that both the character of changer in the crystallization patter and the fact of such changes can be used as recognition elements in analysis of protein molecules.

  8. Noisy Speech Recognition Based on Integration/Selection of Multiple Noise Suppression Methods Using Noise GMMs

    NASA Astrophysics Data System (ADS)

    Kitaoka, Norihide; Hamaguchi, Souta; Nakagawa, Seiichi

    To achieve high recognition performance for a wide variety of noise and for a wide range of signal-to-noise ratio, this paper presents methods for integration of four noise reduction algorithms: spectral subtraction with smoothing of time direction, temporal domain SVD-based speech enhancement, GMM-based speech estimation and KLT-based comb-filtering. In this paper, we proposed two types of combination methods of noise suppression algorithms: selection of front-end processor and combination of results from multiple recognition processes. Recognition results on the CENSREC-1 task showed the effectiveness of our proposed methods.kn-abstract=

  9. Support vector machine-based facial-expression recognition method combining shape and appearance

    NASA Astrophysics Data System (ADS)

    Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

    2010-11-01

    Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

  10. A New Freshwater Biodiversity Indicator Based on Fish Community Assemblages

    PubMed Central

    Clavel, Joanne; Poulet, Nicolas; Porcher, Emmanuelle; Blanchet, Simon; Grenouillet, Gaël; Pavoine, Sandrine; Biton, Anne; Seon-Massin, Nirmala; Argillier, Christine; Daufresne, Martin; Teillac-Deschamps, Pauline; Julliard, Romain

    2013-01-01

    Biodiversity has reached a critical state. In this context, stakeholders need indicators that both provide a synthetic view of the state of biodiversity and can be used as communication tools. Using river fishes as model, we developed community indicators that aim at integrating various components of biodiversity including interactions between species and ultimately the processes influencing ecosystem functions. We developed indices at the species level based on (i) the concept of specialization directly linked to the niche theory and (ii) the concept of originality measuring the overall degree of differences between a species and all other species in the same clade. Five major types of originality indices, based on phylogeny, habitat-linked and diet-linked morphology, life history traits, and ecological niche were analyzed. In a second step, we tested the relationship between all biodiversity indices and land use as a proxy of human pressures. Fish communities showed no significant temporal trend for most of these indices, but both originality indices based on diet- and habitat- linked morphology showed a significant increase through time. From a spatial point of view, all indices clearly singled out Corsica Island as having higher average originality and specialization. Finally, we observed that the originality index based on niche traits might be used as an informative biodiversity indicator because we showed it is sensitive to different land use classes along a landscape artificialization gradient. Moreover, its response remained unchanged over two other land use classifications at the global scale and also at the regional scale. PMID:24278356

  11. Stimulus-based similarity and the recognition of spoken words

    NASA Astrophysics Data System (ADS)

    Auer, Edward T.

    2003-10-01

    Spoken word recognition has been hypothesized to be achieved via a competitive process amongst perceptually similar lexical candidates in the mental lexicon. In this process, lexical candidates are activated as a function of their perceived similarity to the spoken stimulus. The evidence supporting this hypothesis has largely come from studies of auditory word recognition. In this talk, evidence from our studies of visual spoken word recognition will be reviewed. Visual speech provides the opportunity to highlight the importance of stimulus-driven perceptual similarity because it presents a different pattern of segmental similarity than is afforded by auditory speech degraded by noise. Our results are consistent with stimulus-driven activation followed by competition as general spoken word recognition mechanism. In addition, results will be presented from recent investigations of the direct prediction of perceptual similarity from measurements of spoken stimuli. High levels of correlation have been observed between the predicted and perceptually obtained distances for a large set of spoken consonants. These results support the hypothesis that the perceptual structure of English consonants and vowels is predicted by stimulus structure without the need for an intervening level of abstract linguistic representation. [Research supported by NSF IIS 9996088 and NIH DC04856.

  12. Evaluating a county-based Healthy nail Salon Recognition Program

    EPA Science Inventory

    To determine whether nail solons that participate in the SF recognition program have reduced measured levels of toluene, methyl methacrylate (MMA), and total volatile organic compounds (TVOC)as compared to nail salons that do not participate. We also evaluated changes in worker ...

  13. Development of a Fish Based Lake Typology for Natural Austrian Lakes >50 ha Based on the Reconstructed Historical Fish Communities

    NASA Astrophysics Data System (ADS)

    Gassner, Hubert; Wanzenböck, Josef; Zick, Daniela; Tischler, Gerhard; Pamminger-Lahnsteiner, Barbara

    2005-08-01

    Based on the reconstructed native fish communities all natural Austrian lakes >50 ha (n = 43) were classified into four groups using cluster analysis methods. Sentinel species (i.e. species with highest discriminating value for lake types and characteristic for a specific lake group) and type specific fish species (accompanying species with additional value for characterising lake groups) were defined by a newly developed index and by similarity analysis. The first group included 16 lakes of high altitude, small surface area and low fish species number with arctic char as a sentinel species. The second group (n = 10) was characterized by intermediate altitude, large surface area and high maximum water depth with the minnow as sentinel species. The third group contained 14 lakes with low maximum water depths and a long retention time. For this group the bleak was found as a sentinel species. The lakes of the eastern part of Austria represented the last group (n = 3) and were characterized by low altitude and very shallow water depth with pike-perch as a sentinel species.

  14. Comparison of computer-based and optical face recognition paradigms

    NASA Astrophysics Data System (ADS)

    Alorf, Abdulaziz A.

    The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB(c) software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers

  15. Development of a sonar-based object recognition system

    NASA Astrophysics Data System (ADS)

    Ecemis, Mustafa Ihsan

    2001-02-01

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

  16. [Research on Anti-Camouflaged Target System Based on Spectral Detection and Image Recognition].

    PubMed

    Wang, Bo; Gao, Yu-bin; Lu, Xu-tao

    2015-05-01

    To be able to quickly and efficiently identify Enemy camouflaged maneuvering targets in the wild environment, target recognition system was designed based on spectral detection technology and video target recognition method. System was composed of the visible light image acquisition module and static interferometer module. The system used image recognition technology to obtain two dimensional video images of measurement region, and through spectrum detection technology to identify targets. Ultimately, measured target was rebuilt on the corresponding position in the image, so the visual target recognition was realized. After the theoretical derivation, identifiable target function formula of the system was obtained, and based on the functional relationship to complete the quantitative experiments for target recognition. In the experiments, maneuvering target in the battlefield environment was simulated by a car. At different distances, the background was respectively selected to detect a flat wasteland, bushes and abandoned buildings. Obvious target, coated camouflage target and covered disguises target was respectively spectrum detection. Experimental results show that spectrum detection technology can overcome the shortcomings of unrecognized the camouflaged target by traditional image target recognition method. Testing background had some influence on spectrum detection results, and the continuity of the background was conducive to target recognition. Covered disguises target was the hardest to identify in various camouflage mode. As the distance between the target and the system increases, signal to noise ratio of the system was reduced. In summary, the system can achieve effective recognition of camouflaged targets to meet the design requirements. PMID:26415476

  17. Performance evaluation of image-based location recognition approaches based on large-scale UAV imagery

    NASA Astrophysics Data System (ADS)

    Hesse, Nikolas; Bodensteiner, Christoph; Arens, Michael

    2014-10-01

    Recognizing the location where an image was taken, solely based on visual content, is an important problem in computer vision, robotics and remote sensing. This paper evaluates the performance of standard approaches for location recognition when applied to large-scale aerial imagery in both electro-optical (EO) and infrared (IR) domains. We present guidelines towards optimizing the performance and explore how well a standard location recognition system is suited to handle IR data. We show on three datasets that the performance of the system strongly increases if SIFT descriptors computed on Hessian-Affine regions are used instead of SURF features. Applications are widespread and include vision-based navigation, precise object geo-referencing or mapping.

  18. Exploring polypharmacology using a ROCS-based target fishing approach.

    PubMed

    AbdulHameed, Mohamed Diwan M; Chaudhury, Sidhartha; Singh, Narender; Sun, Hongmao; Wallqvist, Anders; Tawa, Gregory J

    2012-02-27

    Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction. PMID:22196353

  19. Optical character recognition of camera-captured images based on phase features

    NASA Astrophysics Data System (ADS)

    Diaz-Escobar, Julia; Kober, Vitaly

    2015-09-01

    Nowadays most of digital information is obtained using mobile devices specially smartphones. In particular, it brings the opportunity for optical character recognition in camera-captured images. For this reason many recognition applications have been recently developed such as recognition of license plates, business cards, receipts and street signal; document classification, augmented reality, language translator and so on. Camera-captured images are usually affected by geometric distortions, nonuniform illumination, shadow, noise, which make difficult the recognition task with existing systems. It is well known that the Fourier phase contains a lot of important information regardless of the Fourier magnitude. So, in this work we propose a phase-based recognition system exploiting phase-congruency features for illumination/scale invariance. The performance of the proposed system is tested in terms of miss classifications and false alarms with the help of computer simulation.

  20. Secondary iris recognition method based on local energy-orientation feature

    NASA Astrophysics Data System (ADS)

    Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing

    2015-01-01

    This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories-a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

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

    PubMed Central

    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. PMID:25942404

  2. 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. PMID:25942404

  3. Design and implementation of face recognition system based on Windows

    NASA Astrophysics Data System (ADS)

    Zhang, Min; Liu, Ting; Li, Ailan

    2015-07-01

    In view of the basic Windows login password input way lacking of safety and convenient operation, we will introduce the biometrics technology, face recognition, into the computer to login system. Not only can it encrypt the computer system, also according to the level to identify administrators at all levels. With the enhancement of the system security, user input can neither be a cumbersome nor worry about being stolen password confidential.

  4. Markov Network-Based Unified Classifier for Face Recognition.

    PubMed

    Hwang, Wonjun; Kim, Junmo

    2015-11-01

    In this paper, we propose a novel unifying framework using a Markov network to learn the relationships among multiple classifiers. In face recognition, we assume that we have several complementary classifiers available, and assign observation nodes to the features of a query image and hidden nodes to those of gallery images. Under the Markov assumption, we connect each hidden node to its corresponding observation node and the hidden nodes of neighboring classifiers. For each observation-hidden node pair, we collect the set of gallery candidates most similar to the observation instance, and capture the relationship between the hidden nodes in terms of a similarity matrix among the retrieved gallery images. Posterior probabilities in the hidden nodes are computed using the belief propagation algorithm, and we use marginal probability as the new similarity value of the classifier. The novelty of our proposed framework lies in the method that considers classifier dependence using the results of each neighboring classifier. We present the extensive evaluation results for two different protocols, known and unknown image variation tests, using four publicly available databases: 1) the Face Recognition Grand Challenge ver. 2.0; 2) XM2VTS; 3) BANCA; and 4) Multi-PIE. The result shows that our framework consistently yields improved recognition rates in various situations. PMID:26219095

  5. A Fast Goal Recognition Technique Based on Interaction Estimates

    NASA Technical Reports Server (NTRS)

    E-Martin, Yolanda; R-Moreno, Maria D.; Smith, David E.

    2015-01-01

    Goal Recognition is the task of inferring an actor's goals given some or all of the actor's observed actions. There is considerable interest in Goal Recognition for use in intelligent personal assistants, smart environments, intelligent tutoring systems, and monitoring user's needs. In much of this work, the actor's observed actions are compared against a generated library of plans. Recent work by Ramirez and Geffner makes use of AI planning to determine how closely a sequence of observed actions matches plans for each possible goal. For each goal, this is done by comparing the cost of a plan for that goal with the cost of a plan for that goal that includes the observed actions. This approach yields useful rankings, but is impractical for real-time goal recognition in large domains because of the computational expense of constructing plans for each possible goal. In this paper, we introduce an approach that propagates cost and interaction information in a plan graph, and uses this information to estimate goal probabilities. We show that this approach is much faster, but still yields high quality results.

  6. FIELD INFORMATION-BASED SYSTEM FOR ESTIMATING FISH TEMPERATURE REQUIREMENTS

    EPA Science Inventory

    In 1979, Biesinger et al. described a technique for spatial and temporal matching of records of stream temperatures and fish sampling events to obtain estimates of yearly temperature regimes for freshwater fishes of the United States. his article describes the state of this Fish ...

  7. Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

    NASA Astrophysics Data System (ADS)

    Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

    2014-09-01

    Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

  8. Study on recognition algorithm for paper currency numbers based on neural network

    NASA Astrophysics Data System (ADS)

    Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

    2008-12-01

    Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

  9. Robust and Effective Component-based Banknote Recognition for the Blind

    PubMed Central

    Hasanuzzaman, Faiz M.; Yang, Xiaodong; Tian, YingLi

    2012-01-01

    We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users. PMID:22661884

  10. Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

    PubMed Central

    Xu, Xin; Tang, Jinshan; Zhang, Xiaolong; Liu, Xiaoming; Zhang, Hong; Qiu, Yimin

    2013-01-01

    With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activities, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation of the performance of human activity recognition. PMID:23353144

  11. Passive Acoustic Recognition of Fishing Vessel Activity in the Shallow Waters of the Arabian Sea: A Statistical Approach

    NASA Astrophysics Data System (ADS)

    Kannan, R.; Sanjana, M. C.; Latha, G.

    2015-09-01

    The ambient noise system consisting of a vertical linear hydrophone array (VLA) with 12 elements was deployed in the waters of the Arabian Sea at a depth of 16m, off Goa, India, for extracting the ambient noise during the fishing season (March, April and May, 2013 before the onset of the south west monsoon). This study focuses on fishing vessel activity by finding the domination of the vessel and wind noise at two 12 hourly periodic cycles that start at midnight and noon, using the statistical analysis. It is performed using statistical parameters, like the mean, median, and standard deviation, skewness, percentile and spread of data. It is observed that the vessel noise dominates during the 12h period that starts at midnight and is an indication of the activity of fishing vessels while the wind generated noise is more during the 12h period that starts at noon, which is a sign of the domination of the sea breeze effects. This is the first time that a statistical analysis has been carried out to study the ambient noise data collected off Goa, in order to find the fishing vessel activity during the pre-monsoon season. The results are verified with the fishing information from the Directorate of Fisheries, Goa, ship traffic data from the Mormugao Port Trust and wind speed measurements.

  12. Gait-based person recognition using arbitrary view transformation model.

    PubMed

    Muramatsu, Daigo; Shiraishi, Akira; Makihara, Yasushi; Uddin, Md Zasim; Yagi, Yasushi

    2015-01-01

    Gait recognition is a useful biometric trait for person authentication because it is usable even with low image resolution. One challenge is robustness to a view change (cross-view matching); view transformation models (VTMs) have been proposed to solve this. The VTMs work well if the target views are the same as their discrete training views. However, the gait traits are observed from an arbitrary view in a real situation. Thus, the target views may not coincide with discrete training views, resulting in recognition accuracy degradation. We propose an arbitrary VTM (AVTM) that accurately matches a pair of gait traits from an arbitrary view. To realize an AVTM, we first construct 3D gait volume sequences of training subjects, disjoint from the test subjects in the target scene. We then generate 2D gait silhouette sequences of the training subjects by projecting the 3D gait volume sequences onto the same views as the target views, and train the AVTM with gait features extracted from the 2D sequences. In addition, we extend our AVTM by incorporating a part-dependent view selection scheme (AVTM_PdVS), which divides the gait feature into several parts, and sets part-dependent destination views for transformation. Because appropriate destination views may differ for different body parts, the part-dependent destination view selection can suppress transformation errors, leading to increased recognition accuracy. Experiments using data sets collected in different settings show that the AVTM improves the accuracy of cross-view matching and that the AVTM_PdVS further improves the accuracy in many cases, in particular, verification scenarios. PMID:25423652

  13. Exploiting vibration-based spectral signatures for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Crider, Lauren; Kangas, Scott

    2014-06-01

    Feature extraction algorithms for vehicle classification techniques represent a large branch of Automatic Target Recognition (ATR) efforts. Traditionally, vehicle ATR techniques have assumed time series vibration data collected from multiple accelerometers are a function of direct path, engine driven signal energy. If data, however, is highly dependent on measurement location these pre-established feature extraction algorithms are ineffective. In this paper, we examine the consequences of analyzing vibration data potentially contingent upon transfer path effects by exploring the sensitivity of sensor location. We summarize our analysis of spectral signatures from each accelerometer and investigate similarities within the data.

  14. Robust Speaker Authentication Based on Combined Speech and Voiceprint Recognition

    NASA Astrophysics Data System (ADS)

    Malcangi, Mario

    2009-08-01

    Personal authentication is becoming increasingly important in many applications that have to protect proprietary data. Passwords and personal identification numbers (PINs) prove not to be robust enough to ensure that unauthorized people do not use them. Biometric authentication technology may offer a secure, convenient, accurate solution but sometimes fails due to its intrinsically fuzzy nature. This research aims to demonstrate that combining two basic speech processing methods, voiceprint identification and speech recognition, can provide a very high degree of robustness, especially if fuzzy decision logic is used.

  15. Single-Sample Face Recognition Based on Intra-Class Differences in a Variation Model

    PubMed Central

    Cai, Jun; Chen, Jing; Liang, Xing

    2015-01-01

    In this paper, a novel random facial variation modeling system for sparse representation face recognition is presented. Although recently Sparse Representation-Based Classification (SRC) has represented a breakthrough in the field of face recognition due to its good performance and robustness, there is the critical problem that SRC needs sufficiently large training samples to achieve good performance. To address these issues, we challenge the single-sample face recognition problem with intra-class differences of variation in a facial image model based on random projection and sparse representation. In this paper, we present a developed facial variation modeling systems composed only of various facial variations. We further propose a novel facial random noise dictionary learning method that is invariant to different faces. The experiment results on the AR, Yale B, Extended Yale B, MIT and FEI databases validate that our method leads to substantial improvements, particularly in single-sample face recognition problems. PMID:25580904

  16. Low-quality fingerprint recognition using a limited ellipse-band-based matching method.

    PubMed

    He, Zaixing; Zhao, Xinyue; Zhang, Shuyou

    2015-06-01

    Current fingerprint recognition technologies are based mostly on the minutia algorithms, which cannot recognize fingerprint images in low-quality conditions. This paper proposes a novel recognition algorithm using a limited ellipse-band-based matching method. It uses the Fourier-Mellin transformation method to improve the limitation of the original algorithm, which cannot resist rotation changes. Furthermore, an ellipse band on the frequency amplitude is used to suppress noise that is introduced by the high-frequency parts of images. Finally, the recognition result is obtained by considering both the contrast and position correlation peaks. The experimental results show that the proposed algorithm can increase the recognition accuracy, particularly of images in low-quality conditions. PMID:26367052

  17. A speech recognition system based on hybrid wavelet network including a fuzzy decision support system

    NASA Astrophysics Data System (ADS)

    Jemai, Olfa; Ejbali, Ridha; Zaied, Mourad; Ben Amar, Chokri

    2015-02-01

    This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.

  18. Novel image fusion scheme based on maximum ratio combining for robust multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Omri, Faten; Foufou, Sebti

    2015-04-01

    Recently, the research in multispectral face recognition has focused on developing efficient frameworks for improving face recognition performance at close-up distances. However, few studies have investigated the multispectral face images captured at long distance. In fact, great challenges still exist in recognizing human face in images captured at long distance as the image quality might be affected and some important features masked. Therefore, multispectral face recognition tools and algorithms should evolve from close-up distances to long distances. To address these issues, we present in this paper a novel image fusion scheme based on Maximum Ratio Combining algorithm and improve multispectral face recognition at long distance. The proposed method is compared with similar super-resolution method based on the Maximum likelihood algorithm. Simulation results show the efficiency of the proposed approach in term of average variance of detection error.

  19. Posture recognition based on fuzzy logic for home monitoring of the elderly.

    PubMed

    Brulin, Damien; Benezeth, Yannick; Courtial, Estelle

    2012-09-01

    We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the person's morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home. PMID:22997188

  20. A size-based probabilistic assessment of PCB exposure from Lake Michigan fish consumption

    SciTech Connect

    Stow, C.A.; Qian, S.S.

    1998-08-01

    The state of Wisconsin has recently issued a fish consumption advisory that includes suggested consumption rates for Lake Michigan fish, based on fish size and PCB concentration. To evaluate the size-based exposure risk from Lake Michigan fish consumption, the authors estimated PCB exposure probabilities for five Lake Michigan fish species using two Bayesian models. The models confirm that very few individuals of any of the five species are likely to have PCB concentrations low enough to fall into the category in which consumption is unrestricted. Among smaller fish (<50 cm), brown trout have the highest PCB levels, while lake trout are the most contaminated among larger fish (>60 cm). Eating meals from multiple individuals of some species results in a high probability that at least one of the meals will exceed 1.9 mg/kg, the upper PCB concentration recommended for consumption in the advisory.

  1. Experimental verifications of noise suppression in retinal recognition by using compression-based joint transform correlator

    NASA Astrophysics Data System (ADS)

    Widjaja, Joewono; Kaewphaluk, Komin

    2014-03-01

    Noise suppression in retinal recognition by using a compression-based joint transform correlator (CBJTC) is experimentally studied. The experimental results show that the noise suppression can be done by compressing targets into a joint-photographic expert group (JPEG) format with appropriate image compression quality. In the case of the weak noise suppression, the improved recognition performance is as high as that of the classical JTC.

  2. Joint transform correlator based on CIELAB model with encoding technique for color pattern recognition

    NASA Astrophysics Data System (ADS)

    Lin, Tiengsheng; Chen, Chulung; Liu, Chengyu; Chen, Yuming

    2010-10-01

    The CIELAB standard color vision model instead of the traditional RGB color model is utilized for polychromatic pattern recognition. The image encoding technique is introduced. The joint transform correlator is set to be the optical configuration. To achieve the distortion invariance in discrimination processes, we have used the minimum average correlation energy approach to yield sharp correlation peak. From the numerical results, it is found that the recognition ability based on CIELAB color specification system is accepted.

  3. Automatic 3D object recognition and reconstruction based on neuro-fuzzy modelling

    NASA Astrophysics Data System (ADS)

    Samadzadegan, Farhad; Azizi, Ali; Hahn, Michael; Lucas, Curo

    Three-dimensional object recognition and reconstruction (ORR) is a research area of major interest in computer vision and photogrammetry. Virtual cities, for example, is one of the exciting application fields of ORR which became very popular during the last decade. Natural and man-made objects of cities such as trees and buildings are complex structures and automatic recognition and reconstruction of these objects from digital aerial images but also other data sources is a big challenge. In this paper a novel approach for object recognition is presented based on neuro-fuzzy modelling. Structural, textural and spectral information is extracted and integrated in a fuzzy reasoning process. The learning capability of neural networks is introduced to the fuzzy recognition process by taking adaptable parameter sets into account which leads to the neuro-fuzzy approach. Object reconstruction follows recognition seamlessly by using the recognition output and the descriptors which have been extracted for recognition. A first successful application of this new ORR approach is demonstrated for the three object classes 'buildings', 'cars' and 'trees' by using aerial colour images of an urban area of the town of Engen in Germany.

  4. Infrared image recognition based on structure sparse and atomic sparse parallel

    NASA Astrophysics Data System (ADS)

    Wu, Yalu; Li, Ruilong; Xu, Yi; Wang, Liping

    2015-12-01

    Use the redundancy of the super complete dictionary can capture the structural features of the image effectively, can achieving the effective representation of the image. However, the commonly used atomic sparse representation without regard the structure of the dictionary and the unrelated non-zero-term in the process of the computation, though structure sparse consider the structure feature of dictionary, the majority coefficients of the blocks maybe are non-zero, it may affect the identification efficiency. For the disadvantages of these two sparse expressions, a weighted parallel atomic sparse and sparse structure is proposed, and the recognition efficiency is improved by the adaptive computation of the optimal weights. The atomic sparse expression and structure sparse expression are respectively, and the optimal weights are calculated by the adaptive method. Methods are as follows: training by using the less part of the identification sample, the recognition rate is calculated by the increase of the certain step size and t the constraint between weight. The recognition rate as the Z axis, two weight values respectively as X, Y axis, the resulting points can be connected in a straight line in the 3 dimensional coordinate system, by solving the highest recognition rate, the optimal weights can be obtained. Through simulation experiments can be known, the optimal weights based on adaptive method are better in the recognition rate, weights obtained by adaptive computation of a few samples, suitable for parallel recognition calculation, can effectively improve the recognition rate of infrared images.

  5. Feature based recognition of submerged objects in holographic imagery

    NASA Astrophysics Data System (ADS)

    Ratto, Christopher R.; Beagley, Nathaniel; Baldwin, Kevin C.; Shipley, Kara R.; Sternberger, Wayne I.

    2014-05-01

    The ability to autonomously sense and characterize underwater objects in situ is desirable in applications of unmanned underwater vehicles (UUVs). In this work, underwater object recognition was explored using a digital holographic system. Two experiments were performed in which several objects of varying size, shape, and material were submerged in a 43,000 gallon test tank. Holograms were collected from each object at multiple distances and orientations, with the imager located either outside the tank (looking through a porthole) or submerged (looking downward). The resultant imagery from these holograms was preprocessed to improve dynamic range, mitigate speckle, and segment out the image of the object. A collection of feature descriptors were then extracted from the imagery to characterize various object properties (e.g., shape, reflectivity, texture). The features extracted from images of multiple objects, collected at different imaging geometries, were then used to train statistical models for object recognition tasks. The resulting classification models were used to perform object classification as well as estimation of various parameters of the imaging geometry. This information can then be used to inform the design of autonomous sensing algorithms for UUVs employing holographic imagers.

  6. Fish tracking by combining motion based segmentation and particle filtering

    NASA Astrophysics Data System (ADS)

    Bichot, E.; Mascarilla, L.; Courtellemont, P.

    2006-01-01

    In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.

  7. A Comparative Study of 2D PCA Face Recognition Method with Other Statistically Based Face Recognition Methods

    NASA Astrophysics Data System (ADS)

    Senthilkumar, R.; Gnanamurthy, R. K.

    2015-07-01

    In this paper, two-dimensional principal component analysis (2D PCA) is compared with other algorithms like 1D PCA, Fisher discriminant analysis (FDA), independent component analysis (ICA) and Kernel PCA (KPCA) which are used for image representation and face recognition. As opposed to PCA, 2D PCA is based on 2D image matrices rather than 1D vectors, so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices and its Eigen vectors are derived for image feature extraction. To test 2D PCA and evaluate its performance, a series of experiments are performed on three face image databases: ORL, Senthil, and Yale face databases. The recognition rate across all trials higher using 2D PCA than PCA, FDA, ICA and KPCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2D PCA than PCA.

  8. Determining optimally orthogonal discriminant vectors in DCT domain for multiscale-based face recognition

    NASA Astrophysics Data System (ADS)

    Niu, Yanmin; Wang, Xuchu

    2011-02-01

    This paper presents a new face recognition method that extracts multiple discriminant features based on multiscale image enhancement technique and kernel-based orthogonal feature extraction improvements with several interesting characteristics. First, it can extract more discriminative multiscale face feature than traditional pixel-based or Gabor-based feature. Second, it can effectively deal with the small sample size problem as well as feature correlation problem by using eigenvalue decomposition on scatter matrices. Finally, the extractor handles nonlinearity efficiently by using kernel trick. Multiple recognition experiments on open face data set with comparison to several related methods show the effectiveness and superiority of the proposed method.

  9. Hough-based recognition of complex 3-D road scenes

    NASA Astrophysics Data System (ADS)

    Foresti, Gian L.; Regazzoni, Carlo S.

