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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 Central

    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. 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

  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. 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

  6. 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

  7. 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

  8. Frequency-Based Fingerprint Recognition

    NASA Astrophysics Data System (ADS)

    Aguilar, Gualberto; Sánchez, Gabriel; Toscano, Karina; Pérez, Héctor

    abstract Fingerprint recognition is one of the most popular methods used for identification with greater success degree. Fingerprint has unique characteristics called minutiae, which are points where a curve track ends, intersects, or branches off. In this chapter a fingerprint recognition method is proposed in which a combination of Fast Fourier Transform (FFT) and Gabor filters is used for image enhancement. A novel recognition stage using local features for recognition is also proposed. Also a verification stage is introduced to be used when the system output has more than one person.

  9. Handwritten digits recognition based on immune network

    NASA Astrophysics Data System (ADS)

    Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

    2011-11-01

    With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

  10. 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.

  11. Face recognition based tensor structure

    NASA Astrophysics Data System (ADS)

    Yang, De-qiang; Ye, Zhi-xia; Zhao, Yang; Liu, Li-mei

    2012-01-01

    Face recognition has broad applications, and it is a difficult problem since face image can change with photographic conditions, such as different illumination conditions, pose changes and camera angles. How to obtain some invariable features for a face image is the key issue for a face recognition algorithm. In this paper, a novel tensor structure of face image is proposed to represent image features with eight directions for a pixel value. The invariable feature of the face image is then obtained from gradient decomposition to make up the tensor structure. Then the singular value decomposition (SVD) and principal component analysis (PCA) of this tensor structure are used for face recognition. The experimental results from this study show that many difficultly recognized samples can correctly be recognized, and the recognition rate is increased by 9%-11% in comparison with same type of algorithms.

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

    PubMed

    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.

  13. 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

  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. Manifold based methods in facial expression recognition

    NASA Astrophysics Data System (ADS)

    Xie, Kun

    2013-07-01

    This paper describes a novel method for facial expression recognition based on non-linear manifold techniques. The graph-based algorithms are designed to treat structure in data, and regularize accordingly. This same goal is shared by several other algorithms, from linear method principal components analysis (PCA) to modern variants such as Laplacian eigenmaps. In this paper we focus on manifold learning for dimensionality reduction and clustering using Laplacian eigenmaps for facial expression recognition. We evaluate the algorithm by using all the pixels and selected features respectively and compare the performance of the proposed non-linear manifold method with the previous linear manifold approach, and the non linear method produces higher recognition rate than the facial expression representation using linear methods.

  16. 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

  17. Optical correlation recognition based on LCOS

    NASA Astrophysics Data System (ADS)

    Tang, Mingchuan; Wu, Jianhong

    2013-08-01

    Vander-Lugt correlator[1] plays an important role in optical pattern recognition due to the characteristics of accurate positioning and high signal-to-noise ratio. The ideal Vander-Lugt correlator should have the ability of outputting strong and sharp correlation peak in allusion to the true target, in the existing Spatial Light Modulators[2], Liquid Crystal On Silicon(LCOS) has been the most competitive candidate for the matched filter owing to the continuous phase modulation peculiarity. Allowing for the distortions of the target to be identified including rotations, scaling changes, perspective changes, which can severely impact the correlation recognition results, herein, we present a modified Vander-Lugt correlator based on the LCOS by means of applying an iterative algorithm to the design of the filter so that the correlator can invariant to the distortions while maintaining good performance. The results of numerical simulation demonstrate that the filter could get the similar recognition results for all the training images. And the experiment shows that the modified correlator achieves the 180° rotating tolerance significantly improving the recognition efficiency of the correlator.

  18. Simplified Pattern Recognition Based On Multiaperture Optics

    NASA Astrophysics Data System (ADS)

    Schneider, Richard T.; Lin, Shih-Chao

    1987-05-01

    Multiaperture optics systems are similar in design to the concepts applying to the insect eye. Digitizing at the detector level is inherent in these systems. The fact that each eyelet forms one pixel of the overall image lends itself to optical preprocessing. There-fore a simplified pattern recognition scheme can be used in connection with multiaperture optics systems. The pattern recognition system used is based on the conjecture that all shapes encountered can be dissected into a set of rectangles. This is accomplished by creating a binary image and comparing each row of numbers starting at the top of the frame with the next row below. A set of rules is established which decides if the binary ones of the next row are to be incorporated in the present rectangle or start a new rectangle. The number and aspect ratios of the rectangles formed constitute a recognition code. These codes are kept and updated in a library. Since the same shape may give rise to different recognition codes depending on the attitude of the shape in respect to the detector grid, all shapes are rotated and normalized prior to dissecting. The rule is that the pattern is turned to maximize the number of straight edges which line up with the detector grid. The mathematical mechanism for rotation of the shape is described. Assuming a-priori knowledge of the size of the object exists, the normalization procedure can be used for distance determination. The description of the hardware for acquisition of the image is provided.

  19. 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.

  20. 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

  1. Photoswitchable gel assembly based on molecular recognition.

    PubMed

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

    2012-01-03

    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.

  2. 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

  3. 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

  4. Wavelet-based multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Liu, Dian-Ting; Zhou, Xiao-Dan; Wang, Cheng-Wen

    2008-09-01

    This paper proposes a novel wavelet-based face recognition method using thermal infrared (IR) and visible-light face images. The method applies the combination of Gabor and the Fisherfaces method to the reconstructed IR and visible images derived from wavelet frequency subbands. Our objective is to search for the subbands that are insensitive to the variation in expression and in illumination. The classification performance is improved by combining the multispectal information coming from the subbands that attain individually low equal error rate. Experimental results on Notre Dame face database show that the proposed wavelet-based algorithm outperforms previous multispectral images fusion method as well as monospectral method.

  5. Spectral face recognition using orthogonal subspace bases

    NASA Astrophysics Data System (ADS)

    Wimberly, Andrew; Robila, Stefan A.; Peplau, Tansy

    2010-04-01

    We present an efficient method for facial recognition using hyperspectral imaging and orthogonal subspaces. Projecting the data into orthogonal subspaces has the advantage of compactness and reduction of redundancy. We focus on two approaches: Principal Component Analysis and Orthogonal Subspace Projection. Our work is separated in three stages. First, we designed an experimental setup that allowed us to create a hyperspectral image database of 17 subjects under different facial expressions and viewing angles. Second, we investigated approaches to employ spectral information for the generation of fused grayscale images. Third, we designed and tested a recognition system based on the methods described above. The experimental results show that spectral fusion leads to improvement of recognition accuracy when compared to regular imaging. The work expands on previous band extraction research and has the distinct advantage of being one of the first that combines spatial information (i.e. face characteristics) with spectral information. In addition, the techniques are general enough to accommodate differences in skin spectra.

  6. 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

  7. 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.

  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. Novel biometrics based on nose pore recognition

    NASA Astrophysics Data System (ADS)

    Song, Shangling; Ohnuma, Kazuhiko; Liu, Zhi; Mei, Liangmo; Kawada, Akira; Monma, Tomoyuki

    2009-05-01

    We present a new member of the biometrics family-i.e., nose pores-which uses particularly interesting properties of nose pores as a basis for noninvasive biometric assessment. The pore distribution on the nose is stable and easily inspected. More important, nose pore distribution features are distinguishable between different persons. Thus, these features can be used for personal identification. However, little work has been done on nose pores as a biometric identifier. We have developed an end-to-end recognition system based on nose pore features. We also made use of a database of nose pore images obtained over a long period to examine the performance of nose pores as a biometric identifier. This research showed that the nose pore is a promising candidate for biometric identification and deserves further research. The experimental results based on the unique nose pores database demonstrated that nose pores can give an 88.07% correct recognition rate for biometric identification, which showed this biometric identifier's feasibility and effectiveness.

  11. Sex recognition and neuronal coding of electric organ discharge waveform in the pulse-type weakly electric fish, Hypopomus occidentalis.

    PubMed

    Shumway, C A; Zelick, R D

    1988-08-01

    1. Hypopomus occidentalis, a weakly electric gymnotiform fish with a pulse-type discharge, has a sexually dimorphic electric organ discharge (Hagedorn 1983). The electric organ discharges (EODs) of males in the breeding season are longer in duration and have a lower peak-power frequency than the EODs of females. We tested reproductively mature fish in the field by presenting electronically generated stimuli in which the only cue for sex recognition was the waveshape of individual EOD-like pulses in a train. We found that gravid females could readily discriminate male-like from female-like EOD waveshapes, and we conclude that this feature of the electric signal is sufficient for sex recognition. 2. To understand the possible neural bases for discrimination of male and female EODs by H . occidentalis, we conducted a neurophysiological examination of both peripheral and central neurons. Our studies show that there are sets of neurons in this species which can discriminate male or female EODs by coding either temporal or spectral features of the EOD. 3. Temporal encoding of stimulus duration was observed in evoked field potential recordings from the magnocellular nucleus of the midbrain torus semicircularis. This nucleus indirectly receives pulse marker electroreceptor information. The field potentials suggest that comparison is possible between pulse marker activity on opposite sides of the body. 4. From standard frequency-threshold curves, spectral encoding of stimulus peak-power frequency was measured in burst duration coder electroreceptor afferents. In both male and female fish, the best frequencies of the narrow-band population of electroreceptors were lower than the peak-power frequency of the EOD. Based on this observation, and the presence of a population of wide-band receptors which can serve as a frequency-independent amplitude reference, a slope-detection model of frequency discrimination is advanced. 5. Spectral discrimination of EOD peak-power frequency

  12. 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.

  13. 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.

  14. 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.

  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. Face recognition based on fringe pattern analysis

    NASA Astrophysics Data System (ADS)

    Guo, Hong; Huang, Peisen

    2010-03-01

    Two-dimensional face-recognition techniques suffer from facial texture and illumination variations. Although 3-D techniques can overcome these limitations, the reconstruction and storage expenses of 3-D information are extremely high. We present a novel face-recognition method that directly utilizes 3-D information encoded in face fringe patterns without having to reconstruct 3-D geometry. In the proposed method, a digital video projector is employed to sequentially project three phase-shifted sinusoidal fringe patterns onto the subject's face. Meanwhile, a camera is used to capture the distorted fringe patterns from an offset angle. Afterward, the face fringe images are analyzed by the phase-shifting method and the Fourier transform method to obtain a spectral representation of the 3-D face. Finally, the eigenface algorithm is applied to the face-spectrum images to perform face recognition. Simulation and experimental results demonstrate that the proposed method achieved satisfactory recognition rates with reduced computational complexity and storage expenses.

  17. Random-profiles-based 3D face recognition system.

    PubMed

    Kim, Joongrock; Yu, Sunjin; Lee, Sangyoun

    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.

  18. 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

  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. 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)

  3. 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…

  4. 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.

  5. 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.

  6. Experimental study on GMM-based speaker recognition

    NASA Astrophysics Data System (ADS)

    Ye, Wenxing; Wu, Dapeng; Nucci, Antonio

    2010-04-01

    Speaker recognition plays a very important role in the field of biometric security. In order to improve the recognition performance, many pattern recognition techniques have be explored in the literature. Among these techniques, the Gaussian Mixture Model (GMM) is proved to be an effective statistic model for speaker recognition and is used in most state-of-the-art speaker recognition systems. The GMM is used to represent the 'voice print' of a speaker through modeling the spectral characteristic of speech signals of the speaker. In this paper, we implement a speaker recognition system, which consists of preprocessing, Mel-Frequency Cepstrum Coefficients (MFCCs) based feature extraction, and GMM based classification. We test our system with TIDIGITS data set (325 speakers) and our own recordings of more than 200 speakers; our system achieves 100% correct recognition rate. Moreover, we also test our system under the scenario that training samples are from one language but test samples are from a different language; our system also achieves 100% correct recognition rate, which indicates that our system is language independent.

  7. 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.

  8. 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.

  9. Ear recognition based on Gabor features and KFDA.

    PubMed

    Yuan, Li; Mu, Zhichun

    2014-01-01

    We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on improved Adaboost algorithm detects the ear part under complex background using two steps: offline cascaded classifier training and online ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature extraction and orientation selection, Gabor filter based ear feature extraction is presented in this paper. Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. Finally distance based classifier is applied for ear recognition. Experimental results of ear recognition on two datasets (USTB and UND datasets) and the performance of the ear authentication system show the feasibility and effectiveness of the proposed approach.

  10. Tracheal activity recognition based on acoustic signals.

    PubMed

    Olubanjo, Temiloluwa; Ghovanloo, Maysam

    2014-01-01

    Tracheal activity recognition can play an important role in continuous health monitoring for wearable systems and facilitate the advancement of personalized healthcare. Neck-worn systems provide access to a unique set of health-related data that other wearable devices simply cannot obtain. Activities including breathing, chewing, clearing the throat, coughing, swallowing, speech and even heartbeat can be recorded from around the neck. In this paper, we explore tracheal activity recognition using a combination of promising acoustic features from related work and apply simplistic classifiers including K-NN and Naive Bayes. For wearable systems in which low power consumption is of primary concern, we show that with a sub-optimal sampling rate of 16 kHz, we have achieved average classification results in the range of 86.6% to 87.4% using 1-NN, 3-NN, 5-NN and Naive Bayes. All classifiers obtained the highest recognition rate in the range of 97.2% to 99.4% for speech classification. This is promising to mitigate privacy concerns associated with wearable systems interfering with the user's conversations.

  11. 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

  12. 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%.

  13. 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.

  14. Automatic emotion recognition based on body movement analysis: a survey.

    PubMed

    Zacharatos, Haris; Gatzoulis, Christos; Chrysanthou, Yiorgos L

    2014-01-01

    Humans are emotional beings, and their feelings influence how they perform and interact with computers. One of the most expressive modalities for humans is body posture and movement, which researchers have recently started exploiting for emotion recognition. This survey describes emerging techniques and modalities related to emotion recognition based on body movement, as well as recent advances in automatic emotion recognition. It also describes application areas and notation systems and explains the importance of movement segmentation. It then discusses unsolved problems and provides promising directions for future research. The Web extra (a PDF file) contains tables with additional information related to the article. PMID:25216477

  15. 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.

  16. 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.

  17. 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.

  18. Parallel computing-based sclera recognition for human identification

    NASA Astrophysics Data System (ADS)

    Lin, Yong; Du, Eliza Y.; Zhou, Zhi

    2012-06-01

    Compared to iris recognition, sclera recognition which uses line descriptor can achieve comparable recognition accuracy in visible wavelengths. However, this method is too time-consuming to be implemented in a real-time system. In this paper, we propose a GPU-based parallel computing approach to reduce the sclera recognition time. We define a new descriptor in which the information of KD tree structure and sclera edge are added. Registration and matching task is divided into subtasks in various sizes according to their computation complexities. Every affine transform parameters are generated by searching on KD tree. Texture memory, constant memory, and shared memory are used to store templates and transform matrixes. The experiment results show that the proposed method executed on GPU can dramatically improve the sclera matching speed in hundreds of times without accuracy decreasing.

  19. A recurrent dynamic model for correspondence-based face recognition.

    PubMed

    Wolfrum, Philipp; Wolff, Christian; Lücke, Jörg; von der Malsburg, Christoph

    2008-01-01

    Our aim here is to create a fully neural, functionally competitive, and correspondence-based model for invariant face recognition. By recurrently integrating information about feature similarities, spatial feature relations, and facial structure stored in memory, the system evaluates face identity ("what"-information) and face position ("where"-information) using explicit representations for both. The network consists of three functional layers of processing, (1) an input layer for image representation, (2) a middle layer for recurrent information integration, and (3) a gallery layer for memory storage. Each layer consists of cortical columns as functional building blocks that are modeled in accordance with recent experimental findings. In numerical simulations we apply the system to standard benchmark databases for face recognition. We find that recognition rates of our biologically inspired approach lie in the same range as recognition rates of recent and purely functionally motivated systems. PMID:19146266

  20. 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.

  1. 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

  2. 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.

  3. 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

  4. 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

  5. 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.

  6. 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

  7. 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

  8. 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%.

  9. Design of speaker recognition system based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Chen, Yanhong; Wang, Li; Lin, Han; Li, Jinlong

    2012-10-01

    Speaker recognition is to recognize speaker's identity from its voice which contains physiological and behavioral characteristics unique to each individual. In this paper, the artificial neural network model, which has very good capacity of non-linear division in characteristic space, is used for pattern matching. The speaker's sample characteristic domain is built for his mixed voice characteristic signals based on Kmeanlbg algorithm. Then the dimension of the inputting eigenvector is reduced, and the redundant information is got rid of. On this basis, BP neural network is used to divide capacity area for characteristic space nonlinearly, and the BP neural network acts as a classifier for the speaker. Finally, a speaker recognition system based on the neural network is realized and the experiment results validate the recognition performance and robustness of the system.

