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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.

  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.

  4. Social recognition in wild fish populations

    PubMed Central

    Ward, Ashley J.W; Webster, Michael M; Hart, Paul J.B

    2007-01-01

    The ability of animals to gather information about their social and physical environment is essential for their ecological function. Odour cues are an important component of this information gathering across taxa. Recent laboratory studies have revealed the importance of flexible chemical cues in facilitating social recognition of fishes. These cues are known to be mediated by recent habitat experience and fishes are attracted to individuals that smell like themselves. However, to be relevant to wild populations, where animals may move and forage freely, these cues would have to be temporally flexible and allow spatial resolution. Here, we present data from a study of social recognition in wild populations of three-spined sticklebacks (Gasterosteus aculeatus). Focal fish preferentially associated with conspecifics from the same habitat as themselves. These preferences were changed and updated following translocation of the focal fish to a different site. Further investigation revealed that association preferences changed after 3 h of exposure to different habitat cues. In addition to temporal flexibility, the cues also allowed a high degree of spatial resolution: fish taken from sites 200 m apart produced cues that were sufficiently different to enable the focal fish to discriminate and associate with fish captured near their own home site. The adaptive benefits of this social recognition mechanism remain unclear, though they may allow fish to orient within their social environment and gain current local information. PMID:17284411

  5. Dioxin screening in fish product by pattern recognition of biomarkers.

    PubMed

    Bassompierre, Marc; Tomasi, Giorgio; Munck, Lars; Bro, Rasmus; Engelsen, Søren Balling

    2007-04-01

    Two alternative, cost- and time-effective dioxin screening methods relying on two categories of potential lipid biomarkers were investigated. A dioxin range varying from 1.1 to 47.1 pg PCDD/F TEQ-WHO/g lipid using 64 fish meal samples was used for model calibration. The methods were based on multivariate models using either (1) fatty acid composition monitored by GC-FID or (2) fluorescence landscape signals analysed using the PARAFAC model and in both cases predicting dioxin content as pgPCDD/F TEQ-WHO/g lipid. In both cases, Partial Least Squares (PLS) regression was performed for predicting the dioxin content of a sample. The GC-FID data analyses was based on automatic peak alignment and integration, enabling extraction of the area of 140 peaks from the gas chromatograms, as opposed to the 31 fatty acids usually considered for fish oil characterisation. In addition to classic PLS employing the whole dataset for calibration, a two-step local PLS modeling approach was performed based upon an initial selection of k number of calibration samples providing the best match to the prediction sample using a so-called k Nearest Neighbors (kNN) approach, then followed by PLS calibration on these kNN selected samples for dioxin prediction. Fluorescence spectroscopy offers a promising non-invasive and ultra-rapid technique, with less than two minutes analysis time. However, fluorescence spectroscopy using the pattern recognition "kNN-PLS" yielded a correlation of 0.76 (r2) and a high root mean square error of prediction of 11.4 pg PCDD/F TEQ-WHO/g lipid. The predictions were improved when the PLS calibration was performed on all the sample with a root mean square error of prediction of 7.0 pg PCDD/F TEQ-WHO/g lipid. Unfortunately, these results failed to demonstrate the potential of fluorophore monitoring as a screening method. In contrast, the overall best screening performance was obtained with the fatty acid profile, when the kNN-PLS combination employed for pattern

  6. Evolution of kin recognition mechanisms in a fish.

    PubMed

    Hain, Timothy J A; Garner, Shawn R; Ramnarine, Indar W; Neff, Bryan D

    2017-03-01

    Both selection and phylogenetic history can influence the evolution of phenotypic traits. Here we used recently characterized variation in kin recognition mechanisms among six guppy populations to explore the phylogenetic history of this trait. Guppies can use two different kin recognition mechanisms: either phenotype matching, in which individuals are identified based on comparison with a recognition template, or familiarity, in which individuals are remembered based on previous interactions. Across the six populations, we identified four transitions in recognition mechanism: phenotype matching evolved once and was subsequently lost in a single population, whereas familiarity evolved twice. Based on a molecular clock, these transitions occurred among populations that had diverged on a timescale of hundreds of thousands of years, which is two orders of magnitude faster than previously documented transitions in recognition mechanisms. A randomization test provided no evidence that recognition mechanisms were constrained by phylogeny, suggesting that recognition mechanisms have the capacity to evolve rapidly, although the specific selection pressures that may be contributing to variation in recognition mechanisms across populations remain unknown.

  7. Microscopic recognition and identification of fish meal in compound feeds.

    PubMed

    van Raamsdonk, L W D; Prins, T W; van de Rhee, N; Vliege, J J M; Pinckaers, V G Z

    2017-02-22

    Fish meal is an accepted ingredient in compound feed. Unauthorised application is primarily enforced by visual inspection, i.e., microscopy. In order to document the visually available diversity, fragments of bones and scales of 17 teleost fish species belonging to seven different orders were investigated for their diversity in the presence of structural elements: lacunae and canaliculae in bone fragments and type of growth rings and teeth of scale fragments. Despite the classical division into cellular bones and acellular bones of teleost fish, i.e., whether or not possessing osteocytes, the current examinations revealed patterns of lacunae, in some types accompanied with canaliculae, in all 17 species investigated. In total seven types of bone structures were defined, and six types of scale structures. Profiles with the relative frequency of each bone type per species were established. The share of acellular bone fragments appeared to be related to the evolutionary position of the species. Results of proficiency tests for the detection of fish meal reveal that in most cases the sensitivity and specificity for the detection of fish meal ranges from sufficient to perfect. Only some specified circumstances can hamper proper recognition and identification, most notably salmon bone fragments mimicking bone fragments from terrestrial animals, and pieces of hydrolysed proteins or minerals mimicking acellular fish bone fragments. The expertise gained in this study would help to improve the distinction between fish meal and terrestrial animal material in compound feed, and it supports the application of the species-to-species ban with respect to the valorisation of by-products from fish farms in aquafeed. In a broader perspective, the current expertise might be helpful to detect fraud throughout the feed/food production chain. The matrix of characteristics versus species is implemented in a data model running in the expert system 'Determinator' for facilitating

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

    PubMed

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

    2008-01-07

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

  9. Image Recognition Based on Biometric Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Sun, Shuliang; Chen, Zhong; Liu, Chenglian; Guo, Yongning; Lin, Xueyun

    2011-09-01

    A new method, biomimetric pattern recognition, is mentioned to recognize images. At first, the image is pretreatment and feature extraction, then a high vector is got. A biomimetric pattern recognition model is designed. The judgment function is used to discriminate the classification of the samples. It is showed that the method is effective for little samples by experiment. It would be useful in many fields in future.

  10. Distance, shape and more: recognition of object features during active electrolocation in a weakly electric fish.

    PubMed

    von der Emde, Gerhard; Fetz, Steffen

    2007-09-01

    In the absence of light, the weakly electric fish Gnathonemus petersii detects and distinguishes objects in the environment through active electrolocation. In order to test which features of an object the fish use under these conditions to discriminate between differently shaped objects, we trained eight individuals in a food-rewarded, two-alternative, forced-choice procedure. All fish learned to discriminate between two objects of different shapes and volumes. When new object combinations were offered in non-rewarded test trials, fish preferred those objects that resembled the one they had been trained to (S+) and avoided objects resembling the one that had not been rewarded (S-). For a decision, fish paid attention to the relative differences between the two objects they had to discriminate. For discrimination, fish used several object features, the most important ones being volume, material and shape. The importance of shape was demonstrated by reducing the objects to their 3-dimensional contours, which sufficed for the fish to distinguish differently shaped objects. Our results also showed that fish attended strongly to the feature ;volume', because all individuals tended to avoid the larger one of two objects. When confronted with metal versus plastic objects, all fish avoided metal and preferred plastic objects, irrespective of training. In addition to volume, material and shape, fish attended to additional parameters, such as corners or rounded edges. When confronted with two unknown objects, fish weighed up the positive and negative properties of these novel objects and based their decision on the outcome of this comparison. Our results suggest that fish are able to link and assemble local features of an electrolocation pattern to construct a representation of an object, suggesting that some form of a feature extraction mechanism enables them to solve a complex object recognition task.