    1992-02-01

    In this paper, we address the problem of the object recognition in a complex 3-D scene by detecting the 2-D object projection on the image-plane for an autonomous vehicle driving; in particular, the problems of road detection and obstacle avoidance in natural road scenes are investigated. A new implementation of the Hough Transform (HT), called Labeled Hough Transform (LHT), to extract and group symbolic features is here presented; the novelty of this method, in respect to the traditional approach, consists in the capability of splitting a maximum in the parameter space into noncontiguous segments, while performing voting. Results are presented on a road image containing obstacles which show the efficiency, good quality, and time performances of the algorithm.

  10. A novel rosamine based fluorescent sensor for Ag+ recognition

    NASA Astrophysics Data System (ADS)

    Li, Lian-Qing; Gao, Le-Jun

    2016-01-01

    Rosamine derivative, N-(9-(4-(bis(2-(ethylthio)ethyl)amino)phenyl)-6-(diethylamino)-3H-xanthen-3-ylidene)-N-ethylethanaminium hexafluorophosphate, L, bearing an NS2 group as receptor, was synthesized as a turn on chemosensor for silver ion in ethanol solution. Sensor L exhibited high selectivity toward Ag+ in comparison to other metal cations (Cd2+, Cu2+, Hg2+, Na+, Mg2+, Ni2+, Pb2+, Fe3+, and Zn2+). The detection limit for Ag+ was in 10-7 level. The binding properties between silver ion and L were further studied by 1HNMR titration experiments. The chemosensor L can be used as a potential material for silver recognition.

  11. 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.

  12. Mobile-based text recognition from water quality devices

    NASA Astrophysics Data System (ADS)

    Dhakal, Shanti; Rahnemoonfar, Maryam

    2015-03-01

    Measuring water quality of bays, estuaries, and gulfs is a complicated and time-consuming process. YSI Sonde is an instrument used to measure water quality parameters such as pH, temperature, salinity, and dissolved oxygen. This instrument is taken to water bodies in a boat trip and researchers note down different parameters displayed by the instrument's display monitor. In this project, a mobile application is developed for Android platform that allows a user to take a picture of the YSI Sonde monitor, extract text from the image and store it in a file on the phone. The image captured by the application is first processed to remove perspective distortion. Probabilistic Hough line transform is used to identify lines in the image and the corner of the image is then obtained by determining the intersection of the detected horizontal and vertical lines. The image is warped using the perspective transformation matrix, obtained from the corner points of the source image and the destination image, hence, removing the perspective distortion. Mathematical morphology operation, black-hat is used to correct the shading of the image. The image is binarized using Otsu's binarization technique and is then passed to the Optical Character Recognition (OCR) software for character recognition. The extracted information is stored in a file on the phone and can be retrieved later for analysis. The algorithm was tested on 60 different images of YSI Sonde with different perspective features and shading. Experimental results, in comparison to ground-truth results, demonstrate the effectiveness of the proposed method.

  13. Modes of Visual Recognition and Perceptually Relevant Sketch-based Coding for Images

    NASA Technical Reports Server (NTRS)

    Jobson, Daniel J.

    1991-01-01

    A review of visual recognition studies is used to define two levels of information requirements. These two levels are related to two primary subdivisions of the spatial frequency domain of images and reflect two distinct different physical properties of arbitrary scenes. In particular, pathologies in recognition due to cerebral dysfunction point to a more complete split into two major types of processing: high spatial frequency edge based recognition vs. low spatial frequency lightness (and color) based recognition. The former is more central and general while the latter is more specific and is necessary for certain special tasks. The two modes of recognition can also be distinguished on the basis of physical scene properties: the highly localized edges associated with reflectance and sharp topographic transitions vs. smooth topographic undulation. The extreme case of heavily abstracted images is pursued to gain an understanding of the minimal information required to support both modes of recognition. Here the intention is to define the semantic core of transmission. This central core of processing can then be fleshed out with additional image information and coding and rendering techniques.

  14. Face recognition in simulated prosthetic vision: face detection-based image processing strategies

    NASA Astrophysics Data System (ADS)

    Wang, Jing; Wu, Xiaobei; Lu, Yanyu; Wu, Hao; Kan, Han; Chai, Xinyu

    2014-08-01

    Objective. Given the limited visual percepts elicited by current prosthetic devices, it is essential to optimize image content in order to assist implant wearers to achieve better performance of visual tasks. This study focuses on recognition of familiar faces using simulated prosthetic vision. Approach. Combined with region-of-interest (ROI) magnification, three face extraction strategies based on a face detection technique were used: the Viola-Jones face region, the statistical face region (SFR) and the matting face region. Main results. These strategies significantly enhanced recognition performance compared to directly lowering resolution (DLR) with Gaussian dots. The inclusion of certain external features, such as hairstyle, was beneficial for face recognition. Given the high recognition accuracy achieved and applicable processing speed, SFR-ROI was the preferred strategy. DLR processing resulted in significant face gender recognition differences (i.e. females were more easily recognized than males), but these differences were not apparent with other strategies. Significance. Face detection-based image processing strategies improved visual perception by highlighting useful information. Their use is advisable for face recognition when using low-resolution prosthetic vision. These results provide information for the continued design of image processing modules for use in visual prosthetics, thus maximizing the benefits for future prosthesis wearers.

  15. A Novel Word Based Arabic Handwritten Recognition System Using SVM Classifier

    NASA Astrophysics Data System (ADS)

    Khalifa, Mahmoud; Bingru, Yang

    Every language script has its structure, characteristic, and feature. Character based word recognition depends on the feature available to be extracted from character. Word based script recognition overcome the problem of character segmenting and can be applied for several languages (Arabic, Urdu, Farsi... est.). In this paper Arabic handwritten is classified as word based system. Firstly, words segmented and normalized in size to fit the DCT input. Then extract feature characteristic by computing the Euclidean distance between pairs of objects in n-by-m data matrix X. Based on the point's operator of extrema, feature was extracted. Then apply one to one-Class Support Vector Machines (SVMs) as a discriminative framework in order to address feature classification. The approach was tested with several public databases and we get high efficiency rate recognition.

  16. Infrared face recognition based on LBP histogram and KW feature selection

    NASA Astrophysics Data System (ADS)

    Xie, Zhihua

    2014-07-01

    The conventional LBP-based feature as represented by the local binary pattern (LBP) histogram still has room for performance improvements. This paper focuses on the dimension reduction of LBP micro-patterns and proposes an improved infrared face recognition method based on LBP histogram representation. To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).

  17. The effect of rights-based fisheries management on risk taking and fishing safety

    PubMed Central

    Pfeiffer, Lisa; Gratz, Trevor

    2016-01-01

    Commercial fishing is a dangerous occupation despite decades of regulatory initiatives aimed at making it safer. We posit that rights-based fisheries management (the individual allocation of fishing quota to vessels or fishing entities, also called catch shares) can improve safety by solving many of the problems associated with the competitive race to fish experienced in fisheries around the world. The competitive nature of such fisheries results in risky behavior such as fishing in poor weather, overloading vessels with fishing gear, and neglecting maintenance. Although not necessarily intended to address safety issues, catch shares eliminate many of the economic incentives to fish as rapidly as possible. We develop a dataset and methods to empirically evaluate the effects of the adoption of catch shares management on a particularly risky type of behavior: the propensity to fish in stormy weather. After catch shares was implemented in an economically important US West Coast fishery, a fisherman’s probability of taking a fishing trip in high wind conditions decreased by 82% compared with only 31% in the former race to fish fishery. Overall, catch shares caused the average annual rate of fishing on high wind days to decrease by 79%. These results are evidence that institutional changes can significantly reduce individual, voluntary risk exposure and result in safer fisheries. PMID:26884188

  18. The effect of rights-based fisheries management on risk taking and fishing safety.

    PubMed

    Pfeiffer, Lisa; Gratz, Trevor

    2016-03-01

    Commercial fishing is a dangerous occupation despite decades of regulatory initiatives aimed at making it safer. We posit that rights-based fisheries management (the individual allocation of fishing quota to vessels or fishing entities, also called catch shares) can improve safety by solving many of the problems associated with the competitive race to fish experienced in fisheries around the world. The competitive nature of such fisheries results in risky behavior such as fishing in poor weather, overloading vessels with fishing gear, and neglecting maintenance. Although not necessarily intended to address safety issues, catch shares eliminate many of the economic incentives to fish as rapidly as possible. We develop a dataset and methods to empirically evaluate the effects of the adoption of catch shares management on a particularly risky type of behavior: the propensity to fish in stormy weather. After catch shares was implemented in an economically important US West Coast fishery, a fisherman's probability of taking a fishing trip in high wind conditions decreased by 82% compared with only 31% in the former race to fish fishery. Overall, catch shares caused the average annual rate of fishing on high wind days to decrease by 79%. These results are evidence that institutional changes can significantly reduce individual, voluntary risk exposure and result in safer fisheries. PMID:26884188

  19. Face Pose Recognition Based on Monocular Digital Imagery and Stereo-Based Estimation of its Precision

    NASA Astrophysics Data System (ADS)

    Gorbatsevich, V.; Vizilter, Yu.; Knyaz, V.; Zheltov, S.

    2014-06-01

    A technique for automated face detection and its pose estimation using single image is developed. The algorithm includes: face detection, facial features localization, face/background segmentation, face pose estimation, image transformation to frontal view. Automatic face/background segmentation is performed by original graph-cut technique based on detected feature points. The precision of face orientation estimation based on monocular digital imagery is addressed. The approach for precision estimation is developed based on comparison of synthesized facial 2D images and scanned face 3D model. The software for modelling and measurement is developed. The special system for non-contact measurements is created. Required set of 3D real face models and colour facial textures is obtained using this system. The precision estimation results demonstrate the precision of face pose estimation enough for further successful face recognition.

  20. Emotion recognition in frontotemporal dementia and Alzheimer's disease: A new film-based assessment.

    PubMed

    Goodkind, Madeleine S; Sturm, Virginia E; Ascher, Elizabeth A; Shdo, Suzanne M; Miller, Bruce L; Rankin, Katherine P; Levenson, Robert W

    2015-08-01

    Deficits in recognizing others' emotions are reported in many psychiatric and neurological disorders, including autism, schizophrenia, behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). Most previous emotion recognition studies have required participants to identify emotional expressions in photographs. This type of assessment differs from real-world emotion recognition in important ways: Images are static rather than dynamic, include only 1 modality of emotional information (i.e., visual information), and are presented absent a social context. Additionally, existing emotion recognition batteries typically include multiple negative emotions, but only 1 positive emotion (i.e., happiness) and no self-conscious emotions (e.g., embarrassment). We present initial results using a new task for assessing emotion recognition that was developed to address these limitations. In this task, respondents view a series of short film clips and are asked to identify the main characters' emotions. The task assesses multiple negative, positive, and self-conscious emotions based on information that is multimodal, dynamic, and socially embedded. We evaluate this approach in a sample of patients with bvFTD, AD, and normal controls. Results indicate that patients with bvFTD have emotion recognition deficits in all 3 categories of emotion compared to the other groups. These deficits were especially pronounced for negative and self-conscious emotions. Emotion recognition in this sample of patients with AD was indistinguishable from controls. These findings underscore the utility of this approach to assessing emotion recognition and suggest that previous findings that recognition of positive emotion was preserved in dementia patients may have resulted from the limited sampling of positive emotion in traditional tests. PMID:26010574

  1. Emotion Recognition in Frontotemporal Dementia and Alzheimer's Disease: A New Film-Based Assessment

    PubMed Central

    Goodkind, Madeleine S.; Sturm, Virginia E.; Ascher, Elizabeth A.; Shdo, Suzanne M.; Miller, Bruce L.; Rankin, Katherine P.; Levenson, Robert W.

    2015-01-01

    Deficits in recognizing others' emotions are reported in many psychiatric and neurological disorders, including autism, schizophrenia, behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). Most previous emotion recognition studies have required participants to identify emotional expressions in photographs. This type of assessment differs from real-world emotion recognition in important ways: Images are static rather than dynamic, include only 1 modality of emotional information (i.e., visual information), and are presented absent a social context. Additionally, existing emotion recognition batteries typically include multiple negative emotions, but only 1 positive emotion (i.e., happiness) and no self-conscious emotions (e.g., embarrassment). We present initial results using a new task for assessing emotion recognition that was developed to address these limitations. In this task, respondents view a series of short film clips and are asked to identify the main characters' emotions. The task assesses multiple negative, positive, and self-conscious emotions based on information that is multimodal, dynamic, and socially embedded. We evaluate this approach in a sample of patients with bvFTD, AD, and normal controls. Results indicate that patients with bvFTD have emotion recognition deficits in all 3 categories of emotion compared to the other groups. These deficits were especially pronounced for negative and self-conscious emotions. Emotion recognition in this sample of patients with AD was indistinguishable from controls. These findings underscore the utility of this approach to assessing emotion recognition and suggest that previous findings that recognition of positive emotion was preserved in dementia patients may have resulted from the limited sampling of positive emotion in traditional tests. PMID:26010574

  2. A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework

    PubMed Central

    Wei, Shengjing; Chen, Xiang; Yang, Xidong; Cao, Shuai; Zhang, Xu

    2016-01-01

    Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user’s training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system. PMID:27104534

  3. A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework.

    PubMed

    Wei, Shengjing; Chen, Xiang; Yang, Xidong; Cao, Shuai; Zhang, Xu

    2016-01-01

    Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 ± 13.2)% and (79.7 ± 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 ± 13.7)% and (86.3 ± 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user's training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system. PMID:27104534

  4. Speech Recognition-based and Automaticity Programs to Help Students with Severe Reading and Spelling Problems

    ERIC Educational Resources Information Center

    Higgins, Eleanor L.; Raskind, Marshall H.

    2004-01-01

    This study was conducted to assess the effectiveness of two programs developed by the Frostig Center Research Department to improve the reading and spelling of students with learning disabilities (LD): a computer Speech Recognition-based Program (SRBP) and a computer and text-based Automaticity Program (AP). Twenty-eight LD students with reading…

  5. Stage boundary recognition in the Eastern Americas realm based on rugose corals

    USGS Publications Warehouse

    Oliver, W.A., Jr.

    2000-01-01

    Most Devonian stages contain characteristic coral assemblages but these tend to be geographically and facies limited and may or may not be useful for recognising stage boundaries. Within eastern North America, corals contribute to the recognition of two boundaries: the base of the Lochkovian (Silurian-Devonian boundary) and the base of the Eifelian (Lower-Middle Devonian Series boundary).

  6. The Effects of Semantic Transparency and Base Frequency on the Recognition of English Complex Words

    ERIC Educational Resources Information Center

    Xu, Joe; Taft, Marcus

    2015-01-01

    A visual lexical decision task was used to examine the interaction between base frequency (i.e., the cumulative frequencies of morphologically related forms) and semantic transparency for a list of derived words. Linear mixed effects models revealed that high base frequency facilitates the recognition of the complex word (i.e., a "base…

  7. A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database.

    PubMed

    Huang, Zhiwu; Shan, Shiguang; Wang, Ruiping; Zhang, Haihong; Lao, Shihong; Kuerban, Alifu; Chen, Xilin

    2015-12-01

    Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX(1) Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation. PMID:26513790

  8. A Full-Body Layered Deformable Model for Automatic Model-Based Gait Recognition

    NASA Astrophysics Data System (ADS)

    Lu, Haiping; Plataniotis, Konstantinos N.; Venetsanopoulos, Anastasios N.

    2007-12-01

    This paper proposes a full-body layered deformable model (LDM) inspired by manually labeled silhouettes for automatic model-based gait recognition from part-level gait dynamics in monocular video sequences. The LDM is defined for the fronto-parallel gait with 22 parameters describing the human body part shapes (widths and lengths) and dynamics (positions and orientations). There are four layers in the LDM and the limbs are deformable. Algorithms for LDM-based human body pose recovery are then developed to estimate the LDM parameters from both manually labeled and automatically extracted silhouettes, where the automatic silhouette extraction is through a coarse-to-fine localization and extraction procedure. The estimated LDM parameters are used for model-based gait recognition by employing the dynamic time warping for matching and adopting the combination scheme in AdaBoost.M2. While the existing model-based gait recognition approaches focus primarily on the lower limbs, the estimated LDM parameters enable us to study full-body model-based gait recognition by utilizing the dynamics of the upper limbs, the shoulders and the head as well. In the experiments, the LDM-based gait recognition is tested on gait sequences with differences in shoe-type, surface, carrying condition and time. The results demonstrate that the recognition performance benefits from not only the lower limb dynamics, but also the dynamics of the upper limbs, the shoulders and the head. In addition, the LDM can serve as an analysis tool for studying factors affecting the gait under various conditions.

  9. Locality Constrained Joint Dynamic Sparse Representation for Local Matching Based Face Recognition

    PubMed Central

    Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

    2014-01-01

    Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC. PMID:25419662

  10. Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

    PubMed

    Jia, Qi; Gao, Xinkai; Guo, He; Luo, Zhongxuan; Wang, Yi

    2015-01-01

    In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach. PMID:25808772

  11. Aminobenzohydrazide based colorimetric and 'turn-on' fluorescence chemosensor for selective recognition of fluoride.

    PubMed

    Anand, Thangaraj; Sivaraman, Gandhi; Iniya, Murugan; Siva, Ayyanar; Chellappa, Duraisamy

    2015-05-30

    Chemosensors based on aminobenzohydrazide Schiff bases bearing pyrene/anthracene as fluorophores have been designed and synthesized for F(-) ion recognition. The addition of fluoride ions to the receptors causes a dramatically observable colour change from pale yellow to brown/red. (1)H NMR studies confirm that the F(-) ion facilitates its recognition by forming hydrogen bond with hydrogens of amide and amine groups. Moreover these sensors have also been successfully applied to detection of fluoride ion in commercial tooth paste solution. PMID:25998453

  12. Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees.

    PubMed

    Zhang, M; Fulcher, J

    1996-01-01

    Recent artificial neural network research has focused on simple models, but such models have not been very successful in describing complex systems (such as face recognition). This paper introduces the artificial neural network group-based adaptive tolerance (GAT) tree model for translation-invariant face recognition, suitable for use in an airport security system. GAT trees use a two-stage divide-and-conquer tree-type approach. The first stage determines general properties of the input, such as whether the facial image contains glasses or a beard. The second stage identifies the individual. Face perception classification, detection of front faces with glasses and/or beards, and face recognition results using GAT trees under laboratory conditions are presented. We conclude that the neural network group-based model offers significant improvement over conventional neural network trees for this task. PMID:18263454

  13. Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

    PubMed Central

    Jalali, M. Ali; Ierodiaconou, Daniel; Gorfine, Harry; Monk, Jacquomo; Rattray, Alex

    2015-01-01

    Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics. PMID:25992800

  14. Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model.

    PubMed

    Jalali, M Ali; Ierodiaconou, Daniel; Gorfine, Harry; Monk, Jacquomo; Rattray, Alex

    2015-01-01

    Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100's of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs. Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics. PMID:25992800

  15. A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera.

    PubMed

    Ar, Ilktan; Akgul, Yusuf Sinan

    2014-11-01

    Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However, most methods in the literature view this task as a special case of motion recognition. In contrast, we propose to employ the three main components of a physiotherapy exercise (the motion patterns, the stance knowledge, and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then, we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level, which takes the advantage of domain knowledge for a more robust system. Finally, a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red, green, and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation, bodypart tracking, joint detection, and temporal segmentation methods. In the end, favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained. PMID:24860037

  16. A modular non-negative matrix factorization for parts-based object recognition using subspace representation

    NASA Astrophysics Data System (ADS)

    Bajla, Ivan; Soukup, Daniel

    2008-02-01

    Non-negative matrix factorization of an input data matrix into a matrix of basis vectors and a matrix of encoding coefficients is a subspace representation method that has attracted attention of researches in pattern recognition in the recent period. We have explored crucial aspects of NMF on massive recognition experiments with the ORL database of faces which include intuitively clear parts constituting the whole. Using a principal changing of the learning stage structure and by formulating NMF problems for each of a priori given parts separately, we developed a novel modular NMF algorithm. Although this algorithm provides uniquely separated basis vectors which code individual face parts in accordance with the parts-based principle of the NMF methodology applied to object recognition problems, any significant improvement of recognition rates for occluded parts, predicted in several papers, was not reached. We claim that using the parts-based concept in NMF as a basis for solving recognition problems with occluded objects has not been justified.

  17. Recognition of Watson-Crick base pairs: constraints and limits due to geometric selection and tautomerism

    PubMed Central

    Yusupov, Marat; Yusupova, Gulnara

    2014-01-01

    The natural bases of nucleic acids have a strong preference for one tautomer form, guaranteeing fidelity in their hydrogen bonding potential. However, base pairs observed in recent crystal structures of polymerases and ribosomes are best explained by an alternative base tautomer, leading to the formation of base pairs with Watson-Crick-like geometries. These observations set limits to geometric selection in molecular recognition of complementary Watson-Crick pairs for fidelity in replication and translation processes. PMID:24765524

  18. Fish body surface data measurement based on 3D digital image correlation

    NASA Astrophysics Data System (ADS)

    Jiang, Ming; Qian, Chen; Yang, Wenkai

    2016-01-01

    To film the moving fish in the glass tank, light will be bent at the interface of air and glass, glass and water. Based on binocular stereo vision and refraction principle, we establish a mathematical model of 3D image correlation to reconstruct the 3D coordinates of samples in the water. Marking speckle in fish surface, a series of real-time speckle images of swimming fish will be obtained by two high-speed cameras, and instantaneous 3D shape, strain, displacement etc. of fish will be reconstructed.

  19. Development of young oil palm tree recognition using Haar- based rectangular windows

    NASA Astrophysics Data System (ADS)

    Daliman, S.; Abu-Bakar, S. A. R.; Nor Azam, S. H. Md

    2016-06-01

    This paper presents development of Haar-based rectangular windows for recognition of young oil palm tree based on WorldView-2 imagery data. Haar-based rectangular windows or also known as Haar-like rectangular features have been popular in face recognition as used in Viola-Jones object detection framework. Similar to face recognition, the oil palm tree recognition would also need a suitable Haar-based rectangular windows that best suit to the characteristics of oil palm tree. A set of seven Haar-based rectangular windows have been designed to better match specifically the young oil palm tree as the crown size is much smaller compared to the matured ones. Determination of features for oil palm tree is an essential task to ensure a high successful rate of correct oil palm tree detection. Furthermore, features that reflects the identification of oil palm tree indicate distinctiveness between an oil palm tree and other objects in the image such as buildings, roads and drainage. These features will be trained using support vector machine (SVM) to model the oil palm tree for classifying the testing set and subimages of WorldView-2 imagery data. The resulting classification of young oil palm tree with sensitivity of 98.58% and accuracy of 92.73% shows a promising result that it can be used for intention of developing automatic young oil palm tree counting.

  20. Log-linear model based behavior selection method for artificial fish swarm algorithm.

    PubMed

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

    Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

  1. Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

    PubMed Central

    Huang, Zhehuang; Chen, Yidong

    2015-01-01

    Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

  2. MODELLING THE UPTAKE AND DISPOSITION OF HYDROPHOBIC ORGANIC CHEMICALS IN FISH USING A PHYSIOLOGICALLY BASED APPROACH

    EPA Science Inventory

    The development of physiologically based toxicokinetic (PBTK) models for hydrophobic chemicals in fish requires: 1) an understanding of chemical efflux at fish gills; 2) knowledge of the factors that limit chemical exchange between blood and tissues; and, 3) a mechanistic descrip...

  3. Size-Based Hydroacoustic Measures of Within-Season Fish Abundance in a Boreal Freshwater Ecosystem

    PubMed Central

    Pollom, Riley A.; Rose, George A.

    2015-01-01

    Eleven sequential size-based hydroacoustic surveys conducted with a 200 kHz split-beam transducer during the summers of 2011 and 2012 were used to quantify seasonal declines in fish abundance in a boreal reservoir in Manitoba, Canada. Fish densities were sufficiently low to enable single target resolution and tracking. Target strengths converted to log2-based size-classes indicated that smaller fish were consistently more abundant than larger fish by a factor of approximately 3 for each halving of length. For all size classes, in both years, abundance (natural log) declined linearly over the summer at rates that varied from -0.067.day-1 for the smallest fish to -0.016.day-1 for the largest (R2 = 0.24–0.97). Inter-annual comparisons of size-based abundance suggested that for larger fish (>16 cm), mean winter decline rates were an order of magnitude lower (-0.001.day-1) and overall survival higher (71%) than in the main summer fishing season (mean loss rate -0.038.day-1; survival 33%). We conclude that size-based acoustic survey methods have the potential to assess within-season fish abundance dynamics, and may prove useful in long-term monitoring of productivity and hence management of boreal aquatic ecosystems. PMID:25875467

  4. Development of an Index of Ecological Condition based on Great River Fish Assemblages

    EPA Science Inventory

    As part of the Environmental Monitoring and Assessment Program for Great River Ecosystems we developed a fish-assemblage based multimetric index (Great River Fish Index,GRFIn) as an indicator of ecological conditions in the Lower Missouri, impounded Upper Mississippi,.unimpoun...

  5. Development of an Index of Ecological Condition Based on Great River Fish Assemblages, Presentation

    EPA Science Inventory

    As part of the Environmental Monitoring and Assessment Program for Great River Ecosystems we developed a fish-assemblage based multimetric index (Great River Fish Index,GRFIn) as an indicator of ecological conditions in the Lower Missouri, impounded Upper Mississippi,.unimpounded...

  6. Research on marine and freshwater fish identification model based on hyper-spectral imaging technology

    NASA Astrophysics Data System (ADS)

    Fu, Yan; Guo, Pei-yuan; Xiang, Ling-zi; Bao, Man; Chen, Xing-hai

    2013-08-01

    With the gradually mature of hyper spectral image technology, the application of the meat nondestructive detection and recognition has become one of the current research focuses. This paper for the study of marine and freshwater fish by the pre-processing and feature extraction of the collected spectral curve data, combined with BP network structure and LVQ network structure, a predictive model of hyper spectral image data of marine and freshwater fish has been initially established and finally realized the qualitative analysis and identification of marine and freshwater fish quality. The results of this study show that hyper spectral imaging technology combined with the BP and LVQ Artificial Neural Network Model can be used for the identification of marine and freshwater fish detection. Hyper-spectral data acquisition can be carried out without any pretreatment of the samples, thus hyper-spectral imaging technique is the lossless, high- accuracy and rapid detection method for quality of fish. In this study, only 30 samples are used for the exploratory qualitative identification of research, although the ideal study results are achieved, we will further increase the sample capacity to take the analysis of quantitative identification and verify the feasibility of this theory.

  7. KD-tree based clustering algorithm for fast face recognition on large-scale data

    NASA Astrophysics Data System (ADS)

    Wang, Yuanyuan; Lin, Yaping; Yang, Junfeng

    2015-07-01

    This paper proposes an acceleration method for large-scale face recognition system. When dealing with a large-scale database, face recognition is time-consuming. In order to tackle this problem, we employ the k-means clustering algorithm to classify face data. Specifically, the data in each cluster are stored in the form of the kd-tree, and face feature matching is conducted with the kd-tree based nearest neighborhood search. Experiments on CAS-PEAL and self-collected database show the effectiveness of our proposed method.