  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. A Random Forest-based ensemble method for activity recognition.

    PubMed

    Feng, Zengtao; Mo, Lingfei; Li, Meng

    2015-01-01

    This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation. PMID:26737432

  12. 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.

  13. 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.

  14. Research on face recognition based on singular value decomposition

    NASA Astrophysics Data System (ADS)

    Liang, Yixiong; Gong, Weiguo; Pan, Yingjun; Liu, Jiamin; Li, Weihong; Zhang, Hongmei

    2004-08-01

    Singular values (SVs) feature vectors of face image have been used for face recognition as the feature recently. Although SVs have some important properties of algebraic and geometric invariance and insensitiveness to noise, they are the representation of face image in its own eigen-space spanned by the two orthogonal matrices of singular value decomposition (SVD) and clearly contain little useful information for face recognition. This study concentrates on extracting more informational feature from a frontal and upright view image based on SVD and proposing an improving method for face recognition. After standardized by intensity normalization, all training and testing face images are projected onto a uniform eigen-space that is obtained from SVD of standard face image. To achieve more computational efficiency, the dimension of the uniform eigen-space is reduced by discarding the eigenvectors that the corresponding eigenvalue is close to zero. Euclidean distance classifier is adopted in recognition. Two standard databases from Yale University and Olivetti research laboratory are selected to evaluate the recognition accuracy of the proposed method. These databases include face images with different expressions, small occlusion, different illumination condition and different poses. Experimental results on the two face databases show the effectiveness of the method and its insensitivity to the face expression, illumination and posture.

  15. 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.

  16. 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…

  17. Micro-Based Speech Recognition: Instructional Innovation for Handicapped Learners.

    ERIC Educational Resources Information Center

    Horn, Carin E.; Scott, Brian L.

    A new voice based learning system (VBLS), which allows the handicapped user to interact with a microcomputer by voice commands, is described. Speech or voice recognition is the computerized process of identifying a spoken word or phrase, including those resulting from speech impediments. This new technology is helpful to the severely physically…

  18. Offline grammar-based recognition of handwritten sentences.

    PubMed

    Zimmermann, Matthias; Chappelier, Jean-Cédric; Bunke, Horst

    2006-05-01

    This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Stochastic Context-Free Grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.

  19. Quaternion-Based Discriminant Analysis Method for Color Face Recognition

    PubMed Central

    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. 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

  2. Visual-size molecular recognition based on gels.

    PubMed

    Tu, Tao; Fang, Weiwei; Sun, Zheming

    2013-10-01

    Since their discovery, stimuli-responsive organogels have garnered considerable and increasing attention from a broad range of research fields. In consideration of an one-dimensional ordered relay in anisotropic phase, the assembled gel networks can amplify various properties of the functional moieties possessed by the gelator molecules. Recently, substantial efforts have been focused on the development of facile, straightforward, and low-cost molecular recognition approaches by using nanostructured gel matrices as visual sensing platforms. In this research news, the recent progresses in macroscopic or visual-size molecular recognition for a number of homologues, isomers, and anions, as well as extremely challenging chiral enantiomers, using polymer and molecular gels are reviewed. Several strategies--including guest molecular competition, hydrogen-bonding blocking, and metal-coordination--for visual discrimination are included. Finally, the future trends and potential application in facile visual-size molecular recognition based on organogel matrices are highlighted. PMID:24089348

  3. 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.

  4. Improvements on EMG-based handwriting recognition with DTW algorithm.

    PubMed

    Li, Chengzhang; Ma, Zheren; Yao, Lin; Zhang, Dingguo

    2013-01-01

    Previous works have shown that Dynamic Time Warping (DTW) algorithm is a proper method of feature extraction for electromyography (EMG)-based handwriting recognition. In this paper, several modifications are proposed to improve the classification process and enhance recognition accuracy. A two-phase template making approach has been introduced to generate templates with more salient features, and modified Mahalanobis Distance (mMD) approach is used to replace Euclidean Distance (ED) in order to minimize the interclass variance. To validate the effectiveness of such modifications, experiments were conducted, in which four subjects wrote lowercase letters at a normal speed and four-channel EMG signals from forearms were recorded. Results of offline analysis show that the improvements increased the average recognition accuracy by 9.20%.

  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. 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

  8. 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

  9. 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.

  10. 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).

  11. 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.

  12. Feature-based syntactic and metric shape recognition

    NASA Astrophysics Data System (ADS)

    Prasad, Lakshman; Skourikhine, Alexei N.; Schlei, Bernd R.

    2000-10-01

    We present a syntactic and metric two-dimensional shape recognition scheme based on shape features. The principal features of a shape can be extracted and semantically labeled by means of the chordal axis transform (CAT), with the resulting generic features, namely torsos and limbs, forming the primitive segmented features of the shape. We introduce a context-free universal language for representing all connected planar shapes in terms of their external features, based on a finite alphabet of generic shape feature primitives. Shape exteriors are then syntactically represented as strings in this language. Although this representation of shapes is not complete, in that it only describes their external features, it effectively captures shape embeddings, which are important properties of shapes for purposes of recognition. The elements of the syntactic strings are associated with attribute feature vectors that capture the metrical attributes of the corresponding features. We outline a hierarchical shape recognition scheme, wherein the syntactical representation of shapes may be 'telescoped' to yield a coarser or finer description for hierarchical comparison and matching. We finally extend the syntactic representation and recognition to completely represent all planar shapes, albeit without a generative context-free grammar for this extension.

  13. 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.

  14. 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 .

  15. 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.

  16. 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

  17. A wavelet-based method for multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Zhang, Chaoyang; Zhou, Zhaoxian

    2012-06-01

    A wavelet-based method is proposed for multispectral face recognition in this paper. Gabor wavelet transform is a common tool for orientation analysis of a 2D image; whereas Hamming distance is an efficient distance measurement for face identification. Specifically, at each frequency band, an index number representing the strongest orientational response is selected, and then encoded in binary format to favor the Hamming distance calculation. Multiband orientation bit codes are then organized into a face pattern byte (FPB) by using order statistics. With the FPB, Hamming distances are calculated and compared to achieve face identification. The FPB algorithm was initially created using thermal images, while the EBGM method was originated with visible images. When two or more spectral images from the same subject are available, the identification accuracy and reliability can be enhanced using score fusion. We compare the identification performance of applying five recognition algorithms to the three-band (visible, near infrared, thermal) face images, and explore the fusion performance of combing the multiple scores from three recognition algorithms and from three-band face images, respectively. The experimental results show that the FPB is the best recognition algorithm, the HMM yields the best fusion result, and the thermal dataset results in the best fusion performance compared to other two datasets.

  18. 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.

  19. 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

  20. New Robust Face Recognition Methods Based on Linear Regression

    PubMed Central

    Mi, Jian-Xun; Liu, Jin-Xing; Wen, Jiajun

    2012-01-01

    Nearest subspace (NS) classification based on linear regression technique is a very straightforward and efficient method for face recognition. A recently developed NS method, namely the linear regression-based classification (LRC), uses downsampled face images as features to perform face recognition. The basic assumption behind this kind method is that samples from a certain class lie on their own class-specific subspace. Since there are only few training samples for each individual class, which will cause the small sample size (SSS) problem, this problem gives rise to misclassification of previous NS methods. In this paper, we propose two novel LRC methods using the idea that every class-specific subspace has its unique basis vectors. Thus, we consider that each class-specific subspace is spanned by two kinds of basis vectors which are the common basis vectors shared by many classes and the class-specific basis vectors owned by one class only. Based on this concept, two classification methods, namely robust LRC 1 and 2 (RLRC 1 and 2), are given to achieve more robust face recognition. Unlike some previous methods which need to extract class-specific basis vectors, the proposed methods are developed merely based on the existence of the class-specific basis vectors but without actually calculating them. Experiments on three well known face databases demonstrate very good performance of the new methods compared with other state-of-the-art methods. PMID:22879992

  1. [Freshwater fish freshness on-line detection method based on near-infrared spectroscopy].

    PubMed

    Huang, Tao; Li, Xiao-Yu; Peng, Yi; Tao, Hai-Long; Li, Peng; Xiong, Shan-Bai

    2014-10-01

    In the present study, the near infrared spectrum of freshwater fish was used to detect the freshness on line, and the near infrared spectra on-line acquisition device was built to get the fish spectrum. In the process of spectrum acquisition, experiment samples move at a speed of 0.5 m · s(-1), the near-infrared diffuse reflection spectrum (900-2,500 nm) could be got for the next analyzing, and SVM was used to build on-line detection model. Sample set partitioning based on joint X-Y distances algo- rithm (SPXY) was used to divide sample set, there were 111 samples in calibration set (57 fresh samples and 54 bad samples), and 37 samples in test set (19 fresh samples and 18 bad samples). Seven spectral preprocessing methods were utilized to prepro- cess the spectrum, and the influences of different methods were compared. Model results indicated that first derivative (FD) with autoscale was the best preprocessing method, the model recognition rate of calibration set was 97.96%, and the recognition rate of test set was 95.92%. In order to improve the modeling speed, it is necessary to optimize the spectra variables. Therefore genetic algorithm (GA), successive projection algorithm (SPA) and competitive adaptive reweighed sampling (CARS) were adopted to select characteristic variables respectively. Finally CARS was proved to be the optimal variable selection method, 10 characteristic wavelengths were selected to develop SVM model, recognition rate of calibration set reached 100%, and recognition rate of test set was 93.88%. The research provided technical reference for freshwater fish freshness online detection.

  2. Biometric verification based on grip-pattern recognition

    NASA Astrophysics Data System (ADS)

    Veldhuis, Raymond N.; Bazen, Asker M.; Kauffman, Joost A.; Hartel, Pieter

    2004-06-01

    This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 × 44 piezoresistive elements is used to measure the grip pattern. An interface has been developed to acquire pressure images from the sensor. The values of the pixels in the pressure-pattern images are used as inputs for a verification algorithm, which is currently implemented in software on a PC. The verification algorithm is based on a likelihoodratio classifier for Gaussian probability densities. First results indicate that it is feasible to use grip-pattern recognition for biometric verification.

  3. Production ready feature recognition based automatic group technology part coding

    SciTech Connect

    Ames, A.L.

    1990-01-01

    During the past four years, a feature recognition based expert system for automatically performing group technology part coding from solid model data has been under development. The system has become a production quality tool, capable of quickly the geometry based portions of a part code with no human intervention. It has been tested on over 200 solid models, half of which are models of production Sandia designs. Its performance rivals that of humans performing the same task, often surpassing them in speed and uniformity. The feature recognition capability developed for part coding is being extended to support other applications, such as manufacturability analysis, automatic decomposition (for finite element meshing and machining), and assembly planning. Initial surveys of these applications indicate that the current capability will provide a strong basis for other applications and that extensions toward more global geometric reasoning and tighter coupling with solid modeler functionality will be necessary.

  4. Speckle-learning-based object recognition through scattering media.

    PubMed

    Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

    2015-12-28

    We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning. PMID:26832049

  5. Part-based set matching for face recognition in surveillance

    NASA Astrophysics Data System (ADS)

    Zheng, Fei; Wang, Guijin; Lin, Xinggang

    2013-12-01

    Face recognition in surveillance is a hot topic in computer vision due to the strong demand for public security and remains a challenging task owing to large variations in viewpoint and illumination of cameras. In surveillance, image sets are the most natural form of input by incorporating tracking. Recent advances in set-based matching also show its great potential for exploring the feature space for face recognition by making use of multiple samples of subjects. In this paper, we propose a novel method that exploits the salient features (such as eyes, noses, mouth) in set-based matching. To represent image sets, we adopt the affine hull model, which can general unseen appearances in the form of affine combinations of sample images. In our proposal, a robust part detector is first used to find four salient parts for each face image: two eyes, nose, and mouth. For each part, we construct an affine hull model by using the local binary pattern histograms of multiple samples of the part. We also construct an affine model for the whole face region. Then, we find the closest distance between the corresponding affine hull models to measure the similarity between parts/face regions, and a weighting scheme is introduced to combine the five distances (four parts and the whole face region) to obtain the final distance between two subjects. In the recognition phase, a nearest neighbor classifier is used. Experiments on the public ChokePoint dataset and our dataset demonstrate the superior performance of our method.

  6. Emotion recognition based on the sample entropy of EEG.

    PubMed

    Jie, Xiang; Cao, Rui; Li, Li

    2014-01-01

    A sample entropy (SampEn)-based emotion recognition approach was presented. The SampEn results of notable EEG channels screened by K-S test were fed to the support vector machine (SVM)-weight classifier for training, after which it was applied to two emotion recognition tasks. One is to distinguish positive and negative emotion with high arousal and the other genitive emotion with different arousal status. Results showed that channels related to emotions were mostly located on the prefrontal region, i.e., F3, CP5, FP2, FZ, and FC2. And they were applied to form the input vectors of SVM-weight classifier. The accuracies of the present algorithm for the two tasks were 80.43% and 79.11%, respectively indicated by the leave-one-person-out validation procedure, demonstrating that the present algorithm had a reasonable generalization capability. PMID:24212012

  7. RNA structural motif recognition based on least-squares distance.

    PubMed

    Shen, Ying; Wong, Hau-San; Zhang, Shaohong; Zhang, Lin

    2013-09-01

    RNA structural motifs are recurrent structural elements occurring in RNA molecules. RNA structural motif recognition aims to find RNA substructures that are similar to a query motif, and it is important for RNA structure analysis and RNA function prediction. In view of this, we propose a new method known as RNA Structural Motif Recognition based on Least-Squares distance (LS-RSMR) to effectively recognize RNA structural motifs. A test set consisting of five types of RNA structural motifs occurring in Escherichia coli ribosomal RNA is compiled by us. Experiments are conducted for recognizing these five types of motifs. The experimental results fully reveal the superiority of the proposed LS-RSMR compared with four other state-of-the-art methods.

  8. Optical fingerprint recognition based on local minutiae structure coding.

    PubMed

    Yi, Yao; Cao, Liangcai; Guo, Wei; Luo, Yaping; Feng, Jianjiang; He, Qingsheng; Jin, Guofan

    2013-07-15

    A parallel volume holographic optical fingerprint recognition system robust to fingerprint translation, rotation and nonlinear distortion is proposed. The optical fingerprint recognition measures the similarity by using the optical filters of multiplexed holograms recorded in the holographic media. A fingerprint is encoded into multiple template data pages based on the local minutiae structure coding method after it is adapted for the optical data channel. An improved filter recording time schedule and a post-filtering calibration technology are combined to suppress the calculating error from the large variations in data page filling ratio. Experimental results tested on FVC2002 DB1 and a forensic database comprising 270,216 fingerprints demonstrate the robustness and feasibility of the system.

  9. 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.

  10. A Vocal-Based Analytical Method for Goose Behaviour Recognition

    PubMed Central

    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

  11. 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.

  12. 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

  13. New algorithm for iris recognition based on video sequences

    NASA Astrophysics Data System (ADS)

    Bourennane, Salah; Fossati, Caroline; Ketchantang, William

    2010-07-01

    Among existing biometrics, iris recognition systems are among the most accurate personal biometric identification systems. However, the acquisition of a workable iris image requires strict cooperation of the user; otherwise, the image will be rejected by a verification module because of its poor quality, inducing a high false reject rate (FRR). The FRR may also increase when iris localization fails or when the pupil is too dilated. To improve the existing methods, we propose to use video sequences acquired in real time by a camera. In order to keep the same computational load to identify the iris, we propose a new method to estimate the iris characteristics. First, we propose a new iris texture characterization based on Fourier-Mellin transform, which is less sensitive to pupil dilatations than previous methods. Then, we develop a new iris localization algorithm that is robust to variations of quality (partial occlusions due to eyelids and eyelashes, light reflects, etc.), and finally, we introduce a fast and new criterion of suitable image selection from an iris video sequence for an accurate recognition. The accuracy of each step of the algorithm in the whole proposed recognition process is tested and evaluated using our own iris video database and several public image databases, such as CASIA, UBIRIS, and BATH.

  14. 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.

  15. 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.

  16. 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…

  17. 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.

  18. 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

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. A novel polar-based human face recognition computational model.