  11. 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

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

    PubMed Central

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

    2016-01-01

    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

  13. Electric signals and species recognition in the wave-type gymnotiform fish Apteronotus leptorhynchus.

    PubMed

    Fugère, V; Krahe, R

    2010-01-15

    Gymnotiformes are South American weakly electric fish that produce weak electric organ discharges (EOD) for orientation, foraging and communication purposes. It has been shown that EOD properties vary widely across species and could thus be used as species recognition signals. We measured and quantified the electric signals of various species using a landmark-based approach. Using discriminant function analysis to verify whether these signals are species specific based on different signal parameters, we found that the EOD waveform is a more specific cue than EOD frequency, which shows large overlap across species. Using Apteronotus leptorhynchus as a focal species, we then performed a series of playback experiments using stimuli of different species (varying in frequency, waveform, or both). In an experiment with restrained fish, we found, in contrast to what we predicted, that the choice of stimulus waveform did not affect the production of communication signals. In an experiment with free-swimming fish, the animals spent more time near the playback electrodes and produced more communication signals when the stimuli were within their conspecific frequency range. Waveform again had no measurable effect. The production of communication signals correlated with the frequency difference between the stimulus and the fish's own EOD, but approach behavior did not.

  14. Underwater color constancy: enhancement of automatic live fish recognition

    NASA Astrophysics Data System (ADS)

    Chambah, Majed; Semani, Dahbia; Renouf, Arnaud; Courtellemont, Pierre; Rizzi, Alessandro

    2003-12-01

    We present in this paper some advances in color restoration of underwater images, especially with regard to the strong and non uniform color cast which is typical of underwater images. The proposed color correction method is based on ACE model, an unsupervised color equalization algorithm. ACE is a perceptual approach inspired by some adaptation mechanisms of the human visual system, in particular lightness constancy and color constancy. A perceptual approach presents a lot of advantages: it is unsupervised, robust and has local filtering properties, that lead to more effective results. The restored images give better results when displayed or processed (fish segmentation and feature extraction). The presented preliminary results are satisfying and promising.

  15. 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

  16. Optoelectronic-based face recognition versus electronic PCA-based face recognition

    NASA Astrophysics Data System (ADS)

    Alsamman, A.

    2003-11-01

    Face recognition based on principal component analysis (PCA) using eigenfaces is popular in face recognition markets. In this paper we present a comparison between various optoelectronic face recognition techniques and principal component analysis (PCA) based technique for face recognition. Computer simulations are used to study the effectiveness of PCA based technique especially for facial images with a high level of distortion. Results are then compared to various distortion-invariant optoelectronic face recognition algorithms such as synthetic discriminant functions (SDF), projection-slice SDF, optical correlator based neural networks, and pose estimation based correlation.

  17. DCT-based iris recognition.

    PubMed

    Monro, Donald M; Rakshit, Soumyadip; Zhang, Dexin

    2007-04-01

    This paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent Correct Recognition Rate (CRR) and perfect Receiver-Operating Characteristic (ROC) Curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical Equal Error Rate (EER) is predicted to be as low as 2.59 x 10(-4) on the available data sets.

  18. Rule-Based Orientation Recognition Of A Moving Object

    NASA Astrophysics Data System (ADS)

    Gove, Robert J.

    1989-03-01

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

  19. Gait recognition based on integral outline

    NASA Astrophysics Data System (ADS)

    Ming, Guan; Fang, Lv

    2017-02-01

    Biometric identification technology replaces traditional security technology, which has become a trend, and gait recognition also has become a hot spot of research because its feature is difficult to imitate and theft. This paper presents a gait recognition system based on integral outline of human body. The system has three important aspects: the preprocessing of gait image, feature extraction and classification. Finally, using a method of polling to evaluate the performance of the system, and summarizing the problems existing in the gait recognition and the direction of development in the future.

  20. 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.

  1. A two-stage exon recognition model based on synergetic neural network.

    PubMed

    Huang, Zhehuang; Chen, Yidong

    2014-01-01

    Exon recognition is a fundamental task in bioinformatics to identify the exons of DNA sequence. Currently, exon recognition algorithms based on digital signal processing techniques have been widely used. Unfortunately, these methods require many calculations, resulting in low recognition efficiency. In order to overcome this limitation, a two-stage exon recognition model is proposed and implemented in this paper. There are three main works. Firstly, we use synergetic neural network to rapidly determine initial exon intervals. Secondly, adaptive sliding window is used to accurately discriminate the final exon intervals. Finally, parameter optimization based on artificial fish swarm algorithm is used to determine different species thresholds and corresponding adjustment parameters of adaptive windows. Experimental results show that the proposed model has better performance for exon recognition and provides a practical solution and a promising future for other recognition tasks.

  2. Practical vision based degraded text recognition system

    NASA Astrophysics Data System (ADS)

    Mohammad, Khader; Agaian, Sos; Saleh, Hani

    2011-02-01

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

  3. Object recognition approach based on feature fusion

    NASA Astrophysics Data System (ADS)

    Wang, Runsheng

    2001-09-01

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

  4. 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

  5. Toll-like receptor recognition of bacteria in fish: ligand specificity and signal pathways.

    PubMed

    Zhang, Jie; Kong, Xianghui; Zhou, Chuanjiang; Li, Li; Nie, Guoxing; Li, Xuejun

    2014-12-01

    Pattern recognition receptors (PRRs) recognize the conserved molecular structure of pathogens and trigger the signaling pathways that activate immune cells in response to pathogen infection. Toll-like receptors (TLRs) are the first and best characterized innate immune receptors. To date, at least 20 TLR types (TLR1, 2, 3, 4, 5M, 5S, 7, 8, 9, 13, 14, 18, 19, 20, 21, 22, 23, 24, 25, and 26) have been found in more than a dozen of fish species. However, of the TLRs identified in fish, direct evidence of ligand specificity has only been shown for TLR2, TLR3, TLR5M, TLR5S, TLR9, TLR21, and TLR22. Some studies have suggested that TLR2, TLR5M, TLR5S, TLR9, and TLR21 could specifically recognize PAMPs from bacteria. In addition, other TLRs including TLR1, TLR4, TLR14, TLR18, and TLR25 may also be sensors of bacteria. TLR signaling pathways in fish exhibit some particular features different from that in mammals. In this review, the ligand specificity and signal pathways of TLRs that recognize bacteria in fish are summarized. References for further studies on the specificity for recognizing bacteria using TLRs and the following reactions triggered are discussed. In-depth studies should be continuously performed to identify the ligand specificity of all TLRs in fish, particularly non-mammalian TLRs, and their signaling pathways. The discovery of TLRs and their functions will contribute to the understanding of disease resistance mechanisms in fish and provide new insights for drug intervention to manipulate immune responses.

  6. Automatic Speech Recognition Based on Electromyographic Biosignals

    NASA Astrophysics Data System (ADS)

    Jou, Szu-Chen Stan; Schultz, Tanja

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

  7. A neural network based speech recognition system

    NASA Astrophysics Data System (ADS)

    Carroll, Edward J.; Coleman, Norman P., Jr.; Reddy, G. N.

    1990-02-01

    An overview is presented of the development of a neural network based speech recognition system. The two primary tasks involved were the development of a time invariant speech encoder and a pattern recognizer or detector. The speech encoder uses amplitude normalization and a Fast Fourier Transform to eliminate amplitude and frequency shifts of acoustic clues. The detector consists of a back-propagation network which accepts data from the encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection time is no more than a few network time constants, and its recognition speed is independent of the number of the words in the vocabulary. The completed system has functioned as expected with high tolerance to input variation and with error rates comparable to a commercial system when used in a noisy environment.

  8. 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.

  9. Neurocomputational bases of object and face recognition.

    PubMed Central

    Biederman, I; Kalocsai, P

    1997-01-01

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

  10. 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.

  11. Study on Information Fusion Based Check Recognition System

    NASA Astrophysics Data System (ADS)

    Wang, Dong

    Automatic check recognition techniques play an important role in financial systems, especially in risk management. This paper presents a novel check recognition system based on multi-cue information fusion theory. For Chinese bank check, the amount can be independently determined by legal amount, courtesy amount, or E13B code. The check recognition algorithm consists of four steps: preprocessing, check layout analysis, segmentation and recognition, and information fusion. For layout analysis, an adaptive template matching algorithm is presented to locate the target recognition regions on the check. The hidden markov model is used to segment and recognize legal amount. Courtesy and E13B code are recognized by artificial neural network method, respectively. Finally, D-S evidence theory is then introduced to fuse above three recognition results for better recognition performance. Experimental results demonstrate that the system can robustly recognize checks and the information fusion based algorithm improves the recognition rate by 5~10 percent.