  8. Gels based on anion recognition between triurea receptor and phosphate anion.

    PubMed

    Yang, Cuiling; Wu, Biao; Chen, Yongming; Zhang, Ke

    2015-04-01

    Anion recognition between the triurea receptor and phosphate anion is demonstrated as the cross-linkage to build supramolecular polymer gels for the first time. A novel multi-block copolymer (3) is designed to have functional triurea groups as cross-linking units along the polymer main chain. By virtue of anion coordination between the triurea receptor and phosphate anion with a binding mode of 2:1, supramolecular polymer gels are then prepared based on anion recognition using 3 as the building block. PMID:25694389

  9. Note: Gaussian mixture model for event recognition in optical time-domain reflectometry based sensing systems

    NASA Astrophysics Data System (ADS)

    Fedorov, A. K.; Anufriev, M. N.; Zhirnov, A. A.; Stepanov, K. V.; Nesterov, E. T.; Namiot, D. E.; Karasik, V. E.; Pnev, A. B.

    2016-03-01

    We propose a novel approach to the recognition of particular classes of non-conventional events in signals from phase-sensitive optical time-domain-reflectometry-based sensors. Our algorithmic solution has two main features: filtering aimed at the de-nosing of signals and a Gaussian mixture model to cluster them. We test the proposed algorithm using experimentally measured signals. The results show that two classes of events can be distinguished with the best-case recognition probability close to 0.9 at sufficient numbers of training samples.

  10. Note: Gaussian mixture model for event recognition in optical time-domain reflectometry based sensing systems.

    PubMed

    Fedorov, A K; Anufriev, M N; Zhirnov, A A; Stepanov, K V; Nesterov, E T; Namiot, D E; Karasik, V E; Pnev, A B

    2016-03-01

    We propose a novel approach to the recognition of particular classes of non-conventional events in signals from phase-sensitive optical time-domain-reflectometry-based sensors. Our algorithmic solution has two main features: filtering aimed at the de-nosing of signals and a Gaussian mixture model to cluster them. We test the proposed algorithm using experimentally measured signals. The results show that two classes of events can be distinguished with the best-case recognition probability close to 0.9 at sufficient numbers of training samples. PMID:27036840

  11. Wavelet decomposition based principal component analysis for face recognition using MATLAB

    NASA Astrophysics Data System (ADS)

    Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

    2016-03-01

    For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

  12. Dialog-Based 3D-Image Recognition Using a Domain Ontology

    NASA Astrophysics Data System (ADS)

    Hois, Joana; Wünstel, Michael; Bateman, John A.; Röfer, Thomas

    The combination of vision and speech, together with the resulting necessity for formal representations, builds a central component of an autonomous system. A robot that is supposed to navigate autonomously through space must be able to perceive its environment as automatically as possible. But each recognition system has its own inherent limits. Especially a robot whose task is to navigate through unknown terrain has to deal with unidentified or even unknown objects, thus compounding the recognition problem still further. The system described in this paper takes this into account by trying to identify objects based on their functionality where possible. To handle cases where recognition is insufficient, we examine here two further strategies: on the one hand, the linguistic reference and labeling of the unidentified objects and, on the other hand, ontological deduction. This approach then connects the probabilistic area of object recognition with the logical area of formal reasoning. In order to support formal reasoning, additional relational scene information has to be supplied by the recognition system. Moreover, for a sound ontological basis for these reasoning tasks, it is necessary to define a domain ontology that provides for the representation of real-world objects and their corresponding spatial relations in linguistic and physical respects. Physical spatial relations and objects are measured by the visual system, whereas linguistic spatial relations and objects are required for interactions with a user.

  13. A sparse Bayesian learning based scheme for multi-movement recognition using sEMG.

    PubMed

    Ding, Shuai; Wang, Liang

    2016-03-01

    This paper proposed a feature extraction scheme based on sparse representation considering the non-stationary property of surface electromyography (sEMG). Sparse Bayesian learning was introduced to extract the feature with optimal class separability to improve recognition accuracy of multi-movement patterns. The extracted feature, sparse representation coefficients (SRC), represented time-varying characteristics of sEMG effectively because of the compressibility (or weak sparsity) of the signal in some transformed domains. We investigated the effect of the proposed feature by comparing with other fourteen individual features in offline recognition. The results demonstrated the proposed feature revealed important dynamic information in the sEMG signals. The multi-feature sets formed by the SRC and other single feature yielded more superior performance on recognition accuracy, compared with the single features. The best average recognition accuracy of 94.33 % was gained by using SVM classifier with the multi-feature set combining the feature SRC, Williston amplitude (WAMP), wavelength (WL) and the coefficients of the fourth order autoregressive model (ARC4) via multiple kernel learning framework. The proposed feature extraction scheme (known as SRC + WAMP + WL + ARC4) is a promising method for multi-movement recognition with high accuracy. PMID:26577712

  14. Localization and Recognition of Dynamic Hand Gestures Based on Hierarchy of Manifold Classifiers

    NASA Astrophysics Data System (ADS)

    Favorskaya, M.; Nosov, A.; Popov, A.

    2015-05-01

    Generally, the dynamic hand gestures are captured in continuous video sequences, and a gesture recognition system ought to extract the robust features automatically. This task involves the highly challenging spatio-temporal variations of dynamic hand gestures. The proposed method is based on two-level manifold classifiers including the trajectory classifiers in any time instants and the posture classifiers of sub-gestures in selected time instants. The trajectory classifiers contain skin detector, normalized skeleton representation of one or two hands, and motion history representing by motion vectors normalized through predetermined directions (8 and 16 in our case). Each dynamic gesture is separated into a set of sub-gestures in order to predict a trajectory and remove those samples of gestures, which do not satisfy to current trajectory. The posture classifiers involve the normalized skeleton representation of palm and fingers and relative finger positions using fingertips. The min-max criterion is used for trajectory recognition, and the decision tree technique was applied for posture recognition of sub-gestures. For experiments, a dataset "Multi-modal Gesture Recognition Challenge 2013: Dataset and Results" including 393 dynamic hand-gestures was chosen. The proposed method yielded 84-91% recognition accuracy, in average, for restricted set of dynamic gestures.

  15. A Computer-Based Gaming System for Assessing Recognition Performance (RECOG).

    ERIC Educational Resources Information Center

    Little, Glenn A.; And Others

    This report documents a computer-based gaming system for assessing recognition performance (RECOG). The game management system is programmed in a modular manner to: instruct the student on how to play the game, retrieve and display individual images, keep track of how well individuals play and provide them feedback, and link these components by…

  16. Evaluating Automatic Speech Recognition-Based Language Learning Systems: A Case Study

    ERIC Educational Resources Information Center

    van Doremalen, Joost; Boves, Lou; Colpaert, Jozef; Cucchiarini, Catia; Strik, Helmer

    2016-01-01

    The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to…

  17. 38 CFR 52.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Adult Day Health Care in State Homes § 52.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing adult day health care to eligible veterans in...

  18. 38 CFR 52.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Adult Day Health Care in State Homes § 52.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing adult day health care to eligible veterans in...

  19. Genome filtering using methylation-sensitive restriction enzymes with six-base pair recognition sites

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The large fraction of repetitive DNA in many plant genomes has complicated all aspects of DNA sequencing and assembly, and thus techniques that enrich for genes and low-copy sequences have been employed to isolate gene space. Methyl sensitive restriction enzymes with six base pair recognition sites...

  20. Distinctiveness and the Recognition Mirror Effect: Evidence for an Item-Based Criterion Placement Heuristic

    ERIC Educational Resources Information Center

    Dobbins, Ian G.; Kroll, Neal E. A.

    2005-01-01

    Superior detection and rejection of 1 versus another class of items during recognition is called the mirror effect. Some mirror effects may involve strategic criterion adjustments based on item distinctiveness and its relation to memorability. Three experiments demonstrated mirror effects for known versus unknown scenes and 1 suggested a similar…

  1. A photochromic supramolecular polymer based on bis-p-sulfonatocalix[4]arene recognition in aqueous solution.

    PubMed

    Yao, Xuyang; Li, Teng; Wang, Sheng; Ma, Xiang; Tian, He

    2014-07-11

    A photochromic supramolecular polymer based on bis-p-sulfonatocalix[4]arene recognition with a dithienylethene derivative in aqueous solution was fabricated. The resultant polymer showed good photochromic behaviour with obvious colour switching and a morphology change under alternative UV/Vis light stimuli. PMID:24853232

  2. 38 CFR 51.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing nursing home care to eligible veterans in a facility if...

  3. 38 CFR 51.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing nursing home care to eligible veterans in a facility if...

  4. 38 CFR 51.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing nursing home care to eligible veterans in a facility if...

  5. 38 CFR 51.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing nursing home care to eligible veterans in a facility if...

  6. 38 CFR 51.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in State Homes § 51.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing nursing home care to eligible veterans in a facility if...

  7. When Does Modality Matter? Perceptual versus Conceptual Fluency-Based Illusions in Recognition Memory

    ERIC Educational Resources Information Center

    Miller, Jeremy K.; Lloyd, Marianne E.; Westerman, Deanne L.

    2008-01-01

    Previous research has shown that illusions of recognition memory based on enhanced perceptual fluency are sensitive to the perceptual match between the study and test phases of an experiment. The results of the current study strengthen that conclusion, as they show that participants will not interpret enhanced perceptual fluency as a sign of…

  8. 38 CFR 52.10 - Per diem based on recognition and certification.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Adult Day Health Care in State Homes § 52.10 Per diem based on recognition and certification. VA will pay per diem to a State for providing adult day health care to eligible veterans in...

  9. Optimal ligand descriptor for pocket recognition based on the Beta-shape.

    PubMed

    Kim, Jae-Kwan; Won, Chung-In; Cha, Jehyun; Lee, Kichun; Kim, Deok-Soo

    2015-01-01

    Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test. PMID:25835497

  10. Facial expression recognition based on fused Feature of PCA and LDP

    NASA Astrophysics Data System (ADS)

    Yi, Zhang; Mao, Hou-lin; Luo, Yuan

    2014-11-01

    Facial expression recognition is an important part of the study in man-machine interaction. Principal component analysis (PCA) is an extraction method based on statistical features which were extracted from the global grayscale features of the whole image .But the grayscale global features are environmentally sensitive. In order to recognize facial expression accurately, a fused method of principal component analysis and local direction pattern (LDP) is introduced in this paper. First, PCA extracts the global features of the whole grayscale image; LDP extracts the local grayscale texture features of the mouth and eyes region, which contribute most to facial expression recognition, to complement the global grayscale features of PCA. Then we adopt Support Vector Machine (SVM) classifier for expression classification. Experimental results demonstrate that this method can classify different expressions more effectively and get higher recognition rate compared with the traditional method.

  11. Kernel-based discriminant image filter learning: application in face recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Lingchen; Wei, Sui; Qu, Lei

    2014-11-01

    The extraction of discriminative and robust feature is a crucial issue in pattern recognition and classification. In this paper, we propose a kernel based discriminant image filter learning method (KDIFL) for local feature enhancement and demonstrate its superiority in the application of face recognition. Instead of designing the image filter in a handcraft or analytical way, we propose to learn the image filter so that after filtering the between-class difference is attenuated and the within-class difference is amplified, thus facilitate the following recognition. During filter learning, the kernel trick is employed to cope with the nonlinear feature space problem caused by expression, pose, illumination, and so on. We show that the proposed filter is generalized and it can be concatenated with classic feature descriptors (e.g. LBP) to further increase the discriminability of extracted features. Our extensive experiments on Yale, ORL and AR face databases validate the effectiveness and robustness of the proposed method.

  12. Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning

    PubMed Central

    Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

    2016-01-01

    Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches. PMID:26927111

  13. An effective approach for iris recognition using phase-based image matching.

    PubMed

    Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

    2008-10-01

    This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology. PMID:18703828

  14. Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

    PubMed

    Yang, Zhutian; Qiu, Wei; Sun, Hongjian; Nallanathan, Arumugam

    2016-01-01

    Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches. PMID:26927111

  15. Body-Based Gender Recognition Using Images from Visible and Thermal Cameras

    PubMed Central

    Nguyen, Dat Tien; Park, Kang Ryoung

    2016-01-01

    Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems. PMID:26828487

  16. Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex

    PubMed Central

    Xie, Shan Juan; Lu, Yu; Yoon, Sook; Yang, Jucheng; Park, Dong Sun

    2015-01-01

    Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy. PMID:26184226

  17. Enhanced iris recognition method based on multi-unit iris images

    NASA Astrophysics Data System (ADS)

    Shin, Kwang Yong; Kim, Yeong Gon; Park, Kang Ryoung

    2013-04-01

    For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris's image is frequently rotated because of the user's head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the

  18. A Variance Based Active Learning Approach for Named Entity Recognition

    NASA Astrophysics Data System (ADS)

    Hassanzadeh, Hamed; Keyvanpour, Mohammadreza

    The cost of manually annotating corpora is one of the significant issues in many text based tasks such as text mining, semantic annotation and generally information extraction. Active Learning is an approach that deals with reduction of labeling costs. In this paper we proposed an effective active learning approach based on minimal variance that reduces manual annotation cost by using a small number of manually labeled examples. In our approach we use a confidence measure based on the model's variance that reaches a considerable accuracy for annotating entities. Conditional Random Field (CRF) is chosen as the underlying learning model due to its promising performance in many sequence labeling tasks. The experiments show that the proposed method needs considerably fewer manual labeled samples to produce a desirable result.

  19. Image Processing Strategies Based on a Visual Saliency Model for Object Recognition Under Simulated Prosthetic Vision.

    PubMed

    Wang, Jing; Li, Heng; Fu, Weizhen; Chen, Yao; Li, Liming; Lyu, Qing; Han, Tingting; Chai, Xinyu

    2016-01-01

    Retinal prostheses have the potential to restore partial vision. Object recognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to object recognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients. PMID:25981202

  20. Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

    PubMed

    Selvaraj, Lokesh; Ganesan, Balakrishnan

    2014-01-01

    Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy. PMID:25478588

  1. SIFT-based error compensation for ear feature matching and recognition system

    NASA Astrophysics Data System (ADS)

    Jiang, Jingying; Zhang, Qi; Ma, Congcong; Lu, Junsheng; Xu, Kexin

    2015-03-01

    The current ear feature matching and recognition system, based on Scale Invariant Feature Transform (SIFT) image matching algorithm, can realize the human ear feature matching and detect the displacement of the human ear so as to reproduce the human ear position and posture. However, due to the influence of image acquisition equipment performance and lighting conditions, too dark or too bright background could bring the locally underexposed or overexposed image. This could result in the loss of some image details so as to make it impossible to identity the image and the recognition rate would be reduced. In this talk, the application of image gray level normalization processing can reduce the sensitivity of imaging to light intensity. Accordingly, it will greatly improve the recognition rate of human ears. Furthermore, it has been found that even if the object is stationary, the image matching results still have certain fluctuation changes, which could be caused by the system error. In order to reduce the error, the Background-based Compensation Model (BCM) has been established based on the investigation of the system error brought by the working environment changes. The results show that, BCM can be used to compensate the system errors of ear recognition matching and further improve the matching accuracy of human ear.

  2. Robust and Effective Component-based Banknote Recognition by SURF Features

    PubMed Central

    Hasanuzzaman, Faiz M.; Yang, Xiaodong; Tian, YingLi

    2013-01-01

    Camera-based computer vision technology is able to assist visually impaired people to automatically recognize banknotes. A good banknote recognition algorithm for blind or visually impaired people should have the following features: 1) 100% accuracy, and 2) robustness to various conditions in different environments and occlusions. Most existing algorithms of banknote recognition are limited to work for restricted conditions. In this paper we propose a component-based framework for banknote recognition by using Speeded Up Robust Features (SURF). The component-based framework is effective in collecting more class-specific information and robust in dealing with partial occlusion and viewpoint changes. Furthermore, the evaluation of SURF demonstrates its effectiveness in handling background noise, image rotation, scale, and illumination changes. To authenticate the robustness and generalizability of the proposed approach, we have collected a large dataset of banknotes from a variety of conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves 100% recognition rate on our challenging dataset. PMID:25531008

  3. Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

    PubMed Central

    Selvaraj, Lokesh; Ganesan, Balakrishnan

    2014-01-01

    Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy. PMID:25478588

  4. Development of a PLATO Based Curriculum for Tactile Speech Recognition.

    ERIC Educational Resources Information Center

    Saunders, Frank A.; And Others

    1978-01-01

    Describes a PLATO-based curriculum for teaching profoundly deaf children to understand speech sounds, which are presented as touch patterns on the abdomen. PLATO's auditory disk output is used to speak words and phrases which are converted to touch patterns via a new sensory aid, the teletactor. (Author/JEG)

  5. Word Recognition Reflects Dimension-Based Statistical Learning

    ERIC Educational Resources Information Center

    Idemaru, Kaori; Holt, Lori L.

    2011-01-01

    Speech processing requires sensitivity to long-term regularities of the native language yet demands listeners to flexibly adapt to perturbations that arise from talker idiosyncrasies such as nonnative accent. The present experiments investigate whether listeners exhibit "dimension-based statistical learning" of correlations between acoustic…

  6. Recognition-Based Physical Response to Facilitate EFL Learning

    ERIC Educational Resources Information Center

    Hwang, Wu-Yuin; Shih, Timothy K.; Yeh, Shih-Ching; Chou, Ke-Chien; Ma, Zhao-Heng; Sommool, Worapot

    2014-01-01

    This study, based on total physical response and cognitive psychology, proposed a Kinesthetic English Learning System (KELS), which utilized Microsoft's Kinect technology to build kinesthetic interaction with life-related contexts in English. A subject test with 39 tenth-grade students was conducted following empirical research method in…

  7. Oxoanion Recognition by Benzene-based Tripodal Pyrrolic Receptors

    SciTech Connect

    Bill, Nathan; Kim, Dae-Sik; Kim, Sung Kuk; Park, Jung Su; Lynch, Vincent M.; Young, Neil J; Hay, Benjamin; Yang, Youjun; Anslyn, Eric; Sessler, Jonathan L.

    2012-01-01

    Two new tripodal receptors based on pyrrole- and dipyrromethane-functionalised derivatives of a sterically geared precursor, 1,3,5-tris(aminomethyl)-2,4,6-triethylbenzene, are reported; these systems, compounds 1 and 2, display high affinity and selectivity for tetrahedral anionic guests, in particular dihydrogen phosphate, pyrophosphate and hydrogen sulphate, in acetonitrile as inferred from isothermal titration calorimetry measurements. Support for the anion-binding ability of these systems comes from theoretical calculations and a single-crystal X-ray diffraction structure of the 2:2 (host:guest) dihydrogen phosphate complex is obtained in the case of the pyrrole-based receptor system, 1. Keywords anion receptors, dihydrogen phosphate, hydrogen sulphate, X-ray structure, theoretical calculations.

  8. Road shape recognition based on scene self-similarity

    NASA Astrophysics Data System (ADS)

    Postnikov, Vassili V.; Krohina, Darya A.; Prun, Victor E.

    2015-02-01

    A method of determining of the road shape and direction is proposed. The road can potentially have curved shape as well as be seen unclearly due to weather effects or relief features. The proposed method uses video taken from frontal camera that is rigidly placed in car as an input data. The method is based on self-similarity of typical road image, i.e. the smaller image inside the road is close to downscaled initial image.

  9. A content-based image retrieval method for optical colonoscopy images based on image recognition techniques

    NASA Astrophysics Data System (ADS)

    Nosato, Hirokazu; Sakanashi, Hidenori; Takahashi, Eiichi; Murakawa, Masahiro

    2015-03-01

    This paper proposes a content-based image retrieval method for optical colonoscopy images that can find images similar to ones being diagnosed. Optical colonoscopy is a method of direct observation for colons and rectums to diagnose bowel diseases. It is the most common procedure for screening, surveillance and treatment. However, diagnostic accuracy for intractable inflammatory bowel diseases, such as ulcerative colitis (UC), is highly dependent on the experience and knowledge of the medical doctor, because there is considerable variety in the appearances of colonic mucosa within inflammations with UC. In order to solve this issue, this paper proposes a content-based image retrieval method based on image recognition techniques. The proposed retrieval method can find similar images from a database of images diagnosed as UC, and can potentially furnish the medical records associated with the retrieved images to assist the UC diagnosis. Within the proposed method, color histogram features and higher order local auto-correlation (HLAC) features are adopted to represent the color information and geometrical information of optical colonoscopy images, respectively. Moreover, considering various characteristics of UC colonoscopy images, such as vascular patterns and the roughness of the colonic mucosa, we also propose an image enhancement method to highlight the appearances of colonic mucosa in UC. In an experiment using 161 UC images from 32 patients, we demonstrate that our method improves the accuracy of retrieving similar UC images.

  10. Graph - Based High Resolution Satellite Image Segmentation for Object Recognition

    NASA Astrophysics Data System (ADS)

    Ravali, K.; Kumar, M. V. Ravi; Venugopala Rao, K.

    2014-11-01

    Object based image processing and analysis is challenging research in very high resolution satellite utilisation. Commonly ei ther pixel based classification or visual interpretation is used to recognize and delineate land cover categories. The pixel based classification techniques use rich spectral content of satellite images and fail to utilise spatial relations. To overcome th is drawback, traditional time consuming visual interpretation methods are being used operational ly for preparation of thematic maps. This paper addresses computational vision principles to object level image segmentation. In this study, computer vision algorithms are developed to define the boundary between two object regions and segmentation by representing image as graph. Image is represented as a graph G (V, E), where nodes belong to pixels and, edges (E) connect nodes belonging to neighbouring pixels. The transformed Mahalanobis distance has been used to define a weight function for partition of graph into components such that each component represents the region of land category. This implies that edges between two vertices in the same component have relatively low weights and edges between vertices in different components should have higher weights. The derived segments are categorised to different land cover using supervised classification. The paper presents the experimental results on real world multi-spectral remote sensing images of different landscapes such as Urban, agriculture and mixed land cover. Graph construction done in C program and list the run time for both graph construction and segmentation calculation on dual core Intel i7 system with 16 GB RAM, running 64bit window 7.

  11. An overlapping-feature-based phonological model incorporating linguistic constraints: applications to speech recognition.

    PubMed

    Sun, Jiping; Deng, Li

    2002-02-01

    Modeling phonological units of speech is a critical issue in speech recognition. In this paper, our recent development of an overlapping-feature-based phonological model that represents long-span contextual dependency in speech acoustics is reported. In this model, high-level linguistic constraints are incorporated in automatic construction of the patterns of feature-overlapping and of the hidden Markov model (HMM) states induced by such patterns. The main linguistic information explored includes word and phrase boundaries, morpheme, syllable, syllable constituent categories, and word stress. A consistent computational framework developed for the construction of the feature-based model and the major components of the model are described. Experimental results on the use of the overlapping-feature model in an HMM-based system for speech recognition show improvements over the conventional triphone-based phonological model. PMID:11863165

  12. An overlapping-feature-based phonological model incorporating linguistic constraints: Applications to speech recognition

    NASA Astrophysics Data System (ADS)

    Sun, Jiping; Deng, Li

    2002-02-01

    Modeling phonological units of speech is a critical issue in speech recognition. In this paper, our recent development of an overlapping-feature-based phonological model that represents long-span contextual dependency in speech acoustics is reported. In this model, high-level linguistic constraints are incorporated in automatic construction of the patterns of feature-overlapping and of the hidden Markov model (HMM) states induced by such patterns. The main linguistic information explored includes word and phrase boundaries, morpheme, syllable, syllable constituent categories, and word stress. A consistent computational framework developed for the construction of the feature-based model and the major components of the model are described. Experimental results on the use of the overlapping-feature model in an HMM-based system for speech recognition show improvements over the conventional triphone-based phonological model.

  13. Multi-class remote sensing object recognition based on discriminative sparse representation.

    PubMed

    Wang, Xin; Shen, Siqiu; Ning, Chen; Huang, Fengchen; Gao, Hongmin

    2016-02-20

    The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class object recognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing object recognition. PMID:26906591

  14. Effects of sampling methodology on fish based IBI metrics

    Technology Transfer Automated Retrieval System (TEKTRAN)

    It often difficult to determine the environmental soundness of rivers and streams particularly those that may have been impaired by farming as might be the case in the Mississippi Delta. Analysis of fish data can be simplified by calculating mathematical indices that provide a simple number that in...

  15. Robust Face Recognition via Minimum Error Entropy-Based Atomic Representation.

    PubMed

    Wang, Yulong; Tang, Yuan Yan; Li, Luoqing

    2015-12-01

    Representation-based classifiers (RCs) have attracted considerable attention in face recognition in recent years. However, most existing RCs use the mean square error (MSE) criterion as the cost function, which relies on the Gaussianity assumption of the error distribution and is sensitive to non-Gaussian noise. This may severely degrade the performance of MSE-based RCs in recognizing facial images with random occlusion and corruption. In this paper, we present a minimum error entropy-based atomic representation (MEEAR) framework for face recognition. Unlike existing MSE-based RCs, our framework is based on the minimum error entropy criterion, which is not dependent on the error distribution and shown to be more robust to noise. In particular, MEEAR can produce discriminative representation vector by minimizing the atomic norm regularized Renyi's entropy of the reconstruction error. The optimality conditions are provided for general atomic representation model. As a general framework, MEEAR can also be used as a platform to develop new classifiers. Two effective MEE-based RCs are proposed by defining appropriate atomic sets. The experimental results on popular face databases show that MEEAR can improve both the recognition accuracy and the reconstructed results compared with the state-of-the-art MSE-based RCs. PMID:26513784

  16. Speech Emotion Recognition Based on Parametric Filter and Fractal Dimension

    NASA Astrophysics Data System (ADS)

    Mao, Xia; Chen, Lijiang

    In this paper, we propose a new method that employs two novel features, correlation density (Cd) and fractal dimension (Fd), to recognize emotional states contained in speech. The former feature obtained by a list of parametric filters reflects the broad frequency components and the fine structure of lower frequency components, contributed by unvoiced phones and voiced phones, respectively; the latter feature indicates the non-linearity and self-similarity of a speech signal. Comparative experiments based on Hidden Markov Model and K Nearest Neighbor methods are carried out. The results show that Cd and Fd are much more closely related with emotional expression than the features commonly used.