    PubMed

    Zana, Y; Mena-Chalco, J P; Cesar, R M

    2009-07-01

    Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1- type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing. PMID:19578643

  6. Embedded knowledge-based system for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Aboutalib, A. O.

    1990-10-01

    The development of a reliable Automatic Target Recognition (ATE) system is considered a very critical and challenging problem. Existing ATE Systems have inherent limitations in terms of recognition performance and the ability to learn and adapt. Artificial Intelligence Techniques have the potential to improve the performance of ATh Systems. In this paper, we presented a novel Knowledge-Engineering tool, termed, the Automatic Reasoning Process (ARP) , that can be used to automatically develop and maintain a Knowledge-Base (K-B) for the ATR Systems. In its learning mode, the ARP utilizes Learning samples to automatically develop the ATR K-B, which consists of minimum size sets of necessary and sufficient conditions for each target class. In its operational mode, the ARP infers the target class from sensor data using the ATh K-B System. The ARP also has the capability to reason under uncertainty, and can support both statistical and model-based approaches for ATR development. The capabilities of the ARP are compared and contrasted to those of another Knowledge-Engineering tool, termed, the Automatic Rule Induction (ARI) which is based on maximizing the mutual information. The AR? has been implemented in LISP on a VAX-GPX workstation.

  7. 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.

  8. 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.

  9. Handwritten character recognition based on hybrid neural networks

    NASA Astrophysics Data System (ADS)

    Wang, Peng; Sun, Guangmin; Zhang, Xinming

    2001-09-01

    A hybrid neural network system for the recognition of handwritten character using SOFM,BP and Fuzzy network is presented. The horizontal and vertical project of preprocessed character and 4_directional edge project are used as feature vectors. In order to improve the recognition effect, the GAT algorithm is applied. Through the hybrid neural network system, the recognition rate is improved visibly.

  10. 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

  11. Human activity recognition based on human shape dynamics

    NASA Astrophysics Data System (ADS)

    Cheng, Zhiqing; Mosher, Stephen; Cheng, Huaining; Webb, Timothy

    2013-05-01

    Human activity recognition based on human shape dynamics was investigated in this paper. The shape dynamics describe the spatial-temporal shape deformation of a human body during its movement and thus provide important information about the identity of a human subject and the motions performed by the subject. The dynamic shapes of four subjects in five activities (digging, jogging, limping, throwing, and walking) were created via 3-D motion replication. The Paquet Shape Descriptor (PSD) was used to describe subject shapes in each frame. The principal component analysis was performed on the calculated PSDs and principal components (PCs) were used to characterize PSDs. The PSD calculation was then reasonably approximated by its significant projections in the eigen-space formed by PCs and represented by the corresponding projection coefficients. As such, the dynamic human shapes for each activity were described by these projection coefficients, which in turn, along with their derivatives were used to form the feature vectors (attribute sets) for activity classification. Data mining technology was employed with six classification methods used. Seven attribute sets were evaluated with high classification accuracy attained for most of them. The results from this investigation illustrate the great potential of human shape dynamics for activity recognition.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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%.

  18. 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...

  19. 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.…

  20. 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

  1. Fish species identification in surimi-based products.

    PubMed

    Pepe, Tiziana; Trotta, Michele; Di Marco, Isolina; Anastasio, Aniello; Bautista, José Manuel; Cortesi, Maria Luisa

    2007-05-01

    Whole fish morphologically identified as belonging to Theragra chalcogramma, Merluccius merluccius, Merluccius hubbsi, and Merluccius capensis and 19 fish products commercialized as surimi with different commercial brands and labeled as T. chalcogramma were analyzed by direct sequence analysis of the cytochrome b gene. A phylogenetic analysis of surimi products was performed as well. Results demonstrated that mislabeling is a large-scale phenomenon, since 84.2% of surimi-based fish products sold as T. chalcogramma (16/19) were prepared with species different from the one declared. In fact, only three samples (samples 15-17) were found to belong to T. chalcogramma. In the remaining samples, Merluccidae (samples 4-14), Gadidae (samples 18 and 19), Sparidae (sample 1), and Pomacentridae (samples 2 and 3) families were detected. A phylogenetic tree was constructed, and the bootstrap value was calculated. According to this methodology, 11 samples were grouped in the same clade as Merluccius spp.

  2. An exclusively based parenteral fish-oil emulsion reverses cholestasis.

    PubMed

    Triana Junco, Miryam; García Vázquez, Natalia; Zozaya, Carlos; Ybarra Zabala, Marta; Abrams, Steven; García de Lorenzo, Abelardo; Sáenz de Pipaón Marcos, Miguel

    2014-10-25

    Prolonged parenteral nutrition (PN) leads to liver damage. Recent interest has focused on the lipid component of PN. A lipid emulsion based on w-3 fatty acids decrease conjugated bilirubin. A mixed lipid emulsion derived from soybean, coconut, olive, and fish oils reverses jaundice. Here we report the reversal of cholestasis and the improvement of enteral feeding tolerance in 1 infant with intestinal failure-associated liver disease. Treatment involved the substitution of a mixed lipid emulsion with one containing primarily omega-3 fatty acids during 37 days. Growth and biochemical tests of liver function improved significantly. This suggests that fat emulsions made from fish oils may be more effective means of treating this condition compared with an intravenous lipid emulsion containing soybean oil, medium -chain triglycerides, olive oil, and fish oil.

  3. 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.

  4. 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…

  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. 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).

  7. 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.

  8. Carbonic anhydrase and acid-base regulation in fish.

    PubMed

    Gilmour, K M; Perry, S F

    2009-06-01

    Carbonic anhydrase (CA) is the zinc metalloenzyme that catalyses the reversible reactions of CO(2) with water. CA plays a crucial role in systemic acid-base regulation in fish by providing acid-base equivalents for exchange with the environment. Unlike air-breathing vertebrates, which frequently utilize alterations of breathing (respiratory compensation) to regulate acid-base status, acid-base balance in fish relies almost entirely upon the direct exchange of acid-base equivalents with the environment (metabolic compensation). The gill is the critical site of metabolic compensation, with the kidney playing a supporting role. At the gill, cytosolic CA catalyses the hydration of CO(2) to H(+) and HCO(3)(-) for export to the water. In the kidney, cytosolic and membrane-bound CA isoforms have been implicated in HCO(3)(-) reabsorption and urine acidification. In this review, the CA isoforms that have been identified to date in fish will be discussed together with their tissue localizations and roles in systemic acid-base regulation.

  9. 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…

  10. Fast traffic sign recognition with a rotation invariant binary pattern based feature.

    PubMed

    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.

  11. 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

  12. 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.

  13. 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.

  14. A context-based approach to text recognition

    SciTech Connect

    Rose, T.G.; Evett, L.J.; Jobbins, A.C.

    1994-12-31

    The performance of text recognition systems may be improved by applying higher-level knowledge in the form of contextual information. However, the acquisition of such information for a realistically sized vocabulary presents a major problem, since hand-coding is feasible for only the smallest of vocabularies. This paper describes a number of methods for extracting contextual knowledge from text corpora, and compares the effect of each on the performance of text recognition systems.

  15. 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.

  16. 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.

  17. EEG-based emotion recognition in music listening.

    PubMed

    Lin, Yuan-Pin; Wang, Chi-Hong; Jung, Tzyy-Ping; Wu, Tien-Lin; Jeng, Shyh-Kang; Duann, Jeng-Ren; Chen, Jyh-Horng

    2010-07-01

    Ongoing brain activity can be recorded as electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% +/- 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.

  18. 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

  19. Recent advances in molecular recognition based on nanoengineered platforms.

    PubMed

    Mu, Bin; Zhang, Jingqing; McNicholas, Thomas P; Reuel, Nigel F; Kruss, Sebastian; Strano, Michael S

    2014-04-15

    they are able to obtain loading curves similar to surface plasmon resonance measurements. They demonstrate the sensitivity and specificity of this platform with two higher-affined glycan-lectin pairs: fucose (Fuc) to PA-IIL and N-acetylglucosamine (GlcNAc) to GafD. Lastly, we discuss how developments in protein biomarker detection in general are benefiting specifically from label-free molecular recognition. Electrical field effect transistors, chemi-resistive and fluorometric nanosensors based on various nanomaterials have demonstrated substantial progress in recent years in addressing this challenging problem. In this Account, we compare the balance between sensitivity, selectivity, and nonspecific adsorption for various applications. In particular, our group has utilized SWNTs as fluorescence sensors for label-free protein-protein interaction measurements. In this assay, we have encapsulated each nanotube in a biocompatible polymer, chitosan, which has been further modified to conjugate nitrilotriacetic acid (NTA) groups. After Ni(2+) chelation, NTA Ni(2+) complexes bind to his-tagged proteins, resulting in a local environment change of the SWNT array, leading to optical fluorescence modulation with detection limit down to 100 nM. We have further engineered the platform to monitor single protein binding events, with an even lower detection limit down to 10 pM.

  20. 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.

  1. 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

  2. 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…

  3. 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…

  4. 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...

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

  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. 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

  12. 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.

  13. 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

  14. SPME technique for analyzing headspace volatiles in fish miso, a Japanese fish meat-based fermented product.

    PubMed

    Giri, Anupam; Osako, Kazufumi; Ohshima, Toshiaki

    2010-01-01

    The optimized conditions were evaluated for solid-phase microextraction (SPME) to investigate the headspace volatiles in fish miso, a Japanese fish meat-based fermented product. The influence on the efficiency for microextraction of such parameters as the sample size, isolation time and temperature, sensitivity and selectivity of several SPME fibers of different liquid phases as well as several extraction techniques was evaluated. Suitable reproducibility and sensitivity of SPME were achieved by combining carbowax/divenylbenzene of 65 µm thickness as the liquid phase of SPME, 3 g of fish miso, 40 °C of isolation temperature and 40 min of isolation time. The headspace volatiles of fish miso prepared from spotted mackerel were analyzed under the optimized conditions. Although several volatiles contributed to fish miso, certain volatile esters might have played the greatest role in imparting the sweet-fruity aroma to the product.

  15. 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.

  16. 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

  17. Face Image Gender Recognition Based on Gabor Transform and SVM

    NASA Astrophysics Data System (ADS)

    Yan, Chunjuan

    In order to overcome the disturbance of non-essential information such as illumination variations and facial expression changing, a new algorithm is proposed in this paper for face image gender recognition. That is, the 2-D Gabor transform is used for extracting the face features; a new method is put forwards to decrease dimensions of Gabor transform output for speeding up SVM training; finally gender recognition is accomplished with SVM classifier. Good performance of gender classification test is achieved on a relative large scale and low-resolution face database.

  18. 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.

  19. 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.

  20. 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.

  1. Highly permselective membrane surface modification by cold plasma-induced grafting polymerization of molecularly imprinted polymer for recognition of pyrethroid insecticides in fish.

    PubMed

    Zhang, Rongrong; Guo, Xiaoqing; Shi, Xizhi; Sun, Aili; Wang, Lin; Xiao, Tingting; Tang, Zigang; Pan, Daodong; Li, Dexiang; Chen, Jiong

    2014-12-01

    Specific molecularly imprinted membranes (MIMs) for pyrethroid insecticides were developed and characterized for the first time in this study by cold plasma-induced grafting polymerization using methacrylic acid as a functional monomer and cypermethrin (CYP) as a template. The nonimprinted membranes (NIMs) were also synthesized using the same procedure without the template. Meanwhile, AFM, XPS, ATR-FTIR, contact angle, and permselectivity experiments were conducted to elucidate the imprinting and recognition properties of MIMs. Results demonstrated that MIMs exhibited excellent imprinting effect and high permselectivity. A molecularly imprinted-membrane-assisted solvent extraction (MI-MASE) method based on the MIMs was established. The operating conditions were optimized for group-selective extraction of the five pyrethroid insecticides. Compared with NIMs, higher extraction recoveries (83.8% to 100.6%) of the five pyrethroid insecticides by gas chromatography-electron capture detector (GC-ECD) were obtained using MIMs at three spiked levels in fish samples; the RSD values were lower than 8.3%. The limits of detection (LOD) and quantification (LOQ) defined as the concentrations at which the signal-to-noise (S/N) ratio is 3:1 and 10:1, respectively, were in the range of 0.26 to 0.42 μg/kg and 0.77 to 1.27 μg/kg, respectively. No matrix effect of the developed MI-MASE was observed by gas chromatography/tandem mass spectrometry (GC/MS/MS). These results demonstrated a highly selective, efficient, and environment-friendly MI-MASE technique for preconcentration and purification of pyrethroid insecticides from seafood, followed by GC-ECD and GC/MS/MS. The excellent applicability and potential of MI-MASE for routine monitoring of pyrethroid pesticides in food samples has also been confirmed.

  2. 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.

  3. Research on pavement crack recognition methods based on image processing

    NASA Astrophysics Data System (ADS)

    Cai, Yingchun; Zhang, Yamin

    2011-06-01

    In order to overview and analysis briefly pavement crack recognition methods , then find the current existing problems in pavement crack image processing, the popular methods of crack image processing such as neural network method, morphology method, fuzzy logic method and traditional image processing .etc. are discussed, and some effective solutions to those problems are presented.

  4. 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.

  5. 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 ...

  6. 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

  7. 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

  8. Robust and discriminating method for face recognition based on correlation technique and independent component analysis model.

    PubMed

    Alfalou, A; Brosseau, C

    2011-03-01

    We demonstrate a novel technique for face recognition. Our approach relies on the performances of a strongly discriminating optical correlation method along with the robustness of the independent component analysis (ICA) model. Simulations were performed to illustrate how this algorithm can identify a face with images from the Pointing Head Pose Image Database. While maintaining algorithmic simplicity, this approach based on ICA representation significantly increases the true recognition rate compared to that obtained using our previously developed all-numerical ICA identity recognition method and another method based on optical correlation and a standard composite filter. PMID:21368935

  9. 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.

  10. 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.

  11. The disruptive effects of processing fluency on familiarity-based recognition in amnesia.

    PubMed

    Ozubko, Jason D; Yonelinas, Andrew P

    2014-02-01

    Amnesia leads to a deficit in recollection that leaves familiarity-based recognition relatively spared. Familiarity is thought to be based on the fluent processing of studied items compared to novel items. However, whether amnesic patients respond normally to direct manipulations of processing fluency is not yet known. In the current study, we manipulated processing fluency by preceding each test item with a semantically related or unrelated prime item, and measured both recollection and familiarity using a remember-know recognition procedure. In healthy controls, enhancing processing fluency increased familiarity-based recognition responses for both old and new words, leaving familiarity-based accuracy constant. However, in patients with MTL damage, enhancing fluency only increased familiarity-based recognition responses for new items, resulting in decreased familiarity-based recognition accuracy. Importantly, this fluency-related decrease in recognition accuracy was not due to overall lower levels of performance or impaired recollection of studied items because it was not observed in healthy subjects that studied words under conditions that lowered performance by reducing recollection. The results indicate that direct manipulations of processing fluency can disrupt familiarity-based discrimination in amnesia. Potential accounts of these findings are discussed.

  12. 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.

  13. 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

  14. 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.

  15. Wavelet-based learning vector quantization for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Chan, Lipchen A.; Nasrabadi, Nasser M.; Mirelli, Vincent

    1996-06-01

    An automatic target recognition classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands. A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. A recognition rate of 69.0 percent is achieved on a highly cluttered test set.

  16. Model Based Object Recognition Using LORD LTS-300 Touch Sensor

    NASA Astrophysics Data System (ADS)

    Roach, J. W.; Paripati, P. K.; Wade, M.

    1988-03-01

    This paper reports the result of a model driven touch sensor recognition experiment. The touch sensor employed is a large field tactile array. Object features appropriate for touch sensor recognition are extracted from a geometric model of an object, the dual spherical image. Both geometric and dynamic features are used to identify objects and their position and orientation on the touch sensor. Experiments show that geometric features extracted from the model are effective but that dynamic features must be determined empirically. Correct object identification rates even for very similar objects exceed ninety percent, a success rate much higher than we would have expected from only two-dimensional contact patterns. Position and orientation of objects once identified are very reliable. We conclude that large field tactile sensors could prove very useful in the automatic palletizing problem when object models (from a CAD system, for example) can be utilized.

  17. Indirect mate choice, direct mate choice and species recognition in a bower-building cichlid fish lek.

    PubMed

    Genner, M J; Young, K A; Haesler, M P; Joyce, D A

    2008-09-01

    Sexual selection arising through female mate choice typically favours males with larger, brighter and louder signals. A critical challenge in sexual selection research is to determine the degree to which this pattern results from direct mate choice, where females select individual males based on variation in signalling traits, or indirect mate choice, where male competition governs access to reproductively active females. We investigated female mate choice in a lekking Lake Malawi cichlid fish, Hemitilapia oxyrhynchus, in which males build and aggressively defend sand 'bowers'. Similar to previous studies, we found that male reproductive success was positively associated with bower height and centrality on the lek. However, this pattern resulted from males holding these territories encountering more females, and thus their greater success was due to indirect mate choice. Following initial male courtship, an increase in the relative mating success of some males was observed, but this relative increase was unrelated to bower size or position. Crucially, experimentally manipulating bowers to resemble those of a co-occurring species had no appreciable effect on direct choice by females or male spawning success. Together, these results suggest indirect mate choice is the dominant force determining male-mating success in this species, and that bowers are not signals used in direct mate choice by females. We propose that, in this species, bowers have a primary function in intraspecific male competition, with the most competitive males maintaining larger and more central bowers that are favoured by sexual selection due to higher female encounter rates.