  12. When false recognition is unopposed by true recognition: gist-based memory distortion in Alzheimer's disease.

    PubMed

    Budson, A E; Daffner, K R; Desikan, R; Schacter, D L

    2000-04-01

    The authors examined false recognition of semantic associates in patients with probable Alzheimer's disease (AD), older adults, and young adults using a paradigm that provided rates of false recognition after single and multiple exposures to word lists. Using corrected false recognition scores to control for unrelated false alarms, the authors found that (a) the level of false recognition after a single list exposure was lower in AD patients than in controls; (b) across 5 trials, false recognition increased in AD patients, decreased in young adults, and showed a fluctuating pattern in older adults; and (c) all groups showed an increase in true recognition over the 5 trials. Analyses suggested that AD patients built up semantic gist across trials, whereas both control groups were able to use increased item-specific recollection and more conservative response criteria to suppress gist-based false alarms.

  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. 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.

  15. Social learning and acquired recognition of a predator by a marine fish.

    PubMed

    Manassa, R P; McCormick, M I

    2012-07-01

    Predation is known to influence the distribution of behavioural traits among prey individuals, populations and communities over both evolutionary and ecological time scales. Prey have evolved mechanisms of rapidly learning the identity of predators. Chemical cues are often used by prey to assess predation risk especially in aquatic systems where visual cues are unreliable. Social learning is a method of threat assessment common among a variety of freshwater fish taxa, which incorporates chemosensory information. Learning predator identities through social learning is beneficial to naïve individuals as it eliminates the need for direct interaction with a potential threat. Although social learning is widespread throughout the animal kingdom, no research on the use of this mechanism exists for marine species. In this study, we examined the role of social learning in predator recognition for a tropical damselfish, Acanthochromis polyacanthus. This species was found to not only possess and respond to conspecific chemical alarm cues, but naïve individuals were able to learn a predators' identity from experienced individuals, the process of social learning. Fish that learned to associate risk with the olfactory cue of a predator responded with the same intensity as conspecifics that were exposed to a chemical alarm cue from a conspecific skin extract.

  16. 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.

  17. Relevance feedback-based building recognition

    NASA Astrophysics Data System (ADS)

    Li, Jing; Allinson, Nigel M.

    2010-07-01

    Building recognition is a nontrivial task in computer vision research which can be utilized in robot localization, mobile navigation, etc. However, existing building recognition systems usually encounter the following two problems: 1) extracted low level features cannot reveal the true semantic concepts; and 2) they usually involve high dimensional data which require heavy computational costs and memory. Relevance feedback (RF), widely applied in multimedia information retrieval, is able to bridge the gap between the low level visual features and high level concepts; while dimensionality reduction methods can mitigate the high-dimensional problem. In this paper, we propose a building recognition scheme which integrates the RF and subspace learning algorithms. Experimental results undertaken on our own building database show that the newly proposed scheme appreciably enhances the recognition accuracy.

  18. A Neural Network Based Speech Recognition System

    DTIC Science & Technology

    1990-02-01

    encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection...environment. Keywords: Artificial intelligence; Neural networks : Back propagation; Speech recognition.

  19. 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.

  20. 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.

  1. 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)

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

    ERIC Educational Resources Information Center

    Khalafi, Lida; Kashani, Samira; Karimi, Javad

    2016-01-01

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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. Learning Distance Functions for Exemplar-Based Object Recognition

    DTIC Science & Technology

    2007-01-01

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

  8. Case-Based Policy and Goal Recognition

    DTIC Science & Technology

    2015-09-30

    Springfield, VA USA 2 ASEE Postdoctoral Fellow 3 Navy Center for Applied Research in Artificial Intelligence ; Naval Research Laboratory (Code 5514...recognition in beyond visual range air combat. In: Proceedings of the Twenty-Eighth Inter- national Florida Artificial Intelligence Research Society...a Navy strategy simulation. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence , AAAI Press (2010) 5. Borck, H., Karneeb

  9. 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

  10. Automatic Target Recognition Based on Cross-Plot

    PubMed Central

    Wong, Kelvin Kian Loong; Abbott, Derek

    2011-01-01

    Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

  11. Automatic target recognition based on cross-plot.

    PubMed

    Wong, Kelvin Kian Loong; Abbott, Derek

    2011-01-01

    Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.

  12. 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.

  13. Dynamic gesture recognition based on multiple sensors fusion technology.

    PubMed

    Wenhui, Wang; Xiang, Chen; Kongqiao, Wang; Xu, Zhang; Jihai, Yang

    2009-01-01

    This paper investigates the roles of a three-axis accelerometer, surface electromyography sensors and a webcam for dynamic gesture recognition. A decision-level multiple sensor fusion method based on action elements is proposed to distinguish a set of 20 kinds of dynamic hand gestures. Experiments are designed and conducted to collect three kinds of sensor data stream simultaneously during gesture implementation and compare the performance of different subsets in gesture recognition. Experimental results from three subjects show that the combination of three kinds of sensor achieves recognition accuracies at 87.5%-91.8%, which are higher largely than that of the single sensor conditions. This study is valuable to realize continuous and dynamic gesture recognition based on multiple sensor fusion technology for multi-model interaction.

  14. Case-Based Plan Recognition Using Action Sequence Graphs

    DTIC Science & Technology

    2014-10-01

    Kumaran, 2007), and probabilistic approaches (e.g., Bui, 2003; Charniak & Goldman, 1991, 1993; Geib & Goldman, 2009; Goldman, Geib & Miller, 1999...Sixteenth UK Workshop on Case-Based Reasoning. Cambridge, UK: Springer. Geib , C. W., & Goldman, R. P. (2009). A probabilistic plan recognition...and practice. San Mateo, CA: Morgan Kaufmann. Goldman, R.P., Geib , C.W., & Miller, C.A. (1999). A new model of plan recognition. Proceedings of the

  15. EEG based topography analysis in string recognition task

    NASA Astrophysics Data System (ADS)

    Ma, Xiaofei; Huang, Xiaolin; Shen, Yuxiaotong; Qin, Zike; Ge, Yun; Chen, Ying; Ning, Xinbao

    2017-03-01

    Vision perception and recognition is a complex process, during which different parts of brain are involved depending on the specific modality of the vision target, e.g. face, character, or word. In this study, brain activities in string recognition task compared with idle control state are analyzed through topographies based on multiple measurements, i.e. sample entropy, symbolic sample entropy and normalized rhythm power, extracted from simultaneously collected scalp EEG. Our analyses show that, for most subjects, both symbolic sample entropy and normalized gamma power in string recognition task are significantly higher than those in idle state, especially at locations of P4, O2, T6 and C4. It implies that these regions are highly involved in string recognition task. Since symbolic sample entropy measures complexity, from the perspective of new information generation, and normalized rhythm power reveals the power distributions in frequency domain, complementary information about the underlying dynamics can be provided through the two types of indices.

  16. 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.

  17. 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.

  18. [Recognition of corn seeds based on pattern recognition and near infrared spectroscopy technology].

    PubMed

    Liu, Tian-ling; Su, Qi-ya; Sun, Qun; Yang, Li-ming

    2012-05-01

    Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling since it has advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds was proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA, PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results of three different spectral regions show that the performances of three methods: PCA+SVM, LLE+SVM, PLS+SVM are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data.

  19. [Recognition of corn seeds based on pattern recognition and near infrared spectroscopy technology].

    PubMed

    Liu, Tian-Ling; Su, Qi-Ya; Sun, Qun; Yang, Li-Ming

    2012-06-01

    Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling due to its advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds is proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA and PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results for three different spectral regions show that the performances of three methods, i. e. PCA+SVM, LLE+SVM, PLS+SVM, are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data.

  20. Finger vein recognition based on a personalized best bit map.

    PubMed

    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.

  1. 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

  2. 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

  3. Uniform design based SVM model selection for face recognition

    NASA Astrophysics Data System (ADS)

    Li, Weihong; Liu, Lijuan; Gong, Weiguo

    2010-02-01

    Support vector machine (SVM) has been proved to be a powerful tool for face recognition. The generalization capacity of SVM depends on the model with optimal hyperparameters. The computational cost of SVM model selection results in application difficulty in face recognition. In order to overcome the shortcoming, we utilize the advantage of uniform design--space filling designs and uniformly scattering theory to seek for optimal SVM hyperparameters. Then we propose a face recognition scheme based on SVM with optimal model which obtained by replacing the grid and gradient-based method with uniform design. The experimental results on Yale and PIE face databases show that the proposed method significantly improves the efficiency of SVM model selection.

  4. Finger vein recognition based on local directional code.

    PubMed

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

    2012-11-05

    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.

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

    NASA Astrophysics Data System (ADS)

    Wang, Chengzhang; Bai, Xiaoming

    2013-03-01

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

  6. Finger Vein Recognition Based on Local Directional Code

    PubMed Central

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

    2012-01-01

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

  7. 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.