  17. From neural-based object recognition toward microelectronic eyes

    NASA Technical Reports Server (NTRS)

    Sheu, Bing J.; Bang, Sa Hyun

    1994-01-01

    Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

  18. Cold-pressor stress after learning enhances familiarity-based recognition memory in men.

    PubMed

    McCullough, Andrew M; Yonelinas, Andrew P

    2013-11-01

    Stress that is experienced after items have been encoded into memory can protect memories from the effects of forgetting. However, very little is known about how stress impacts recognition memory. The current study investigated how an aversive laboratory stressor (i.e., the cold-pressor test) that occurs after information has been encoded into memory affects subsequent recognition memory in an immediate and a delayed test (i.e., 2-h and 3-month retention interval). Recognition was assessed for negative and neutral photographs using a hybrid remember/know confidence procedure in order to characterize overall performance and to separate recollection- and familiarity-based responses. The results indicated that relative to a non-stress control condition, post-encoding stress significantly improved familiarity but not recollection-based recognition memory or free recall. The beneficial effects of stress were observed in males for negative and neutral materials at both immediate and long-term delays, but were not significant in females. The results indicate that aversive stress can have long-lasting beneficial effects on the memory strength of information encountered prior to the stressful event. PMID:23823181

  19. Isolation and characterization of peptidoglycan recognition protein 1 from antler base of sika deer (Cervus nippon).

    PubMed

    Jiang, Wei; Yin, Yongguang; Zhou, Yajun; He, Guidan; Qi, Yue

    2014-03-01

    Peptidoglycan recognition proteins (PGRPs) are secreted innate immunity pattern recognition molecules. In this study, a new peptidoglycan recognition protein 1 named cnPGRP1 was isolated from an antler base of sika deer Cervus nippon. The antler base antimicrobial proteins (AAP) were subjected to consecutive chromatographic methods connected to Sephadex G-25 gel filtration column (CM) anion-exchange column, and RP-HPLC. The molecular weight of cnPGRP1 was 17.2 kDa under SDS-PAGE, and peptide mass fingerprint analysis by MALDI-TOF-MS as peptidoglycan recognition protein 1 matched to Dasypus novemcinctus. The matched amino acids sequences were RLYEIIQKWPHYRA. Both Gram-positive and Gram-negative bacteria can be killed by cnPGRP1 in the 50-250 μg/mL range through in vitro. Furthermore, cnPGRP1 has been found to bind Gram-positive bacteria, Gram-negative bacteria, and even fungus. PMID:24360898

  20. Nonparametric Feature Matching Based Conditional Random Fields for Gesture Recognition from Multi-Modal Video.

    PubMed

    Chang, Ju Yong

    2016-08-01

    We present a new gesture recognition method that is based on the conditional random field (CRF) model using multiple feature matching. Our approach solves the labeling problem, determining gesture categories and their temporal ranges at the same time. A generative probabilistic model is formalized and probability densities are nonparametrically estimated by matching input features with a training dataset. In addition to the conventional skeletal joint-based features, the appearance information near the active hand in an RGB image is exploited to capture the detailed motion of fingers. The estimated likelihood function is then used as the unary term for our CRF model. The smoothness term is also incorporated to enforce the temporal coherence of our solution. Frame-wise recognition results can then be obtained by applying an efficient dynamic programming technique. To estimate the parameters of the proposed CRF model, we incorporate the structured support vector machine (SSVM) framework that can perform efficient structured learning by using large-scale datasets. Experimental results demonstrate that our method provides effective gesture recognition results for challenging real gesture datasets. By scoring 0.8563 in the mean Jaccard index, our method has obtained the state-of-the-art results for the gesture recognition track of the 2014 ChaLearn Looking at People (LAP) Challenge. PMID:26800528

  1. Cold-Pressor Stress After Learning Enhances Familiarity-Based Recognition Memory in Men

    PubMed Central

    McCullough, Andrew M.; Yonelinas, Andrew P.

    2013-01-01

    Stress that is experienced after items have been encoded into memory can protect memories from the effects of forgetting. However, very little is known about how stress impacts recognition memory. The current study investigated how an aversive laboratory stressor (i.e., the cold-pressor test) that occurs after information has been encoded into memory affects subsequent recognition memory in an immediate and a delayed test (i.e., 2-hour and 3-month retention interval). Recognition was assessed for negative and neutral photographs using a hybrid remember/know confidence procedure in order to characterize overall performance and to separate recollection- and familiarity-based responses. The results indicated that relative to a non-stress control condition, post-encoding stress significantly improved familiarity but not recollection-based recognition memory or free recall. The beneficial effects of stress were observed in males for negative and neutral materials at both immediate and long-term delays, but were not significant in females. The results indicate that aversive stress can have long-lasting beneficial effects on the memory strength of information encountered prior to the stressful event. PMID:23823181

  2. Variability sensitivity of dynamic texture based recognition in clinical CT data

    NASA Astrophysics Data System (ADS)

    Kwitt, Roland; Razzaque, Sharif; Lowell, Jeffrey; Aylward, Stephen

    2014-03-01

    Dynamic texture recognition using a database of template models has recently shown promising results for the task of localizing anatomical structures in Ultrasound video. In order to understand its clinical value, it is imperative to study the sensitivity with respect to inter-patient variability as well as sensitivity to acquisition parameters such as Ultrasound probe angle. Fully addressing patient and acquisition variability issues, however, would require a large database of clinical Ultrasound from many patients, acquired in a multitude of controlled conditions, e.g., using a tracked transducer. Since such data is not readily attainable, we advocate an alternative evaluation strategy using abdominal CT data as a surrogate. In this paper, we describe how to replicate Ultrasound variabilities by extracting subvolumes from CT and interpreting the image material as an ordered sequence of video frames. Utilizing this technique, and based on a database of abdominal CT from 45 patients, we report recognition results on an organ (kidney) recognition task, where we try to discriminate kidney subvolumes/videos from a collection of randomly sampled negative instances. We demonstrate that (1) dynamic texture recognition is relatively insensitive to inter-patient variation while (2) viewing angle variability needs to be accounted for in the template database. Since naively extending the template database to counteract variability issues can lead to impractical database sizes, we propose an alternative strategy based on automated identification of a small set of representative models.

  3. FISH TISSUE RESIDUE-BASED WILDLIFE VALUES FOR PISCIVOUOUS WILDLIFE: CHLORDANE, DDT, DIELDRIN, HEXACHLOROBENZENE

    EPA Science Inventory

    Fish tissue residue-based wildlife values were derived for chlordane, DDT, dieldrin, endrin, hexachlorobenzene, mercury and PCBs. Piscivorous wildlife for which these benchmarks were derived include belted kingfisher, river otter and mink. Toxic endpoint selection, criteria for t...

  4. USE OF A PHYSIOLOGICALLY BASED TOXICOKINETIC MODEL TO SIMULATE CHRONIC DIETARY EXPOSURE IN FISH

    EPA Science Inventory

    A physiologically based toxicokinetic (PBTK) model was developed to describe dietary uptake of hydrophobic organic chemicals by fish. The GI tract was modeled as four compartments corresponding to the stomach, pyloric ceca, upper intestine, and lower intestine. Partitioning coeff...

  5. Cascaded classifiers for confidence-based chemical named entity recognition

    PubMed Central

    Corbett, Peter; Copestake, Ann

    2008-01-01

    Background Chemical named entities represent an important facet of biomedical text. Results We have developed a system to use character-based n-grams, Maximum Entropy Markov Models and rescoring to recognise chemical names and other such entities, and to make confidence estimates for the extracted entities. An adjustable threshold allows the system to be tuned to high precision or high recall. At a threshold set for balanced precision and recall, we were able to extract named entities at an F score of 80.7% from chemistry papers and 83.2% from PubMed abstracts. Furthermore, we were able to achieve 57.6% and 60.3% recall at 95% precision, and 58.9% and 49.1% precision at 90% recall. Conclusion These results show that chemical named entities can be extracted with good performance, and that the properties of the extraction can be tuned to suit the demands of the task. PMID:19025690

  6. Signature-extendable technology - Global space-based crop recognition

    NASA Technical Reports Server (NTRS)

    Hall, Forrest G.; Badhwar, Gautam D.

    1987-01-01

    The use of signature-extendable technology to improve the efficiency of machine processing of remotely sensed data is examined. Temporal profile technology is employed to automatically recognize crops; the technique uses the Kauth and Thomas (1976) transform of Landsat, multidata, and parameters derived from a model of each crop's greenness-time trajectory. The basic characteristics of temporal profile technology and the U.S. based labeling algorithm are described. Consideration is given to signature extension, signature-extendable spaces, and signature-extendable features. The greenness and brightness parameters used in temporal profile technology are derived. The signature extendability of the parameters is evaluated by applying them to the analysis of corn and soybean crops in the U.S. and Argentina. It is noted that the technique is an affordable and efficient method for deriving data on crops on a global basis.

  7. A fish-based index for assessing the ecological status of Polish transitional and coastal waters.

    PubMed

    Smoliński, Szymon; Całkiewicz, Joanna

    2015-12-30

    Fish assemblages are considered indicators of aquatic ecosystem quality. Based on how fish communities respond to anthropogenic pressures, we developed a multimetric fish index for evaluating the health of both coastal and transitional waters. Fish data were collected along the Polish coast in the years 2011, 2013 and 2014 using different types of gear. Redundancy analysis showed that the most important environmental factor affecting fish community was salinity. Responses to anthropogenic disturbances of 20 candidate metrics were tested by generalized linear models, taking into account salinity, sampling protocol and the proxy of human pressures described by the Baltic Sea Impact Index (BSII). Five selected metrics were combined in a Multimetric Index, which showed negative significant correlation with BSII. The index presented herein appeared to be a good tool for assessing the ecological state of highly impacted Polish transitional and coastal areas and complies with the Water Framework Directive requirements. PMID:26522163

  8. Image recognition of diseased rice seeds based on color feature

    NASA Astrophysics Data System (ADS)

    Cheng, Fang; Ying, Yibin

    2004-11-01

    The objective of this research is to develop a digital image analysis algorithm for detection of diseased rice seeds based on color features. The rice seeds used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou99 and IIyou3207. Images of rice seeds were acquired with a color machine vision system. Each original RGB image was converted to HSV color space and preprocessed to show, as hue in the seed region while the pixels value of background was zero. The hue values were scaled so that they varied from 0.0 to 1.0. Then six color features were extracted and evaluated for their contributions to seed classification. Determined using Blocks method, the mean hue value shows the strongest classification ability. Parzen windowing function method was used to estimate probability density distribution and a threshold of mean hue was drawn to classify normal seeds and diseased seeds. The average accuracy of test data set is 95% for Jinyou402. Then the feature of hue histogram was extracted for diseased seeds and partitioned into two clusters of spot diseased seeds and severe diseased seeds. Desired results were achieved when the two cancroids locations were used to discriminate the disease degree. Combined with the two features of mean hue and histogram, all seeds could be classified as normal seeds, spot diseased seeds and severe diseased seeds. Finally, the algorithm was implemented for all the five varieties to test the adaptability.

  9. Better image texture recognition based on SVM classification

    NASA Astrophysics Data System (ADS)

    Liu, Kuan; Lu, Bin; Wei, Yaxun

    2013-10-01

    Texture classification is very important in remote sensing images, X-ray photos, cell image interpretation and processing, and is also the active research areas of computer vision, image processing, image analysis, image retrieval, and so on. As to spatial domain image, texture analysis can use statistical methods to calculate the texture feature vector. In this paper, we use the gray level co-occurrence matrix and Gabor filter feature vector to calculate the feature vector. For the feature vector classification under normal circumstances we can use Bayesian method, KNN method, BP neural network. In this paper, we use a statistical classification method which is based on SVM method to classify images. Image classification generally includes image preprocessing, image feature extraction, image feature selection and image classification in four steps. In this paper, we use a gray-scale image, by calculating the image gray level cooccurrence matrix and Gabor filtering method to get feature extraction, and then use SVM to training and classification. From the test results, it shows that the SVM method is the better way to solve the problem of texture features for image classification and it shows strong adaptability and robustness for image classification.

  10. Sensitivity based segmentation and identification in automatic speech recognition

    NASA Astrophysics Data System (ADS)

    Absher, R.

    1984-03-01

    This research program continued an investigation of sensitivity analysis, and its use in the segmentation and identification of the phonetic units of speech, that was initiated during the 1982 Summer Faculty Research Program. The elements of the sensitivity matrix, which express the relative change in each pole of the speech model to a relative change in each coefficient of the characteristic equation, were evaluated for an expanded set of data which consisted of six vowels contained in single words spoken in a simple carrier phrase by five males with differing dialects. The objectives were to evaluate the sensitivity matrix, interpret its changes during the production of the vowels, and to evaluate inter-speaker variations. It was determined that the sensitivity analysis (1) serves to segment the vowel interval, (2) provides a measure of when a vowel is on target, and (3) should provide sufficient information to identify each particular vowel. Based on the results presented, sensitivity analysis should result in more accurate segmentation and identification of phonemes and should provide a practicable framework for incorporation of acoustic-phonetic variance as well as time and talker normalization.

  11. Why Fish Oil Fails: A Comprehensive 21st Century Lipids-Based Physiologic Analysis

    PubMed Central

    Peskin, B. S.

    2014-01-01

    The medical community suffered three significant fish oil failures/setbacks in 2013. Claims that fish oil's EPA/DHA would stop the progression of heart disease were crushed when The Risk and Prevention Study Collaborative Group (Italy) released a conclusive negative finding regarding fish oil for those patients with high risk factors but no previous myocardial infarction. Fish oil failed in all measures of CVD prevention—both primary and secondary. Another major 2013 setback occurred when fish oil's DHA was shown to significantly increase prostate cancer in men, in particular, high-grade prostate cancer, in the Selenium and Vitamin E Cancer Prevention Trial (SELECT) analysis by Brasky et al. Another monumental failure occurred in 2013 whereby fish oil's EPA/DHA failed to improve macular degeneration. In 2010, fish oil's EPA/DHA failed to help Alzheimer's victims, even those with low DHA levels. These are by no means isolated failures. The promise of fish oil and its so-called active ingredients EPA / DHA fails time and time again in clinical trials. This lipids-based physiologic review will explain precisely why there should have never been expectation for success. This review will focus on underpublicized lipid science with a focus on physiology. PMID:24551453

  12. Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

    2007-12-01

    The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

  13. A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition

    PubMed Central

    Qin, Huafeng; Qin, Lan; Xue, Lian; Li, Yantao

    2012-01-01

    This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM). PMID:22969351

  14. EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

    PubMed

    Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

    2016-01-01

    In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms. PMID:26805837

  15. A kernel Gabor-based weighted region covariance matrix for face recognition.

    PubMed

    Qin, Huafeng; Qin, Lan; Xue, Lian; Li, Yantao

    2012-01-01

    This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting matrix by computing the similarity of each pixel within a face sample to emphasize features. We then incorporate the weighting matrices into a region covariance matrix, named weighted region covariance matrix (WRCM), to obtain the discriminative features of faces for recognition. Finally, to further preserve discriminative features in higher dimensional space, we develop the kernel Gabor-based weighted region covariance matrix (KGWRCM). Experimental results show that the KGWRCM outperforms other algorithms including the kernel Gabor-based region covariance matrix (KGCRM). PMID:22969351

  16. Pose recognition of articulated target based on ladar range image with elastic shape analysis

    NASA Astrophysics Data System (ADS)

    Liu, Zheng-Jun; Li, Qi; Wang, Qi

    2014-10-01

    Elastic shape analysis is introduced for pose recognition of articulated target which is based on small samples of ladar range images. Shape deformations caused by poses changes represented as closed elastic curves given by the square-root velocity function geodesics are used to quantify shape differences and the Karcher mean is used to build a model library. Three kinds of moments - Hu moment invariants, affine moment invariants, and Zernike moment invariants based on support vector machines (SVMs) - are applied to evaluate this approach. The experiment results show that no matter what the azimuth angles of the testing samples are, this approach is capable of achieving a high recognition rate using only 3 model samples with different carrier to noise ratios (CNR); the performance of this approach is much better than that of three kinds of moments based on SVM, especially under high noise conditions.

  17. EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals

    PubMed Central

    Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

    2016-01-01

    In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector’s false alarms. PMID:26805837

  18. Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression

    PubMed Central

    Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

    2016-01-01

    In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms. PMID:27525734

  19. Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

    PubMed

    Gao, Guangwei; Yang, Jian; Jing, Xiaoyuan; Huang, Pu; Hua, Juliang; Yue, Dong

    2016-01-01

    In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms. PMID:27525734

  20. Feature and score fusion based multiple classifier selection for iris recognition.

    PubMed

    Islam, Md Rabiul

    2014-01-01

    The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al. PMID:25114676

  1. Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

    PubMed Central

    Islam, Md. Rabiul

    2014-01-01

    The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al. PMID:25114676

  2. CASP2 knowledge-based approach to distant homology recognition and fold prediction in CASP4.

    PubMed

    Murzin, A G; Bateman, A

    2001-01-01

    In 1996, in CASP2, we presented a semimanual approach to the prediction of protein structure that was aimed at the recognition of probable distant homology, where it existed, between a given target protein and a protein of known structure (Murzin and Bateman, Proteins 1997; Suppl 1:105-112). Central to our method was the knowledge of all known structural and probable evolutionary relationships among proteins of known structure classified in the SCOP database (Murzin et al., J Mol Biol 1995;247:536-540). It was demonstrated that a knowledge-based approach could compete successfully with the best computational methods of the time in the correct recognition of the target protein fold. Four years later, in CASP4, we have applied essentially the same knowledge-based approach to distant homology recognition, concentrating our effort on the improvement of the completeness and alignment accuracy of our models. The manifold increase of available sequence and structure data was to our advantage, as well as was the experience and expertise obtained through the classification of these data. In particular, we were able to model most of our predictions from several distantly related structures rather than from a single parent structure, and we could use more superfamily characteristic features for the refinement of our alignments. Our predictions for each of the attempted distant homology recognition targets ranked among the few top predictions for each of these targets, with the predictions for the hypothetical protein HI0065 (T0104) and the C-terminal domain of the ABC transporter MalK (T0121C) being particularly successful. We also have attempted the prediction of protein folds of some of the targets tentatively assigned to new superfamilies. The average quality of our fold predictions was far less than the quality of our distant homology recognition models, but for the two targets, chorismate lyase (T0086) and Appr>p cyclic phosphodiesterase (T0094), our predictions achieved

  3. Anion recognition by simple chromogenic and chromo-fluorogenic salicylidene Schiff base or reduced-Schiff base receptors

    NASA Astrophysics Data System (ADS)

    Dalapati, Sasanka; Jana, Sankar; Guchhait, Nikhil

    2014-08-01

    This review contains extensive application of anion sensing ability of salicylidene type Schiff bases and their reduced forms having various substituents with respect to phenolic sbnd OH group. Some of these molecular systems behave as receptor for recognition or sensing of various anions in organic or aqueous-organic binary solvent mixture as well as in the solid supported test kits. Development of Schiff base or reduced Schiff base receptors for anion recognition event is commonly based on the theory of hydrogen bonding interaction or deprotonation of phenolic -OH group. The process of charge transfer (CT) or inhibition of excited proton transfer (ESIPT) or followed by photo-induced electron transfer (PET) lead to naked-eye color change, UV-vis spectral change, chemical shift in the NMR spectra and fluorescence spectral modifications. In this review we have tried to discuss about the anion sensing properties of Schiff base or reduced Schiff base receptors.

  4. A unified framework for activity recognition-based behavior analysis and action prediction in smart homes.

    PubMed

    Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

    2013-01-01

    In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users" actions to gain knowledge about their habits and preferences. PMID:23435057

  5. 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

  6. A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

    PubMed Central

    Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

    2013-01-01

    In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences. PMID:23435057

  7. A novel genome-wide polyadenylation sites recognition system based on condition random field.

    PubMed

    Han, Jiuqiang; Zhang, Shanxin; Liu, Jun; Liu, Ruiling

    2014-01-01

    Polyadenylation including the cleavage of pre-mRNA and addition of a stretch of adenosines to the 3'-end is an essential step of pre-mRNA processing in eukayotes. The known regulatory role of polyadenylation in mRNA localization, stability, and translation and the emerging link between poly(A) and disease states underline the necessary to fully characterize polyadenylation sites. Several artificial intelligence methods have been proposed for poly(A) sites recognition. However, these methods are suitable to small subsets of genome sequences. It is necessary to propose a method for genome-wide recognition of poly(A) sites. Recent efforts have found a lot of poly(A) related factors on DNA level. Here, we proposed a novel genome-wide poly(A) recognition method based on the Condition Random Field (CRF) by integrating multiple features. Compared with the polya_svm (the most accurate program for prediction of poly(A) sites till date), our method had a higher performance with the area under ROC curve(0.8621 versus 0.6796). The result suggests that our method is an effective method in genome wide poly(A) sites recognition. PMID:25571055

  8. 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. PMID:24940876

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

    PubMed

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

    2016-08-01

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

  10. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    PubMed Central

    Jirayucharoensak, Suwicha; Pan-Ngum, Setha; Israsena, Pasin

    2014-01-01

    Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. PMID:25258728

  11. EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation.

    PubMed

    Jirayucharoensak, Suwicha; Pan-Ngum, Setha; Israsena, Pasin

    2014-01-01

    Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. PMID:25258728

  12. 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.

  13. Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature.

    PubMed

    Yang, Zhihao; Lin, Hongfei; Li, Yanpeng

    2008-08-01

    Bio-entity name recognition is the key step for information extraction from biomedical literature. This paper presents a dictionary-based bio-entity name recognition approach. The approach expands the bio-entity name dictionary via the Abbreviation Definitions identifying algorithm, improves the recall rate through the improved edit distance algorithm and adopts some post-processing methods including Pre-keyword and Post-keyword expansion, Part of Speech expansion, merge of adjacent bio-entity names and the exploitation of the contextual cues to further improve the performance. Experiment results show that with this approach even an internal dictionary-based system could achieve a fairly good performance. PMID:18467180

  14. A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment.

    PubMed

    Hargrove, L; Losier, Y; Lock, B; Englehart, K; Hudgins, B

    2007-01-01

    Pattern recognition based myoelectric control systems have been well researched; however very few systems have been implemented in a clinical environment. Although classification accuracy or classification error is the metric most often reported to describe how well these control systems perform, very little work research has been conducted to relate this measure to the usability of the system. This work presents a virtual clothespin usability test to assess the performance of pattern recognition based myoelectric control systems. The results suggest that users can complete the virtual task in reasonable time frames when using systems with high classification accuracies. Additionally, results indicate that a clinically-supported classifier training approach (inclusion of the transient potion of contraction signals) may reduce classification accuracy but increase real-time performance. PMID:18003090

  15. Dynamic physiological signal analysis based on Fisher kernels for emotion recognition.

    PubMed

    Garcia, Hernan F; Orozco, Álvaro A; Álvarez, Mauricio A

    2013-01-01

    Emotional behavior is an active area of study in the fields of neuroscience and affective computing. This field has the fundamental role of emotion recognition in the maintenance of physical and mental health. Valence/Arousal levels are two orthogonal, independent dimensions of any emotional stimulus and allows an analysis framework in affective research. In this paper we present our framework for emotional regression based on machine learning techniques. Autoregressive coefficients and hidden markov models on physiological signals, based on Fisher Kernels characterization are presented for mapping variable length sequences to new dimension feature vector space. Then, support vector regression is performed over the Fisher Scores for emotional recognition. Also quantitatively we evaluated the accuracy of the proposed model by acomplishing a hold-out cross validation over the dataset. The experimental results show that the proposed model can effectively perform the regression in comparison with static characterization methods. PMID:24110689

  16. Evaluation of Midwater Trawl Selectivity and its Influence on Acoustic-Based Fish Population Surveys

    NASA Astrophysics Data System (ADS)

    Williams, Kresimir

    Trawls are used extensively during fisheries abundance surveys to derive estimates of fish density and, in the case of acoustic-based surveys, to identify acoustically sampled fish populations. However, trawls are selective in what fish they retain, resulting in biased estimates of density, species, and size compositions. Selectivity of the midwater trawl used in acoustic-based surveys of walleye pollock (Theragra chalcogramma) was evaluated using multiple methods. The effects of trawl selectivity on the acoustic-based survey abundance estimates and the stock assessment were evaluated for the Gulf of Alaska walleye pollock population. Selectivity was quantified using recapture, or pocket, nets attached to the outside of the trawl. Pocket net catches were modeled using a hierarchical Bayesian model to provide uncertainty in selectivity parameter estimates. Significant under-sampling of juvenile pollock by the midwater trawl was found, with lengths at 50% retention ranging from 14--26 cm over three experiments. Escapement was found to be light dependent, with more fish escaping in dark conditions. Highest escapement rates were observed in the aft of the trawl near to the codend though the bottom panel of the trawl. The behavioral mechanisms involved in the process of herding and escapement were evaluated using stereo-cameras, a DIDSON high frequency imaging sonar, and pocket nets. Fish maintained greater distances from the trawl panel during daylight, suggesting trawl modifications such as increased visibility of netting materials may evoke stronger herding responses and increased retention of fish. Selectivity and catchability of pollock by the midwater trawl was also investigated using acoustic density as an independent estimate of fish abundance to compare with trawl catches. A modeling framework was developed to evaluate potential explanatory factors for selectivity and catchability. Selectivity estimates were dependent on which vessel was used for the survey

  17. Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

    PubMed

    Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

    2016-06-01

    Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges. PMID:27106584

  18. Polymer-based separations: Synthesis and application of polymers for ionic and molecular recognition

    SciTech Connect

    Alexandratos, S.D.

    1992-01-01

    Polymer-based separations have utilized resins such as sulfonic, acrylic, and iminodiacetic acid resins and the XAD series. Selective polymeric reagents for reaction with a targeted metal ion were synthesized as polymers with two different types of functional groups, each operating on the ions through a different mechanism. There are 3 classes of DMBPs (dual mechanism bifunctional polymers). Research during this period dealing with metal ion recognition focused on two of these classes (reduction of metal ions to metal; selective complexation).

  19. An improved poly(A) motifs recognition method based on decision level fusion.

    PubMed

    Zhang, Shanxin; Han, Jiuqiang; Liu, Jun; Zheng, Jiguang; Liu, Ruiling

    2015-02-01

    Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene regulation. The bioinformatics methods used for poly(A) motifs recognition have demonstrated that information extracted from sequences surrounding the candidate motifs can differentiate true motifs from the false ones greatly. However, these methods depend on either domain features or string kernels. To date, methods combining information from different sources have not been found yet. Here, we proposed an improved poly(A) motifs recognition method by combing different sources based on decision level fusion. First of all, two novel prediction methods was proposed based on support vector machine (SVM): one method is achieved by using the domain-specific features and principle component analysis (PCA) method to eliminate the redundancy (PCA-SVM); the other method is based on Oligo string kernel (Oligo-SVM). Then we proposed a novel machine-learning method for poly(A) motif prediction by marrying four poly(A) motifs recognition methods, including two state-of-the-art methods (Random Forest (RF) and HMM-SVM), and two novel proposed methods (PCA-SVM and Oligo-SVM). A decision level information fusion method was employed to combine the decision values of different classifiers by applying the DS evidence theory. We evaluated our method on a comprehensive poly(A) dataset that consists of 14,740 samples on 12 variants of poly(A) motifs and 2750 samples containing none of these motifs. Our method has achieved accuracy up to 86.13%. Compared with the four classifiers, our evidence theory based method reduces the average error rate by about 30%, 27%, 26% and 16%, respectively. The experimental results suggest that the proposed method is more effective for poly(A) motif recognition. PMID:25594576

  20. Recognition of short-term changes in physiological signals with the wavelet-based multifractal formalism

    NASA Astrophysics Data System (ADS)

    Pavlov, Alexey N.; Sindeeva, Olga A.; Sindeev, Sergey S.; Pavlova, Olga N.; Rybalova, Elena V.; Semyachkina-Glushkovskaya, Oxana V.

    2016-03-01

    In this paper we address the problem of revealing and recognition transitions between distinct physiological states using quite short fragments of experimental recordings. With the wavelet-based multifractal analysis we characterize changes of complexity and correlation properties in the stress-induced dynamics of arterial blood pressure in rats. We propose an approach for association revealed changes with distinct physiological regulatory mechanisms and for quantifying the influence of each mechanism.

  1. Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems

    PubMed Central

    Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

    2015-01-01

    A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What’s more, the improved algorithm can enhance the accuracy of blind recognition obviously. PMID:26154439

  2. Real-time lidar-based place recognition using distinctive shape descriptors

    NASA Astrophysics Data System (ADS)

    Collier, Jack; Se, Stephen; Kotamraju, Vinay; Jasiobedzki, Piotr

    2012-06-01

    A key component in the emerging localization and mapping paradigm is an appearance-based place recognition algorithm that detects when a place has been revisited. This algorithm can run in the background at a low frame rate and be used to signal a global geometric mapping algorithm when a loop is detected. An optimization technique can then be used to correct the map by 'closing the loop'. This allows an autonomous unmanned ground vehicle to improve localization and map accuracy and successfully navigate large environments. Image-based place recognition techniques lack robustness to sensor orientation and varying lighting conditions. Additionally, the quality of range estimates from monocular or stereo imagery can decrease the loop closure accuracy. Here, we present a lidar-based place recognition system that is robust to these challenges. This probabilistic framework learns a generative model of place appearance and determines whether a new observation comes from a new or previously seen place. Highly descriptive features called the Variable Dimensional Local Shape Descriptors are extracted from lidar range data to encode environment features. The range data processing has been implemented on a graphics processing unit to optimize performance. The system runs in real-time on a military research vehicle equipped with a highly accurate, 360 degree field of view lidar and can detect loops regardless of the sensor orientation. Promising experimental results are presented for both rural and urban scenes in large outdoor environments.