  18. 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.

  19. Segment-based acoustic models for continuous speech recognition

    NASA Astrophysics Data System (ADS)

    Ostendorf, Mari; Rohlicek, J. R.

    1994-02-01

    In work, we are interested in the problem of large vocabulary, speaker-independent continuous speech recognition, and primarily in the acoustic modeling component of this problem. In developing acoustic models for speech recognition, we have conflicting goals. On one hand, the models should be robust to inter- and intra-speaker variability, to the use of a different vocabulary in recognition than in training, and to the effects of moderately noisy environments. In order to accomplish this, we need to model gross features and global trends. On the other hand, the models must be sensitive and detailed enough to detect fine acoustic differences between similar words in a large vocabulary task. To answer these opposing demands requires improvements in acoustic modeling at several levels: the frame level (e.g. signal processing), the phoneme level (e.g. modeling feature dynamics), and the utterance level (e.g. defining a structural context for representing the intra-utterance dependence across phonemes). This project address the problem of acoustic modeling specifically focusing on modeling at the segment level and above.

  20. 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.

  1. Team activity recognition in Association Football using a Bag-of-Words-based method.

    PubMed

    Montoliu, Raúl; Martín-Félez, Raúl; Torres-Sospedra, Joaquín; Martínez-Usó, Adolfo

    2015-06-01

    In this paper, a new methodology is used to perform team activity recognition and analysis in Association Football. It is based on pattern recognition and machine learning techniques. In particular, a strategy based on the Bag-of-Words (BoW) technique is used to characterize short Football video clips that are used to explain the team's performance and to train advanced classifiers in automatic recognition of team activities. In addition to the neural network-based classifier, three more classifier families are tested: the k-Nearest Neighbor, the Support Vector Machine and the Random Forest. The results obtained show that the proposed methodology is able to explain the most common movements of a team and to perform the team activity recognition task with high accuracy when classifying three Football actions: Ball Possession, Quick Attack and Set Piece. Random Forest is the classifier obtaining the best classification results.

  2. Bilateral thalamic lesions affect recollection- and familiarity-based recognition memory judgments.

    PubMed

    Kishiyama, Mark M; Yonelinas, Andrew P; Kroll, Neal E A; Lazzara, Michele M; Nolan, Eric C; Jones, Edward G; Jagust, William J

    2005-12-01

    The contribution of the thalamus to different forms of explicit memory is poorly understood. In the current study, explicit memory performance was examined in a 40-year-old male (RG) with bilateral anterior and medial thalamic lesions. Standardized tests indicated that the patient exhibited more severe recall than recognition deficits and his performance was generally worse for verbal compared to nonverbal memory. Recognition memory tests using the remember-know (R/K) procedure and the confidence-based receiver operating characteristic (ROC) procedure were used to examine recollection- and familiarity-based recognition. These tests revealed that RG had deficits in recollection and smaller, but consistent deficits in familiarity. The results are in agreement with models indicating that the anteromedial thalamus is important for both recollection- and familiarity-based recognition memory. PMID:16353367

  3. 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

  4. 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.

  5. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH.

    PubMed

    Bienko, Magda; Crosetto, Nicola; Teytelman, Leonid; Klemm, Sandy; Itzkovitz, Shalev; van Oudenaarden, Alexander

    2013-02-01

    We developed a cost-effective genome-scale PCR-based method for high-definition DNA FISH (HD-FISH). We visualized gene loci with diffraction-limited resolution, chromosomes as spot clusters and single genes together with transcripts by combining HD-FISH with single-molecule RNA FISH. We provide a database of over 4.3 million primer pairs targeting the human and mouse genomes that is readily usable for rapid and flexible generation of probes.

  6. Automatic facial expression recognition based on features extracted from tracking of facial landmarks

    NASA Astrophysics Data System (ADS)

    Ghimire, Deepak; Lee, Joonwhoan

    2014-01-01

    In this paper, we present a fully automatic facial expression recognition system using support vector machines, with geometric features extracted from the tracking of facial landmarks. Facial landmark initialization and tracking is performed by using an elastic bunch graph matching algorithm. The facial expression recognition is performed based on the features extracted from the tracking of not only individual landmarks, but also pair of landmarks. The recognition accuracy on the Extended Kohn-Kanade (CK+) database shows that our proposed set of features produces better results, because it utilizes time-varying graph information, as well as the motion of individual facial landmarks.

  7. 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

  8. Exploring techniques for vision based human activity recognition: methods, systems, and evaluation.

    PubMed

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

    2013-01-25

    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 activity, 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 towards the performance of human activity recognition.

  9. 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.

  10. 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

  11. Driver fatigue recognition based on supervised LPP and MKSVM

    NASA Astrophysics Data System (ADS)

    Huang, Wei; Zhang, Wei

    2011-06-01

    Driver fatigue is a significant factor in many traffic accidents. In this paper, a novel approach is proposed to recognize driver fatigue. First of all, in order to extract effective feature of fatigue expression from face images, supervised locality preserving projections (SLPP) is adopted, which can solve the problem that LPP ignores the within-class local structure by adopting prior class label information. And then multiple kernels support vector machines (MKSVM) is employed to recognizing fatigue expression, Compared to SVM, which can improve the interpretability of decision function and performance of fatigue recognition. Experimental results are shown to demonstrate the effectiveness of the proposed method.

  12. 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.

  13. Novel, ERP-based, concealed information detection: Combining recognition-based and feedback-evoked ERPs.

    PubMed

    Sai, Liyang; Lin, Xiaohong; Rosenfeld, J Peter; Sang, Biao; Hu, Xiaoqing; Fu, Genyue

    2016-02-01

    The present study introduced a novel variant of the concealed information test (CIT), called the feedback-CIT. By providing participants with feedbacks regarding their memory concealment performance during the CIT, we investigated the feedback-related neural activity underlying memory concealment. Participants acquired crime-relevant memories via enacting a lab crime, and were tested with the feedback-CIT while EEGs were recorded. We found that probes (e.g., crime-relevant memories) elicited larger recognition-P300s than irrelevants among guilty participants. Moreover, feedback-related negativity (FRN) and feedback-P300 could also discriminate probes from irrelevants among guilty participants. Both recognition- and feedback-ERPs were highly effective in distinguishing between guilty and innocent participants (recognition-P300: AUC=.73; FRN: AUC=.95; feedback-P300: AUC=.97). This study sheds new light on brain-based memory detection, such that feedback-related neural signals can be employed to detect concealed memories.

  14. 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

  15. Single-sample face recognition based on intra-class differences in a variation model.

    PubMed

    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.

  17. 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

  18. [Low frequency-based non-uniform sampling strategy to improve Chinese recognition in cochlear implant].

    PubMed

    Ni, Saihua; Sun, Wenye; Sun, Baoyin; Zhou, Qiang; Wang, Qiang; Wang, Zhenming; Gu, Jihua; Tao, Zhi

    2014-06-01

    To enhance speech recognition, as well as Mandarin tone recognition in noice, we proposed a speech coding strategy called zero-crossing of fine structure in low frequency (LFFS) for cochlear implant based on low frequency non-uniform sampling (LFFS for short). In the range of frequency perceived boundary of human ear, we used zero-crossing time of the fine structure to generate the stimulus pulse sequences based on the frequency selection rule. Acoustic simulation results showed that although on quiet background the performance of LFFS was similar to continuous interleaved sampling (CIS), on the noise background the performance of LFFS in Chinese tones, words and sentences were significantly better than CIS. In addition to this, we also got better Mandarin recognition factors distribution by using the improved index distribution model. LFFS contains more tonal information which was able to effectively improve Mandarin recognition of the cochlear implant. PMID:25219227

  19. Multiple base-recognition sites in a biological nanopore – two heads are better than one

    PubMed Central

    Stoddart, David; Maglia, Giovanni; Mikhailova, Ellina; Heron, Andrew J.; Bayley, Hagan

    2011-01-01

    Ultra-rapid sequencing of DNA strands with nanopores is under intense investigation. The αHL protein nanopore is a leading candidate sensor for this approach. Multiple base-recognition sites have been identified in engineered αHL pores. By using immobilized synthetic oligonucleotides, we show here that additional sequence information can be gained when two recognition sites, rather than one, are employed within a single nanopore. PMID:20014084

  20. Towards a smart glove: arousal recognition based on textile Electrodermal Response.

    PubMed

    Valenza, Gaetano; Lanata, Antonio; Scilingo, Enzo Pasquale; De Rossi, Danilo

    2010-01-01

    This paper investigates the possibility of using Electrodermal Response, acquired by a sensing fabric glove with embedded textile electrodes, as reliable means for emotion recognition. Here, all the essential steps for an automatic recognition system are described, from the recording of physiological data set to a feature-based multiclass classification. Data were collected from 35 healthy volunteers during arousal elicitation by means of International Affective Picture System (IAPS) pictures. Experimental results show high discrimination after twenty steps of cross validation. PMID:21096840

  1. 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.

  2. 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.

  3. 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.

    2016-09-01

    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.

  4. 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.

  5. 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.

  6. 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.

  7. Speaker-Adaptive Speech Recognition Based on Surface Electromyography

    NASA Astrophysics Data System (ADS)

    Wand, Michael; Schultz, Tanja

    We present our recent advances in silent speech interfaces using electromyographic signals that capture the movements of the human articulatory muscles at the skin surface for recognizing continuously spoken speech. Previous systems were limited to speaker- and session-dependent recognition tasks on small amounts of training and test data. In this article we present speaker-independent and speaker-adaptive training methods which allow us to use a large corpus of data from many speakers to train acoustic models more reliably. We use the speaker-dependent system as baseline, carefully tuning the data preprocessing and acoustic modeling. Then on our corpus we compare the performance of speaker-dependent and speaker-independent acoustic models and carry out model adaptation experiments.

  8. 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.

  9. 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.

  10. Application of food waste based diets in polyculture of low trophic level fish: effects on fish growth, water quality and plankton density.

    PubMed

    Mo, Wing Yin; Cheng, Zhang; Choi, Wai Ming; Man, Yu Bon; Liu, Yihui; Wong, Ming Hung

    2014-08-30

    Food waste was collected from local hotels and fish feed pellets were produced for a 6 months long field feeding trial. Three types of fish feed pellets (control diet: Jinfeng® 613 formulated feed, contains mainly fish meal, plant product and fish oil; Diet A: food waste based diet without meat and 53% cereal; Diet B: food waste based diet with 25% meat and 28% cereal) were used in polyculture fish ponds to investigate the growth of fish (grass carp, bighead and mud carp), changes in water quality and plankton density. No significant differences in the levels of nitrogen and phosphorous compounds of water body were observed between 3 fish ponds after the half-year feeding trial, while pond receiving Diet A had the highest density of plankton. The food waste combination of Diet B seems to be a better formulation in terms of the overall performance on fish growth.

  11. Application of food waste based diets in polyculture of low trophic level fish: effects on fish growth, water quality and plankton density.

    PubMed

    Mo, Wing Yin; Cheng, Zhang; Choi, Wai Ming; Man, Yu Bon; Liu, Yihui; Wong, Ming Hung

    2014-08-30

    Food waste was collected from local hotels and fish feed pellets were produced for a 6 months long field feeding trial. Three types of fish feed pellets (control diet: Jinfeng® 613 formulated feed, contains mainly fish meal, plant product and fish oil; Diet A: food waste based diet without meat and 53% cereal; Diet B: food waste based diet with 25% meat and 28% cereal) were used in polyculture fish ponds to investigate the growth of fish (grass carp, bighead and mud carp), changes in water quality and plankton density. No significant differences in the levels of nitrogen and phosphorous compounds of water body were observed between 3 fish ponds after the half-year feeding trial, while pond receiving Diet A had the highest density of plankton. The food waste combination of Diet B seems to be a better formulation in terms of the overall performance on fish growth. PMID:24492151

  12. 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.

  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. 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.

  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. Facial recognition using multisensor images based on localized kernel eigen spaces.

    PubMed

    Gundimada, Satyanadh; Asari, Vijayan K

    2009-06-01

    A feature selection technique along with an information fusion procedure for improving the recognition accuracy of a visual and thermal image-based facial recognition system is presented in this paper. A novel modular kernel eigenspaces approach is developed and implemented on the phase congruency feature maps extracted from the visual and thermal images individually. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the visual and thermal face recognition techniques. AR and Equinox databases are used for experimentation and evaluation of the proposed technique. The proposed feature selection procedure has greatly improved the recognition accuracy for both the visual and thermal images when compared to conventional techniques. Also, a decision level fusion methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy. PMID:19366643

  17. Hierarchical Vision-based Algorithm for Vehicle Model Type Recognition from Time-sequence Road Images

    NASA Astrophysics Data System (ADS)

    Zheng, Mingxie; Gotoh, Toshiyuki; Shiohara, Morito

    This paper describes a vision-based algorithm for recognizing the vehicle model type from time-sequence road images. Many types of vehicle models are offered commercially, and some of them are resemble in shape. This prevents us to discriminate their model types from the others easily. To solve these problems, we proposes a hierarchical recognition method with training process, in which the resemble model groups are firstly generated and the effective features to discriminate the models in the each group are then selected using the subspace method in training. In the recognition process, a front area is firstly detected from each frame of the input time-sequence images, then a hierarchical recognition which consists of a group and a category discrimination is performed. Finally, the results of frame recognition are integrated to realize stable recognition. The experimental results using time-sequence road images show the proposed method is effective: the recognition rate for the registered model types is more than 99%, and the rejection rate for unregistered vehicle type is more than 92%.

  18. 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.

  19. 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

  20. Bimodal biometrics based on a representation and recognition approach

    NASA Astrophysics Data System (ADS)

    Xu, Yong; Zhong, Aini; Yang, Jian; Zhang, David

    2011-03-01

    It has been demonstrated that multibiometrics can produce higher accuracy than single biometrics. This is mainly because the use of multiple biometric traits of the subject enables more information to be used for identification or verification. In this paper, we focus on bimodal biometrics and propose a novel representation and recognition approach to bimodal biometrics. This approach first denotes the biometric trait sample by a complex vector. Then, it represents the test sample through the training samples and classifies the test sample as follows: let the test sample be expressed as a linear combination of all the training samples each being a complex vector. The proposed approach obtains the solution by solving a linear system. After evaluating the effect, in representing the test sample of each class, the approach classifies the test sample into the class that makes the greatest effect. The approach proposed is not only novel but also simple and computationally efficient. A large number of experiments show that our method can obtain promising results.

  1. 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

  2. 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…

  3. 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…

  4. 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.

  5. A neural-network appearance-based 3-D object recognition using independent component analysis.

    PubMed

    Sahambi, H S; Khorasani, K

    2003-01-01

    This paper presents results on appearance-based three-dimensional (3-D) object recognition (3DOR) accomplished by utilizing a neural-network architecture developed based on independent component analysis (ICA). ICA has already been applied for face recognition in the literature with encouraging results. In this paper, we are exploring the possibility of utilizing the redundant information in the visual data to enhance the view based object recognition. The underlying premise here is that since ICA uses high-order statistics, it should in principle outperform principle component analysis (PCA), which does not utilize statistics higher than two, in the recognition task. Two databases of images captured by a CCD camera are used. It is demonstrated that ICA did perform better than PCA in one of the databases, but interestingly its performance was no better than PCA in the case of the second database. Thus, suggesting that the use of ICA may not necessarily always give better results than PCA, and that the application of ICA is highly data dependent. Various factors affecting the differences in the recognition performance using both methods are also discussed. PMID:18237997

  6. Locality constrained joint dynamic sparse representation for local matching based face recognition.

    PubMed

    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

  7. 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

  8. 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.

  9. 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

  10. 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

  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. High-Precise and Robust Face-Recognition System Based on Optical Parallel Correlator

    NASA Astrophysics Data System (ADS)

    Kodate, Kashiko

    2005-10-01

    Facial recognition is applied in a wide range of security systems, and has been studied since the 1970s, with extensive research into and development of digital processing. However, there is only available a 1:1 verification system combined with ID card identification, or an ID-less system with a small number of images in the database. The number of images that can be stored is limited, and recognition has to be improved to account for photos taken at different angles. Commercially available facial recognition systems for the most part utilize digital computers performing electronic pattern recognition. In contrast, optical analog operations can process two-dimensional images instantaneously in parallel using a lens-based Fourier transform function. In the 1960s two methods were proposed, the Vanderlugt correlator and the joint transform correlator (JTC). We present a new scheme using a multi-channel parallel JTC to make better use of spatial parallelism, through the use of a diffraction-type multi-level zone-plate array to extend a single-channel JTC. Our project's objectives were: (i) to design a matched filter which equips the system with high recognition capability at a faster calculation speed by analyzing the spatial frequency of facial image elements, and (ii) to create a four-channel Vanderlugt correlator with super-high-speed (1000 frame/s) optical parallel facial recognition system, robust enough for 1:N identification, for a large database with 4000 images. Automation was also achieved for the entire process via a practical controlling system. The achieved super-high-speed facial recognition system based on optical parallelism is faster in its processing time than the JTC optical correlator.