  8. Optical character recognition based on nonredundant correlation measurements.

    PubMed

    Braunecker, B; Hauck, R; Lohmann, A W

    1979-08-15

    The essence of character recognition is a comparison between the unknown character and a set of reference patterns. Usually, these reference patterns are all possible characters themselves, the whole alphabet in the case of letter characters. Obviously, N analog measurements are highly redundant, since only K = log(2)N binary decisions are enough to identify one out of N characters. Therefore, we devised K reference patterns accordingly. These patterns, called principal components, are found by digital image processing, but used in an optical analog computer. We will explain the concept of principal components, and we will describe experiments with several optical character recognition systems, based on this concept.

  9. 3D object recognition based on local descriptors

    NASA Astrophysics Data System (ADS)

    Jakab, Marek; Benesova, Wanda; Racev, Marek

    2015-01-01

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

  10. A face recognition algorithm based on thermal and visible data

    NASA Astrophysics Data System (ADS)

    Sochenkov, Ilya; Tihonkih, Dmitrii; Vokhmintcev, Aleksandr; Melnikov, Andrey; Makovetskii, Artyom

    2016-09-01

    In this work we present an algorithm of fusing thermal infrared and visible imagery to identify persons. The proposed face recognition method contains several components. In particular this is rigid body image registration. The rigid registration is achieved by a modified variant of the iterative closest point (ICP) algorithm. We consider an affine transformation in three-dimensional space that preserves the angles between the lines. An algorithm of matching is inspirited by the recent results of neurophysiology of vision. Also we consider the ICP minimizing error metric stage for the case of an arbitrary affine transformation. Our face recognition algorithm also uses the localized-contouring algorithms to segment the subject's face; thermal matching based on partial least squares discriminant analysis. Thermal imagery face recognition methods are advantageous when there is no control over illumination or for detecting disguised faces. The proposed algorithm leads to good matching accuracies for different person recognition scenarios (near infrared, far infrared, thermal infrared, viewed sketch). The performance of the proposed face recognition algorithm in real indoor environments is presented and discussed.

  11. 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…

  12. 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.

  13. Hypergraph-Based Recognition Memory Model for Lifelong Experience

    PubMed Central

    2014-01-01

    Cognitive agents are expected to interact with and adapt to a nonstationary dynamic environment. As an initial process of decision making in a real-world agent interaction, familiarity judgment leads the following processes for intelligence. Familiarity judgment includes knowing previously encoded data as well as completing original patterns from partial information, which are fundamental functions of recognition memory. Although previous computational memory models have attempted to reflect human behavioral properties on the recognition memory, they have been focused on static conditions without considering temporal changes in terms of lifelong learning. To provide temporal adaptability to an agent, in this paper, we suggest a computational model for recognition memory that enables lifelong learning. The proposed model is based on a hypergraph structure, and thus it allows a high-order relationship between contextual nodes and enables incremental learning. Through a simulated experiment, we investigate the optimal conditions of the memory model and validate the consistency of memory performance for lifelong learning. PMID:25371665

  14. Evolution of lipopolysaccharide (LPS) recognition and signaling: fish TLR4 does not recognize LPS and negatively regulates NF-kappaB activation.

    PubMed

    Sepulcre, María P; Alcaraz-Pérez, Francisca; López-Muñoz, Azucena; Roca, Francisco J; Meseguer, José; Cayuela, María L; Mulero, Victoriano

    2009-02-15

    It has long been established that lower vertebrates, most notably fish and amphibians, are resistant to the toxic effect of LPS. Furthermore, the lack of a TLR4 ortholog in some fish species and the lack of the essential costimulatory molecules for LPS activation via TLR4 (i.e., myeloid differentiation protein 2 (MD-2) and CD14) in all the fish genomes and expressed sequence tag databases available led us to hypothesize that the mechanism of LPS recognition in fish may be different from that of mammals. To shed light on the role of fish TLRs in LPS recognition, a dual-luciferase reporter assay to study NF-kappaB activation in whole zebrafish embryos was developed and three different bony fish models were studied: 1) the gilthead seabream (Sparus aurata, Perciformes), an immunological-tractable teleost model in which the presence of a TLR4 ortholog is unknown; 2) the spotted green pufferfish (Tetraodon nigroviridis, Tetraodontiformes), which lacks a TLR4 ortholog; and 3) the zebrafish (Danio rerio, Cypriniformes), which possesses two TLR4 orthologs. Our results show that LPS signaled via a TLR4- and MyD88-independent manner in fish, and, surprisingly, that the zebrafish TLR4 orthologs negatively regulated the MyD88-dependent signaling pathway. We think that the identification of TLR4 as a negative regulator of TLR signaling in the zebrafish, together with the absence of this receptor in most fish species, explains the resistance of fish to endotoxic shock and supports the idea that the TLR4 receptor complex for LPS recognition arose after the divergence of fish and tetrapods.

  15. 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

  16. Scale Invariant Gabor Descriptor-Based Noncooperative Iris Recognition

    NASA Astrophysics Data System (ADS)

    Du, Yingzi; Belcher, Craig; Zhou, Zhi

    2010-12-01

    A new noncooperative iris recognition method is proposed. In this method, the iris features are extracted using a Gabor descriptor. The feature extraction and comparison are scale, deformation, rotation, and contrast-invariant. It works with off-angle and low-resolution iris images. The Gabor wavelet is incorporated with scale-invariant feature transformation (SIFT) for feature extraction to better extract the iris features. Both the phase and magnitude of the Gabor wavelet outputs were used in a novel way for local feature point description. Two feature region maps were designed to locally and globally register the feature points and each subregion in the map is locally adjusted to the dilation/contraction/deformation. We also developed a video-based non-cooperative iris recognition system by integrating video-based non-cooperative segmentation, segmentation evaluation, and score fusion units. The proposed method shows good performance for frontal and off-angle iris matching. Video-based recognition methods can improve non-cooperative iris recognition accuracy.

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

    PubMed

    Sprager, Sebastijan; Juric, Matjaz B

    2015-09-02

    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.

  18. 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

  19. 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).

  20. Multifeature-based high-resolution palmprint recognition.

    PubMed

    Dai, Jifeng; Zhou, Jie

    2011-05-01

    Palmprint is a promising biometric feature for use in access control and forensic applications. Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e.g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted. In this paper, we propose a novel recognition algorithm for high-resolution palmprint. The main contributions of the proposed algorithm include the following: 1) use of multiple features, namely, minutiae, density, orientation, and principal lines, for palmprint recognition to significantly improve the matching performance of the conventional algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the existing algorithm in case of regions with a large number of creases. 3) Use of a novel fusion scheme for an identification application which performs better than conventional fusion methods, e.g., weighted sum rule, SVMs, or Neyman-Pearson rule. Besides, we analyze the discriminative power of different feature combinations and find that density is very useful for palmprint recognition. Experimental results on the database containing 14,576 full palmprints show that the proposed algorithm has achieved a good performance. In the case of verification, the recognition system's False Rejection Rate (FRR) is 16 percent, which is 17 percent lower than the best existing algorithm at a False Acceptance Rate (FAR) of 10(-5), while in the identification experiment, the rank-1 live-scan partial palmprint recognition rate is improved from 82.0 to 91.7 percent.

  1. 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.

  2. A novel fingerprint recognition algorithm based on VK-LLE

    NASA Astrophysics Data System (ADS)

    Luo, Jing; Lin, Shu-zhong; Ni, Jian-yun; Song, Li-mei

    2009-07-01

    It is a challenging problem to overcome shift and rotation and nonlinearity in fingerprint images. By analyzing the shortcoming of fingerprint recognition algorithm on shift or rotation images at present, manifold learning algorithm is introduced. A fingerprint recognition algorithm has been proposed based on locally linear embedding of variable neighbourhood k (VK-LLE). Firstly, approximate geodesic distance between any two points is computed by ISOMAP ( isometric feature mapping) and then the neighborhood is determined for each point by the relationship between its local estimated geodesic distance matrix and local Euclidean distance matrix. Secondly, the dimension of fingerprint image is reduced by nonlinear dimension-reduction method. And the best projected features of original fingerprint data of large dimension are acquired. By analyzing the changes of recognition accuracy with the neighborhood and embedding dimension, the neighborhood and embedding dimension is determined at last. Finally, fingerprint recognition is accomplished by Euclidean distance Classifier. The experimental results based on standard fingerprint datasets have verified the proposed algorithm had a better robustness to those fingerprint images of shift or rotation or nonlinearity than the algorithm using LLE, thus this method has some values in practice.