  3. Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

    PubMed Central

    Jurek, Anna; Nugent, Chris; Bi, Yaxin; Wu, Shengli

    2014-01-01

    Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks. PMID:25014095

  4. A state-based approach to trend recognition and failure prediction for the Space Station Freedom

    NASA Technical Reports Server (NTRS)

    Nelson, Kyle S.; Hadden, George D.

    1992-01-01

    A state-based reasoning approach to trend recognition and failure prediction for the Altitude Determination, and Control System (ADCS) of the Space Station Freedom (SSF) is described. The problem domain is characterized by features (e.g., trends and impending failures) that develop over a variety of time spans, anywhere from several minutes to several years. Our state-based reasoning approach, coupled with intelligent data screening, allows features to be tracked as they develop in a time-dependent manner. That is, each state machine has the ability to encode a time frame for the feature it detects. As features are detected, they are recorded and can be used as input to other state machines, creating a hierarchical feature recognition scheme. Furthermore, each machine can operate independently of the others, allowing simultaneous tracking of features. State-based reasoning was implemented in the trend recognition and the prognostic modules of a prototype Space Station Freedom Maintenance and Diagnostic System (SSFMDS) developed at Honeywell's Systems and Research Center.

  5. A review on electronic bio-sensing approaches based on non-antibody recognition elements.

    PubMed

    Chen, Hu; Huang, Jingfeng; Palaniappan, Alagappan; Wang, Yi; Liedberg, Bo; Platt, Mark; Tok, Alfred Iing Yoong

    2016-04-21

    In this review, recent advances in the development of electronic detection methodologies based on non-antibody recognition elements such as functional liposomes, aptamers and synthetic peptides are discussed. Particularly, we highlight the progress of field effect transistor (FET) sensing platforms where possible as the number of publications on FET-based platforms has increased rapidly. Biosensors involving antibody-antigen interactions have been widely applied in diagnostics and healthcare in virtue of their superior selectivity and sensitivity, which can be attributed to their high binding affinity and extraordinary specificity, respectively. However, antibodies typically suffer from fragile and complicated functional structures, large molecular size and sophisticated preparation approaches (resource-intensive and time-consuming), resulting in limitations such as short shelf-life, insufficient stability and poor reproducibility. Recently, bio-sensing approaches based on synthetic elements have been intensively explored. In contrast to existing reports, this review provides a comprehensive overview of recent advances in the development of biosensors utilizing synthetic recognition elements and a detailed comparison of their assay performances. Therefore, this review would serve as a good summary of the efforts for the development of electronic bio-sensing approaches involving synthetic recognition elements. PMID:27002177

  6. Clustering-based ensemble learning for activity recognition in smart homes.

    PubMed

    Jurek, Anna; Nugent, Chris; Bi, Yaxin; Wu, Shengli

    2014-01-01

    Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks. PMID:25014095

  7. A knowledge-based object recognition system for applications in the space station

    NASA Astrophysics Data System (ADS)

    Dhawan, Atam P.

    1988-02-01

    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.

  8. 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.

  9. Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

    NASA Astrophysics Data System (ADS)

    Chude-Olisah, Chollette C.; Sulong, Ghazali; Chude-Okonkwo, Uche A. K.; Hashim, Siti Z. M.

    2014-12-01

    Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

  10. Vibrodiagnostics of gearboxes using NBV-based classifier: A pattern recognition approach

    NASA Astrophysics Data System (ADS)

    Dybała, Jacek

    2013-07-01

    Gearbox faults are one of the major factors causing breakdown of industrial machinery and gearbox diagnosing is one of the most important topics in machine condition monitoring. This paper presents a new pattern recognition approach to the condition monitoring of technical objects working under time varying load. The approach shows potential for the fault detection of the high-power planetary gearbox used in bucket wheel excavators. In the presented pattern recognition approach, relations between spectral components of the gearbox vibration signal were investigated in the full range of gearbox operating conditions. A novel Noise-Assisted Feature Subset Evaluation (NAFSE) method addressed for the extraction of diagnostic parameters was introduced. The NAFSE method integrates the feature subset evaluation with the NBV-based classifier and extracts the diagnostic parameter set useful for this classifier. The NBV-based classifier conducted the final recognition of the gearbox condition on the basis of the diagnostic parameters obtained from the NAFSE method. The NBV-based classifier is, in its essence, the condensed 1-NN classifier based on Nearest Boundary Vector algorithm. The elaborated algorithms for determining basic and supplemental boundary vectors together with the original editing procedure of the training set reduction create the original hybrid prototype selection method. The effectiveness of this method has been confirmed in the classification task of the benchmark dataset. In contrast to the traditional hard classifier that assigns only a single-value class label to an investigated pattern, the NBV-based classifier enables the semi-soft classification which offers the possibility of evaluating classification certainty. The offered possibility of evaluating classification certainty has a significant diagnostic meaning. In diagnostic practice it is often not enough merely to recognize the object's condition, but the information about the certainty of the

  11. Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition

    NASA Astrophysics Data System (ADS)

    Xiao, Pengfeng; Zhang, Xueliang; Wang, Dongguang; Yuan, Min; Feng, Xuezhi; Kelly, Maggi

    2016-09-01

    This study proposed a new framework that combines pixel-level change detection and object-level recognition to detect changes of built-up land from high-spatial resolution remote sensing images. First, an adaptive differencing method was designed to detect changes at the pixel level based on both spectral and textural features. Next, the changed pixels were subjected to a set of morphological operations to improve the completeness and to generate changed objects, achieving the transition of change detection from the pixel level to the object level. The changed objects were further recognised through the difference of morphological building index in two phases to indicate changed objects on built-up land. The transformation from changed pixels to changed objects makes the proposed framework distinct with both the pixel-based and the object-based change detection methods. Compared with the pixel-based methods, the proposed framework can improve the change detection capability through the transformation and successive recognition of objects. Compared with the object-based method, the proposed framework avoids the issue of multitemporal segmentation and can generate changed objects directly from changed pixels. The experimental results show the effectiveness of the transformation from changed pixels to changed objects and the successive object-based recognition on improving the detection accuracy, which justify the application potential of the proposed change detection framework.

  12. Gaze Estimation for Off-Angle Iris Recognition Based on the Biometric Eye Model

    SciTech Connect

    Karakaya, Mahmut; Barstow, Del R; Santos-Villalobos, Hector J; Thompson, Joseph W; Bolme, David S; Boehnen, Chris Bensing

    2013-01-01

    Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-angle iris recognition framework based on the ANONYMIZED biometric eye model. Gaze estimation is an important prerequisite step to correct an off-angle iris images. To achieve the accurate frontal reconstruction of an off-angle iris image, we first need to estimate the eye gaze direction from elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.

  13. Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions.

    PubMed

    Daqrouq, K; Dobaie, A

    2016-01-01

    An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB. PMID:26949412

  14. Infrared face recognition based on intensity of local micropattern-weighted local binary pattern

    NASA Astrophysics Data System (ADS)

    Xie, Zhihua; Liu, Guodong

    2011-07-01

    The traditional local binary pattern (LBP) histogram representation extracts the local micropatterns and assigns the same weight to all local micropatterns. To combine the different contributions of local micropatterns to face recognition, this paper proposes a weighted LBP histogram based on Weber's law. First, inspired by psychological Weber's law, intensity of local micropattern is defined by the ratio between two terms: one is relative intensity differences of a central pixel against its neighbors and the other is intensity of local central pixel. Second, regarding the intensity of local micropattern as its weight, the weighted LBP histogram is constructed with the defined weight. Finally, to make full use of the space location information and lessen the complexity of recognition, the partitioning and locality preserving projection are applied to get final features. The proposed method is tested on our infrared face databases and yields the recognition rate of 99.2% for same-session situation and 96.4% for elapsed-time situation compared to the 97.6 and 92.1% produced by the method based on traditional LBP.

  15. Spacetime texture representation and recognition based on a spatiotemporal orientation analysis.

    PubMed

    Derpanis, Konstantinos G; Wildes, Richard P

    2012-06-01

    This paper is concerned with the representation and recognition of the observed dynamics (i.e., excluding purely spatial appearance cues) of spacetime texture based on a spatiotemporal orientation analysis. The term "spacetime texture" is taken to refer to patterns in visual spacetime, (x,y,t), that primarily are characterized by the aggregate dynamic properties of elements or local measurements accumulated over a region of spatiotemporal support, rather than in terms of the dynamics of individual constituents. Examples include image sequences of natural processes that exhibit stochastic dynamics (e.g., fire, water, and windblown vegetation) as well as images of simpler dynamics when analyzed in terms of aggregate region properties (e.g., uniform motion of elements in imagery, such as pedestrians and vehicular traffic). Spacetime texture representation and recognition is important as it provides an early means of capturing the structure of an ensuing image stream in a meaningful fashion. Toward such ends, a novel approach to spacetime texture representation and an associated recognition method are described based on distributions (histograms) of spacetime orientation structure. Empirical evaluation on both standard and original image data sets shows the promise of the approach, including significant improvement over alternative state-of-the-art approaches in recognizing the same pattern from different viewpoints. PMID:22064801

  16. Weighted sparse representation for human ear recognition based on local descriptor

    NASA Astrophysics Data System (ADS)

    Mawloud, Guermoui; Djamel, Melaab

    2016-01-01

    A two-stage ear recognition framework is presented where two local descriptors and a sparse representation algorithm are combined. In a first stage, the algorithm proceeds by deducing a subset of the closest training neighbors to the test ear sample. The selection is based on the K-nearest neighbors classifier in the pattern of oriented edge magnitude feature space. In a second phase, the co-occurrence of adjacent local binary pattern features are extracted from the preselected subset and combined to form a dictionary. Afterward, sparse representation classifier is employed on the developed dictionary in order to infer the closest element to the test sample. Thus, by splitting up the ear image into a number of segments and applying the described recognition routine on each of them, the algorithm finalizes by attributing a final class label based on majority voting over the individual labels pointed out by each segment. Experimental results demonstrate the effectiveness as well as the robustness of the proposed scheme over leading state-of-the-art methods. Especially when the ear image is occluded, the proposed algorithm exhibits a great robustness and reaches the recognition performances outlined in the state of the art.

  17. FPGA and DSP based an intelligent visual sensor design for laser welding seam recognition

    NASA Astrophysics Data System (ADS)

    Jiang, Fang; Jiang, Chunying; Ge, Hanlin

    2010-10-01

    A kind of intelligent visual sensor system, which is mainly composed of a laser lighting source and an intelligent camera based on FPGA and DSP, is designed using the triangle structured light principle for laser welding seam recognition. Intending to meet the high-precision demands, on one hand, the components of the sensor such as the lighting source, the optical lens, the optic sensor of the camera are selected and the errors caused by these parts are analyzed. Furthermore, the triangle structured light principle models are built and the simulation is given to optimize the light path parameters. On the other hand, the methods of the image processing for seam recognition using intelligent camera based on FPGA and DSP are explained. Experiments results proved that the sensor design is reasonable and the image recognition methods are rational and effective. Very compact structure, high-precision and high-real-time performance are integrated in this sensor, so it can be applied well in the seam tracking and inspection system in the laser welding and arc welding fields.

  18. Speech recognition method based on genetic vector quantization and BP neural network

    NASA Astrophysics Data System (ADS)

    Gao, Li'ai; Li, Lihua; Zhou, Jian; Zhao, Qiuxia

    2009-07-01

    Vector Quantization is one of popular codebook design methods for speech recognition at present. In the process of codebook design, traditional LBG algorithm owns the advantage of fast convergence, but it is easy to get the local optimal result and be influenced by initial codebook. According to the understanding that Genetic Algorithm has the capability of getting the global optimal result, this paper proposes a hybrid clustering method GA-L based on Genetic Algorithm and LBG algorithm to improve the codebook.. Then using genetic neural networks for speech recognition. consequently search a global optimization codebook of the training vector space. The experiments show that neural network identification method based on genetic algorithm can extricate from its local maximum value and the initial restrictions, it can show superior to the standard genetic algorithm and BP neural network algorithm from various sources, and the genetic BP neural networks has a higher recognition rate and the unique application advantages than the general BP neural network in the same GA-VQ codebook, it can achieve a win-win situation in the time and efficiency.

  19. Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

    PubMed Central

    García-Garrido, Miguel A.; Ocaña, Manuel; Llorca, David F.; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

    2012-01-01

    This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance. PMID:22438704

  20. Design of a hand-shape acquisition and recognition system based on DSP

    NASA Astrophysics Data System (ADS)

    Li, Wenwen; Liu, Fu; Gao, Lei

    2013-10-01

    In this paper, we design a hand-shape image acquisition and processing system based on DSP for solving the problem of hand-shape recognition. Acquisition configuration was designed, and the key parts (encoder, decoder, memory chip etc.) are selected. And a new method for hand-shape recognition based on wavelet moment is presented to overcome some shortage in present method for hand shape recognition. Firstly, image processing including binary processing and segment of hand silhouette are used, and then translation and scale normalization algorithms is implemented on the palms and fingers image. After that the wavelet moment characteristics of image are extracted. At last, support vector is achieved by training 100 images data in images database, 10 testing images were selected randomly to verify validity and feasibility of algorithms. Experimental results indicate that the 10 testing images are all classified correctly. The new method of extracting hand shape wavelet moment as characteristic matrix is easy to realize with characteristic of high utility and accuracy, and solve the problem of translation, rotation and scaling during the image acquisition process without positioning aids.

  1. The development of adaptive decision making: Recognition-based inference in children and adolescents.

    PubMed

    Horn, Sebastian S; Ruggeri, Azzurra; Pachur, Thorsten

    2016-09-01

    Judgments about objects in the world are often based on probabilistic information (or cues). A frugal judgment strategy that utilizes memory (i.e., the ability to discriminate between known and unknown objects) as a cue for inference is the recognition heuristic (RH). The usefulness of the RH depends on the structure of the environment, particularly the predictive power (validity) of recognition. Little is known about developmental differences in use of the RH. In this study, the authors examined (a) to what extent children and adolescents recruit the RH when making judgments, and (b) around what age adaptive use of the RH emerges. Primary schoolchildren (M = 9 years), younger adolescents (M = 12 years), and older adolescents (M = 17 years) made comparative judgments in task environments with either high or low recognition validity. Reliance on the RH was measured with a hierarchical multinomial model. Results indicated that primary schoolchildren already made systematic use of the RH. However, only older adolescents adaptively adjusted their strategy use between environments and were better able to discriminate between situations in which the RH led to correct versus incorrect inferences. These findings suggest that the use of simple heuristics does not progress unidirectionally across development but strongly depends on the task environment, in line with the perspective of ecological rationality. Moreover, adaptive heuristic inference seems to require experience and a developed base of domain knowledge. (PsycINFO Database Record PMID:27505696

  2. A co-expression modules based gene selection for cancer recognition.

    PubMed

    Lu, Xinguo; Deng, Yong; Huang, Lei; Feng, Bingtao; Liao, Bo

    2014-12-01

    Gene expression profiles are used to recognize patient samples for cancer diagnosis and therapy. Gene selection is crucial to high recognition performance. In usual gene selection methods the genes are considered as independent individuals and the correlation among genes is not used efficiently. In this description, a co-expression modules based gene selection method for cancer recognition is proposed. First, in the cancer dataset a weighted correlation network is constructed according to the correlation between each pair of genes, different modules from this network are identified and the significant modules are selected for following exploration. Second, based on these informative modules information gain is applied to selecting the feature genes for cancer recognition. Then using LOOCV, the experiments with different classification algorithms are conducted and the results show that the proposed method makes better classification accuracy than traditional gene selection methods. At last, via gene ontology enrichment analysis the biological significance of the co-expressed genes in specific modules was verified. PMID:24440175

  3. Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

    PubMed Central

    Daqrouq, K.; Dobaie, A.

    2016-01-01

    An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB. PMID:26949412

  4. SD-MSAEs: Promoter recognition in human genome based on deep feature extraction.

    PubMed

    Xu, Wenxuan; Zhang, Li; Lu, Yaping

    2016-06-01

    The prediction and recognition of promoter in human genome play an important role in DNA sequence analysis. Entropy, in Shannon sense, of information theory is a multiple utility in bioinformatic details analysis. The relative entropy estimator methods based on statistical divergence (SD) are used to extract meaningful features to distinguish different regions of DNA sequences. In this paper, we choose context feature and use a set of methods of SD to select the most effective n-mers distinguishing promoter regions from other DNA regions in human genome. Extracted from the total possible combinations of n-mers, we can get four sparse distributions based on promoter and non-promoters training samples. The informative n-mers are selected by optimizing the differentiating extents of these distributions. Specially, we combine the advantage of statistical divergence and multiple sparse auto-encoders (MSAEs) in deep learning to extract deep feature for promoter recognition. And then we apply multiple SVMs and a decision model to construct a human promoter recognition method called SD-MSAEs. Framework is flexible that it can integrate new feature extraction or new classification models freely. Experimental results show that our method has high sensitivity and specificity. PMID:27018214

  5. Appearance-based human gesture recognition using multimodal features for human computer interaction

    NASA Astrophysics Data System (ADS)

    Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

    2011-03-01

    The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

  6. Complete vision-based traffic sign recognition supported by an I2V communication system.

    PubMed

    García-Garrido, Miguel A; Ocaña, Manuel; Llorca, David F; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

    2012-01-01

    This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance. PMID:22438704

  7. Static and Dynamic Features for Improved HMM based Visual Speech Recognition

    NASA Astrophysics Data System (ADS)

    Rajavel, R.; Sathidevi, P. S.

    Visual speech recognition refers to the identification of utterances through the movements of lips, tongue, teeth, and other facial muscles of the speaker without using the acoustic signal. This work shows the relative benefits of both static and dynamic visual speech features for improved visual speech recognition. Two approaches for visual feature extraction have been considered: (1) an image transform based static feature approach in which Discrete Cosine Transform (DCT) is applied to each video frame and 6×6 triangle region coefficients are considered as features. Principal Component Analysis (PCA) is applied over all 60 features corresponding to the video frame to reduce the redundancy; the resultant 21 coefficients are taken as the static visual features. (2) Motion segmentation based dynamic feature approach in which the facial movements are segmented from the video file using motion history images (MHI). DCT is applied to the MHI and triangle region coefficients are taken as the dynamic visual features. Two types of experiments were done one with concatenated features and another with dimension reduced feature by using PCA to identify the utterances. The left-right continuous HMMs are used as visual speech classifier to classify nine MPEG-4 standard viseme consonants. The experimental result shows that the concatenated as well as dimension reduced features improve te visual speech recognition with a high accuracy of 92.45% and 92.15% respectively.

  8. ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering

    PubMed Central

    Ren, Xiang; El-Kishky, Ahmed; Wang, Chi; Tao, Fangbo; Voss, Clare R.; Ji, Heng; Han, Jiawei

    2015-01-01

    Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres—news, Yelp reviews and tweets—demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method. PMID:26705503

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

    NASA Astrophysics Data System (ADS)

    Mohd Zin, Zalhan; Ibrahim, Ratnawati

    2011-06-01

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

  10. Gaze estimation for off-angle iris recognition based on the biometric eye model

    NASA Astrophysics Data System (ADS)

    Karakaya, Mahmut; Barstow, Del; Santos-Villalobos, Hector; Thompson, Joseph; Bolme, David; Boehnen, Christopher

    2013-05-01

    Iris recognition is among the highest accuracy biometrics. However, its accuracy relies on controlled high quality capture data and is negatively affected by several factors such as angle, occlusion, and dilation. Non-ideal iris recognition is a new research focus in biometrics. In this paper, we present a gaze estimation method designed for use in an off-angle iris recognition framework based on the ORNL biometric eye model. Gaze estimation is an important prerequisite step to correct an off-angle iris images. To achieve the accurate frontal reconstruction of an off-angle iris image, we first need to estimate the eye gaze direction from elliptical features of an iris image. Typically additional information such as well-controlled light sources, head mounted equipment, and multiple cameras are not available. Our approach utilizes only the iris and pupil boundary segmentation allowing it to be applicable to all iris capture hardware. We compare the boundaries with a look-up-table generated by using our biologically inspired biometric eye model and find the closest feature point in the look-up-table to estimate the gaze. Based on the results from real images, the proposed method shows effectiveness in gaze estimation accuracy for our biometric eye model with an average error of approximately 3.5 degrees over a 50 degree range.

  11. Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

    PubMed

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

    2014-01-01

    For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. PMID:24693243

  12. Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

    PubMed

    Gutta, Sandeep; Cheng, Qi

    2016-03-01

    Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database. PMID:25680220

  13. Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

    PubMed Central

    2014-01-01

    For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. PMID:24693243

  14. Multi-Scale Locality-Constrained Spatiotemporal Coding for Local Feature Based Human Action Recognition

    PubMed Central

    Liu, Yu; Wang, Wei; Xu, Wei; Zhang, Maojun

    2013-01-01

    We propose a Multiscale Locality-Constrained Spatiotemporal Coding (MLSC) method to improve the traditional bag of features (BoF) algorithm which ignores the spatiotemporal relationship of local features for human action recognition in video. To model this spatiotemporal relationship, MLSC involves the spatiotemporal position of local feature into feature coding processing. It projects local features into a sub space-time-volume (sub-STV) and encodes them with a locality-constrained linear coding. A group of sub-STV features obtained from one video with MLSC and max-pooling are used to classify this video. In classification stage, the Locality-Constrained Group Sparse Representation (LGSR) is adopted to utilize the intrinsic group information of these sub-STV features. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the competing local spatiotemporal feature-based human action recognition methods. PMID:24194681

  15. A method of recognition based on the feature layer fusion of palmprint and hand vein

    NASA Astrophysics Data System (ADS)

    Ma, Hua; Yang, Xiaoping; Shi, Guangyuan

    2013-12-01

    In this paper, a method of recognition of multi-modal biometrics for palmprint and hand vein based on the feature layer fusion is proposed, combined with the characteristics of an improved canonical correlation analysis (CCA) and two dimensional principal component analysis (2DPCA). After pretreatment respectively, feature vectors of palmprint and hand vein images are extracted using two dimensional principal component analysis (2DPCA),then fused in the feature level using the improved canonical correlation analysis(CCA), so identification can be done by a adjacent classifier finally. Using this method, two biometric information can be fused and the redundancy of information between features can effectively eliminated, the problem of the high-dimensional and small sample size can be overcome too. Simulation experimental results show that the proposed method in this paper can effectively improve the recognition rate of identification.

  16. Knowledge-based recognition algorithm for long-range infrared bridge images

    NASA Astrophysics Data System (ADS)

    Cao, Zhiguo; Sun, Qi; Zhang, Tianxu

    2001-10-01

    The recognition of bridge in long-range infrared images presents a number of problems due to the complexity of background, high noise interference, small size of target and low contrast between bridge and its surrounding water area. To counter these barriers, we have developed a new knowledge-based recognition algorithm. It first detects the candidate bridge sub-regions and then focus on them. According to the degree to which they match with our pre-built framework, different credits are given, so the false objects are excluded and eventually real target is found. The experimental results demonstrate our localized method is always superior to the traditional global algorithms adopted by most former researchers.

  17. A high precision feature based on LBP and Gabor theory for face recognition.

    PubMed

    Xia, Wei; Yin, Shouyi; Ouyang, Peng

    2013-01-01

    How to describe an image accurately with the most useful information but at the same time the least useless information is a basic problem in the recognition field. In this paper, a novel and high precision feature called BG2D2LRP is proposed, accompanied with a corresponding face recognition system. The feature contains both static texture differences and dynamic contour trends. It is based on Gabor and LBP theory, operated by various kinds of transformations such as block, second derivative, direct orientation, layer and finally fusion in a particular way. Seven well-known face databases such as FRGC, AR, FERET and so on are used to evaluate the veracity and robustness of the proposed feature. A maximum improvement of 29.41% is achieved comparing with other methods. Besides, the ROC curve provides a satisfactory figure. Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method. PMID:23552103

  18. Image preprocessing based on spatial statistics for the optical correlation recognition of aviation photographs

    NASA Astrophysics Data System (ADS)

    Sun, Yifan; Tan, Qiaofeng; Cao, Liangcai; Ni, Kai; He, Qingsheng; Jin, Guofan

    2008-03-01

    A new method of digital image processing is developed for the correlation recognition of aviation photographs under volume holographic correlator. The physical characteristics of semivariogram of the image are analyzed according to spatial statistics, which suggests the principle of the image segmentation, including the dimension of the templates, the space between each template and the image restoration. Then the residual space of each template is calculated directly based on the 2D-images to enlarge and to scatter the differences between the correlativity. After that, the residual spaces are reconstructed to form the final set of templates. Finally the proposed method is tested on a series of aviation photographs. The experimental results come with rather higher precision as well as rather lower computational complexity, which supports the real-time capability of the optical correlation recognition of aviation photographs under volume holographic correlator.

  19. Fractal geometry-based classification approach for the recognition of lung cancer cells

    NASA Astrophysics Data System (ADS)

    Xia, Deshen; Gao, Wenqing; Li, Hua

    1994-05-01

    This paper describes a new fractal geometry based classification approach for the recognition of lung cancer cells, which is used in the health inspection for lung cancers, because cancer cells grow much faster and more irregularly than normal cells do, the shape of the segmented cancer cells is very irregular and considered as a graph without characteristic length. We use Texture Energy Intensity Rn to do fractal preprocessing to segment the cells from the image and to calculate the fractal dimention value for extracting the fractal features, so that we can get the figure characteristics of different cancer cells and normal cells respectively. Fractal geometry gives us a correct description of cancer-cell shapes. Through this method, a good recognition of Adenoma, Squamous, and small cancer cells can be obtained.

  20. Fuzzy-based latent-dynamic conditional random fields for continuous gesture recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Shengjun; He, Xiaohai; Teng, Qizhi

    2012-06-01

    We show an original method for automatic hand gesture recognition that makes use of fuzzified latent-dynamic conditional random fields (LDCRF). In this method, fuzzy linguistic variables are used to model the features of hand gestures and then to modify the potential function in LDCRFs. By combining LDCRFs and fuzzy sets, these fuzzy-based LDCRFs (FLDCRF) have the advantages of LDCRFs in sequence labeling along with the advantage of retaining the imprecise character of gestures. The efficiency of the proposed method was tested with unsegmented gesture sequences in three different hand gesture data sets. The experimental results demonstrate that FLDCRFs compare favorably with support vector machines, hidden conditional random fields, and LDCRFs on hand gesture recognition tasks.

  1. ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.

    SciTech Connect

    Gonzales, Antonio Ignacio; Reeves, Paul C.; Jones, John J.; Farkas, Benjamin D.

    2004-06-01

    This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.

  2. SkateBase, an elasmobranch genome project and collection of molecular resources for chondrichthyan fishes

    PubMed Central

    Wyffels, Jennifer; L. King, Benjamin; Vincent, James; Chen, Chuming; Wu, Cathy H.; Polson, Shawn W.