  13. 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.

  14. Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

    NASA Astrophysics Data System (ADS)

    Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

    We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

  15. Surface versus Edge-Based Determinants of Visual Recognition.

    ERIC Educational Resources Information Center

    Biederman, Irving; Ju, Ginny

    1988-01-01

    The latency at which objects could be identified by 126 subjects was compared through line drawings (edge-based) or color photography (surface depiction). The line drawing was identified about as quickly as the photograph; primal access to a mental representation of an object can be modeled from an edge-based description. (SLD)

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. Fuzzy difference-of-Gaussian-based iris recognition method for noisy iris images

    NASA Astrophysics Data System (ADS)

    Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Moon, Kiyoung

    2010-06-01

    Iris recognition is used for information security with a high confidence level because it shows outstanding recognition accuracy by using human iris patterns with high degrees of freedom. However, iris recognition accuracy can be reduced by noisy iris images with optical and motion blurring. We propose a new iris recognition method based on the fuzzy difference-of-Gaussian (DOG) for noisy iris images. This study is novel in three ways compared to previous works: (1) The proposed method extracts iris feature values using the DOG method, which is robust to local variations of illumination and shows fine texture information, including various frequency components. (2) When determining iris binary codes, image noises that cause the quantization error of the feature values are reduced with the fuzzy membership function. (3) The optimal parameters of the DOG filter and the fuzzy membership function are determined in terms of iris recognition accuracy. Experimental results showed that the performance of the proposed method was better than that of previous methods for noisy iris images.

  3. 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.

  4. 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...

  5. 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…

  6. 38 CFR 52.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 2 2014-07-01 2014-07-01 false Application for recognition based on certification. 52.20 Section 52.20 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining...

  7. 38 CFR 52.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 2 2012-07-01 2012-07-01 false Application for recognition based on certification. 52.20 Section 52.20 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining...

  8. 38 CFR 52.20 - Application for recognition based on certification.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 2 2013-07-01 2013-07-01 false Application for recognition based on certification. 52.20 Section 52.20 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining...

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

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 2 2011-07-01 2011-07-01 false Application for recognition based on certification. 52.20 Section 52.20 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS (CONTINUED) PER DIEM FOR ADULT DAY HEALTH CARE OF VETERANS IN STATE HOMES Obtaining...

  10. 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…

  11. 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…

  12. 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

  13. 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...

  14. 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...

  15. 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...

  16. 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...

  17. 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...

  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. 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

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

    PubMed

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

    2016-02-25

    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.

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

    PubMed

    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

  2. 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

  3. 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

  4. Study on the classification algorithm of degree of arteriosclerosis based on fuzzy pattern recognition

    NASA Astrophysics Data System (ADS)

    Ding, Li; Zhou, Runjing; Liu, Guiying

    2010-08-01

    Pulse wave of human body contains large amount of physiological and pathological information, so the degree of arteriosclerosis classification algorithm is study based on fuzzy pattern recognition in this paper. Taking the human's pulse wave as the research object, we can extract the characteristic of time and frequency domain of pulse signal, and select the parameters with a better clustering effect for arteriosclerosis identification. Moreover, the validity of characteristic parameters is verified by fuzzy ISODATA clustering method (FISOCM). Finally, fuzzy pattern recognition system can quantitatively distinguish the degree of arteriosclerosis with patients. By testing the 50 samples in the built pulse database, the experimental result shows that the algorithm is practical and achieves a good classification recognition result.

  5. A Genetic-Algorithm-Based Explicit Description of Object Contour and its Ability to Facilitate Recognition.

    PubMed

    Wei, Hui; Tang, Xue-Song

    2015-11-01

    Shape representation is an extremely important and longstanding problem in the field of pattern recognition. Closed contour, which refers to shape contour, plays a crucial role in the comparison of shapes. Because shape contour is the most stable, distinguishable, and invariable feature of an object, it is useful to incorporate it into the recognition process. This paper proposes a method based on genetic algorithms. The proposed method can be used to identify the most common contour fragments, which can be used to represent the contours of a shape category. The common fragments clarify the particular logics included in the contours. This paper shows that the explicit representation of the shape contour contributes significantly to shape representation and object recognition.

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

    PubMed

    Nguyen, Dat Tien; Park, Kang Ryoung

    2016-01-27

    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.

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. A mixture of physicochemical and evolutionary-based feature extraction approaches for protein fold recognition.

    PubMed

    Dehzangi, Abdollah; Sharma, Alok; Lyons, James; Paliwal, Kuldip K; Sattar, Abdul

    2015-01-01

    Recent advancement in the pattern recognition field stimulates enormous interest in Protein Fold Recognition (PFR). PFR is considered as a crucial step towards protein structure prediction and drug design. Despite all the recent achievements, the PFR still remains as an unsolved issue in biological science and its prediction accuracy still remains unsatisfactory. Furthermore, the impact of using a wide range of physicochemical-based attributes on the PFR has not been adequately explored. In this study, we propose a novel mixture of physicochemical and evolutionary-based feature extraction methods based on the concepts of segmented distribution and density. We also explore the impact of 55 different physicochemical-based attributes on the PFR. Our results show that by providing more local discriminatory information as well as obtaining benefit from both physicochemical and evolutionary-based features simultaneously, we can enhance the protein fold prediction accuracy up to 5% better than previously reported results found in the literature.

  12. 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...

  13. 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...

  14. 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

  15. 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

  16. 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...

  17. SETTING EXPECTATIONS FOR THE OHIO RIVER FISH INDEX BASED ON IN-STREAM HABITAT

    EPA Science Inventory

    The use of habitat criteria for setting fish community assessment expectations is common for streams, but a standard approach for great rivers remains largely undeveloped. We developed assessment expectations for the Ohio River Fish Index (ORFIN) based on measures of in-stream h...

  18. 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

  19. 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.

  20. Combining feature- and correspondence-based methods for visual object recognition.

    PubMed

    Westphal, Günter; Würtz, Rolf P

    2009-07-01

    We present an object recognition system built on a combination of feature- and correspondence-based pattern recognizers. The feature-based part, called preselection network, is a single-layer feedforward network weighted with the amount of information contributed by each feature to the decision at hand. For processing arbitrary objects, we employ small, regular graphs whose nodes are attributed with Gabor amplitudes, termed parquet graphs. The preselection network can quickly rule out most irrelevant matches and leaves only the ambiguous cases, so-called model candidates, to be verified by a rudimentary version of elastic graph matching, a standard correspondence-based technique for face and object recognition. According to the model, graphs are constructed that describe the object in the input image well. We report the results of experiments on standard databases for object recognition. The method achieved high recognition rates on identity and pose. Unlike many other models, it can also cope with varying background, multiple objects, and partial occlusion.

  1. 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

  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. 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…

  5. 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)

  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. Purification and characterization of a mannose recognition lectin from Oreochromis niloticus (tilapia fish): cytokine production in mice splenocytes.

    PubMed

    da Silva, Cynarha Daysy Cardoso; Coriolano, Marília Cavalcanti; da Silva Lino, Mércia Andréa; de Melo, Cristiane Moutinho Lagos; de Souza Bezerra, Ranilson; de Carvalho, Elba Verônica Matoso Maciel; Dos Santos, Athiê Jorge Guerra; Pereira, Valéria Rêgo Alves; Coelho, Luana Cassandra Breitenbach Barroso

    2012-01-01

    The aim of this work was to purify and partially characterize a mannose recognition lectin from Nile tilapia (Oreochromis niloticus) serum, named OniL. OniL was isolated through precipitation with ammonium sulfate and affinity chromatography (Concanavalin A-Sepharose 4B). In addition, we evaluated carbohydrate specificity, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) profiles, and in vitro immunomodulatory activity on mice splenocyte experimental cultures through cytotoxic assays and cytokine production. The ammonium sulfate fraction F2 showed the highest specific hemagglutinating activity (331) and was applied to affinity matrix. Adsorbed proteins (OniL) were eluted with methyl-α-D: -mannopyranoside. OniL, a 17-kDa protein by SDS-PAGE constituted by subunits of 11 and 6.6 kDa, showed highest affinity for methyl-α-D: -mannopyranoside and D: -mannose. Immunological assays, in vitro, showed that OniL did not show cytotoxicity against splenocytes, induced higher IFN-γ production and lower IL-10 as well as nitrite release. In conclusion, OniL lectin was successfully purified and showed a preferential Th1 response in mice splenocytes. PMID:22081327

  8. 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.

  9. Evaluation of nitrogenous substrates such as peptones from fish:a new method based on Gompertz modeling of microbial growth.

    PubMed

    Dufossé, L; De La Broise, D; Guerard, F

    2001-01-01

    Fish peptones from tuna, cod, salmon, and unspecified fish were compared with a casein one by using a new method based on Gompertz modeling of microbial growth. Cumulative results obtained from six species of bacteria, yeasts, and fungi showed that, in most cases, these fish peptones are very effective. Nevertheless, this study raised some questions about the standardization of fish raw material, the enzymatic hydrolysis of fish proteins, and the composition of the culture medium used for testing the peptones.

  10. 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.

  11. 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

  12. 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.

  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.

  14. Vision-based object detection and recognition system for intelligent vehicles

    NASA Astrophysics Data System (ADS)

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

    1999-01-01

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

  15. 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

  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. Defects' geometric feature recognition based on infrared image edge detection

    NASA Astrophysics Data System (ADS)

    Junyan, Liu; Qingju, Tang; Yang, Wang; Yumei, Lu; Zhiping, Zhang

    2014-11-01

    Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects' edges in infrared images effectively enables the identification of defects' geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects' edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects' geometric feature much more completely and clearly. The defects' diameters have been calculated based on the image edge detection results.

  18. 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.

  19. 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.

  20. [Automated recognition of quasars based on adaptive radial basis function neural networks].

    PubMed

    Zhao, Mei-Fang; Luo, A-Li; Wu, Fu-Chao; Hu, Zhan-Yi

    2006-02-01

    Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space. At first, the K-means clustering is used for the initialization, then based on the sum of squares errors and a gradient descent optimization technique, the number of neurons in the hidden layer is adaptively increased to improve the recognition performance; (3) The quasar spectra recognition is effectively carried out by the above trained RBFN. The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm, but also increase the stability and accuracy of recognition of quasars. Besides, the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project, such as our LAMOST, due to its efficiency.

  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. 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

  3. Activity reductions in perirhinal cortex predict conceptual priming and familiarity-based recognition

    PubMed Central

    Wang, Wei-chun; Ranganath, Charan; Yonelinas, Andrew P

    2013-01-01

    Although it is well established that regions in the medial temporal lobes are critical for explicit memory, recent work has suggested that one medial temporal lobe subregion – the perirhinal cortex (PRC) – may also support conceptual priming, a form of implicit memory. Here, we sought to investigate whether activity reductions in PRC, previously linked to familiarity-based recognition, might also support conceptual implicit memory retrieval. Using a free association priming task, the current study tested the prediction that PRC indexes conceptual priming independent of contributions from perceptual and response repetition. Participants first completed an incidental semantic encoding task outside of the MRI scanner. Next, they were scanned during performance of a free association priming task, followed by a recognition memory test. Results indicated successful conceptual priming was associated with decreased PRC activity, and that an overlapping region within the PRC also exhibited activity reductions that covaried with familiarity during the recognition memory test. Our results demonstrate that the PRC contributes to both conceptual priming and familiarity-based recognition, which may reflect a common role of this region in implicit and explicit memory retrieval. PMID:24157537

  4. Activity reductions in perirhinal cortex predict conceptual priming and familiarity-based recognition.

    PubMed

    Wang, Wei-Chun; Ranganath, Charan; Yonelinas, Andrew P

    2014-01-01

    Although it is well established that regions in the medial temporal lobes are critical for explicit memory, recent work has suggested that one medial temporal lobe subregion--the perirhinal cortex (PRC)--may also support conceptual priming, a form of implicit memory. Here, we sought to investigate whether activity reductions in PRC, previously linked to familiarity-based recognition, might also support conceptual implicit memory retrieval. Using a free association priming task, the current study tested the prediction that PRC indexes conceptual priming independent of contributions from perceptual and response repetition. Participants first completed an incidental semantic encoding task outside of the MRI scanner. Next, they were scanned during performance of a free association priming task, followed by a recognition memory test. Results indicated successful conceptual priming was associated with decreased PRC activity, and that an overlapping region within the PRC also exhibited activity reductions that covaried with familiarity during the recognition memory test. Our results demonstrate that the PRC contributes to both conceptual priming and familiarity-based recognition, which may reflect a common role of this region in implicit and explicit memory retrieval.

  5. Face Recognition Using Sparse Representation-Based Classification on K-Nearest Subspace

    PubMed Central

    Mi, Jian-Xun; Liu, Jin-Xing

    2013-01-01

    The sparse representation-based classification (SRC) has been proven to be a robust face recognition method. However, its computational complexity is very high due to solving a complex -minimization problem. To improve the calculation efficiency, we propose a novel face recognition method, called sparse representation-based classification on k-nearest subspace (SRC-KNS). Our method first exploits the distance between the test image and the subspace of each individual class to determine the nearest subspaces and then performs SRC on the selected classes. Actually, SRC-KNS is able to reduce the scale of the sparse representation problem greatly and the computation to determine the nearest subspaces is quite simple. Therefore, SRC-KNS has a much lower computational complexity than the original SRC. In order to well recognize the occluded face images, we propose the modular SRC-KNS. For this modular method, face images are partitioned into a number of blocks first and then we propose an indicator to remove the contaminated blocks and choose the nearest subspaces. Finally, SRC is used to classify the occluded test sample in the new feature space. Compared to the approach used in the original SRC work, our modular SRC-KNS can greatly reduce the computational load. A number of face recognition experiments show that our methods have five times speed-up at least compared to the original SRC, while achieving comparable or even better recognition rates. PMID:23555671

  6. 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.

  7. Pattern Recognition-Based Approach for Identifying Metabolites in Nuclear Magnetic Resonance-Based Metabolomics.

    PubMed

    Dubey, Abhinav; Rangarajan, Annapoorni; Pal, Debnath; Atreya, Hanudatta S

    2015-07-21

    Identification and assignments of metabolites is an important step in metabolomics and is necessary for the discovery of new biomarkers. In nuclear magnetic resonance (NMR) spectroscopy-based studies, the conventional approach involves a database search, wherein chemical shifts are assigned to specific metabolites by use of a tolerance limit. This is inefficient because deviation in chemical shifts associated with pH or temperature variations, as well as missing peaks, impairs a robust comparison with the database. We propose here a novel method based on matching the pattern of peaks rather than absolute tolerance thresholds, using a combination of geometric hashing and similarity scoring techniques. Tests with 719 metabolites from the Human Metabolome Database (HMDB) show that 100% of the metabolites can be assigned correctly when accurate data are available. A high success rate is obtained even in the presence of large chemical shift deviations such as 0.5 ppm in (1)H and 3 ppm in (13)C and missing peaks (up to 50%), compared to nearly no assignments obtained under these conditions with existing methods that employ a direct database search approach. The method was evaluated on experimental data on a mixture of 16 metabolites at eight different combinations of pH and temperature conditions. The pattern recognition approach thus helps in identification and assignment of metabolites independent of the pH, temperature, and ionic strength used, thereby obviating the need for spectral calibration with internal or external standards.

  8. 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

  9. Protein−DNA binding in the absence of specific base-pair recognition

    PubMed Central

    Afek, Ariel; Schipper, Joshua L.; Horton, John; Gordân, Raluca; Lukatsky, David B.