  3. Component-based target recognition inspired by human vision

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Agyepong, Kwabena

    2009-05-01

    In contrast with machine vision, human can recognize an object from complex background with great flexibility. For example, given the task of finding and circling all cars (no further information) in a picture, you may build a virtual image in mind from the task (or target) description before looking at the picture. Specifically, the virtual car image may be composed of the key components such as driver cabin and wheels. In this paper, we propose a component-based target recognition method by simulating the human recognition process. The component templates (equivalent to the virtual image in mind) of the target (car) are manually decomposed from the target feature image. Meanwhile, the edges of the testing image can be extracted by using a difference of Gaussian (DOG) model that simulates the spatiotemporal response in visual process. A phase correlation matching algorithm is then applied to match the templates with the testing edge image. If all key component templates are matched with the examining object, then this object is recognized as the target. Besides the recognition accuracy, we will also investigate if this method works with part targets (half cars). In our experiments, several natural pictures taken on streets were used to test the proposed method. The preliminary results show that the component-based recognition method is very promising.

  4. Predicting fish acute toxicity using a fish gill cell line-based toxicity assay.

    PubMed

    Tanneberger, Katrin; Knöbel, Melanie; Busser, Frans J M; Sinnige, Theo L; Hermens, Joop L M; Schirmer, Kristin

    2013-01-15

    The OECD test guideline 203 for determination of fish acute toxicity requires substantial numbers of fish and uses death as an apical end point. One potential alternative are fish cell lines; however, several studies indicated that these appear up to several orders of magnitude less sensitive than fish. We developed a fish gill cell line-based (RTgill-W1) assay, using several measures to improve sensitivity. The optimized assay was applied to determine the toxicity of 35 organic chemicals, having a wide range of toxicity to fish, mode of action and physicochemical properties. We found a very good agreement between in vivo and in vitro effective concentrations. For up to 73% of the tested compounds, the difference between the two approaches was less than 5-fold, covering baseline toxicants but as well compounds with presumed specific modes of action, including reactivity, inhibition of acetylcholine esterase or uncoupling of oxidative phosphorylation. Accounting for measured chemical concentrations eliminated two outliers, the hydrophobic 4-decylaniline and the volatile 2,3-dimethyl-1,3-butadiene, with an outlier being operationally defined as a substance showing a more than 10-fold difference between in vivo/in vitro effect concentrations. Few outliers remained. The most striking were allyl alcohol (2700-fold), which likely needs to be metabolically activated, and permethrin (190-fold) and lindane (63-fold), compounds acting, respectively, on sodium and chloride channels in the brain of fish. We discuss further developments of this assay and suggest its use beyond predicting acute toxicity to fish, for example, as part of adverse outcome pathways to replace, reduce, or refine chronic fish tests.

  5. Learning Distance Functions for Exemplar-Based Object Recognition

    DTIC Science & Technology

    2007-08-08

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

  6. 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.

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

    PubMed

    Tarr, M J; Bülthoff, H H

    1998-07-01

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

  8. 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.

  9. Wavelet-based illumination invariant preprocessing in face recognition

    NASA Astrophysics Data System (ADS)

    Goh, Yi Zheng; Teoh, Andrew Beng Jin; Goh, Kah Ong Michael

    2009-04-01

    Performance of a contemporary two-dimensional face-recognition system has not been satisfied due to the variation in lighting. As a result, many works of solving illumination variation in face recognition have been carried out in past decades. Among them, the Illumination-Reflectance model is one of the generic models that is used to separate the individual reflectance and illumination components of an object. The illumination component can be removed by means of image-processing techniques to regain an intrinsic face feature, which is depicted by the reflectance component. We present a wavelet-based illumination invariant algorithm as a preprocessing technique for face recognition. On the basis of the multiresolution nature of wavelet analysis, we decompose both illumination and reflectance components from a face image in a systematic way. The illumination component wherein resides in the low-spatial-frequency subband can be eliminated efficiently. This technique works out very advantageously for achieving higher recognition performance on YaleB, CMU PIE, and FRGC face databases.

  10. 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.

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

    PubMed

    Zhou, Pan; Lin, Zhouchen; Zhang, Chao

    2016-05-01

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

  12. 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.

  13. Aircraft recognition based on the discrepancy of polygon intersection area

    NASA Astrophysics Data System (ADS)

    Deng, Xiujian; Wang, Yanfang; Feng, Qi

    2017-01-01

    In this paper, a new algorithm that based on discrepancy of polygon intersection area for aircraft recognition is presented. The recognition algorithm process involves three parts: generating polygon of aircraft, placing overlapping plane polygons and computing the area of total intersecting polygons. For the purpose of getting the polygon of aircraft, the picture that was ready to be recognized has gone through a series of pre-processing and the smallest circumference polygon algorithm was used to get approximate polygon of the target contour. To make the two compared polygons have the approximate area, the similar principle was utilized. The matching procedure was divided into four steps including computing intersecting points, computing polygon intersecting sets, computing the intersecting area and getting the intersecting rate to recognize the aircraft. The data structure of algorithm is based on doubly liked list principle. A mass of simulations illustrate that the proposed algorithm is effective and reasonable.

  14. Active AU Based Patch Weighting for Facial Expression Recognition

    PubMed Central

    Xie, Weicheng; Shen, Linlin; Yang, Meng; Lai, Zhihui

    2017-01-01

    Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+) databases, respectively. Better cross-database performance has also been observed. PMID:28146094

  15. 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.

  16. Design of embedded intelligent monitoring system based on face recognition

    NASA Astrophysics Data System (ADS)

    Liang, Weidong; Ding, Yan; Zhao, Liangjin; Li, Jia; Hu, Xuemei

    2017-01-01

    In this paper, a new embedded intelligent monitoring system based on face recognition is proposed. The system uses Pi Raspberry as the central processor. A sensors group has been designed with Zigbee module in order to assist the system to work better and the two alarm modes have been proposed using the Internet and 3G modem. The experimental results show that the system can work under various light intensities to recognize human face and send alarm information in real time.

  17. 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

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

    PubMed

    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.

  19. LPI Radar Waveform Recognition Based on Time-Frequency Distribution.

    PubMed

    Zhang, Ming; Liu, Lutao; Diao, Ming

    2016-10-12

    In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo-Zernike moments, etc., the features are extracted from the Choi-Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of -2 dB.

  20. LPI Radar Waveform Recognition Based on Time-Frequency Distribution

    PubMed Central

    Zhang, Ming; Liu, Lutao; Diao, Ming

    2016-01-01

    In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB. PMID:27754325

  1. Segment-based acoustic models for continuous speech recognition

    NASA Astrophysics Data System (ADS)

    Ostendorf, Mari; Rohlicek, J. R.

    1993-07-01

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

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

    NASA Astrophysics Data System (ADS)

    Wan, Khairunizam; Sawada, Hideyuki

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

  3. 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.

  4. Excavation Equipment Recognition Based on Novel Acoustic Statistical Features.

    PubMed

    Cao, Jiuwen; Wang, Wei; Wang, Jianzhong; Wang, Ruirong

    2016-09-30

    Excavation equipment recognition attracts increasing attentions in recent years due to its significance in underground pipeline network protection and civil construction management. In this paper, a novel classification algorithm based on acoustics processing is proposed for four representative excavation equipments. New acoustic statistical features, namely, the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse are first developed to characterize acoustic signals. Then, probability density distributions of these acoustic features are analyzed and a novel classifier is presented. Experiments on real recorded acoustics of the four excavation devices are conducted to demonstrate the effectiveness of the proposed algorithm. Comparisons with two popular machine learning methods, support vector machine and extreme learning machine, combined with the popular linear prediction cepstral coefficients are provided to show the generalization capability of our method. A real surveillance system using our algorithm is developed and installed in a metro construction site for real-time recognition performance validation.

  5. Hazardous Odor Recognition by CMAC Based Neural Networks.

    PubMed

    Bucak, Ihsan Ömür; Karlık, Bekir

    2009-01-01

    Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks.

  6. Human activity recognition based on Evolving Fuzzy Systems.

    PubMed

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

    2010-10-01

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

  7. 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.

  8. Programmable molecular recognition based on the geometry of DNA nanostructures

    NASA Astrophysics Data System (ADS)

    Woo, Sungwook; Rothemund, Paul W. K.

    2011-08-01

    From ligand-receptor binding to DNA hybridization, molecular recognition plays a central role in biology. Over the past several decades, chemists have successfully reproduced the exquisite specificity of biomolecular interactions. However, engineering multiple specific interactions in synthetic systems remains difficult. DNA retains its position as the best medium with which to create orthogonal, isoenergetic interactions, based on the complementarity of Watson-Crick binding. Here we show that DNA can be used to create diverse bonds using an entirely different principle: the geometric arrangement of blunt-end stacking interactions. We show that both binary codes and shape complementarity can serve as a basis for such stacking bonds, and explore their specificity, thermodynamics and binding rules. Orthogonal stacking bonds were used to connect five distinct DNA origami. This work, which demonstrates how a single attractive interaction can be developed to create diverse bonds, may guide strategies for molecular recognition in systems beyond DNA nanostructures.