    2014-01-01

    Chondrichthyan fishes are a diverse class of gnathostomes that provide a valuable perspective on fundamental characteristics shared by all jawed and limbed vertebrates. Studies of phylogeny, species diversity, population structure, conservation, and physiology are accelerated by genomic, transcriptomic and protein sequence data. These data are widely available for many sarcopterygii (coelacanth, lungfish and tetrapods) and actinoptergii (ray-finned fish including teleosts) taxa, but limited for chondrichthyan fishes.  In this study, we summarize available data for chondrichthyes and describe resources for one of the largest projects to characterize one of these fish, Leucoraja erinacea, the little skate.  SkateBase ( http://skatebase.org) serves as the skate genome project portal linking data, research tools, and teaching resources. PMID:25309735

  3. SkateBase, an elasmobranch genome project and collection of molecular resources for chondrichthyan fishes.

    PubMed

    Wyffels, Jennifer; King, Benjamin L; Vincent, James; Chen, Chuming; Wu, Cathy H; Polson, Shawn W

    2014-01-01

    Chondrichthyan fishes are a diverse class of gnathostomes that provide a valuable perspective on fundamental characteristics shared by all jawed and limbed vertebrates. Studies of phylogeny, species diversity, population structure, conservation, and physiology are accelerated by genomic, transcriptomic and protein sequence data. These data are widely available for many sarcopterygii (coelacanth, lungfish and tetrapods) and actinoptergii (ray-finned fish including teleosts) taxa, but limited for chondrichthyan fishes.  In this study, we summarize available data for chondrichthyes and describe resources for one of the largest projects to characterize one of these fish, Leucoraja erinacea, the little skate.  SkateBase ( http://skatebase.org) serves as the skate genome project portal linking data, research tools, and teaching resources. PMID:25309735

  4. Analysis of Documentation Speed Using Web-Based Medical Speech Recognition Technology: Randomized Controlled Trial

    PubMed Central

    Kaisers, Wolfgang; Wassmuth, Ralf; Mayatepek, Ertan

    2015-01-01

    Background Clinical documentation has undergone a change due to the usage of electronic health records. The core element is to capture clinical findings and document therapy electronically. Health care personnel spend a significant portion of their time on the computer. Alternatives to self-typing, such as speech recognition, are currently believed to increase documentation efficiency and quality, as well as satisfaction of health professionals while accomplishing clinical documentation, but few studies in this area have been published to date. Objective This study describes the effects of using a Web-based medical speech recognition system for clinical documentation in a university hospital on (1) documentation speed, (2) document length, and (3) physician satisfaction. Methods Reports of 28 physicians were randomized to be created with (intervention) or without (control) the assistance of a Web-based system of medical automatic speech recognition (ASR) in the German language. The documentation was entered into a browser’s text area and the time to complete the documentation including all necessary corrections, correction effort, number of characters, and mood of participant were stored in a database. The underlying time comprised text entering, text correction, and finalization of the documentation event. Participants self-assessed their moods on a scale of 1-3 (1=good, 2=moderate, 3=bad). Statistical analysis was done using permutation tests. Results The number of clinical reports eligible for further analysis stood at 1455. Out of 1455 reports, 718 (49.35%) were assisted by ASR and 737 (50.65%) were not assisted by ASR. Average documentation speed without ASR was 173 (SD 101) characters per minute, while it was 217 (SD 120) characters per minute using ASR. The overall increase in documentation speed through Web-based ASR assistance was 26% (P=.04). Participants documented an average of 356 (SD 388) characters per report when not assisted by ASR and 649 (SD

  5. Fish community-based measures of estuarine ecological quality and pressure-impact relationships

    NASA Astrophysics Data System (ADS)

    Fonseca, Vanessa F.; Vasconcelos, Rita P.; Gamito, Rita; Pasquaud, Stéphanie; Gonçalves, Catarina I.; Costa, José L.; Costa, Maria J.; Cabral, Henrique N.

    2013-12-01

    Community-based responses of fish fauna to anthropogenic pressures have been extensively used to assess the ecological quality of estuarine ecosystems. Several methodologies have been developed recently combining metrics reflecting community structure and function. A fish community facing significant environmental disturbances will be characterized by a simplified structure, with lower diversity and complexity. However, estuaries are naturally dynamic ecosystems exposed to numerous human pressures, making it difficult to distinguish between natural and anthropogenic-induced changes to the biological community. In the present work, the variability of several fish metrics was assessed in relation to different pressures in estuarine sites. The response of a multimetric index (Estuarine Fish Assessment Index) was also analysed. Overall, fish metrics and the multimetric index signalled anthropogenic stress, particularly environmental chemical pollution. The fish assemblage associated with this type of pressure was characterized by lower species diversity, lower number of functional guilds, lower abundance of marine migrants and of piscivorous individuals, and higher abundance of estuarine resident species. A decreased ecological quality status, based on the EFAI, was also determined for sites associated with this pressure group. Ultimately, the definition of each pressure groups favoured a stressor-specific analysis, evidencing pressure patterns and accounting for multiple factors in a highly dynamic environment.

  6. Face Recognition System for Set-Top Box-Based Intelligent TV

    PubMed Central

    Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Park, Kang Ryoung

    2014-01-01

    Despite the prevalence of smart TVs, many consumers continue to use conventional TVs with supplementary set-top boxes (STBs) because of the high cost of smart TVs. However, because the processing power of a STB is quite low, the smart TV functionalities that can be implemented in a STB are very limited. Because of this, negligible research has been conducted regarding face recognition for conventional TVs with supplementary STBs, even though many such studies have been conducted with smart TVs. In terms of camera sensors, previous face recognition systems have used high-resolution cameras, cameras with high magnification zoom lenses, or camera systems with panning and tilting devices that can be used for face recognition from various positions. However, these cameras and devices cannot be used in intelligent TV environments because of limitations related to size and cost, and only small, low cost web-cameras can be used. The resulting face recognition performance is degraded because of the limited resolution and quality levels of the images. Therefore, we propose a new face recognition system for intelligent TVs in order to overcome the limitations associated with low resource set-top box and low cost web-cameras. We implement the face recognition system using a software algorithm that does not require special devices or cameras. Our research has the following four novelties: first, the candidate regions in a viewer's face are detected in an image captured by a camera connected to the STB via low processing background subtraction and face color filtering; second, the detected candidate regions of face are transmitted to a server that has high processing power in order to detect face regions accurately; third, in-plane rotations of the face regions are compensated based on similarities between the left and right half sub-regions of the face regions; fourth, various poses of the viewer's face region are identified using five templates obtained during the initial user

  7. Trends in correlation-based pattern recognition and tracking in forward-looking infrared imagery.

    PubMed

    Alam, Mohammad S; Bhuiyan, Sharif M A

    2014-01-01

    In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

  8. Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery

    PubMed Central

    Alam, Mohammad S.; Bhuiyan, Sharif M. A.

    2014-01-01

    In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

  9. Nonspecific base recognition mediated by water bridges and hydrophobic stacking in ribonuclease I from Escherichia coli

    PubMed Central

    Rodriguez, Sergio Martinez; Panjikar, Santosh; Van Belle, Karolien; Wyns, Lode; Messens, Joris; Loris, Remy

    2008-01-01

    The crystal structure of Escherichia coli ribonuclease I (EcRNase I) reveals an RNase T2-type fold consisting of a conserved core of six β-strands and three α-helices. The overall architecture of the catalytic residues is very similar to the plant and fungal RNase T2 family members, but the perimeter surrounding the active site is characterized by structural elements specific for E. coli. In the structure of EcRNase I in complex with a substrate-mimicking decadeoxynucleotide d(CGCGATCGCG), we observe a cytosine bound in the B2 base binding site and mixed binding of thymine and guanine in the B1 base binding site. The active site residues His55, His133, and Glu129 interact with the phosphodiester linkage only through a set of water molecules. Residues forming the B2 base recognition site are well conserved among bacterial homologs and may generate limited base specificity. On the other hand, the B1 binding cleft acquires true base aspecificity by combining hydrophobic van der Waals contacts at its sides with a water-mediated hydrogen-bonding network at the bottom. This B1 base recognition site is highly variable among bacterial sequences and the observed interactions are unique to EcRNaseI and a few close relatives. PMID:18305191

  10. [Recognition of water-injected meat based on visible/near-infrared spectrum and sparse representation].

    PubMed

    Hao, Dong-mei; Zhou, Ya-nan; Wang, Yu; Zhang, Song; Yang, Yi-min; Lin, Ling; Li, Gang; Wang, Xiu-li

    2015-01-01

    The present paper proposed a new nondestructive method based on visible/near infrared spectrum (Vis/NIRS) and sparse representation to rapidly and accurately discriminate between raw meat and water-injected meat. Water-injected meat model was built by injecting water into non-destructed meat samples comprising pigskin, fat layer and muscle layer. Vis/NIRS data were collected from raw meat and six scales of water-injected meat with spectrometers. To reduce the redundant information in the spectrum and improve the difference between the samples,. some preprocessing steps were performed for the spectral data, including light modulation and normalization. Effective spectral bands were extracted from the preprocessed spectral data. The meat samples were classified as raw meat and water-injected meat, and further, water-injected meat with different water injection rates. All the training samples were used to compose an atom dictionary, and test samples were represented by the sparsest linear combinations of these atoms via l1-minimization. Projection errors of test samples with respect to each category were calculated. A test sample was classified to the category with the minimum projection error, and leave-one-out cross-validation was conducted. The recognition performance from sparse representation was compared with that from support vector machine (SVM).. Experimental results showed that the overall recognition accuracy of sparse representation for raw meat and water-injected meat was more than 90%, which was higher than that of SVM. For water-injected meat samples with different water injection rates, the recognition accuracy presented a positive correlation with the water injection rate difference. Spare representation-based classifier eliminates the need for the training and feature extraction steps required by conventional pattern recognition models, and is suitable for processing data of high dimensionality and small sample size. Furthermore, it has a low

  11. Gabor-based kernel PCA with fractional power polynomial models for face recognition.

    PubMed

    Liu, Chengjun

    2004-05-01

    This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power

  12. 3D automatic anatomy recognition based on iterative graph-cut-ASM

    NASA Astrophysics Data System (ADS)

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

    2010-02-01

    We call the computerized assistive process of recognizing, delineating, and quantifying organs and tissue regions in medical imaging, occurring automatically during clinical image interpretation, automatic anatomy recognition (AAR). The AAR system we are developing includes five main parts: model building, object recognition, object delineation, pathology detection, and organ system quantification. In this paper, we focus on the delineation part. For the modeling part, we employ the active shape model (ASM) strategy. For recognition and delineation, we integrate several hybrid strategies of combining purely image based methods with ASM. In this paper, an iterative Graph-Cut ASM (IGCASM) method is proposed for object delineation. An algorithm called GC-ASM was presented at this symposium last year for object delineation in 2D images which attempted to combine synergistically ASM and GC. Here, we extend this method to 3D medical image delineation. The IGCASM method effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost function, which effectively integrates the specific image information with the ASM shape model information. The proposed methods are tested on a clinical abdominal CT data set. The preliminary results show that: (a) it is feasible to explicitly bring prior 3D statistical shape information into the GC framework; (b) the 3D IGCASM delineation method improves on ASM and GC and can provide practical operational time on clinical images.

  13. A Star Recognition Method Based on the Adaptive Ant Colony Algorithm for Star Sensors

    PubMed Central

    Quan, Wei; Fang, Jiancheng

    2010-01-01

    A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms. PMID:22294908

  14. Line-Based Object Recognition using Hausdorff Distance: From Range Images to Molecular Secondary Structure

    SciTech Connect

    Guerra, C; Pascucci, V

    2004-12-13

    Object recognition algorithms are fundamental tools in automatic matching of geometric shapes within a background scene. Many approaches have been proposed in the past to solve the object recognition problem. Two of the key aspects that distinguish them in terms of their practical usability are: (i) the type of input model description and (ii) the comparison criteria used. In this paper we introduce a novel scheme for 3D object recognition based on line segment representation of the input shapes and comparison using the Hausdor distance. This choice of model representation provides the flexibility to apply the scheme in different application areas. We define several variants of the Hausdor distance to compare the models within the framework of well defined metric spaces. We present a matching algorithm that efficiently finds a pattern in a 3D scene. The algorithm approximates a minimization procedure of the Hausdor distance. The output error due to the approximation is guaranteed to be within a known constant bound. Practical results are presented for two classes of objects: (i) polyhedral shapes extracted from segmented range images and (ii) secondary structures of large molecules. In both cases the use of our approximate algorithm allows to match correctly the pattern in the background while achieving the efficiency necessary for practical use of the scheme. In particular the performance is improved substantially with minor degradation of the quality of the matching.

  15. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    PubMed Central

    Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  16. Bridge recognition based on Gabor filter in forward-looking infrared images

    NASA Astrophysics Data System (ADS)

    Liu, Songlin; Sun, Gang; Niu, Zhaodong; Chen, Zengping

    2013-10-01

    Conventional methods often assume that water region is homogeneous and bridge is brighter than background. They usually recognize target by parallel lines detection. But grayscale of bridge has bipolar problem in FLIR images due to interference of complex background and constraints of imaging conditions, which means that it can be greater or lower than river. Furthermore, water is not a homogeneous area as a whole because of the interference of water clutter and shoals. This paper proposes a novel algorithm of bridge recognition based on Gabor filter. Firstly, we obtain target ROI by extracting the horizontal line. And then ROI sub-images are enhanced by Gabor filter and target polarity is determined by bridge body detection. Finally, bridge recognition can be achieved by pier detection according to the target polarity and location of bridge body. Experimental results of nearly 3000 frames show that the proposed algorithm can effectively overcome problems such as bipolar target and low image contrast. It offers a good practicability and accuracy in bridge recognition in FLIR images.

  17. A voxel-based lesion study on facial emotion recognition after penetrating brain injury.

    PubMed

    Dal Monte, Olga; Krueger, Frank; Solomon, Jeffrey M; Schintu, Selene; Knutson, Kristine M; Strenziok, Maren; Pardini, Matteo; Leopold, Anne; Raymont, Vanessa; Grafman, Jordan

    2013-08-01

    The ability to read emotions in the face of another person is an important social skill that can be impaired in subjects with traumatic brain injury (TBI). To determine the brain regions that modulate facial emotion recognition, we conducted a whole-brain analysis using a well-validated facial emotion recognition task and voxel-based lesion symptom mapping (VLSM) in a large sample of patients with focal penetrating TBIs (pTBIs). Our results revealed that individuals with pTBI performed significantly worse than normal controls in recognizing unpleasant emotions. VLSM mapping results showed that impairment in facial emotion recognition was due to damage in a bilateral fronto-temporo-limbic network, including medial prefrontal cortex (PFC), anterior cingulate cortex, left insula and temporal areas. Beside those common areas, damage to the bilateral and anterior regions of PFC led to impairment in recognizing unpleasant emotions, whereas bilateral posterior PFC and left temporal areas led to impairment in recognizing pleasant emotions. Our findings add empirical evidence that the ability to read pleasant and unpleasant emotions in other people's faces is a complex process involving not only a common network that includes bilateral fronto-temporo-limbic lobes, but also other regions depending on emotional valence. PMID:22496440

  18. Enhancing HMM-based biomedical named entity recognition by studying special phenomena.

    PubMed

    Zhang, Jie; Shen, Dan; Zhou, Guodong; Su, Jian; Tan, Chew-Lim

    2004-12-01

    The purpose of this research is to enhance an HMM-based named entity recognizer in the biomedical domain. First, we analyze the characteristics of biomedical named entities. Then, we propose a rich set of features, including orthographic, morphological, part-of-speech, and semantic trigger features. All these features are integrated via a Hidden Markov Model with back-off modeling. Furthermore, we propose a method for biomedical abbreviation recognition and two methods for cascaded named entity recognition. Evaluation on the GENIA V3.02 and V1.1 shows that our system achieves 66.5 and 62.5 F-measure, respectively, and outperforms the previous best published system by 8.1 F-measure on the same experimental setting. The major contribution of this paper lies in its rich feature set specially designed for biomedical domain and the effective methods for abbreviation and cascaded named entity recognition. To our best knowledge, our system is the first one that copes with the cascaded phenomena. PMID:15542015

  19. A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

    PubMed

    Quan, Wei; Fang, Jiancheng

    2010-01-01

    A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms. PMID:22294908

  20. Action recognition based on a selective sampling strategy for real-time video surveillance

    NASA Astrophysics Data System (ADS)

    Zhang, Bo; Zhang, Hong; Yuan, Ding

    2015-12-01

    Action recognition is a very challenging task in the field of real-time video surveillance. The traditional models on action recognition are constructed of Spatial-temporal features and Bag-of-Feature representations. Based on this model, current research work tends to introduce dense sampling to achieve better performance. However, such approaches are computationally intractable when dealing with large video dataset. Hence, there are some recent works focused on feature reduction to speed up the algorithm without reducing accuracy. In this paper, we proposed a novel selective feature sampling strategy on action recognition. Firstly, the optical flow field is estimated throughout the input video. And then the sparse FAST (Features from Accelerated Segment Test) points are selected within the motion regions detected by using the optical flows on the temporally down-sampled image sequences. The selective features, sparse FAST points, are the seeds to generate the 3D patches. Consequently, the simplified LPM (Local Part Model) which greatly speeds up the model is formed via 3D patches. Moreover, MBHs (Motion Boundary Histograms) calculated by optical flows are also adopted in the framework to further improve the efficiency. Experimental results on UCF50 dataset and our artificial dataset show that our method could reach more real-time effect and achieve a higher accuracy compared with the other competitive methods published recently.

  1. Motion-sensor fusion-based gesture recognition and its VLSI architecture design for mobile devices

    NASA Astrophysics Data System (ADS)

    Zhu, Wenping; Liu, Leibo; Yin, Shouyi; Hu, Siqi; Tang, Eugene Y.; Wei, Shaojun

    2014-05-01

    With the rapid proliferation of smartphones and tablets, various embedded sensors are incorporated into these platforms to enable multimodal human-computer interfaces. Gesture recognition, as an intuitive interaction approach, has been extensively explored in the mobile computing community. However, most gesture recognition implementations by now are all user-dependent and only rely on accelerometer. In order to achieve competitive accuracy, users are required to hold the devices in predefined manner during the operation. In this paper, a high-accuracy human gesture recognition system is proposed based on multiple motion sensor fusion. Furthermore, to reduce the energy overhead resulted from frequent sensor sampling and data processing, a high energy-efficient VLSI architecture implemented on a Xilinx Virtex-5 FPGA board is also proposed. Compared with the pure software implementation, approximately 45 times speed-up is achieved while operating at 20 MHz. The experiments show that the average accuracy for 10 gestures achieves 93.98% for user-independent case and 96.14% for user-dependent case when subjects hold the device randomly during completing the specified gestures. Although a few percent lower than the conventional best result, it still provides competitive accuracy acceptable for practical usage. Most importantly, the proposed system allows users to hold the device randomly during operating the predefined gestures, which substantially enhances the user experience.

  2. Contact-Free Palm-Vein Recognition Based on Local Invariant Features

    PubMed Central

    Kang, Wenxiong; Liu, Yang; Wu, Qiuxia; Yue, Xishun

    2014-01-01

    Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach. PMID:24866176

  3. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

    PubMed

    Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

    2016-01-01

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

  4. An automatic recognition method of pointer instrument based on improved Hough transform

    NASA Astrophysics Data System (ADS)

    Xu, Li; Fang, Tian; Gao, Xiaoyu

    2015-10-01

    For the automatic recognition of pointer instrument, the method for the automatic recognition of pointer instrument based on improved Hough Transform was proposed in this paper. The automatic recognition of pointer instrument is applied to all kinds of lighting conditions, but the accuracy of it binaryzation will be influenced when the light is too strong or too dark. Therefore, the improved Ostu method was suggested to realize recognition for adaptive thresholding of pointer instrument under all kinds of lighting conditions. On the basis of dial image characteristics, Otsu method is used to get the value of maximum between-cluster variance and initial threshold than analyze its maximum between-cluster variance value to determine the light and shade of the image. When the images are too bright or too dark, the smaller pixels should be given up and then calculate the initial threshold by Otsu method again and again until the best binaryzation image was obtained. Hence, transform the pointer straight line of the binaryzation image to Hough parameter space through improved Hough Transform to determine the position of the pointer straight line by searching the maximum value of arrays of the same angle. Finally, according to angle method, the pointer reading was obtained by the linear relationship for the initial scale and angle of the pointer instrument. Results show that the improved Otsu method make pointer instrument possible to obtained the accuracy binaryzation image even though the light is too bright or too dark , which improves the adaptability of pointer instrument to automatic recognize the light under different conditions. For the pressure gauges with range of 60MPa, the relative error identification reached to 0.005 when use the improved Hough Transform Algorithm.

  5. Noise Robust Feature Scheme for Automatic Speech Recognition Based on Auditory Perceptual Mechanisms

    NASA Astrophysics Data System (ADS)

    Cai, Shang; Xiao, Yeming; Pan, Jielin; Zhao, Qingwei; Yan, Yonghong

    Mel Frequency Cepstral Coefficients (MFCC) are the most popular acoustic features used in automatic speech recognition (ASR), mainly because the coefficients capture the most useful information of the speech and fit well with the assumptions used in hidden Markov models. As is well known, MFCCs already employ several principles which have known counterparts in the peripheral properties of human hearing: decoupling across frequency, mel-warping of the frequency axis, log-compression of energy, etc. It is natural to introduce more mechanisms in the auditory periphery to improve the noise robustness of MFCC. In this paper, a k-nearest neighbors based frequency masking filter is proposed to reduce the audibility of spectra valleys which are sensitive to noise. Besides, Moore and Glasberg's critical band equivalent rectangular bandwidth (ERB) expression is utilized to determine the filter bandwidth. Furthermore, a new bandpass infinite impulse response (IIR) filter is proposed to imitate the temporal masking phenomenon of the human auditory system. These three auditory perceptual mechanisms are combined with the standard MFCC algorithm in order to investigate their effects on ASR performance, and a revised MFCC extraction scheme is presented. Recognition performances with the standard MFCC, RASTA perceptual linear prediction (RASTA-PLP) and the proposed feature extraction scheme are evaluated on a medium-vocabulary isolated-word recognition task and a more complex large vocabulary continuous speech recognition (LVCSR) task. Experimental results show that consistent robustness against background noise is achieved on these two tasks, and the proposed method outperforms both the standard MFCC and RASTA-PLP.

  6. Real-time distortion correction of fish-eye lens based on Bayer image signal

    NASA Astrophysics Data System (ADS)

    Lai, Shiming; Xiong, Zhihui; Chen, Lidong; Tan, Xin; Zhang, Maojun

    2014-03-01

    The imaging system based on a fish-eye lens generally has to correct the distortion of fish-eye images. The distortion correction based on the Bayer image signal is valuable, such as reducing the computation burden of image signal processing chips and providing a new imaging system structure of fish-eye lens. In this paper, a distortion correction method of fish-eye lens based on the Bayer image signal is proposed. Firstly, a distortion correction method that focuses on vertical straight lines and processing delay is proposed. Secondly, according to the correlation among color channels of the Bayer image, a novel Hermite interpolation method appropriate for Bayer image signal is proposed. Finally, a prototype system of fish-eye-lens-based imaging is established and the real-time field-programmable gate array (FPGA) implementation of the proposed method is demonstrated. The experiment demonstrates that the proposed distortion correction is not only characteristic of real-time processing and the smaller computation amount, but also applicable to embedded hardware.

  7. A neural network based artificial vision system for licence plate recognition.

    PubMed

    Draghici, S

    1997-02-01

    This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%. PMID:9228583

  8. New generation of human machine interfaces for controlling UAV through depth-based gesture recognition

    NASA Astrophysics Data System (ADS)

    Mantecón, Tomás.; del Blanco, Carlos Roberto; Jaureguizar, Fernando; García, Narciso

    2014-06-01

    New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

  9. Active destabilization of base pairs by a DNA glycosylase wedge initiates damage recognition

    PubMed Central

    Kuznetsov, Nikita A.; Bergonzo, Christina; Campbell, Arthur J.; Li, Haoquan; Mechetin, Grigory V.; de los Santos, Carlos; Grollman, Arthur P.; Fedorova, Olga S.; Zharkov, Dmitry O.; Simmerling, Carlos

    2015-01-01

    Formamidopyrimidine-DNA glycosylase (Fpg) excises 8-oxoguanine (oxoG) from DNA but ignores normal guanine. We combined molecular dynamics simulation and stopped-flow kinetics with fluorescence detection to track the events in the recognition of oxoG by Fpg and its mutants with a key phenylalanine residue, which intercalates next to the damaged base, changed to either alanine (F110A) or fluorescent reporter tryptophan (F110W). Guanine was sampled by Fpg, as evident from the F110W stopped-flow traces, but less extensively than oxoG. The wedgeless F110A enzyme could bend DNA but failed to proceed further in oxoG recognition. Modeling of the base eversion with energy decomposition suggested that the wedge destabilizes the intrahelical base primarily through buckling both surrounding base pairs. Replacement of oxoG with abasic (AP) site rescued the activity, and calculations suggested that wedge insertion is not required for AP site destabilization and eversion. Our results suggest that Fpg, and possibly other DNA glycosylases, convert part of the binding energy into active destabilization of their substrates, using the energy differences between normal and damaged bases for fast substrate discrimination. PMID:25520195

  10. A simple web-based tool to compare freshwater fish data collected using AFS standard methods

    USGS Publications Warehouse

    Bonar, Scott A.; Mercado-Silva, Norman; Rahr, Matt; Torrey, Yuta T.; Cate, Averill, Jr.

    2016-01-01

    The American Fisheries Society (AFS) recently published Standard Methods for Sampling North American Freshwater Fishes. Enlisting the expertise of 284 scientists from 107 organizations throughout Canada, Mexico, and the United States, this text was developed to facilitate comparisons of fish data across regions or time. Here we describe a user-friendly web tool that automates among-sample comparisons in individual fish condition, population length-frequency distributions, and catch per unit effort (CPUE) data collected using AFS standard methods. Currently, the web tool (1) provides instantaneous summaries of almost 4,000 data sets of condition, length frequency, and CPUE of common freshwater fishes collected using standard gears in 43 states and provinces; (2) is easily appended with new standardized field data to update subsequent queries and summaries; (3) compares fish data from a particular water body with continent, ecoregion, and state data summaries; and (4) provides additional information about AFS standard fish sampling including benefits, ongoing validation studies, and opportunities to comment on specific methods. The web tool—programmed in a PHP-based Drupal framework—was supported by several AFS Sections, agencies, and universities and is freely available from the AFS website and fisheriesstandardsampling.org. With widespread use, the online tool could become an important resource for fisheries biologists.