    2014-01-01

    Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)−DNA binding. Contrary to this assumption, here we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TF−DNA binding preferences. We used high-throughput protein−DNA binding assays to measure the binding levels and free energies of binding for several human TFs to tens of thousands of short DNA sequences with varying repeat symmetries. Based on statistical mechanics modeling, we identify a new protein−DNA binding mechanism induced by DNA sequence symmetry in the absence of specific base-pair recognition, and experimentally demonstrate that this mechanism indeed governs protein−DNA binding preferences. PMID:25313048

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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

  15. 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

  16. Fast vision through frameless event-based sensing and convolutional processing: application to texture recognition.

    PubMed

    Perez-Carrasco, Jose Antonio; Acha, Begona; Serrano, Carmen; Camunas-Mesa, Luis; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe

    2010-04-01

    Address-event representation (AER) is an emergent hardware technology which shows a high potential for providing in the near future a solid technological substrate for emulating brain-like processing structures. When used for vision, AER sensors and processors are not restricted to capturing and processing still image frames, as in commercial frame-based video technology, but sense and process visual information in a pixel-level event-based frameless manner. As a result, vision processing is practically simultaneous to vision sensing, since there is no need to wait for sensing full frames. Also, only meaningful information is sensed, communicated, and processed. Of special interest for brain-like vision processing are some already reported AER convolutional chips, which have revealed a very high computational throughput as well as the possibility of assembling large convolutional neural networks in a modular fashion. It is expected that in a near future we may witness the appearance of large scale convolutional neural networks with hundreds or thousands of individual modules. In the meantime, some research is needed to investigate how to assemble and configure such large scale convolutional networks for specific applications. In this paper, we analyze AER spiking convolutional neural networks for texture recognition hardware applications. Based on the performance figures of already available individual AER convolution chips, we emulate large scale networks using a custom made event-based behavioral simulator. We have developed a new event-based processing architecture that emulates with AER hardware Manjunath's frame-based feature recognition software algorithm, and have analyzed its performance using our behavioral simulator. Recognition rate performance is not degraded. However, regarding speed, we show that recognition can be achieved before an equivalent frame is fully sensed and transmitted.

  17. 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

  18. 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

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

    PubMed

    Dalapati, Sasanka; Jana, Sankar; Guchhait, Nikhil

    2014-08-14

    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 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.

  20. 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.

  1. 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...

  2. 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

  3. A new approach for modulation recognition based on ant colony algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Shu; Wang, Hongyuan

    2007-11-01

    A New Approach based on ant colony algorithm for the automatic modulation recognition of communications signals is presented. This approach can discriminate between continuous wave (CW), Amplitude Modulation (AM), Frequency Modulation (FM), Frequency Shift Keying (FSK), Binary Phase Shift Keying (BPSK) and Quaternary Phase Shift Keying (QPSK) modulations. Requirements for a priori knowledge of the signals are minimized by the inclusion of an efficient carrier frequency estimator and low sensitivity to variations in the sampling epochs. Computer simulations indicate good performance on an AWGN channel, even at signal-to-noise ratios as low as 5 dB. This compares favorably with the performance obtained with most algorithms based on pattern recognition techniques.

  4. Automatic target recognition algorithm based on statistical dispersion of infrared multispectral image

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Cao, Le-lin; Wu, Chun-feng; Hou, Qing-yu

    2009-07-01

    A novel automatic target recognition algorithm based on statistical dispersion of infrared multispectral images(SDOIMI) is proposed. Firstly, infrared multispectral characteristic matrix of the scenario is constructed based on infrared multispectral characteristic information (such as radiation intensity and spectral distribution etc.) of targets, background and decoys. Then the infrared multispectral characteristic matrix of targets is reconstructed after segmenting image by maximum distance method and fusing spatial and spectral information. Finally, an statistical dispersion of infrared multispectral images(SDOIMI) recognition criteria is formulated in terms of spectral radiation difference of interesting targets. In simulation, nine sub-bands multispectral images of real ship target and shipborne aerosol infrared decoy modulated by laser simulating ship geometry appearance are obtained via using spectral radiation curves. Digital simulation experiment result verifies that the algorithm is effective and feasible.

  5. An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device.

    PubMed

    Li, Zhen; Wei, Zhiqiang; Yue, Yaofeng; Wang, Hao; Jia, Wenyan; Burke, Lora E; Baranowski, Thomas; Sun, Mingui

    2015-05-01

    Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.

  6. Video-based convolutional neural networks for activity recognition from robot-centric videos

    NASA Astrophysics Data System (ADS)

    Ryoo, M. S.; Matthies, Larry

    2016-05-01

    In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

  7. 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

  8. 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.

  9. A risk-based sampling plan for monitoring of histamine in fish products.

    PubMed

    Guillier, L; Thébault, A; Gauchard, F; Pommepuy, M; Guignard, A; Malle, P

    2011-02-01

    In 2008, the French Institute for Public Health Surveillance reported an increase in the number of histamine food poisoning outbreaks and cases in France. The aim of this study was to propose a new monitoring plan for characterizing consumers' exposure to histamine through fishery products. As fish products of concern are numerous, we proposed that the number of samples allocated for a fish category be chosen based on the risk associated with the category. Point risk estimates of histamine poisoning were assessed with the Risk Ranger tool. Fresh fish with high histidine content was found to contribute most to the number of cases. The (estimated) risks associated with the consumption of canned and deep-frozen fish appear marginal as compared with the risk associated with fresh fish with high histidine concentrations. Accordingly, we recommend excluding canned and deep-frozen fish from the monitoring plan, although these risk estimates can be biased. Within a category, samples were proportional to the relative food consumption of the different fishes. The spatial and seasonal consumption patterns were also taken into account for the design of the new monitoring plan. By testing appropriate numbers of samples from categories of fish products of concern, this plan will permit investigation of trends or comparison of product categories presenting risks of histamine poisoning.

  10. SNP microarray-based 24 chromosome aneuploidy screening is significantly more consistent than FISH

    PubMed Central

    Treff, Nathan R.; Levy, Brynn; Su, Jing; Northrop, Lesley E.; Tao, Xin; Scott, Richard T.

    2010-01-01

    Many studies estimate that chromosomal mosaicism within the cleavage-stage human embryo is high. However, comparison of two unique methods of aneuploidy screening of blastomeres within the same embryo has not been conducted and may indicate whether mosaicism has been overestimated due to technical inconsistency rather than the biological phenomena. The present study investigates the prevalence of chromosomal abnormality and mosaicism found with two different single cell aneuploidy screening techniques. Thirteen arrested cleavage-stage embryos were studied. Each was biopsied into individual cells (n = 160). The cells from each embryo were randomized into two groups. Those destined for FISH-based aneuploidy screening (n = 75) were fixed, one cell per slide. Cells for SNP microarray-based aneuploidy screening (n = 85) were put into individual tubes. Microarray was significantly more reliable (96%) than FISH (83%) for providing an interpretable result (P = 0.004). Markedly different results were obtained when comparing microarray and FISH results from individual embryos. Mosaicism was significantly less commonly observed by microarray (31%) than by FISH (100%) (P = 0.0005). Although FISH evaluated fewer chromosomes per cell and fewer cells per embryo, FISH still displayed significantly more unique genetic diagnoses per embryo (3.2 ± 0.2) than microarray (1.3 ± 0.2) (P < 0.0001). This is the first prospective, randomized, blinded and paired comparison between microarray and FISH-based aneuploidy screening. SNP microarray-based 24 chromosome aneuploidy screening provides more complete and consistent results than FISH. These results also suggest that FISH technology may overestimate the contribution of mitotic error to the origin of aneuploidy at the cleavage stage of human embryogenesis. PMID:20484246

  11. 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.

  12. 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

  13. 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

  14. ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

    PubMed

    Zhang, Jianhai; Chen, Ming; Zhao, Shaokai; Hu, Sanqing; Shi, Zhiguo; Cao, Yu

    2016-01-01

    Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the

  15. 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.

  16. 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).

  17. ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

    PubMed

    Zhang, Jianhai; Chen, Ming; Zhao, Shaokai; Hu, Sanqing; Shi, Zhiguo; Cao, Yu

    2016-01-01

    Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the

  18. 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.

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

    PubMed

    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.

  20. 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

  1. 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.

  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. Improving Eye Motion Sequence Recognition Using Electrooculography Based on Context-Dependent HMM

    PubMed Central

    Shinozaki, Takahiro; Horiuchi, Yasuo; Kuroiwa, Shingo; Furui, Sadaoki; Musha, Toshimitsu

    2016-01-01

    Eye motion-based human-machine interfaces are used to provide a means of communication for those who can move nothing but their eyes because of injury or disease. To detect eye motions, electrooculography (EOG) is used. For efficient communication, the input speed is critical. However, it is difficult for conventional EOG recognition methods to accurately recognize fast, sequentially input eye motions because adjacent eye motions influence each other. In this paper, we propose a context-dependent hidden Markov model- (HMM-) based EOG modeling approach that uses separate models for identical eye motions with different contexts. Because the influence of adjacent eye motions is explicitly modeled, higher recognition accuracy is achieved. Additionally, we propose a method of user adaptation based on a user-independent EOG model to investigate the trade-off between recognition accuracy and the amount of user-dependent data required for HMM training. Experimental results show that when the proposed context-dependent HMMs are used, the character error rate (CER) is significantly reduced compared with the conventional baseline under user-dependent conditions, from 36.0 to 1.3%. Although the CER increases again to 17.3% when the context-dependent but user-independent HMMs are used, it can be reduced to 7.3% by applying the proposed user adaptation method. PMID:27774099

  4. M pathway and areas 44 and 45 are involved in stereoscopic recognition based on binocular disparity.

    PubMed

    Negawa, Tsuneo; Mizuno, Shinji; Hahashi, Tomoya; Kuwata, Hiromi; Tomida, Mihoko; Hoshi, Hiroaki; Era, Seiichi; Kuwata, Kazuo

    2002-04-01

    We characterized the visual pathways involved in the stereoscopic recognition of the random dot stereogram based on the binocular disparity employing a functional magnetic resonance imaging (fMRI). The V2, V3, V4, V5, intraparietal sulcus (IPS) and the superior temporal sulcus (STS) were significantly activated during the binocular stereopsis, but the inferotemporal gyrus (ITG) was not activated. Thus a human M pathway may be part of a network involved in the stereoscopic processing based on the binocular disparity. It is intriguing that areas 44 (Broca's area) and 45 in the left hemisphere were also active during the binocular stereopsis. However, it was reported that these regions were inactive during the monocular stereopsis. To separate the specific responses directly caused by the stereoscopic recognition process from the nonspecific ones caused by the memory load or the intention, we designed a novel frequency labeled tasks (FLT) sequence. The functional MRI using the FLT indicated that the activation of areas 44 and 45 is correlated with the stereoscopic recognition based on the binocular disparity but not with the intention artifacts, suggesting that areas 44 and 45 play an essential role in the binocular disparity. PMID:12139777

  5. 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.

  6. M pathway and areas 44 and 45 are involved in stereoscopic recognition based on binocular disparity.

    PubMed

    Negawa, Tsuneo; Mizuno, Shinji; Hahashi, Tomoya; Kuwata, Hiromi; Tomida, Mihoko; Hoshi, Hiroaki; Era, Seiichi; Kuwata, Kazuo

    2002-04-01

    We characterized the visual pathways involved in the stereoscopic recognition of the random dot stereogram based on the binocular disparity employing a functional magnetic resonance imaging (fMRI). The V2, V3, V4, V5, intraparietal sulcus (IPS) and the superior temporal sulcus (STS) were significantly activated during the binocular stereopsis, but the inferotemporal gyrus (ITG) was not activated. Thus a human M pathway may be part of a network involved in the stereoscopic processing based on the binocular disparity. It is intriguing that areas 44 (Broca's area) and 45 in the left hemisphere were also active during the binocular stereopsis. However, it was reported that these regions were inactive during the monocular stereopsis. To separate the specific responses directly caused by the stereoscopic recognition process from the nonspecific ones caused by the memory load or the intention, we designed a novel frequency labeled tasks (FLT) sequence. The functional MRI using the FLT indicated that the activation of areas 44 and 45 is correlated with the stereoscopic recognition based on the binocular disparity but not with the intention artifacts, suggesting that areas 44 and 45 play an essential role in the binocular disparity.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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.

  15. [A new automatic quasars recognition technique based on PCA and Hough transform].

    PubMed

    Huang, Ling-yun; Hu, Zhan-yi

    2003-02-01

    The main purpose of quasar recognition is to determine the observed quasar spectrum's redshift value. Previously the template of quasar rest frame in the literature was basically constructed based on astronomers' hypotheses. Due to the inaccuracy of such a template, it is hard to determine the redshift value by matching the observed quasar spectrum with the template directly. This paper's main contributions are two-fold: Firstly, the template in our paper is constructed by the principal component analysis (PCA) method from some selected spectra with known redshift values, hence the obtained template is more realistic. Secondly, a 2D standard Hough transform, rather than a 1D Hough transform, is used. This is because although only redshift needs to be determined in our system, based on our observations, the magnitude of emission peak is also changed, hence a new parameter, namely scale parameter, is also introduced to the Hough transform to enhance the reliability of the recognition. The experiments show that the proposed technique is workable and the correct recognition rate can reach about as high as 90%.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. Age differences in hippocampal activation during gist-based false recognition.

    PubMed

    Paige, Laura E; Cassidy, Brittany S; Schacter, Daniel L; Gutchess, Angela H

    2016-10-01

    Age-related increases in reliance on gist-based processes can cause increased false recognition. Understanding the neural basis for this increase helps to elucidate a mechanism underlying this vulnerability in memory. We assessed age differences in gist-based false memory by increasing image set size at encoding, thereby increasing the rate of false alarms. False alarms during a recognition test elicited increased hippocampal activity for older adults as compared to younger adults for the small set sizes, whereas the age groups had similar hippocampal activation for items associated with larger set sizes. Interestingly, younger adults had stronger connectivity between the hippocampus and posterior temporal regions relative to older adults during false alarms for items associated with large versus small set sizes. With increased gist, younger adults might rely more on additional processes (e.g., semantic associations) during recognition than older adults. Parametric modulation revealed that younger adults had increased anterior cingulate activity than older adults with decreasing set size, perhaps indicating difficulty in using monitoring processes in error-prone situations. PMID:27460152

  2. 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.

  3. Age differences in hippocampal activation during gist-based false recognition.

    PubMed

    Paige, Laura E; Cassidy, Brittany S; Schacter, Daniel L; Gutchess, Angela H

    2016-10-01

    Age-related increases in reliance on gist-based processes can cause increased false recognition. Understanding the neural basis for this increase helps to elucidate a mechanism underlying this vulnerability in memory. We assessed age differences in gist-based false memory by increasing image set size at encoding, thereby increasing the rate of false alarms. False alarms during a recognition test elicited increased hippocampal activity for older adults as compared to younger adults for the small set sizes, whereas the age groups had similar hippocampal activation for items associated with larger set sizes. Interestingly, younger adults had stronger connectivity between the hippocampus and posterior temporal regions relative to older adults during false alarms for items associated with large versus small set sizes. With increased gist, younger adults might rely more on additional processes (e.g., semantic associations) during recognition than older adults. Parametric modulation revealed that younger adults had increased anterior cingulate activity than older adults with decreasing set size, perhaps indicating difficulty in using monitoring processes in error-prone situations.

  4. 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

  5. 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

  6. 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.

  7. Human action recognition based on spatial-temporal descriptors using key poses

    NASA Astrophysics Data System (ADS)

    Hu, Shuo; Chen, Yuxin; Wang, Huaibao; Zuo, Yaqing

    2014-11-01

    Human action recognition is an important area of pattern recognition today due to its direct application and need in various occasions like surveillance and virtual reality. In this paper, a simple and effective human action recognition method is presented based on the key poses of human silhouette and the spatio-temporal feature. Firstly, the contour points of human silhouette have been gotten, and the key poses are learned by means of K-means clustering based on the Euclidean distance between each contour point and the centre point of the human silhouette, and then the type of each action is labeled for further match. Secondly, we obtain the trajectories of centre point of each frame, and create a spatio-temporal feature value represented by W to describe the motion direction and speed of each action. The value W contains the information of location and temporal order of each point on the trajectories. Finally, the matching stage is performed by comparing the key poses and W between training sequences and test sequences, the nearest neighbor sequences is found and its label supplied the final result. Experiments on the public available Weizmann datasets show the proposed method can improve accuracy by distinguishing amphibious poses and increase suitability for real-time applications by reducing the computational cost.

  8. 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%.

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Molecular recognition of arginine by supramolecular complexation with calixarene crown ether based on surface plasmon resonance.

    PubMed

    Chen, Hongxia; Gu, Limin; Yin, Yongmei; Koh, Kwangnak; Lee, Jaebeom

    2011-01-01

    Arginine plays an important role in cell division and the functioning of the immune system. We describe a novel method by which arginine can be identified using an artificial monolayer based on surface plasmon resonance (SPR). The affinity of arginine binding its recognition molecular was compared to that of lysine. In fabrication of an arginine sensing interface, a calix[4]crown ether monolayer was anchored onto a gold surface and then characterized by Fourier Transform infrared reflection absorption spectroscopy, atomic force microscopy, and cyclic voltammetry. The interaction between arginine and its host compound was investigated by SPR. The calix[4]crown ether was found to assemble as a monolayer on the gold surface. Recognition of calix[4]crown monolayer was assessed by the selective binding of arginine. Modification of the SPR chip with the calix[4]crown monolayer provides a reliable and simple experimental platform for investigation of arginine under aqueous conditions.