  9. 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

  10. Fingerprint recognition using model-based density map.

    PubMed

    Wan, Dingrui; Zhou, Jie

    2006-06-01

    Utilizing more information other than minutiae is much helpful for large-scale fingerprint recognition applications. In this paper, we proposed a polynomial model to approximate the density map of fingerprints and used the model's parameters as a novel kind of feature for fingerprint representation. Thus, the density information can be utilized into the matching stage with a low additional storage cost. A decision-level fusion scheme is further used to combine the density map matching with conventional minutiae-based matching and experimental results showed a much better performance than using single minutiae-based matching.

  11. Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation

    PubMed Central

    Liu, Qian; Pineda-García, Garibaldi; Stromatias, Evangelos; Serrano-Gotarredona, Teresa; Furber, Steve B.

    2016-01-01

    Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarksand that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware

  12. Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation.

    PubMed

    Liu, Qian; Pineda-García, Garibaldi; Stromatias, Evangelos; Serrano-Gotarredona, Teresa; Furber, Steve B

    2016-01-01

    Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarksand that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware

  13. 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…

  14. 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.

  15. 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

  16. Web-based environmental health education: fish facts.

    PubMed

    Anderko, Laura; Otter, Ada; Chalupka, Stephanie; Anderko, Chris; Fahey, Carrie

    2013-03-01

    Nurses and other health professionals are often asked about the benefits and risks of fish consumption. The combination of conflicting media messages about these risks and benefits and limited knowledge has led to confusion about how to properly advise people about safe fish consumption. "Fish Facts for Health Professionals" was the result of a collaborative effort of environmental, public health, medical, nursing, and media experts to create a web-based educational series to address the need for reliable information on fish consumption. Using interviews and real case studies, the 3- to 5-minute media modules provided a strong visual element while remaining conversational. The modules were viewed worldwide, and 121 participants successfully completed the requirements for professional continuing education credit.

  17. 3D face recognition by projection-based methods

    NASA Astrophysics Data System (ADS)

    Dutagaci, Helin; Sankur, Bülent; Yemez, Yücel

    2006-02-01

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

  18. 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.

  19. A robust HOG-based descriptor for pattern recognition

    NASA Astrophysics Data System (ADS)

    Diaz-Escobar, Julia; Kober, Vitaly

    2016-09-01

    The Histogram of Oriented Gradients (HOG) is a popular feature descriptor used in computer vision and image processing. The technique counts occurrences of gradient orientation in localized portions of an image. The descriptor is sensible to the presence in images of noise, nonuniform illumination, and low contrast. In this work, we propose a robust HOG-based descriptor using the local energy model and phase congruency approach. Computer simulation results are presented for recognition of objects in images affected by additive noise, nonuniform illumination, and geometric distortions using the proposed and conventional HOG descriptors.

  20. Implementation of pattern recognition algorithm based on RBF neural network

    NASA Astrophysics Data System (ADS)

    Bouchoux, Sophie; Brost, Vincent; Yang, Fan; Grapin, Jean Claude; Paindavoine, Michel

    2002-12-01

    In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240x320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.

  1. Structural bases for substrate and inhibitor recognition by matrix metalloproteinases.

    PubMed

    Aureli, Loretta; Gioia, Magda; Cerbara, Ilaria; Monaco, Susanna; Fasciglione, Giovanni Francesco; Marini, Stefano; Ascenzi, Paolo; Topai, Alessandra; Coletta, Massimo

    2008-01-01

    Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases which are involved in the proteolytic processing of several components of the extracellular matrix. As a consequence, MMPs are implicated in several physiological and pathological processes, like skeletal growth and remodelling, wound healing, cancer, arthritis, and multiple sclerosis, raising a very widespread interest toward this class of enzymes as potential therapeutic targets. Here, structure-function relationships are discussed to highlight the role of different MMP domains on substrate/inhibitor recognition and processing and to attempt the formulation of advanced guidelines, based on natural substrates, for the design of inhibitors more efficient in vivo.

  2. 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.

  3. 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

  4. 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.

  5. Beacon recognition and tracking based on omni-vision

    NASA Astrophysics Data System (ADS)

    Zhang, Baofeng; Liu, Yuli; Cao, Zuoliang

    2008-12-01

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

  6. Biometric recognition based on free-text keystroke dynamics.

    PubMed

    Ahmed, Ahmed A; Traore, Issa

    2014-04-01

    Accurate recognition of free text keystroke dynamics is challenging due to the unstructured and sparse nature of the data and its underlying variability. As a result, most of the approaches published in the literature on free text recognition, except for one recent one, have reported extremely high error rates. In this paper, we present a new approach for the free text analysis of keystrokes that combines monograph and digraph analysis, and uses a neural network to predict missing digraphs based on the relation between the monitored keystrokes. Our proposed approach achieves an accuracy level comparable to the best results obtained through related techniques in the literature, while achieving a far lower processing time. Experimental evaluation involving 53 users in a heterogeneous environment yields a false acceptance ratio (FAR) of 0.0152% and a false rejection ratio (FRR) of 4.82%, at an equal error rate (EER) of 2.46%. Our follow-up experiment, in a homogeneous environment with 17 users, yields FAR=0% and FRR=5.01%, at EER=2.13%.

  7. Evaluation of Anomaly Detection Method Based on Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Fontugne, Romain; Himura, Yosuke; Fukuda, Kensuke

    The number of threats on the Internet is rapidly increasing, and anomaly detection has become of increasing importance. High-speed backbone traffic is particularly degraded, but their analysis is a complicated task due to the amount of data, the lack of payload data, the asymmetric routing and the use of sampling techniques. Most anomaly detection schemes focus on the statistical properties of network traffic and highlight anomalous traffic through their singularities. In this paper, we concentrate on unusual traffic distributions, which are easily identifiable in temporal-spatial space (e.g., time/address or port). We present an anomaly detection method that uses a pattern recognition technique to identify anomalies in pictures representing traffic. The main advantage of this method is its ability to detect attacks involving mice flows. We evaluate the parameter set and the effectiveness of this approach by analyzing six years of Internet traffic collected from a trans-Pacific link. We show several examples of detected anomalies and compare our results with those of two other methods. The comparison indicates that the only anomalies detected by the pattern-recognition-based method are mainly malicious traffic with a few packets.

  8. Arabic sign language recognition based on HOG descriptor

    NASA Astrophysics Data System (ADS)

    Ben Jmaa, Ahmed; Mahdi, Walid; Ben Jemaa, Yousra; Ben Hamadou, Abdelmajid

    2017-02-01

    We present in this paper a new approach for Arabic sign language (ArSL) alphabet recognition using hand gesture analysis. This analysis consists in extracting a histogram of oriented gradient (HOG) features from a hand image and then using them to generate an SVM Models. Which will be used to recognize the ArSL alphabet in real-time from hand gesture using a Microsoft Kinect camera. Our approach involves three steps: (i) Hand detection and localization using a Microsoft Kinect camera, (ii) hand segmentation and (iii) feature extraction using Arabic alphabet recognition. One each input image first obtained by using a depth sensor, we apply our method based on hand anatomy to segment hand and eliminate all the errors pixels. This approach is invariant to scale, to rotation and to translation of the hand. Some experimental results show the effectiveness of our new approach. Experiment revealed that the proposed ArSL system is able to recognize the ArSL with an accuracy of 90.12%.

  9. Specificity of Dimension-Based Statistical Learning in Word Recognition

    PubMed Central

    Idemaru, Kaori; Holt, Lori L.