  11. Development and validation of glycoprotein-based native-subunit vaccine for fish against Aeromonas hydrophila.

    PubMed

    Çiftci, A; Onuk, E E; Çiftci, G; Fındık, A; Söğüt, M Ü; Didinen, B I; Aksoy, A; Üstünakın, K; Gülhan, T; Balta, F; Altun, S

    2016-08-01

    Aeromonas hydrophila is known to be causative agent of an infection named as Bacterial haemorrhagic septicaemia or red pest in freshwater fish. The aim of this study was to develop and validate the glycoprotein-based fish vaccine against Aeromonas hydrophila. For this aim, after identification and characterization of A. hydrophila isolates from fish farms, one A. hydrophila isolate was selected as vaccine strain. Antigenic glycoproteins of this vaccine strain were determined by Western blotting and glycan detection kit. The connection types of these glycoproteins were examined by glycoprotein differentiation kit. Two glycoproteins, molecular weights of 19 and 38 kDa, with SNA connection type were selected for use in vaccination trials. After their purification by SNA-specific lectin and size-exclusion chromatography, protection studies with purified proteins were performed. For challenge trials, four experimental fish groups were designated: Group I (with montanide), Group II (with montanide and ginseng), Group III [with Al(OH)3 ] and Group IV [with Al(OH)3 and ginseng]. The survival ratings of fish were determined, and protection was calculated as 21.56%, 29.41%, 69.83% and 78.88% in groups I, II, III and IV, respectively. In conclusion, A. hydrophila glycoproteins with Al(OH)3 and ginseng could be used as a safe and effective vaccine for fish. PMID:27144782

  12. Constructing attribute classes by example learning: the research of attribute-based knowledge-style pattern recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Lijia; Song, Hongxun; Zhao, Shukui

    1991-08-01

    This paper proposes a method to generate a hierarchical knowledge base oriented to pattern recognition based on example attribute learning. The primary goal of this study is to extend the recognition process from the simple low level of the sample''s memory to high levels of their conceptual memory so that the PR process can be brought about on different conceptual levels. The authors combine the traditional AI method with the modern artificial neural network method to make concepts obtained from training samples have very strong descriptive power for objects to be recognized. Algorithms for constructing attribute classes and knowledge bases are given which have been applied in a case study of handwritten character recognition. The test results show that the system proposed can acquire a high recognition rate when it has learned enough training samples.

  13. Managing conflicts arising from fisheries enhancements based on non-native fishes in southern Africa.

    PubMed

    Ellender, B R; Woodford, D J; Weyl, O L F; Cowx, I G

    2014-12-01

    Southern Africa has a long history of non-native fish introductions for the enhancement of recreational and commercial fisheries, due to a perceived lack of suitable native species. This has resulted in some important inland fisheries being based on non-native fishes. Regionally, these introductions are predominantly not benign, and non-native fishes are considered one of the main threats to aquatic biodiversity because they affect native biota through predation, competition, habitat alteration, disease transfer and hybridization. To achieve national policy objectives of economic development, food security and poverty eradication, countries are increasingly looking towards inland fisheries as vehicles for development. As a result, conflicts have developed between economic and conservation objectives. In South Africa, as is the case for other invasive biota, the control and management of non-native fishes is included in the National Environmental Management: Biodiversity Act. Implementation measures include import and movement controls and, more recently, non-native fish eradication in conservation priority areas. Management actions are, however, complicated because many non-native fishes are important components in recreational and subsistence fisheries that contribute towards regional economies and food security. In other southern African countries, little attention has focussed on issues and management of non-native fishes, and this is cause for concern. This paper provides an overview of introductions, impacts and fisheries in southern Africa with emphasis on existing and evolving legislation, conflicts, implementation strategies and the sometimes innovative approaches that have been used to prioritize conservation areas and manage non-native fishes. PMID:25256916

  14. A possible approach for setting a mercury risk-based action level based on tribal fish ingestion rates.

    PubMed

    Harper, Barbara L; Harris, Stuart G

    2008-05-01

    Risks from mercury and other contaminants in fish for a large Columbia River dataset are evaluated in this paper for a range of consumption rates. Extensive ethnohistorical, nutritional, recent ethnographic surveys, and other documentation was reviewed to confirm previous determinations that the traditional subsistence fish consumption rate is 500 pounds per capita annually, or 620 g per day (gpd). Lower comtemporary consumption rates for other population subsets are also discussed. The causes of the current suppression of fish consumption are discussed and the cultural, educational, social, and trade and economic impacts of the loss of fish are considered. Action levels for mercury for riverine Tribes in the Columbia Basin are suggested at 0.1 ppm or less based on the combined risk from mercury plus other contaminants, the higher fish consumption rates, the existing cultural deficit due to loss of salmon and other stressors, the health benefits of fish, and the cultural and economic importance of fish. The goal of fish advisories is to reduce fish consumption even further, which shifts the burden of avoiding risk to the very people who already bear the burdens of contaminant exposure, socio-economic impacts and cultural loss. However, because Tribal communities often do not have the choice of giving up more food, income, religion, culture, and heritage in order to avoid contamination, they are forced into choosing between culture and health. Many tribal members choose to incur chemical risk rather than giving up their culture and religion. We believe that lowering the action level for mercury is part of the federal fiduciary responsibility to American Indian Tribes. PMID:17631290

  15. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

    PubMed Central

    Deng, Ning

    2014-01-01

    In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

  16. Serum albumin 'camouflage' of plant virus based nanoparticles prevents their antibody recognition and enhances pharmacokinetics.

    PubMed

    Pitek, Andrzej S; Jameson, Slater A; Veliz, Frank A; Shukla, Sourabh; Steinmetz, Nicole F

    2016-05-01

    Plant virus-based nanoparticles (VNPs) are a novel class of nanocarriers with unique potential for biomedical applications. VNPs have many advantageous properties such as ease of manufacture and high degree of quality control. Their biocompatibility and biodegradability make them an attractive alternative to synthetic nanoparticles (NPs). Nevertheless, as with synthetic NPs, to be successful in drug delivery or imaging, the carriers need to overcome several biological barriers including innate immune recognition. Plasma opsonization can tag (V)NPs for clearance by the mononuclear phagocyte system (MPS), resulting in shortened circulation half lives and non-specific sequestration in non-targeted organs. PEG coatings have been traditionally used to 'shield' nanocarriers from immune surveillance. However, due to broad use of PEG in cosmetics and other industries, the prevalence of anti-PEG antibodies has been reported, which may limit the utility of PEGylation in nanomedicine. Alternative strategies are needed to tailor the in vivo properties of (plant virus-based) nanocarriers. We demonstrate the use of serum albumin (SA) as a viable alternative. SA conjugation to tobacco mosaic virus (TMV)-based nanocarriers results in a 'camouflage' effect more effective than PEG coatings. SA-'camouflaged' TMV particles exhibit decreased antibody recognition, as well as enhanced pharmacokinetics in a Balb/C mouse model. Therefore, SA-coatings may provide an alternative and improved coating technique to yield (plant virus-based) NPs with improved in vivo properties enhancing drug delivery and molecular imaging. PMID:26950168

  17. Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition.

    PubMed

    Tao, Dapeng; Jin, Lianwen; Yuan, Yuan; Xue, Yang

    2016-06-01

    With the rapid development of mobile devices and pervasive computing technologies, acceleration-based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3-D acceleration-based activity dataset and the naturalistic mobile-devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration-based human activity recognition. PMID:25265635

  18. Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.

    PubMed

    Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

    2014-01-01

    In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

  19. DSP-Based dual-polarity mass spectrum pattern recognition for bio-detection

    SciTech Connect

    Riot, V; Coffee, K; Gard, E; Fergenson, D; Ramani, S; Steele, P

    2006-04-21

    The Bio-Aerosol Mass Spectrometry (BAMS) instrument analyzes single aerosol particles using a dual-polarity time-of-flight mass spectrometer recording simultaneously spectra of thirty to a hundred thousand points on each polarity. We describe here a real-time pattern recognition algorithm developed at Lawrence Livermore National Laboratory that has been implemented on a nine Digital Signal Processor (DSP) system from Signatec Incorporated. The algorithm first preprocesses independently the raw time-of-flight data through an adaptive baseline removal routine. The next step consists of a polarity dependent calibration to a mass-to-charge representation, reducing the data to about five hundred to a thousand channels per polarity. The last step is the identification step using a pattern recognition algorithm based on a library of known particle signatures including threat agents and background particles. The identification step includes integrating the two polarities for a final identification determination using a score-based rule tree. This algorithm, operating on multiple channels per-polarity and multiple polarities, is well suited for parallel real-time processing. It has been implemented on the PMP8A from Signatec Incorporated, which is a computer based board that can interface directly to the two one-Giga-Sample digitizers (PDA1000 from Signatec Incorporated) used to record the two polarities of time-of-flight data. By using optimized data separation, pipelining, and parallel processing across the nine DSPs it is possible to achieve a processing speed of up to a thousand particles per seconds, while maintaining the recognition rate observed on a non-real time implementation. This embedded system has allowed the BAMS technology to improve its throughput and therefore its sensitivity while maintaining a large dynamic range (number of channels and two polarities) thus maintaining the systems specificity for bio-detection.

  20. Development of a brain MRI-based hidden Markov model for dementia recognition

    PubMed Central

    2013-01-01

    Background Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Methods Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. Results The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. Conclusion The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio

  1. Weighted joint sparse representation-based classification method for robust alignment-free face recognition

    NASA Astrophysics Data System (ADS)

    Sun, Bo; Xu, Feng; Zhou, Guoyan; He, Jun; Ge, Fengxiang

    2015-01-01

    This work proposes a weighted joint sparse representation (WJSR)-based classification method for robust alignment-free face recognition, in which an image is represented by a set of scale-invariant feature transform descriptors. The proposed method considers the correlation and the reliability of the query descriptors. The reliability is measured by the similarity information between the query descriptors and the atoms in the dictionary, which is incorporated into the l0∖l2-norm minimization to seek the optimal WJSR. Compared with the related state-of-art methods, the performance is advanced, as verified by the experiments on the benchmark face databases.

  2. Semantic Network Adaptation Based on QoS Pattern Recognition for Multimedia Streams

    NASA Astrophysics Data System (ADS)

    Exposito, Ernesto; Gineste, Mathieu; Lamolle, Myriam; Gomez, Jorge

    This article proposes an ontology based pattern recognition methodology to compute and represent common QoS properties of the Application Data Units (ADU) of multimedia streams. The use of this ontology by mechanisms located at different layers of the communication architecture will allow implementing fine per-packet self-optimization of communication services regarding the actual application requirements. A case study showing how this methodology is used by error control mechanisms in the context of wireless networks is presented in order to demonstrate the feasibility and advantages of this approach.

  3. Optical implementation of a feature-based neural network with application to automatic target recognition

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

    An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

  4. Microprocessor-based single board computer for high energy physics event pattern recognition

    SciTech Connect

    Bernstein, H.; Gould, J.J.; Imossi, R.; Kopp, J.K.; Love, W.A.; Ozaki, S.; Platner, E.D.; Kramer, M.A.

    1981-01-01

    A single board MC 68000 based computer has been assembled and bench marked against the CDC 7600 running portions of the pattern recognition code used at the MPS. This computer has a floating coprocessor to achieve throughputs equivalent to several percent that of the 7600. A major part of this work was the construction of a FORTRAN compiler including assembler, linker and library. The intention of this work is to assemble a large number of these single board computers in a parallel FASTBUS environment to act as an on-line and off-line filter for the raw data from MPS II and ISABELLE experiments.

  5. Automatic target recognition using a feature-based optical neural network

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin

    1992-01-01

    An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

  6. 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.

  7. Wavelength initialization employing wavelength recognition scheme in WDM-PON based on tunable lasers

    NASA Astrophysics Data System (ADS)

    Mun, Sil-Gu; Lee, Eun-Gu; Lee, Jong Hyun; Lee, Sang Soo; Lee, Jyung Chan

    2015-01-01

    We proposed a simple method to initialize the wavelength of tunable lasers in WDM-PON employing wavelength recognition scheme with an optical filter as a function of wavelength and accomplished plug and play operation. We also implemented a transceiver based on our proposed wavelength initialization scheme and then experimentally demonstrated the feasibility in WDM-PON configuration guaranteeing 16 channels with 100 GHz channel spacing. Our proposal is a cost-effective and easy-to-install method to realize the wavelength initialization of ONU. In addition, this method will support compatibility with all kind of tunable laser regardless of their structures and operating principles.

  8. A rule based approach for pattern recognition in planar geometric figures

    NASA Astrophysics Data System (ADS)

    Alvisi, L.; Odorico, R.

    1988-11-01

    An OPS5 expert system has been developed, allowing the recognition of an arbitrary subpattern in a complex planar geometric figure under analysis. For this sake a syntactic representation for images is used by the expert system as a relational data base. The expert system looks for consistent mappings of the set of topological elements/relations representing the given subpattern into a corresponding subset of the topological elements/relations representing the geometric figure under analysis. The basic core of the expert system consists of 30 OPS5 rules.

  9. Sensor-Based Human Activity Recognition in a Multi-user Scenario

    NASA Astrophysics Data System (ADS)

    Wang, Liang; Gu, Tao; Tao, Xianping; Lu, Jian

    Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.

  10. Fish's Muscles Distortion and Pectoral Fins Propulsion of Lift-Based Mode

    NASA Astrophysics Data System (ADS)

    Yang, S. B.; Han, X. Y.; Qiu, J.

    As a sort of MPF(median and/or paired fin propulsion), pectoral fins propulsion makes fish easier to maneuver than other propulsion, according to the well-established classification scheme proposed by Webb in 1984. Pectoral fins propulsion is classified into oscillatory propulsion, undulatory propulsion and compound propulsion. Pectoral fins oscillatory propulsion, is further ascribable to two modes: drag-based mode and lift-based mode. And fish exhibits strong cruise ability by using lift-based mode. Therefore to robot fish design using pectoral fins lift-based mode will bring a new revolution to resources exploration in blue sea. On the basis of the wave plate theory, a kinematic model of fish’s pectoral fins lift-based mode is established associated with the behaviors of cownose ray (Rhinoptera bonasus) in the present work. In view of the power of fish’s locomotion from muscle distortion, it would be helpful benefit to reveal the mechanism of fish’s locomotion variation dependent on muscles distortion. So this study puts forward the pattern of muscles distortion of pectoral fins according to the character of skeletons and muscles of cownose ray in morphology and simulates the kinematics of lift-based mode using nonlinear analysis software. In the symmetrical fluid field, the model is simulated left-right symmetrically or asymmetrically. The results qualitatively show how muscles distortion determines the performance of fish locomotion. Finally the efficient muscles distortion associated with the preliminary dynamics is induced.

  11. Enhancing the Robustness of the Posterior-Based Confidence Measures Using Entropy Information for Speech Recognition

    NASA Astrophysics Data System (ADS)

    Sun, Yanqing; Zhou, Yu; Zhao, Qingwei; Zhang, Pengyuan; Pan, Fuping; Yan, Yonghong

    In this paper, the robustness of the posterior-based confidence measures is improved by utilizing entropy information, which is calculated for speech-unit-level posteriors using only the best recognition result, without requiring a larger computational load than conventional methods. Using different normalization methods, two posterior-based entropy confidence measures are proposed. Practical details are discussed for two typical levels of hidden Markov model (HMM)-based posterior confidence measures, and both levels are compared in terms of their performances. Experiments show that the entropy information results in significant improvements in the posterior-based confidence measures. The absolute improvements of the out-of-vocabulary (OOV) rejection rate are more than 20% for both the phoneme-level confidence measures and the state-level confidence measures for our embedded test sets, without a significant decline of the in-vocabulary accuracy.

  12. Ship formation recognition based on information fusion of spaceborne IMINT and ELINT

    NASA Astrophysics Data System (ADS)

    Zhang, Changfang; Yang, Hongwen; Hu, Weidong; Yu, Wenxian

    2007-11-01

    A method of ship formation recognition is proposed based on the information fusion of spaceborne IMINT (image intelligence) and ELINT (electronic intelligence) in this paper. Firstly, the composition and the battle array of the observed ship formation are derived from the spaceborne IMINT information of each individual ship. The beliefs about the composition and the battle array of the observed formation are calculated separately. A synthetic evaluation method is used to get the BPAF (basic probability assignment function) from IMINT information using the aforementioned two beliefs. Secondly, by computing the matching measure between the observed emitter set and that of the typical formation entity, the BPAF based on ELINT information is obtained. Thirdly, the two BPAFs based on the information of spaceborne IMINT and ELINT are combined with DS (Dempster-Shafer) evidence theory. Finally, a decision is made according to the combined BPAF. The experiment indicates that our proposed methods to obtain the BPAFs are practicable and the formation recognition accuracy is greatly improved compared to the results which use only one of the two sources.

  13. Horror Image Recognition Based on Context-Aware Multi-Instance Learning.

    PubMed

    Li, Bing; Xiong, Weihua; Wu, Ou; Hu, Weiming; Maybank, Stephen; Yan, Shuicheng

    2015-12-01

    Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the fuzzy support vector machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on the tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large-scale image sets collected from the Internet. PMID:26390459

  14. Secure Method for Biometric-Based Recognition with Integrated Cryptographic Functions

    PubMed Central

    Chiou, Shin-Yan

    2013-01-01

    Biometric systems refer to biometric technologies which can be used to achieve authentication. Unlike cryptography-based technologies, the ratio for certification in biometric systems needs not to achieve 100% accuracy. However, biometric data can only be directly compared through proximal access to the scanning device and cannot be combined with cryptographic techniques. Moreover, repeated use, improper storage, or transmission leaks may compromise security. Prior studies have attempted to combine cryptography and biometrics, but these methods require the synchronization of internal systems and are vulnerable to power analysis attacks, fault-based cryptanalysis, and replay attacks. This paper presents a new secure cryptographic authentication method using biometric features. The proposed system combines the advantages of biometric identification and cryptographic techniques. By adding a subsystem to existing biometric recognition systems, we can simultaneously achieve the security of cryptographic technology and the error tolerance of biometric recognition. This method can be used for biometric data encryption, signatures, and other types of cryptographic computation. The method offers a high degree of security with protection against power analysis attacks, fault-based cryptanalysis, and replay attacks. Moreover, it can be used to improve the confidentiality of biological data storage and biodata identification processes. Remote biometric authentication can also be safely applied. PMID:23762851

  15. Gait recognition based on Gabor wavelets and modified gait energy image for human identification

    NASA Astrophysics Data System (ADS)

    Huang, Deng-Yuan; Lin, Ta-Wei; Hu, Wu-Chih; Cheng, Chih-Hsiang

    2013-10-01

    This paper proposes a method for recognizing human identity using gait features based on Gabor wavelets and modified gait energy images (GEIs). Identity recognition by gait generally involves gait representation, extraction, and classification. In this work, a modified GEI convolved with an ensemble of Gabor wavelets is proposed as a gait feature. Principal component analysis is then used to project the Gabor-wavelet-based gait features into a lower-dimension feature space for subsequent classification. Finally, support vector machine classifiers based on a radial basis function kernel are trained and utilized to recognize human identity. The major contributions of this paper are as follows: (1) the consideration of the shadow effect to yield a more complete segmentation of gait silhouettes; (2) the utilization of motion estimation to track people when walkers overlap; and (3) the derivation of modified GEIs to extract more useful gait information. Extensive performance evaluation shows a great improvement of recognition accuracy due to the use of shadow removal, motion estimation, and gait representation using the modified GEIs and Gabor wavelets.

  16. Site-directed mutagenesis of the base recognition loop of ribonuclease from Bacillus intermedius (binase).

    PubMed

    Okorokov, A L; Panov, K I; Kolbanovskaya EYu; Karpeisky MYa; Polyakov, K M; Wilkinson, A J; Dodson, G G

    1996-04-15

    Members of the microbial guanyl-specific ribonuclease family show a high level of structural homology. The structural basis for guanyl base binding by microbial ribonucleases has been established for all members of the family and the existence of a guanine recognition loop was shown. However, bacillar RNases such as binase and barnase show far less specificity towards the guanyl base in hydrolysing oligonucleotides composed of more than 4 or 5 nucleotides. Using site-directed mutagenesis we introduced a number of amino acid substitutions into the base recognition loop of binase. The donor sequence originated from the guanyl specific ribonuclease Sa. Two single, two double and one triple (entire loop substitution) mutants were constructed and overproduced in E. coli. The kinetic properties of the mutant variants are different from the wild-type protein. Amino acid substitutions R61V, G60S, S56Q/R61V, G60S/R61V show 3-fold, 7-fold, 4-fold and 12-fold increased guanyl specificity respectively. However, all mutants retain the ability to catalyse the hydrolysis of a poly(A) substrate. PMID:8612811

  17. Secure method for biometric-based recognition with integrated cryptographic functions.

    PubMed

    Chiou, Shin-Yan

    2013-01-01

    Biometric systems refer to biometric technologies which can be used to achieve authentication. Unlike cryptography-based technologies, the ratio for certification in biometric systems needs not to achieve 100% accuracy. However, biometric data can only be directly compared through proximal access to the scanning device and cannot be combined with cryptographic techniques. Moreover, repeated use, improper storage, or transmission leaks may compromise security. Prior studies have attempted to combine cryptography and biometrics, but these methods require the synchronization of internal systems and are vulnerable to power analysis attacks, fault-based cryptanalysis, and replay attacks. This paper presents a new secure cryptographic authentication method using biometric features. The proposed system combines the advantages of biometric identification and cryptographic techniques. By adding a subsystem to existing biometric recognition systems, we can simultaneously achieve the security of cryptographic technology and the error tolerance of biometric recognition. This method can be used for biometric data encryption, signatures, and other types of cryptographic computation. The method offers a high degree of security with protection against power analysis attacks, fault-based cryptanalysis, and replay attacks. Moreover, it can be used to improve the confidentiality of biological data storage and biodata identification processes. Remote biometric authentication can also be safely applied. PMID:23762851

  18. Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese–Osher Decomposition Model

    PubMed Central

    Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

    2014-01-01

    As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese–Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred–PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition. PMID:24992328

  19. 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.

  20. Blurred palmprint recognition based on stable-feature extraction using a Vese-Osher decomposition model.

    PubMed

    Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

    2014-01-01

    As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese-Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred-PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition. PMID:24992328

  1. Parallel effects of processing fluency and positive affect on familiarity-based recognition decisions for faces

    PubMed Central

    Duke, Devin; Fiacconi, Chris M.; Köhler, Stefan

    2014-01-01

    According to attribution models of familiarity assessment, people can use a heuristic in recognition-memory decisions, in which they attribute the subjective ease of processing of a memory probe to a prior encounter with the stimulus in question. Research in social cognition suggests that experienced positive affect may be the proximal cue that signals fluency in various experimental contexts. In the present study, we compared the effects of positive affect and fluency on recognition-memory judgments for faces with neutral emotional expression. We predicted that if positive affect is indeed the critical cue that signals processing fluency at retrieval, then its manipulation should produce effects that closely mirror those produced by manipulations of processing fluency. In two experiments, we employed a masked-priming procedure in combination with a Remember-Know (RK) paradigm that aimed to separate familiarity- from recollection-based memory decisions. In addition, participants performed a prime-discrimination task that allowed us to take inter-individual differences in prime awareness into account. We found highly similar effects of our priming manipulations of processing fluency and of positive affect. In both cases, the critical effect was specific to familiarity-based recognition responses. Moreover, in both experiments it was reflected in a shift toward a more liberal response bias, rather than in changed discrimination. Finally, in both experiments, the effect was found to be related to prime awareness; it was present only in participants who reported a lack of such awareness on the prime-discrimination task. These findings add to a growing body of evidence that points not only to a role of fluency, but also of positive affect in familiarity assessment. As such they are consistent with the idea that fluency itself may be hedonically marked. PMID:24795678

  2. EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study

    PubMed Central

    2013-01-01

    Background Several studies investigating the use of electromyographic (EMG) signals in robot-based stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether a classical EMG-based patterns recognition approach could be employed to predict patients’ intentions while attempting to generate goal-directed movements in the horizontal plane. Methods Nine right-handed healthy subjects and seven right-handed stroke survivors performed reaching movements in the horizontal plane. EMG signals were recorded and used to identify the intended motion direction of the subjects. To this aim, a standard pattern recognition algorithm (i.e., Support Vector Machine, SVM) was used. Different tests were carried out to understand the role of the inter- and intra-subjects’ variability in affecting classifier accuracy. Abnormal muscular spatial patterns generating misclassification were evaluated by means of an assessment index calculated from the results achieved with the PCA, i.e., the so-called Coefficient of Expressiveness (CoE). Results Processing the EMG signals of the healthy subjects, in most of the cases we were able to build a static functional map of the EMG activation patterns for point-to-point reaching movements on the horizontal plane. On the contrary, when processing the EMG signals of the pathological subjects a good classification was not possible. In particular, patients’ aimed movement direction was not predictable with sufficient accuracy either when using the general map extracted from data of normal subjects and when tuning the classifier on the EMG signals recorded from each patient. Conclusions The experimental findings herein reported show that the use of EMG patterns recognition approach might not be practical to decode movement intention in subjects with neurological injury such as stroke. Rather than estimate motion from EMGs, future scenarios should encourage the utilization of these signals to detect and interpret the normal and

  3. Recognition- and reactivity-based fluorescent probes for studying transition metal signaling in living systems.

    PubMed

    Aron, Allegra T; Ramos-Torres, Karla M; Cotruvo, Joseph A; Chang, Christopher J

    2015-08-18

    Metals are essential for life, playing critical roles in all aspects of the central dogma of biology (e.g., the transcription and translation of nucleic acids and synthesis of proteins). Redox-inactive alkali, alkaline earth, and transition metals such as sodium, potassium, calcium, and zinc are widely recognized as dynamic signals, whereas redox-active transition metals such as copper and iron are traditionally thought of as sequestered by protein ligands, including as static enzyme cofactors, in part because of their potential to trigger oxidative stress and damage via Fenton chemistry. Metals in biology can be broadly categorized into two pools: static and labile. In the former, proteins and other macromolecules tightly bind metals; in the latter, metals are bound relatively weakly to cellular ligands, including proteins and low molecular weight ligands. Fluorescent probes can be useful tools for studying the roles of transition metals in their labile forms. Probes for imaging transition metal dynamics in living systems must meet several stringent criteria. In addition to exhibiting desirable photophysical properties and biocompatibility, they must be selective and show a fluorescence turn-on response to the metal of interest. To meet this challenge, we have pursued two general strategies for metal detection, termed "recognition" and "reactivity". Our design of transition metal probes makes use of a recognition-based approach for copper and nickel and a reactivity-based approach for cobalt and iron. This Account summarizes progress in our laboratory on both the development and application of fluorescent probes to identify and study the signaling roles of transition metals in biology. In conjunction with complementary methods for direct metal detection and genetic and/or pharmacological manipulations, fluorescent probes for transition metals have helped reveal a number of principles underlying transition metal dynamics. In this Account, we give three recent

  4. A RAD-based phylogenetics for Orestias fishes from Lake Titicaca.

    PubMed

    Takahashi, Tetsumi; Moreno, Edmundo

    2015-12-01

    The fish genus Orestias is endemic to the Andes highlands, and Lake Titicaca is the centre of the species diversity of the genus. Previous phylogenetic studies based on a single locus of mitochondrial and nuclear DNA strongly support the monophyly of a group composed of many of species endemic to the Lake Titicaca basin (the Lake Titicaca radiation), but the relationships among the species in the radiation remain unclear. Recently, restriction site-associated DNA (RAD) sequencing, which can produce a vast number of short sequences from various loci of nuclear DNA, has emerged as a useful way to resolve complex phylogenetic problems. To propose a new phylogenetic hypothesis of Orestias fishes of the Lake Titicaca radiation, we conducted a cluster analysis based on morphological similarities among fish samples and a molecular phylogenetic analysis based on RAD sequencing. From a morphological cluster analysis, we recognised four species groups in the radiation, and three of the four groups were resolved as monophyletic groups in maximum-likelihood trees based on RAD sequencing data. The other morphology-based group was not resolved as a monophyletic group in molecular phylogenies, and some members of the group were diverged from its sister group close to the root of the Lake Titicaca radiation. The evolution of these fishes is discussed from the phylogenetic relationships. PMID:26291568

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

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

    This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The

  6. Development of PCR-based methods for detection of Sphaerothecum destruens in fish tissues.

    PubMed

    Mendonca, Holly L; Arkush, Kristen D

    2004-11-01

    Single-round and nested polymerase chain reaction (PCR) tests were developed for amplification of a 434 bp fragment of the small subunit ribosomal RNA (18S rRNA) gene from Sphaerothecum destruens, previously known as the rosette agent, an intracellular parasite of salmonid fishes. Both tests have successfully amplified S. destruens-specific DNA from different isolates of S. destruens but not from related organisms. The limits of detection using the nested PCR test were 1 pg for purified S. destruens genomic DNA and 0.1 fg for plasmid DNA. We conducted 2 experimental transmission studies, consisting of injection or waterborne exposure of juvenile winter-run Chinook salmon Oncorhynchus tshawytscha to spore stages of the parasite. In the injection study, parasite DNA was detected in 100% of kidney samples from exposed fish (n = 83) at 1 and 3 mo post-exposure using nested PCR, versus 98% using microscopic analysis of Gram-stained impression smears made from the kidney. Following waterborne exposure, fish were sampled over the course of a year. From each fish, samples of gill, liver, posterior intestine and kidney were analyzed. S. destruens-specific DNA was detected most often in gill and kidney over the course of the experiment, and 71% (64/90) of the exposed fish were identified as positive for S. destruens using the nested PCR test, versus 16% (14/90) using microscopic analysis of Gram-stained kidney smears. Natural infections in captive broodstock of adult winter-run Chinook salmon, originally diagnosed by examination of Gram-stained kidney smears, were confirmed using the nested PCR test in all fish examined (15/15). Further, the nested test amplified parasite-specific DNA from other tissues in these fish with varying frequencies. This report introduces the first DNA-based detection method for S. destruens, to be used alone as a diagnostic tool or in conjunction with histologic tests for confirmatory identification of the parasite. PMID:15609874

  7. Class Energy Image analysis for video sensor-based gait recognition: a review.

    PubMed

    Lv, Zhuowen; Xing, Xianglei; Wang, Kejun; Guan, Donghai

    2015-01-01

    Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach. PMID:25574935

  8. Class Energy Image Analysis for Video Sensor-Based Gait Recognition: A Review

    PubMed Central

    Lv, Zhuowen; Xing, Xianglei; Wang, Kejun; Guan, Donghai

    2015-01-01

    Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach. PMID:25574935

  9. A novel THz spectroscopy recognition method for transgenic organisms based on APSO combined with SVM

    NASA Astrophysics Data System (ADS)

    Li, T. J.; Liu, J. J.; Shao, G. F.; Fan, L. L.