  17. Trust sensor interface for improving reliability of EMG-based user intent recognition.

    PubMed

    Liu, Yuhong; Zhang, Fan; Sun, Yan Lindsay; Huang, He

    2011-01-01

    To achieve natural and smooth control of prostheses, Electromyographic (EMG) signals have been investigated for decoding user intent. However, EMG signals can be easily contaminated by diverse disturbances, leading to errors in user intent recognition and threatening the safety of prostheses users. To address this problem, we propose a trust sensor interface (TSI) that contains 2 modules: (1) abnormality detector that detects diverse disturbances with high accuracy and low latency and (2) trust evaluation that dynamically evaluates the reliability of EMG sensors. Based on the output of the TSI, the user intention recognition (UIR) algorithm is able to dynamically adjust their operations or decisions. Our experiments on an able-bodied subject have demonstrated that the proposed TSI can effectively detect two types of disturbances (i.e. motion artifacts and baseline shifts) and improve the reliability of the UIR.

  18. 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

  19. [A leukocyte pattern recognition based on feature fusion in multi-color space].

    PubMed

    Hao, Liangwang; Hong, Wenxue

    2013-10-01

    To solve the ineffective problem of leukocytes classification based on multi-feature fusion in a single color space, we proposed an automatic leukocyte pattern recognition by means of feature fusion with color histogram and texture granular in multi-color space. The interactive performance of three color spaces (RGB, HSV and Lab), two features (color histogram and texture granular) and four similarity measured distance metrics (normalized intersection, Euclidean distance, chi2-metric distance and Mahalanobis distance) were discussed. The optimized classification modes of high precision, extensive universality and low cost to different leukocyte types were obtained respectively, and then the recognition system of tree-integration of the optimized modes was established. The experimental results proved that the performance of the fusion classification was improved by 12.3% at least.

  20. A matter of time: antecedents of one-reason decision making based on recognition.

    PubMed

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

    2012-09-01

    The notion of adaptive decision making implies that strategy selection in both inferences and preferences is driven by a trade-off between accuracy and effort. A strategy for probabilistic inferences which is particularly attractive from this point of view is the recognition heuristic (RH). It proposes that judgments rely on recognition in isolation-ignoring any further information that might be available-and thereby allows for substantial effort-reduction. Consequently, it is herein hypothesized that and tested whether increased necessity of effort-reduction-as implemented via time pressure-fosters reliance on the RH. Two experiments corroborated that this was the case, even with relatively mild time pressure. In addition, this result held even when non-compliance with the response deadline did not yield negative monetary consequences. The current investigations are among the first to tackle the largely open question of whether effort-related factors influence the reliance on heuristics in memory-based decisions. PMID:22820454

  1. 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.

  2. The research of multi-frame target recognition based on laser active imaging

    NASA Astrophysics Data System (ADS)

    Wang, Can-jin; Sun, Tao; Wang, Tin-feng; Chen, Juan

    2013-09-01

    Laser active imaging is fit to conditions such as no difference in temperature between target and background, pitch-black night, bad visibility. Also it can be used to detect a faint target in long range or small target in deep space, which has advantage of high definition and good contrast. In one word, it is immune to environment. However, due to the affect of long distance, limited laser energy and atmospheric backscatter, it is impossible to illuminate the whole scene at the same time. It means that the target in every single frame is unevenly or partly illuminated, which make the recognition more difficult. At the same time the speckle noise which is common in laser active imaging blurs the images . In this paper we do some research on laser active imaging and propose a new target recognition method based on multi-frame images . Firstly, multi pulses of laser is used to obtain sub-images for different parts of scene. A denoising method combined homomorphic filter with wavelet domain SURE is used to suppress speckle noise. And blind deconvolution is introduced to obtain low-noise and clear sub-images. Then these sub-images are registered and stitched to combine a completely and uniformly illuminated scene image. After that, a new target recognition method based on contour moments is proposed. Firstly, canny operator is used to obtain contours. For each contour, seven invariant Hu moments are calculated to generate the feature vectors. At last the feature vectors are input into double hidden layers BP neural network for classification . Experiments results indicate that the proposed algorithm could achieve a high recognition rate and satisfactory real-time performance for laser active imaging.

  3. Lunar Phase-Dependent Expression of Cryptochrome and a Photoperiodic Mechanism for Lunar Phase-Recognition in a Reef Fish, Goldlined Spinefoot

    PubMed Central

    Fukushiro, Masato; Takeuchi, Takahiro; Takeuchi, Yuki; Hur, Sung-Pyo; Sugama, Nozomi; Takemura, Akihiro; Kubo, Yoko; Okano, Keiko; Okano, Toshiyuki

    2011-01-01

    Lunar cycle-associated physiology has been found in a wide variety of organisms. Recent study has revealed that mRNA levels of Cryptochrome (Cry), one of the circadian clock genes, were significantly higher on a full moon night than on a new moon night in coral, implying the involvement of a photoreception system in the lunar-synchronized spawning. To better establish the generalities surrounding such a mechanism and explore the underlying molecular mechanism, we focused on the relationship between lunar phase, Cry gene expression, and the spawning behavior in a lunar-synchronized spawner, the goldlined spinefoot (Siganus guttatus), and we identified two kinds of Cry genes in this animal. Their mRNA levels showed lunar cycle-dependent expression in the medial part of the brain (mesencephalon and diencephalon) peaking at the first quarter moon. Since this lunar phase coincided with the reproductive phase of the goldlined spinefoot, Cry gene expression was considered a state variable in the lunar phase recognition system. Based on the expression profiles of SgCrys together with the moonlight's pattern of timing and duration during its nightly lunar cycle, we have further speculated on a model of lunar phase recognition for reproductive control in the goldlined spinefoot, which integrates both moonlight and circadian signals in a manner similar to photoperiodic response. PMID:22163321

  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. Face recognition system for set-top box-based intelligent TV.

    PubMed

    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

  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 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

  8. Individual RNA Base Recognition in Immobilized Oligonucleotides using a Protein Nanopore

    PubMed Central

    Ayub, Mariam; Bayley, Hagan

    2012-01-01

    Protein nanopores are under investigation as key components of rapid, low-cost platforms to sequence DNA molecules. Previously, it has been shown that the α-hemolysin (αHL) nanopore contains three recognition sites, capable of discriminating between individual DNA bases when oligonucleotides are immobilized within the nanopore. However, the direct sequencing of RNA is also of critical importance. Here, we achieve sharply defined current distributions that enable clear discrimination of the four nucleobases, guanine, cytosine, adenine and uracil, in RNA. Further, the modified bases, inosine, N6-methyladenosine and N5-methylcytosine, can be distinguished. PMID:23043363

  9. Fishing the molecular bases of Treacher Collins syndrome.

    PubMed

    Weiner, Andrea M J; Scampoli, Nadia L; Calcaterra, Nora B

    2012-01-01

    Treacher Collins syndrome (TCS) is an autosomal dominant disorder of craniofacial development, and mutations in the TCOF1 gene are responsible for over 90% of TCS cases. The knowledge about the molecular mechanisms responsible for this syndrome is relatively scant, probably due to the difficulty of reproducing the pathology in experimental animals. Zebrafish is an emerging model for human disease studies, and we therefore assessed it as a model for studying TCS. We identified in silico the putative zebrafish TCOF1 ortholog and cloned the corresponding cDNA. The derived polypeptide shares the main structural domains found in mammals and amphibians. Tcof1 expression is restricted to the anterior-most regions of zebrafish developing embryos, similar to what happens in mouse embryos. Tcof1 loss-of-function resulted in fish showing phenotypes similar to those observed in TCS patients, and enabled a further characterization of the mechanisms underlying craniofacial malformation. Besides, we initiated the identification of potential molecular targets of treacle in zebrafish. We found that Tcof1 loss-of-function led to a decrease in the expression of cellular proliferation and craniofacial development. Together, results presented here strongly suggest that it is possible to achieve fish with TCS-like phenotype by knocking down the expression of the TCOF1 ortholog in zebrafish. This experimental condition may facilitate the study of the disease etiology during embryonic development.

  10. Fishing the Molecular Bases of Treacher Collins Syndrome

    PubMed Central

    Weiner, Andrea M. J.; Scampoli, Nadia L.; Calcaterra, Nora B.

    2012-01-01

    Treacher Collins syndrome (TCS) is an autosomal dominant disorder of craniofacial development, and mutations in the TCOF1 gene are responsible for over 90% of TCS cases. The knowledge about the molecular mechanisms responsible for this syndrome is relatively scant, probably due to the difficulty of reproducing the pathology in experimental animals. Zebrafish is an emerging model for human disease studies, and we therefore assessed it as a model for studying TCS. We identified in silico the putative zebrafish TCOF1 ortholog and cloned the corresponding cDNA. The derived polypeptide shares the main structural domains found in mammals and amphibians. Tcof1 expression is restricted to the anterior-most regions of zebrafish developing embryos, similar to what happens in mouse embryos. Tcof1 loss-of-function resulted in fish showing phenotypes similar to those observed in TCS patients, and enabled a further characterization of the mechanisms underlying craniofacial malformation. Besides, we initiated the identification of potential molecular targets of treacle in zebrafish. We found that Tcof1 loss-of-function led to a decrease in the expression of cellular proliferation and craniofacial development. Together, results presented here strongly suggest that it is possible to achieve fish with TCS-like phenotype by knocking down the expression of the TCOF1 ortholog in zebrafish. This experimental condition may facilitate the study of the disease etiology during embryonic development. PMID:22295061

  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. 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

  13. 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.

  14. The application of EMD in activity recognition based on a single triaxial accelerometer.

    PubMed

    Liao, Mengjia; Guo, Yi; Qin, Yajie; Wang, Yuanyuan

    2015-01-01

    Activities recognition using a wearable device is a very popular research field. Among all wearable sensors, the accelerometer is one of the most common sensors due to its versatility and relative ease of use. This paper proposes a novel method for activity recognition based on a single accelerometer. To process the activity information from accelerometer data, two kinds of signal features are extracted. Firstly, five features including the mean, the standard deviation, the entropy, the energy and the correlation are calculated. Then a method called empirical mode decomposition (EMD) is used for the feature extraction since accelerometer data are non-linear and non-stationary. Several time series named intrinsic mode functions (IMFs) can be obtained after the EMD. Additional features will be added by computing the mean and standard deviation of first three IMFs. A classifier called Adaboost is adopted for the final activities recognition. In the experiments, a single sensor is separately positioned in the waist, left thigh, right ankle and right arm. Results show that the classification accuracy is 94.69%, 86.53%, 91.84% and 92.65%, respectively. These relatively high performances demonstrate that activities can be detected irrespective of the position by reducing problems such as the movement constrain and discomfort.

  15. 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.

  16. 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.

  17. 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.

  18. Contact-free palm-vein recognition based on local invariant features.

    PubMed

    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.

  19. A voxel-based lesion study on facial emotion recognition after penetrating brain injury

    PubMed Central

    Dal Monte, Olga; Solomon, Jeffrey M.; Schintu, Selene; Knutson, Kristine M.; Strenziok, Maren; Pardini, Matteo; Leopold, Anne; Raymont, Vanessa; Grafman, Jordan

    2013-01-01

    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

  20. 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

  1. 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

  2. A theophylline quartz crystal microbalance biosensor based on recognition of RNA aptamer and amplification of signal.

    PubMed

    Dong, Zong-Mu; Zhao, Guang-Chao

    2013-04-21

    A quartz crystal microbalance (QCM) biosensor for theophylline was developed by recognition of RNA aptamer and gold nanoparticle amplification technique. Firstly, a designed small single-stranded RNA, RNA1, was immobilized onto the QCM electrode through a thiol linker. Then, the complementary stranded RNA2, which can combine with RNA1 to form a double-stranded RNA with a recognition unit of theophylline, could be self-assembled on the QCM electrode surface through a hybrid reaction in the presence of theophylline. The recognition process could cause a frequency change of QCM to give the signal related to theophylline. When RNA2 was tethered to gold nanoparticles, the signal could be amplified to further enhance the sensitivity of the designed sensor. Under the optimal conditions, the QCM-based biosensor showed excellent sensitivity (limit of detection, 8.2 nM) and specificity with a dissociation constant of Kd = 5.26 × 10(-7) M. The sensor can be used to quantitatively detect theophylline in serum, suggesting that it can be applied in complex biological samples.

  3. 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.

  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. 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.

  6. 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

  7. A history of fish vaccination: science-based disease prevention in aquaculture.

    PubMed

    Gudding, Roar; Van Muiswinkel, Willem B

    2013-12-01

    Disease prevention and control are crucial in order to maintain a sustainable aquaculture, both economically and environmentally. Prophylactic measures based on stimulation of the immune system of the fish have been an effective measure for achieving this goal. Immunoprophylaxis has become an important part in the successful development of the fish-farming industry. The first vaccine for aquaculture, a vaccine for prevention of yersiniosis in salmonid fish, was licensed in USA in 1976. Since then the use of vaccines has expanded to new countries and new species simultaneous with the growth of the aquaculture industry. This paper gives an overview of the achievements in fish vaccinology with particular emphasis on immunoprophylaxis as a practical tool for a successful development of bioproduction of aquatic animals.

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. Identification of a novel V1-type AVP receptor based on the molecular recognition theory.

    PubMed Central

    Herrera, V. L.; Ruiz-Opazo, N.

    2001-01-01

    BACKGROUND: The molecular recognition theory predicts that binding domains of peptide hormones and their corresponding receptor binding domains evolved from complementary strands of genomic DNA, and that a process of selective evolutionary mutational events within these primordial domains gave rise to the high affinity and high specificity of peptide hormone-receptor interactions observed today in different peptide hormone-receptor systems. Moreover, this theory has been broadened as a general hypothesis that could explain the evolution of intermolecular protein-protein and intramolecular peptide interactions. MATERIALS AND METHODS: Applying a molecular cloning strategy based on the molecular recognition theory, we screened a rat kidney cDNA library with a vasopressin (AVP) antisense oligonucleotide probe, expecting to isolate potential AVP receptors. RESULTS: We isolated a rat kidney cDNA encoding a functional V1-type vasopressin receptor. Structural analysis identified a 135 amino acid-long polypeptide with a single transmembrane domain, quite distinct from the rhodopsin-based G protein-coupled receptor superfamily. Functional analysis of the expressed V1-type receptor in Cos-1 cells revealed AVP-specific binding, AVP-specific coupling to Ca2+ mobilizing transduction system, and characteristic V1-type antagonist inhibition. CONCLUSIONS: This is the second AVP receptor cDNA isolated using AVP antipeptide-based oligonucleotide screening, thus providing compelling evidence in support of the molecular recognition theory as the basis of the evolution of this peptide hormone-receptor system, as well as adds molecular complexity and diversity to AVP receptor systems. PMID:11683375

  16. Model-based automatic target recognition using hierarchical foveal machine vision

    NASA Astrophysics Data System (ADS)

    McKee, Douglas C.; Bandera, Cesar; Ghosal, Sugata; Rauss, Patrick J.

    1996-06-01

    This paper presents a target detection and interrogation techniques for a foveal automatic target recognition (ATR) system based on the hierarchical scale-space processing of imagery from a rectilinear tessellated multiacuity retinotopology. Conventional machine vision captures imagery and applies early vision techniques with uniform resolution throughout the field-of-view (FOV). In contrast, foveal active vision features graded acuity imagers and processing coupled with context sensitive gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision can operate more efficiently in dynamic scenarios with localized relevance than uniform acuity vision because resolution is treated as a dynamically allocable resource. Foveal ATR exploits the difference between detection and recognition resolution requirements and sacrifices peripheral acuity to achieve a wider FOV (e.g. faster search), greater localized resolution where needed (e.g., more confident recognition at the fovea), and faster frame rates (e.g., more reliable tracking and navigation) without increasing processing requirements. The rectilinearity of the retinotopology supports a data structure that is a subset of the image pyramid. This structure lends itself to multiresolution and conventional 2-D algorithms, and features a shift invariance of perceived target shape that tolerates sensor pointing errors and supports multiresolution model-based techniques. The detection technique described in this paper searches for regions-of- interest (ROIs) using the foveal sensor's wide FOV peripheral vision. ROIs are initially detected using anisotropic diffusion filtering and expansion template matching to a multiscale Zernike polynomial-based target model. Each ROI is then interrogated to filter out false target ROIs by sequentially pointing a higher acuity region of the sensor at each ROI centroid and conducting a fractal dimension test that distinguishes targets from structured clutter.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Nest-mate recognition based on heritable odors in the termite Microcerotermes arboreus.