    2014-01-01

    Speech perception flexibly adapts to short-term regularities of ambient speech input. Recent research demonstrates that the function of an acoustic dimension for speech categorization at a given time is relative to its relationship to the evolving distribution of dimensional regularity across time, and not simply to a fixed value along the dimension. Two experiments examine the nature of this dimension-based statistical learning in online word recognition, testing generalization of learning across phonetic categories. While engaged in a word recognition task guided by perceptually unambiguous voice-onset time (VOT) acoustics signaling stop voicing in either bilabial rhymes, beer and pier, or alveolar rhymes, deer and tear, listeners were exposed incidentally to an artificial “accent” deviating from English norms in its correlation of the pitch onset of the following vowel (F0) with VOT (Experiment 1). Exposure to the change in the correlation of F0 with VOT led listeners to down-weight reliance on F0 in voicing categorization, indicating dimension-based statistical learning. This learning was observed only for the “accented” contrast varying in its F0/VOT relationship during exposure; learning did not generalize to the other place of articulation. Another group of listeners experienced competing F0/VOT correlations across place of articulation such that the global correlation for voicing was stable, but locally correlations across voicing pairs were opposing (e.g., “accented” beer and pier, “canonical” deer and tear, Experiment 2). Listeners showed dimension-based learning only for the accented pair, not the canonical pair, indicating that they are able to track separate acoustic statistics across place of articulation, that is, for /b-p/ and /d-t/. This suggests that dimension-based learning does not operate obligatorily at the phonological level of stop voicing. PMID:24364708

  10. Towards a FISH-based karyotype of Rosa L. (Rosaceae)

    PubMed Central

    Kirov, Ilya V.; Van Laere, Katrijn; Van Roy, Nadine; Khrustaleva, Ludmila I.

    2016-01-01

    Abstract The genus Rosa Linnaeus, 1753 has important economic value in ornamental sector and many breeding activities are going on supported by molecular studies. However, the cytogenetic studies of rose species are scarce and mainly focused on chromosome counting and chromosome morphology-based karyotyping. Due to the small size of the chromosomes and a high frequency of polyploidy in the genus, karyotyping is very challenging for rose species and requires FISH-based cytogenetic markers to be applied. Therefore, in this work the aim is to establish a FISH-based karyotype for Rosa wichurana (Crépin, 1888), a rose species with several benefits for advanced molecular cytogenetic studies of genus Rosa (Kirov et al. 2015a). It is shown that FISH signals from 5S, 45S and an Arabidopsis-type telomeric repeat are distributed on five (1, 2, 4, 5 and 7) of seven chromosome pairs. In addition, it is demonstrated that the interstitial telomeric repeat sequences (ITR) are located in the centromeric regions of four chromosome pairs. Using low hybridization stringency for ITR visualization, we showed that the number of ITR signals increases four times (1–4 signals). This study is the first to propose a FISH-based Rosa wichurana karyotype for the reliable identification of chromosomes. The possible origin of Rosa wichurana ITR loci is discussed. PMID:28123677

  11. 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...

  12. 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.…

  13. 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.

  14. Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model.

    PubMed

    Uddin, Md; Lee, J J; Kim, T S

    2008-01-01

    In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.

  15. 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.

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

    PubMed

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

    2015-07-15

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

  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. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  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. Familiarity effects in EEG-based emotion recognition.

    PubMed

    Thammasan, Nattapong; Moriyama, Koichi; Fukui, Ken-Ichi; Numao, Masayuki

    2017-03-01

    Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity studies, we allowed subjects to select the same number of familiar and unfamiliar songs; both resulting datasets demonstrated the importance of reporting self-emotion based on the assumption that the emotional state when experiencing music is subjective. We found evidence that music familiarity influences both the power spectra of brainwaves and the brain functional connectivity to a certain level. We conducted an additional experiment using music familiarity in an attempt to recognize emotional states; our empirical results suggested that the use of only songs with low familiarity levels can enhance the performance of EEG-based emotion classification systems that adopt fractal dimension or power spectral density features and support vector machine, multilayer perceptron or C4.5 classifier. This suggests that unfamiliar songs are most appropriate for the construction of an emotion recognition system.

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

    PubMed

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

    2014-01-01

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

  7. 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…

  8. Histogram of Oriented Gradient Based Gist Feature for Building Recognition.

    PubMed

    Li, Bin; Cheng, Kaili; Yu, Zhezhou

    2016-01-01

    We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.

  9. 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.

  10. 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).

  11. Histogram of Oriented Gradient Based Gist Feature for Building Recognition

    PubMed Central

    Cheng, Kaili; Yu, Zhezhou

    2016-01-01

    We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist. PMID:27872639

  12. Apple physalospora recognition by using Gabor feature-based PCA

    NASA Astrophysics Data System (ADS)

    Qin, Xiang; Cai, Cheng; Song, Wei; Hao, Huan; Meng, Yu; Zhu, Junping

    2009-07-01

    In this paper, a novel apple Physalospora recognition approach based on the Gabor feature-based principal component analysis (GBPCA) is proposed. In this method, the principal component analysis (PCA) is a powerful technique for finding patterns in data of high dimensionality and can reduce the high dimensionality of the data space to the low dimensionality of feature space effectively. Gabor filter is an effective tool because of its accurate time-frequency localization and robustness against variations caused by illumination and rotation. Three main steps are taken in the proposed GBPCA: Firstly, Gabor features of different scales and orientations are extracted by convoluting the Gabor filter bank and the original gray images. Then eigenvectors in the direction of the largest variance of the training vectors is computed by PCA. An eigenspace is composed of these eigenvectors. Thirdly, we project the testing images into the constructed eigenspace and the Euclidean distance and nearest neighbor classifier are adopted for classification. Therefore, the proposed method is not only insensitive to illumination and rotation, but also efficient in feature matching. Experimental results demonstrate the effectiveness of the proposed GBPCA.

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

    NASA Astrophysics Data System (ADS)

    Ringeliene, Z.; Lipeika, A.

    2010-09-01

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

  14. 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

  15. Induction of fish biomarkers by synthetic-based drilling muds.

    PubMed

    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.

  16. Iris recognition based on key image feature extraction.

    PubMed

    Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

    2008-01-01

    In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

  17. Color character recognition method based on human perception

    NASA Astrophysics Data System (ADS)

    Yamaba, Kazuo; Miyake, Yoichi

    1993-01-01

    Color is one of the most powerful and important types of visual information in various fields such as image processing, electronic imaging, and robot vision technologies. A new color character recognition system, composed of a camera, optical filters, an image board, a neuro board, and a micro computer was constructed. Using typewriter characters and backgrounds in five colors, two kinds of experiments were performed. The first consisted of preliminary experiments testing the effectiveness of the modified opponent-color theory of the human eye for use in machine character recognition. The second was an experiment in color character recognition, in which the system recognized both characters and their colors.

  18. Continuous speech recognition based on convolutional neural network

    NASA Astrophysics Data System (ADS)

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

    2015-07-01

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

  19. Practical algorithms for algebraic and logical correction in precedent-based recognition problems

    NASA Astrophysics Data System (ADS)

    Ablameyko, S. V.; Biryukov, A. S.; Dokukin, A. A.; D'yakonov, A. G.; Zhuravlev, Yu. I.; Krasnoproshin, V. V.; Obraztsov, V. A.; Romanov, M. Yu.; Ryazanov, V. V.

    2014-12-01

    Practical precedent-based recognition algorithms relying on logical or algebraic correction of various heuristic recognition algorithms are described. The recognition problem is solved in two stages. First, an arbitrary object is recognized independently by algorithms from a group. Then a final collective solution is produced by a suitable corrector. The general concepts of the algebraic approach are presented, practical algorithms for logical and algebraic correction are described, and results of their comparison are given.

  20. The research on high speed underwater target recognition based on fuzzy logic inference

    NASA Astrophysics Data System (ADS)

    Jiang, Xiang-Dong; Yang, De-Sen; Shi, Sheng-Guo; Li, Si-Chun

    2006-06-01

    The underwater target recognition is a key technology in acoustic confrontation and underwater defence. In this article, a recognition system based of fuzzy logic inference (FLI) is set up. This system is mainly composed of three parts: the fuzzy input module, the fuzzy logic inference module with a set of inference rules and the de-fuzzy output module. The inference result shows the recognition system is effective in most conditions.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. Wavelet-Based Signal and Image Processing for Target Recognition

    DTIC Science & Technology

    2002-01-01

    in target recognition applications. Classical spatial and frequency domain image processing algorithms were generalized to process discrete wavelet ... transform (DWT) data. Results include adaptation of classical filtering, smoothing and interpolation techniques to DWT. From 2003 the research

  6. Finger vein recognition based on finger crease location

    NASA Astrophysics Data System (ADS)

    Lu, Zhiying; Ding, Shumeng; Yin, Jing

    2016-07-01

    Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region. Then we use linear fitting to overcome the problem of finger rotation in the plane. The method of modular adaptive histogram equalization (MAHE) is presented to enhance image contrast and reduce computational cost. To extract the finger vein features, we use a fusion method, which can obtain clear and distinguishable vein patterns under different conditions. We used the Hausdorff average distance algorithm to examine the recognition performance of the system. The experimental results demonstrate that MAHE can better balance the recognition accuracy and the expenditure of time compared with three other methods. Our resulting equal error rate throughout the total procedure was 3.268% in a database of 153 finger vein images.