    2016-04-01

    Currently, the transgenic products detection methods are mostly based on visible/near-infrared light spectrum. In addition, it is hard to set up the parameters in the support vector machine (SVM) model and there is a large amount of calculation on spectrum data. To solve these problems, this paper proposed an algorithm based on terahertz (THz) spectrum and SVM using adaptive particle swarm optimize (APSO-SVM) for building up the classifications of transgenic cotton seed. To conduct the transgenic cotton seed classification, within the wavelength region 150 μm—3 mm, the THz spectrums are first sampled from 165 samples of three newest transgenic cotton seeds. Then, the 165 transgenic cotton seeds are recognized based on the APSO-SVM. Experiment results indicate that the total recognition rate is up to 97.3%, which prove that the THz spectrum combined with APSO-SVM can provide a reliable, rapid, simple and nondestructive detection method for transgenic cotton seed.

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

    NASA Astrophysics Data System (ADS)

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

    2008-03-01

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

  11. EMG-based facial gesture recognition through versatile elliptic basis function neural network

    PubMed Central

    2013-01-01

    Background Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating. Methods In this study, EMGs of ten facial gestures were recorded from ten subjects using three pairs of surface electrodes in a bi-polar configuration. The signals were filtered and segmented into distinct portions prior to feature extraction. Ten different time-domain features, namely, Integrated EMG, Mean Absolute Value, Mean Absolute Value Slope, Maximum Peak Value, Root Mean Square, Simple Square Integral, Variance, Mean Value, Wave Length, and Sign Slope Changes were extracted from the EMGs. The statistical relationships between these features were investigated by Mutual Information measure. Then, the feature combinations including two to ten single features were formed based on the feature rankings appointed by Minimum-Redundancy-Maximum-Relevance (MRMR) and Recognition Accuracy (RA) criteria. In the last step, VEBFNN was employed to classify the facial gestures. The effectiveness of single features as well as the feature sets on the system performance was examined by considering the two major metrics, recognition accuracy and training time. Finally, the proposed classifier was assessed and compared with conventional methods support vector machines and multilayer perceptron neural network. Results The average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy

  12. [Research on Multi-Spectral Target Recognition System Based on the Magneto-Optical Modulation].

    PubMed

    Yan, Xiao-yan; Qin, Jian-min; Qiao, Ji-pin

    2016-03-01

    The technology of target recognition based on characteristic multi-spectrum has many advantages, such as strong detection capability and discriminating capability of target species. But there are some problems, it requires that you obtain the background spectrum as a priori knowledge, and it requires that the change of background spectrum is small with time. Thereby its application of real-time object recognition is limited in the new environment, or the complex environment. Based on magneto-optical modulation and characteristic multi-spectrum the method is designed, and the target is identified without prior access to the background spectrum. In order to achieve the function of the target information in the one acquisition time for tested, compared to conventional methods in terms of target detection, it's adaptability is better than before on the battlefield, and it is of more practical significance. Meanwhile, the magneto-optical modulator is used to suppress the interference of stray light background, thereby improving the probability of target recognition. Since the magneto-optical modulation provides incremental iterative target spectral information, therefore, even if the unknown background spectrum or background spectrum change is large, it can significantly improve the recognition accuracy of information through an iterative target spectrum. Different test targets back shimmering light intensity and background intensity values were analyzed during experiments, results showed that three targets for linearly polarized reflectance modulation is significantly stronger than the background. And it was of great influence to visible imaging target identification when measured target used camouflage color, but the system of polarization modulation type can still recognize target well. On this basis, the target range within 0.5 km x 2 km multi-wavelength characteristics of the target species were identified. When using three characteristic wavelengths, the

  13. A food recognition system for diabetic patients based on an optimized bag-of-features model.

    PubMed

    Anthimopoulos, Marios M; Gianola, Lauro; Scarnato, Luca; Diem, Peter; Mougiakakou, Stavroula G

    2014-07-01

    Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset. PMID:25014934

  14. Local binary pattern based face recognition by estimation of facial distinctive information distribution

    NASA Astrophysics Data System (ADS)

    da, Bangyou; Sang, Nong

    2009-11-01

    We present a novel approach for face recognition by combining a local binary pattern (LBP)-based face descriptor and the distinctive information of faces. Several studies of psychophysics have shown that the eyes or mouth can be an important cue in human face perception, and the nose plays an insignificant role. This means that there exists a distinctive information distribution of faces. First, we give a quantitative estimation of the density for each pixel in a fronted face image by combining the Parzen-window approach and scale invariant feature transform detector, which is taken as the measure of the distinctive information of the faces. Second, we integrate the density function in the subwindow region of the face to gain the weight set used in the LBP-based face descriptor to produce weighted chi-square statistics. As an elementary application of the estimation of distinctive information of faces, the proposed method is tested on the FERET FA/FB image sets and yields a recognition rate of 98.2% compared to the 97.3% produced by the method adopted by Ahonen, Hadid, and Pietikainen.

  15. Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks

    PubMed Central

    Luque, Joaquín; Larios, Diego F.; Personal, Enrique; Barbancho, Julio; León, Carlos

    2016-01-01

    Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance. PMID:27213375

  16. Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation

    PubMed Central

    Fernández-Llatas, Carlos; Meneu, Teresa; Traver, Vicente; Benedi, José-Miguel

    2013-01-01

    Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized. PMID:24185841

  17. Recognition Stage for a Speed Supervisor Based on Road Sign Detection

    PubMed Central

    Carrasco, Juan-Pablo; de la Escalera, Arturo; Armingol, José María

    2012-01-01

    Traffic accidents are still one of the main health problems in the World. A number of measures have been applied in order to reduce the number of injuries and fatalities in roads, i.e., implementation of Advanced Driver Assistance Systems (ADAS) based on image processing. In this paper, a real time speed supervisor based on road sign recognition that can work both in urban and non-urban environments is presented. The system is able to recognize 135 road signs, belonging to the danger, yield, prohibition obligation and indication types, and sends warning messages to the driver upon the combination of two pieces of information: the current speed of the car and the road sign symbol. The core of this paper is the comparison between the two main methods which have been traditionally used for detection and recognition of road signs: template matching (TM) and neural networks (NN). The advantages and disadvantages of the two approaches will be shown and commented. Additionally we will show how the use of well-known algorithms to avoid illumination issues reduces the amount of images needed to train a neural network.

  18. Incrementally learning objects by touch: online discriminative and generative models for tactile-based recognition.

    PubMed

    Soh, Harold; Demiris, Yiannis

    2014-01-01

    Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate "early" classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches. PMID:25532151

  19. Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks.

    PubMed

    Luque, Joaquín; Larios, Diego F; Personal, Enrique; Barbancho, Julio; León, Carlos

    2016-01-01

    Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance. PMID:27213375

  20. Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

    PubMed

    Akhondi, Saber A; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F H; Hettne, Kristina M; van Mulligen, Erik M; Kors, Jan A

    2016-01-01

    We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents. PMID:27141091

  1. Simulated Prosthetic Vision: The Benefits of Computer-Based Object Recognition and Localization.

    PubMed

    Macé, Marc J-M; Guivarch, Valérian; Denis, Grégoire; Jouffrais, Christophe

    2015-07-01

    Clinical trials with blind patients implanted with a visual neuroprosthesis showed that even the simplest tasks were difficult to perform with the limited vision restored with current implants. Simulated prosthetic vision (SPV) is a powerful tool to investigate the putative functions of the upcoming generations of visual neuroprostheses. Recent studies based on SPV showed that several generations of implants will be required before usable vision is restored. However, none of these studies relied on advanced image processing. High-level image processing could significantly reduce the amount of information required to perform visual tasks and help restore visuomotor behaviors, even with current low-resolution implants. In this study, we simulated a prosthetic vision device based on object localization in the scene. We evaluated the usability of this device for object recognition, localization, and reaching. We showed that a very low number of electrodes (e.g., nine) are sufficient to restore visually guided reaching movements with fair timing (10 s) and high accuracy. In addition, performance, both in terms of accuracy and speed, was comparable with 9 and 100 electrodes. Extraction of high level information (object recognition and localization) from video images could drastically enhance the usability of current visual neuroprosthesis. We suggest that this method-that is, localization of targets of interest in the scene-may restore various visuomotor behaviors. This method could prove functional on current low-resolution implants. The main limitation resides in the reliability of the vision algorithms, which are improving rapidly. PMID:25900238

  2. Improved Hip-Based Individual Recognition Using Wearable Motion Recording Sensor

    NASA Astrophysics Data System (ADS)

    Gafurov, Davrondzhon; Bours, Patrick

    In todays society the demand for reliable verification of a user identity is increasing. Although biometric technologies based on fingerprint or iris can provide accurate and reliable recognition performance, they are inconvenient for periodic or frequent re-verification. In this paper we propose a hip-based user recognition method which can be suitable for implicit and periodic re-verification of the identity. In our approach we use a wearable accelerometer sensor attached to the hip of the person, and then the measured hip motion signal is analysed for identity verification purposes. The main analyses steps consists of detecting gait cycles in the signal and matching two sets of detected gait cycles. Evaluating the approach on a hip data set consisting of 400 gait sequences (samples) from 100 subjects, we obtained equal error rate (EER) of 7.5% and identification rate at rank 1 was 81.4%. These numbers are improvements by 37.5% and 11.2% respectively of the previous study using the same data set.

  3. Chemical entity recognition in patents by combining dictionary-based and statistical approaches

    PubMed Central

    Akhondi, Saber A.; Pons, Ewoud; Afzal, Zubair; van Haagen, Herman; Becker, Benedikt F.H.; Hettne, Kristina M.; van Mulligen, Erik M.; Kors, Jan A.

    2016-01-01

    We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small. Database URL: http://biosemantics.org/chemdner-patents PMID:27141091

  4. Learning and plan refinement in a knowledge-based system for automatic speech recognition

    SciTech Connect

    De Mori, R.; Lam, L.; Gilloux, M.

    1987-03-01

    This paper shows how a semiautomatic design of a speech recognition system can be done as a planning activity. Recognition performances are used for deciding plan refinement. Inductive learning is performed for setting action preconditions. Experimental results in the recognition of connected letters spoken by 100 speakers are presented.

  5. PHYSIOLOGICALLY BASED TOXICOKINETIC MODEL FOR THE UPTAKE AND DISPOSITION OF WATERBORNE ORGANIC CHEMICALS IN FISH

    EPA Science Inventory

    A physiologically based toxicokinetic model was developed to predict the uptake and disposition of waterborne organic chemicals in fish, he model consists of a set of mass-balance differential equations which describe the time course of chemical concentration within each of five ...

  6. Emergency power for fish produced in intensive, pond-based systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Power failure in a heavily stocked and fed pond-based culture system can result in massive fish losses within minutes. Even in a conventional pond with a stand-by tractor powered aerator, the shock of a sudden loss of power can dramatically affect production resulting in mortalities and reduced perf...

  7. A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Genrang; Lin, ZhengChun

    The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

  8. The response of fish to immunostimulant diets.

    PubMed

    Vallejos-Vidal, Eva; Reyes-López, Felipe; Teles, Mariana; MacKenzie, Simon

    2016-09-01

    In order to maintain fish health and to improve performance immunostimulants have been used as dietary additives to improve weight gain, feed efficiency, and/or disease resistance in cultured fish. In aquaculture, non-specific immunostimulants have been widely used probably due to the limited knowledge of the immune response in fish and the ease of their application. Many studies have been carried out to assess the effect of dietary immunostimulants in fish including algal derivatives, herb and plant extract containing diets using a wide range of downstream analytical techniques. Many immunostimulants are based upon tradition and folklore transferred through generations and specific to certain geographical regions rather than known biological properties. However, there are studies in which it is possible to observe a clear and direct dose-dependent stimulatory effect upon the immune system. Other dietary supplements used contain PAMPs (Pathogen Associated Molecular Patterns) as immunostimulants whose recognition depends upon PRR (pathogen recognition receptor) interactions including the TLRs (Toll-like receptor). Despite the growing interest in the use of immunostimulants across the aquaculture industry the underlying mechanisms of ligand recognition, extract composition and activation of the fish immune response remains fragmented. In this review we focus upon the last 15 years of studies addressing the assessment of: (1) plant, herb and algae extracts; and (2) PAMPs, upon non-specific immune parameters of activation and immunostimulant diet efficacy. PMID:27389620

  9. Target recognition using HRR profile-based incoherent SAR (InSAR) image formation

    NASA Astrophysics Data System (ADS)

    O'Donoughue, Nicholas A.; Kuklinski, Walter S.; Arabadjis, Constantine

    2008-04-01

    Feature-aided target verification is a challenging field of research, with the potential to yield significant increases in the confidence of re-established target tracks after kinematic confusion events. Using appropriate control algorithms airborne multi-mode radars can acquire a library of HRR (High Range Resolution) profiles for targets as they are tracked. When a kinematic confusion event occurs, such as a vehicle dropping below MDV (Minimum Detectable Velocity) for some period of time, or two target tracks crossing, it is necessary to utilize feature-aided tracking methods to correctly associate post-confusion tracks with pre-confusion tracks. Many current HRR profile target recognition methods focus on statistical characteristics of either individual profiles or sets of profiles taken over limited viewing angles. These methods have not proven to be very effective when the pre- and post- confusion libraries do not overlap in azimuth angle. To address this issue we propose a new approach to target recognition from HRR profiles. We present an algorithm that generates 2-D imagery of targets from the pre- and post-confusion libraries. These images are subsequently used as the input to a target recognition/classifier process. Since, center-aligned HRR Profiles, while ideal for processing, are not easily computed in field systems, as they require the airborne platform's center of rotation to line up with the geometric center of the moving target (this is impossible when multiple targets are being tracked), our algorithm is designed to work with HRR profiles that are aligned to the leading edge (the first detection above a threshold, commonly referred to as Edge-Aligned HRR profiles). Our simulated results demonstrate the effectiveness of this method for classifying target vehicles based on simulations using both overlapping and non-overlapping HRR profile sets. The algorithm was tested on several test cases using an input set of .28 m resolution XPATCH generated HRR

  10. Fully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA)

    PubMed Central

    Salimi, Nima; Loh, Kar Hoe; Kaur Dhillon, Sarinder

    2016-01-01

    Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies. PMID:26925315

  11. Fully-automated identification of fish species based on otolith contour: using short-time Fourier transform and discriminant analysis (STFT-DA).

    PubMed

    Salimi, Nima; Loh, Kar Hoe; Kaur Dhillon, Sarinder; Chong, Ving Ching

    2016-01-01

    Background. Fish species may be identified based on their unique otolith shape or contour. Several pattern recognition methods have been proposed to classify fish species through morphological features of the otolith contours. However, there has been no fully-automated species identification model with the accuracy higher than 80%. The purpose of the current study is to develop a fully-automated model, based on the otolith contours, to identify the fish species with the high classification accuracy. Methods. Images of the right sagittal otoliths of 14 fish species from three families namely Sciaenidae, Ariidae, and Engraulidae were used to develop the proposed identification model. Short-time Fourier transform (STFT) was used, for the first time in the area of otolith shape analysis, to extract important features of the otolith contours. Discriminant Analysis (DA), as a classification technique, was used to train and test the model based on the extracted features. Results. Performance of the model was demonstrated using species from three families separately, as well as all species combined. Overall classification accuracy of the model was greater than 90% for all cases. In addition, effects of STFT variables on the performance of the identification model were explored in this study. Conclusions. Short-time Fourier transform could determine important features of the otolith outlines. The fully-automated model proposed in this study (STFT-DA) could predict species of an unknown specimen with acceptable identification accuracy. The model codes can be accessed at http://mybiodiversityontologies.um.edu.my/Otolith/ and https://peerj.com/preprints/1517/. The current model has flexibility to be used for more species and families in future studies. PMID:26925315

  12. Finger-vein and fingerprint recognition based on a feature-level fusion method

    NASA Astrophysics Data System (ADS)

    Yang, Jinfeng; Hong, Bofeng

    2013-07-01

    Multimodal biometrics based on the finger identification is a hot topic in recent years. In this paper, a novel fingerprint-vein based biometric method is proposed to improve the reliability and accuracy of the finger recognition system. First, the second order steerable filters are used here to enhance and extract the minutiae features of the fingerprint (FP) and finger-vein (FV). Second, the texture features of fingerprint and finger-vein are extracted by a bank of Gabor filter. Third, a new triangle-region fusion method is proposed to integrate all the fingerprint and finger-vein features in feature-level. Thus, the fusion features contain both the finger texture-information and the minutiae triangular geometry structure. Finally, experimental results performed on the self-constructed finger-vein and fingerprint databases are shown that the proposed method is reliable and precise in personal identification.

  13. Tautomerization-dependent recognition and excision of oxidation damage in base-excision DNA repair.

    PubMed

    Zhu, Chenxu; Lu, Lining; Zhang, Jun; Yue, Zongwei; Song, Jinghui; Zong, Shuai; Liu, Menghao; Stovicek, Olivia; Gao, Yi Qin; Yi, Chengqi

    2016-07-12

    NEIL1 (Nei-like 1) is a DNA repair glycosylase guarding the mammalian genome against oxidized DNA bases. As the first enzymes in the base-excision repair pathway, glycosylases must recognize the cognate substrates and catalyze their excision. Here we present crystal structures of human NEIL1 bound to a range of duplex DNA. Together with computational and biochemical analyses, our results suggest that NEIL1 promotes tautomerization of thymine glycol (Tg)-a preferred substrate-for optimal binding in its active site. Moreover, this tautomerization event also facilitates NEIL1-catalyzed Tg excision. To our knowledge, the present example represents the first documented case of enzyme-promoted tautomerization for efficient substrate recognition and catalysis in an enzyme-catalyzed reaction. PMID:27354518

  14. Hand biometric recognition based on fused hand geometry and vascular patterns.

    PubMed

    Park, GiTae; Kim, Soowon

    2013-01-01

    A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%. PMID:23449119

  15. Intent and error recognition as part of a knowledge-based cockpit assistant

    NASA Astrophysics Data System (ADS)

    Strohal, Michael; Onken, Reiner

    1998-03-01

    With the Crew Assistant Military Aircraft (CAMA) a knowledge- based cockpit assistant system for future military transport aircraft is developed and tested to enhance situation awareness. Human-centered automation was the central principal for the development of CAMA, an approach to achieve advanced man-machine interaction, mainly by enhancing situation awareness. The CAMA-module Pilot Intent and Error Recognition (PIER) evaluates the pilot's activities and mission events in order to interpret and understand the pilot's actions in the context of the flight situation. Expected crew actions based on the flight plan are compared with the actual behavior shown by the crew. If discrepancies are detected the PIER module tries to figure out, whether the deviation was caused erroneously or by a sensible intent. By monitoring pilot actions as well as the mission context, the system is able to compare the pilot's action with a set of behavioral hypotheses. In case of an intentional deviation from the flight plan, the module checks, whether the behavior matches to the given set of behavior patterns of the pilot. Intent recognition can increase man-machine synergy by anticipating a need for assistance pertinent to the pilot's intent without having a pilot request. The interpretation of all possible situations with respect to intent recognition in terms of a reasoning process is based on a set of decision rules. To cope with the need of inferencing under uncertainty a fuzzy-logic approach is used. A weakness of the fuzzy-logic approach lies in the possibly ill-defined boundaries of the fuzzy sets. Self-Organizing Maps (SOM) as introduced and elaborated on by T. Kohonen are applied to improve the fuzzy set data and rule base complying with observed pilot behavior. Hierarchical cluster analysis is used to locate clusters of similar patterns in the maps. As introduced by Pedrycz, every feature is evaluated using fuzzy sets for each designated cluster. This approach allows to

  16. A novel flavone-based fluorescent probe for relay recognition of HSO3(-) and Al(3+).

    PubMed

    Xu, Shuai; Tang, Ruiren; Wang, Zhen; Zhou, Yin; Yan, Rui

    2015-10-01

    In this work, a new flavone-based fluorescent probe 3-hydroxy-3'-formylflavone (3HFF) was designed to achieve highly selective relay recognition of HSO3(-) and Al(3+) in DMSO-H2O (2:8, v/v) solution. 3HFF displayed a highly selective response to HSO3(-) with a green fluorescence appearing at 524 nm. Moreover, the in situ generated 3HFF+HSO3(-) system demonstrated eminent relay recognition capability for Al(3+) with a blue fluorescence appearing at 453 nm by the formation of a 1:1 complex between 3HFF and Al(3+) in DMSO-H2O (2:8, v/v) solution. However, only slight change was observed in emission intensity with addition of Al(3+) to 3HFF, and indicated HSO3(-) was essential for the sensing of Al(3+). This work achieves the detection of HSO3(-) and Al(3+) by only one probe and provides another example for this rare combination (anion/metal). PMID:25965168

  17. Facial expression recognition based on image Euclidean distance-supervised neighborhood preserving embedding

    NASA Astrophysics Data System (ADS)

    Chen, Li; Li, Yingjie; Li, Haibin

    2014-11-01

    High-dimensional data often lie on relatively low-dimensional manifold, while the nonlinear geometry of that manifold is often embedded in the similarities between the data points. These similar structures are captured by Neighborhood Preserving Embedding (NPE) effectively. But NPE as an unsupervised method can't utilize class information to guide the procedure of nonlinear dimensionality reduction. They ignore the geometrical structure information of local data points and the spatial information of pixels, which leads to the failure of classification. For this problem, a feature extraction method based on Image Euclidean Distance-Supervised NPE (IED-SNPE) is proposed, and is applied to facial expression recognition. Firstly, it employs Image Euclidean Distance (IED) to characterize the dissimilarity of data points. And then the neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points. Finally, it fuses prior nonlinear facial expression manifold of facial expression images and class-label information to extract discriminative features for expression recognition. In the classification experiments on JAFFE facial expression database, IED-SNPE is used for feature extraction and compared with NPE, SNPE, and IED-NPE. The results reveal that IED-SNPE not only the local structure of expression manifold preserves well but also explicitly considers the spatial relationships among pixels in the images. So it excels NPE in feature extraction and is highly competitive with those well-known feature extraction methods.

  18. A new FOD recognition algorithm based on multi-source information fusion and experiment analysis

    NASA Astrophysics Data System (ADS)

    Li, Yu; Xiao, Gang

    2011-08-01

    Foreign Object Debris (FOD) is a kind of substance, debris or article alien to an aircraft or system, which would potentially cause huge damage when it appears on the airport runway. Due to the airport's complex circumstance, quick and precise detection of FOD target on the runway is one of the important protections for airplane's safety. A multi-sensor system including millimeter-wave radar and Infrared image sensors is introduced and a developed new FOD detection and recognition algorithm based on inherent feature of FOD is proposed in this paper. Firstly, the FOD's location and coordinate can be accurately obtained by millimeter-wave radar, and then according to the coordinate IR camera will take target images and background images. Secondly, in IR image the runway's edges which are straight lines can be extracted by using Hough transformation method. The potential target region, that is, runway region, can be segmented from the whole image. Thirdly, background subtraction is utilized to localize the FOD target in runway region. Finally, in the detailed small images of FOD target, a new characteristic is discussed and used in target classification. The experiment results show that this algorithm can effectively reduce the computational complexity, satisfy the real-time requirement and possess of high detection and recognition probability.

  19. Study on pattern recognition method based on fiber optic perimeter system

    NASA Astrophysics Data System (ADS)

    Xu, Haiyan; Zhang, Xuewu; Zhang, Zhuo; Li, Min

    2015-10-01

    All-fiber interferometer sensor system is a new type of system, which could be used in long-distance, strong-EMI condition for monitoring and inspection. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, the universal steps in triggering pattern recognition is introduced, which includes signal characteristics extracting by accurate endpoint detecting, templates establishing by training, and pattern matching. By training the samples acquired in the laboratory, this paper uses the wavelet transformation to decompose the detection signals of the intrusion activities into sub-signals in different frequency bands with multi-resolution analysis. Then extracts the features of the above mentioned intrusions signals by frequency band energy and wavelet information entropy and the system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises such as windy and walk effectively. What's more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.

  20. Electrospun Nanofibers from a Tricyanofuran-Based Molecular Switch for Colorimetric Recognition of Ammonia Gas.

    PubMed

    Khattab, Tawfik A; Abdelmoez, Sherif; Klapötke, Thomas M

    2016-03-14

    A chromophore based on tricyanofuran (TCF) with a hydrazone (H) recognition moiety was developed. Its molecular-switching performance is reversible and has differential sensitivity towards aqueous ammonia at comparable concentrations. Nanofibers were fabricated from the TCF-H chromophore by electrospinning. The film fabricated from these nanofibers functions as a solid-state optical chemosensor for probing ammonia vapor. Recognition of ammonia vapor occurs by proton transfer from the hydrazone fragment of the chromophore to the ammonia nitrogen atom and is facilitated by the strongly electron withdrawing TCF fragment. The TCF-H chromophore was added to a solution of poly(acrylic acid), which was electrospun to obtain a nanofibrous sensor device. The morphology of the nanofibrous sensor was determined by SEM, which showed that nanofibers with a diameter range of 200-450 nm formed a nonwoven mat. The resultant nanofibrous sensor showed very good sensitivity in ammonia-vapor detection. Furthermore, very good reversibility and short response time were also observed. PMID:26864701