    PubMed Central

    Adams, E S

    1991-01-01

    Workers of the Neotropical termite Microcerotermes arboreus distinguish nest mates from other conspecifics by odor. A controlled breeding experiment demonstrated a genetic component to variation in colony odors. Workers were less aggressive toward unfamiliar relatives than toward nonrelatives and distinguished degree of relatedness among unfamiliar workers. Unfamiliar relatives were attacked more often than nest mates, despite similar levels of genetic relatedness; thus, nest-mate recognition is not based solely upon heritable characteristics of individual workers. No difference was detected between the effects of cues inherited through the mother and cues inherited through the father. PMID:11607159

  3. Nest-mate recognition based on heritable odors in the termite Microcerotermes arboreus.

    PubMed

    Adams, E S

    1991-03-01

    Workers of the Neotropical termite Microcerotermes arboreus distinguish nest mates from other conspecifics by odor. A controlled breeding experiment demonstrated a genetic component to variation in colony odors. Workers were less aggressive toward unfamiliar relatives than toward nonrelatives and distinguished degree of relatedness among unfamiliar workers. Unfamiliar relatives were attacked more often than nest mates, despite similar levels of genetic relatedness; thus, nest-mate recognition is not based solely upon heritable characteristics of individual workers. No difference was detected between the effects of cues inherited through the mother and cues inherited through the father.

  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. Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition

    NASA Astrophysics Data System (ADS)

    An, Gaoyun; Wu, Jiying; Ruan, Qiuqi

    In order to overcome the drawback of TVQI and to utilize the property of dimensionality increasing techniques, a novel model for Kernel TV-based Quotient Image employing Gabor analysis is proposed and applied to face recognition with only one sample per subject. To deal with illumination outliers, an enhanced TV-based quotient image (ETVQI) model is first adopted. Then for preprocessed images by ETVQI, a bank of Gabor filters is built to extract features at specified scales and orientations. Lastly, KPCA is introduced to extract final high-order and nonlinear features of extracted Gabor features. According to experiments on the CAS-PEAL face database, our model could outperform Gabor-based KPCA, TVQI and Gabor-based TVQI when they face most outliers (illumination, expression, masking etc.).

  6. 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

  7. 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

    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.

  8. 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

  9. 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

  10. 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

  11. 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.

  12. 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.

  13. Prediction of Period-Doubling Bifurcation Based on Dynamic Recognition and Its Application to Power Systems

    NASA Astrophysics Data System (ADS)

    Chen, Danfeng; Wang, Cong

    In this paper, a bifurcation prediction approach is proposed based on dynamic recognition and further applied to predict the period-doubling bifurcation (PDB) of power systems. Firstly, modeling of the internal dynamics of nonlinear systems is obtained through deterministic learning (DL), and the modeling results are applied for constructing the dynamic training pattern database. Specifically, training patterns are chosen according to the hierarchical structured knowledge representation based on the qualitative property of dynamical systems, which is capable of arranging the dynamical models into a specific order in the pattern database. Then, a dynamic recognition-based bifurcation prediction approach is suggested. As a result, perturbations implying PDB on the testing patterns can be predicted through the minimum dynamic error between the training patterns and testing patterns by recalling the knowledge restored in the pattern database. Finally, the second-order single-machine to infinite bus power system model is introduced to check the effectiveness of this prediction approach, which implies PDB under small periodic parameter perturbations. The key point that determines the prediction effect mainly lies in two methods: (1) accurate approximation of the unknown system dynamics through DL guarantees the feasibility of the prediction process; (2) the qualitative property of PDB and the generalization ability of DL algorithm ensure the validity of the selected training patterns. Simulations are included to illustrate the effectiveness of the proposed approach.

  14. 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.

  15. A quick scan and lane recognition algorithm based on positional distribution and edge features

    NASA Astrophysics Data System (ADS)

    Wang, Jian; Zhang, Yuan; Chen, Xiaomin; Shi, Xiaoying

    2010-08-01

    With the growing number of vehicles on the road, the automatic guided vehicles (AGV) vision system for intelligent vehicles has been given more and more attention. Lane recognition is an important component in the automatic guided vehicles (AGV) vision system for intelligent vehicles. To improve the speed and accuracy of lane recognition, this paper proposed an image segmentation algorithm based on the normalized histogram matching and a specific image scan algorithm based on positional distribution of lanes to reduce runtime. The purpose of image segmentation is extracting useful road information and the algorithm is segmenting the image by calculating the similarity of Cumulative Distribution Function (CDF) of normalization histogram. The main idea of image scan algorithm proposed in this paper is regarding the lanes that have been found as starting points and looking for the new lanes. Then we use a novel lane screen algorithm based on the left and right edges of lanes' geometric feature to remove invalid information and improve the accuracy and promote efficiency effectively. At last, a lane prediction algorithm is proposed to predict the farther lanes which may be lost due to treating as noises. After our tests, this algorithm has better robustness and higher efficiency.

  16. 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

  17. 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

  18. 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

  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 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

  1. 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

  2. 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

  3. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    PubMed

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-07-05

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  4. 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

  5. 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.

  6. [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

  7. [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

  8. 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

  9. 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

  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. 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.

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. Applying evidence-based medicine in telehealth: an interactive pattern recognition approximation.

    PubMed

    Fernández-Llatas, Carlos; Meneu, Teresa; Traver, Vicente; Benedi, José-Miguel

    2013-11-01

    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. 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

  18. 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.

  19. 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

  20. 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.

  1. What's in It for Me? Recognition of Prior Learning in Enterprise-Based Registered Training Organisations

    ERIC Educational Resources Information Center

    Blom, Kaaren; Clayton, Berwyn; Bateman, Andrea; Bedggood, Marie; Hughes, Elvie

    2004-01-01

    Recognition of prior learning is a crucial element in lifelong learning, but limited information exists about skills recognition implementation and outcomes within Australian enterprises. This study examines the nature of recognition within individual enterprises, including the processes employed, strategies in place for promotion and support, and…

  2. 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

  3. A carbohydrate-based mechanism of species recognition in sea urchin fertilization.

    PubMed

    Mourão, P A S

    2007-01-01

    In the present review, we describe a systematic study of the sulfated polysaccharides from marine invertebrates, which led to the discovery of a carbohydrate-based mechanism of sperm-egg recognition during sea urchin fertilization. We have described unique polymers present in these organisms, especially sulfated fucose-rich compounds found in the egg jelly coat of sea urchins. The polysaccharides have simple, linear structures consisting of repeating units of oligosaccharides. They differ among the various species of sea urchins in specific patterns of sulfation and/or position of the glycosidic linkage within their repeating units. These polysaccharides show species specificity in inducing the acrosome reaction in sea urchin sperm, providing a clear-cut example of a signal transduction event regulated by sulfated polysaccharides. This distinct carbohydrate-mediated mechanism of sperm-egg recognition coexists with the bindin-protein system. Possibly, the genes involved in the biosynthesis of these sulfated fucans did not evolve in concordance with evolutionary distance but underwent a dramatic change near the tip of the Strongylocentrotid tree. Overall, we established a direct causal link between the molecular structure of a sulfated polysaccharide and a cellular physiological event - the induction of the sperm acrosome reaction in sea urchins. Small structural changes modulate an entire system of sperm-egg recognition and species-specific fertilization in sea urchins. We demonstrated that sulfated polysaccharides - in addition to their known function in cell proliferation, development, coagulation, and viral infection - mediate fertilization, and respond to evolutionary mechanisms that lead to species diversity.

  4. Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming

    PubMed Central

    Yang, Zhongliang; Chen, Yumiao

    2016-01-01

    Sketching is one of the most important processes in the conceptual stage of design. Previous studies have relied largely on the analyses of sketching process and outcomes; whereas surface electromyographic (sEMG) signals associated with sketching have received little attention. In this study, we propose a method in which 11 basic one-stroke sketching shapes are identified from the sEMG signals generated by the forearm and upper arm muscles from 4 subjects. Time domain features such as integrated electromyography, root mean square and mean absolute value were extracted with analysis windows of two length conditions for pattern recognition. After reducing data dimensionality using principal component analysis, the shapes were classified using Gene Expression Programming (GEP). The performance of the GEP classifier was compared to the Back Propagation neural network (BPNN) and the Elman neural network (ENN). Feature extraction with the short analysis window (250 ms with a 250 ms increment) improved the recognition rate by around 6.4% averagely compared with the long analysis window (2500 ms with a 2500 ms increment). The average recognition rate for the eleven basic one-stroke sketching patterns achieved by the GEP classifier was 96.26% in the training set and 95.62% in the test set, which was superior to the performance of the BPNN and ENN classifiers. The results show that the GEP classifier is able to perform well with either length of the analysis window. Thus, the proposed GEP model show promise for recognizing sketching based on sEMG signals. PMID:27790083

  5. Acid-base and ion balance in fishes with bimodal respiration.

    PubMed

    Shartau, R B; Brauner, C J

    2014-03-01

    The evolution of air breathing during the Devonian provided early fishes with bimodal respiration with a stable O2 supply from air. This was, however, probably associated with challenges and trade-offs in terms of acid-base balance and ionoregulation due to reduced gill:water interaction and changes in gill morphology associated with air breathing. While many aspects of acid-base and ionoregulation in air-breathing fishes are similar to water breathers, the specific cellular and molecular mechanisms involved remain largely unstudied. In general, reduced ionic permeability appears to be an important adaptation in the few bimodal fishes investigated but it is not known if this is a general characteristic. The kidney appears to play an important role in minimizing ion loss to the freshwater environment in the few species investigated, and while ion uptake across the gut is probably important, it has been largely unexplored. In general, air breathing in facultative air-breathing fishes is associated with an acid-base disturbance, resulting in an increased partial pressure of arterial CO2 and a reduction in extracellular pH (pHE ); however, several fishes appear to be capable of tightly regulating tissue intracellular pH (pHI ), despite a large sustained reduction in pHE , a trait termed preferential pHI regulation. Further studies are needed to determine whether preferential pHI regulation is a general trait among bimodal fishes and if this confers reduced sensitivity to acid-base disturbances, including those induced by hypercarbia, exhaustive exercise and hypoxia or anoxia. Additionally, elucidating the cellular and molecular mechanisms may yield insight into whether preferential pHI regulation is a trait ultimately associated with the early evolution of air breathing in vertebrates.

  6. 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.

  7. 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

  8. 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

  9. 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

  10. 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.

  11. Reconstructing three-dimensional wake topology based on planar PIV measurements and pattern recognition analysis

    NASA Astrophysics Data System (ADS)

    Morton, C.; Yarusevych, S.

    2016-10-01

    The present study presents a new technique for reconstructing the salient aspects of three-dimensional wake topology based on time-resolved, planar, two-component particle image velocimetry data collected in multiple orthogonal planes. The technique produces conditionally averaged flow field reconstructions based on a pattern recognition analysis of velocity fields. It is validated on the wake of a low-aspect ratio dual step cylinder geometry, consisting of a large diameter cylinder ( D) with small aspect ratio ( L/ D) attached to the mid-span of a small diameter cylinder ( d). For a dual step cylinders with D/ d = 2, and L/ D = 1, numerical and experimental data are considered for ReD = 150 (laminar wake) and for ReD = 2100 (turbulent wake). The results show that the proposed technique successfully reconstructs the dominant periodic wake vortex interactions and can be extended to a wide range of turbulent flows.

  12. 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.

  13. Stereovision-based 3D field recognition for automatic guidance system of off-road vehicle

    NASA Astrophysics Data System (ADS)

    Zhang, Fangming; Ying, Yibin; Shen, Chuan; Jiang, Huanyu; Zhang, Qin

    2005-11-01

    A stereovision-based disparity evaluation algorithm was developed for rice crop field recognition. The gray level intensities and the correlation relation were integrated to produce the disparities of stereo-images. The surface of ground and rice were though as two rough planes, but their disparities waved in a narrow range. The cut/uncut edges of rice crops were first detected and track through the images. We used a step model to locate those edge positions. The points besides the edges were matched respectively to get disparity values using area correlation method. The 3D camera coordinates were computed based on those disparities. The vehicle coordinates were obtained by multiplying the 3D camera coordinates with a transform formula. It has been implemented on an agricultural robot and evaluated in rice crop field with straight rows. The results indicated that the developed stereovision navigation system is capable of reconstructing the field image.

  14. Conformal mapping-based hand-written word and sentence representation and recognition

    NASA Astrophysics Data System (ADS)

    Megherbi, Dalila B.; Iyassu, Yohannes; Boulenouar, A. J.

    2001-08-01

    In this paper, we introduce a technique for handwritten words and sentences representation and recognition. The proposed method is based on complex variables and conformal mapping methodology. In particular, in a previous work, through a complex variable methodology and conformal mapping process, we demonstrated the ability to recognized shapes and concisely represent shape boundaries using a set of polynomial coefficients derived in the mapping process. In this work we illustrate how these previous results can be applied to hand-written words and sentences. We show that the words/sentences classification techniques used are adapted to the feature-coefficients selected and are based on feature-coefficients similarities in combination with the minimum distance classifier. We use as measures the Euclidean distance as well as the covariance matrix eigen- values distance. Finally, experimental results of handwritten words and sentences are shown to show the power, versatility and robustness of the proposed technique.

  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. Applied learning-based color tone mapping for face recognition in video surveillance system

    NASA Astrophysics Data System (ADS)

    Yew, Chuu Tian; Suandi, Shahrel Azmin

    2012-04-01

    In this paper, we present an applied learning-based color tone mapping technique for video surveillance system. This technique can be applied onto both color and grayscale surveillance images. The basic idea is to learn the color or intensity statistics from a training dataset of photorealistic images of the candidates appeared in the surveillance images, and remap the color or intensity of the input image so that the color or intensity statistics match those in the training dataset. It is well known that the difference in commercial surveillance cameras models, and signal processing chipsets used by different manufacturers will cause the color and intensity of the images to differ from one another, thus creating additional challenges for face recognition in video surveillance system. Using Multi-Class Support Vector Machines as the classifier on a publicly available video surveillance camera database, namely SCface database, this approach is validated and compared to the results of using holistic approach on grayscale images. The results show that this technique is suitable to improve the color or intensity quality of video surveillance system for face recognition.

  17. Emotion recognition from sound stimuli based on back-propagation neural networks and electroencephalograms.

    PubMed

    Di, Guo-Qing; Wu, Si-Xia

    2015-08-01

    This research aims to explore the feasibility of using back-propagation (BP) neural networks and electroencephalograms (EEGs) to recognize the emotional reactions induced by sound stimuli in the dimensions of pleasure and arousal, as well as compare the recognition performance of each method on these two dimensions. It could provide an aided design on choosing proper sounds to induce or regulate individuals' emotional states under specific situations for potential users at the design stage. Emotional reactions to different sound stimuli are investigated by Self-Assessment Manikin. The results of BP neural network indicate that the arousal predictions are more satisfactory than the pleasure predictions, and the recognition rates can be improved by optimizing input parameters. EEG signals induced by sound stimuli are recorded. The results show that when induced by each pleasant sound, the Average Power of Electroencephalogram of the α wave in the left frontal pole electrode is significantly lower than that in the right frontal pole electrode, while when induced by each unpleasant sound, the former is significantly higher than the latter. This finding indicates that pleasant and unpleasant sounds can be identified based on the asymmetry of the α wave between the left and right frontal pole electrodes.

  18. 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.

  19. 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.

  20. Study of environmental sound source identification based on hidden Markov model for robust speech recognition

    NASA Astrophysics Data System (ADS)

    Nishiura, Takanobu; Nakamura, Satoshi

    2003-10-01

    Humans communicate with each other through speech by focusing on the target speech among environmental sounds in real acoustic environments. We can easily identify the target sound from other environmental sounds. For hands-free speech recognition, the identification of the target speech from environmental sounds is imperative. This mechanism may also be important for a self-moving robot to sense the acoustic environments and communicate with humans. Therefore, this paper first proposes hidden Markov model (HMM)-based environmental sound source identification. Environmental sounds are modeled by three states of HMMs and evaluated using 92 kinds of environmental sounds. The identification accuracy was 95.4%. This paper also proposes a new HMM composition method that composes speech HMMs and an HMM of categorized environmental sounds for robust environmental sound-added speech recognition. As a result of the evaluation experiments, we confirmed that the proposed HMM composition outperforms the conventional HMM composition with speech HMMs and a noise (environmental sound) HMM trained using noise periods prior to the target speech in a captured signal. [Work supported by Ministry of Public Management, Home Affairs, Posts and Telecommunications of Japan.