  7. 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

  8. Developing crossmodal expression recognition based on a deep neural model

    PubMed Central

    Barros, Pablo; Wermter, Stefan

    2016-01-01

    A robot capable of understanding emotion expressions can increase its own capability of solving problems by using emotion expressions as part of its own decision-making, in a similar way to humans. Evidence shows that the perception of human interaction starts with an innate perception mechanism, where the interaction between different entities is perceived and categorized into two very clear directions: positive or negative. While the person is developing during childhood, the perception evolves and is shaped based on the observation of human interaction, creating the capability to learn different categories of expressions. In the context of human–robot interaction, we propose a model that simulates the innate perception of audio–visual emotion expressions with deep neural networks, that learns new expressions by categorizing them into emotional clusters with a self-organizing layer. The proposed model is evaluated with three different corpora: The Surrey Audio–Visual Expressed Emotion (SAVEE) database, the visual Bi-modal Face and Body benchmark (FABO) database, and the multimodal corpus of the Emotion Recognition in the Wild (EmotiW) challenge. We use these corpora to evaluate the performance of the model to recognize emotional expressions, and compare it to state-of-the-art research. PMID:27853349

  9. Biomolecular recognition and detection using gold-based nanoprobes

    NASA Astrophysics Data System (ADS)

    Crew, Elizabeth

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

  10. 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.

  11. 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.

  12. 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.

  13. Clustering-based pattern recognition applied to chemical recognition using SAW array signals

    SciTech Connect

    Osbourn, G.C.; Bartholomew, J.W.; Frye, G.C.; Ricco, A.J.

    1994-05-01

    We present a new patter recognition (PR) technique for chemical identification using arrays of microsensors. The technique relies on a new empirical approach to k-dimensional cluster analysis which incorporates measured human visual perceptions of difficult 2- dimensional clusters. The method can handle nonlinear SAW array data, detects both unexpected (outlier) and unreliable array responses, and has no user-adjustable parameters. We use this technique to guide the development of arrays of thin-film-coated SAW (Surface Acoustic Wave) devices that produce optimal PR performance for distinguishing a variety of volatile organic compounds, organophosphonates and water.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  15. Top-down facilitation of visual object recognition: object-based and context-based contributions.

    PubMed

    Fenske, Mark J; Aminoff, Elissa; Gronau, Nurit; Bar, Moshe

    2006-01-01

    The neural mechanisms subserving visual recognition are traditionally described in terms of bottom-up analysis, whereby increasingly complex aspects of the visual input are processed along a hierarchical progression of cortical regions. However, the importance of top-down facilitation in successful recognition has been emphasized in recent models and research findings. Here we consider evidence for top-down facilitation of recognition that is triggered by early information about an object, as well as by contextual associations between an object and other objects with which it typically appears. The object-based mechanism is proposed to trigger top-down facilitation of visual recognition rapidly, using a partially analyzed version of the input image (i.e., a blurred image) that is projected from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC information that is back-projected as "initial guesses" to the temporal cortex where it presensitizes the most likely interpretations of the input object. In addition to this object-based facilitation, a context-based mechanism is proposed to trigger top-down facilitation through contextual associations between objects in scenes. These contextual associations activate predictive information about which objects are likely to appear together, and can influence the "initial guesses" about an object's identity. We have shown that contextual associations are analyzed by a network that includes the parahippocampal cortex and the retrosplenial complex. The integrated proposal described here is that object- and context-based top-down influences operate together, promoting efficient recognition by framing early information about an object within the constraints provided by a lifetime of experience with contextual associations.

  16. 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...

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

    Code of Federal Regulations, 2010 CFR

    2010-07-01

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

  18. 38 CFR 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...

  19. 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...

  20. 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...

  1. 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…

  2. 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...

  3. 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...

  4. A Motion-Based Feature for Event-Based Pattern Recognition

    PubMed Central

    Clady, Xavier; Maro, Jean-Matthieu; Barré, Sébastien; Benosman, Ryad B.

    2017-01-01

    This paper introduces an event-based luminance-free feature from the output of asynchronous event-based neuromorphic retinas. The feature consists in mapping the distribution of the optical flow along the contours of the moving objects in the visual scene into a matrix. Asynchronous event-based neuromorphic retinas are composed of autonomous pixels, each of them asynchronously generating “spiking” events that encode relative changes in pixels' illumination at high temporal resolutions. The optical flow is computed at each event, and is integrated locally or globally in a speed and direction coordinate frame based grid, using speed-tuned temporal kernels. The latter ensures that the resulting feature equitably represents the distribution of the normal motion along the current moving edges, whatever their respective dynamics. The usefulness and the generality of the proposed feature are demonstrated in pattern recognition applications: local corner detection and global gesture recognition. PMID:28101001

  5. A Motion-Based Feature for Event-Based Pattern Recognition.

    PubMed

    Clady, Xavier; Maro, Jean-Matthieu; Barré, Sébastien; Benosman, Ryad B

    2016-01-01

    This paper introduces an event-based luminance-free feature from the output of asynchronous event-based neuromorphic retinas. The feature consists in mapping the distribution of the optical flow along the contours of the moving objects in the visual scene into a matrix. Asynchronous event-based neuromorphic retinas are composed of autonomous pixels, each of them asynchronously generating "spiking" events that encode relative changes in pixels' illumination at high temporal resolutions. The optical flow is computed at each event, and is integrated locally or globally in a speed and direction coordinate frame based grid, using speed-tuned temporal kernels. The latter ensures that the resulting feature equitably represents the distribution of the normal motion along the current moving edges, whatever their respective dynamics. The usefulness and the generality of the proposed feature are demonstrated in pattern recognition applications: local corner detection and global gesture recognition.

  6. Tunable interactions of polyoxometalate-based brushlike hybrids in solvents of variable quality: from self-recognition to supramolecular recognition.

    PubMed

    Zhang, Qian; Liao, Yin; Bu, Weifeng

    2013-08-27

    The controllable interactions of a spherical polymer brush modeled by a poly(styrene-b-4-vinylpyridinium methyl iodide)-polyoxometalate composite micelle, SVP-6, with a polyoxometalate-based supramolecular star polymer, PSP-4, in solvents of variable quality allow us to tune their self-assembly behaviors from self-recognition to supramolecular recognition. In the former case, isolated, contractive spheres together with a few vesicles formed by PSP-4 coexist with multimicelle aggregates formed by SVP-6, whereas SVP-6 is hosted inside the vesicle of PSP-4 in the latter case. This work represents an important step toward the development and understanding of programmable self-assembly of brushlike polymers into complex materials.

  7. 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

  8. Transfer network learning based remote sensing target recognition

    NASA Astrophysics Data System (ADS)

    Gou, Shuiping; Wang, Yuqin; Jiao, Licheng

    2009-10-01

    The target recognition accuracy of remote sensing images is not satisfied. The labels of images acquisition and recollecting are difficult and expensive. In order to solve the problem, we introduce transfer learning into Network Boosting algorithm (NB) and propose Transfer Network Learning algorithm (TNL), in which other out-date data are reused to instruct the remote sensing target recognition. TNL is suitable to improve the performance of remote sensing target recognition, in which instances transfer learning is adopted for domain adaptation. The experimental results on the MSTAR SAR data set and remote sensing data set including two-class planes show that the proposed algorithm has better performance and achieves different domains learning.

  9. Conditional random field-based gesture recognition with depth information

    NASA Astrophysics Data System (ADS)

    Chung, Hyunsook; Yang, Hee-Deok

    2013-01-01

    Gesture recognition is useful for human-computer interaction. The difficulty of gesture recognition is that instances of gestures vary both in motion and shape in three-dimensional (3-D) space. We use depth information generated using Microsoft's Kinect in order to detect 3-D human body components and apply a threshold model with a conditional random field in order to recognize meaningful gestures using continuous motion information. Body gesture recognition is achieved through a framework consisting of two steps. First, a human subject is described by a set of features, encoding the angular relationship between body components in 3-D space. Second, a feature vector is recognized using a threshold model with a conditional random field. In order to show the performance of the proposed method, we use a public data set, the Microsoft Research Cambridge-12 Kinect gesture database. The experimental results demonstrate that the proposed method can efficiently and effectively recognize body gestures automatically.

  10. 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.

  11. A new freshwater biodiversity indicator based on fish community assemblages.

    PubMed

    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.

  12. 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

  13. DNA-based identification of fish species implicated in Puffer fish poisoning.

    PubMed

    Murakami, Taro; Masayama, Atsushi; Ki, Masami; Yamano, Tetsuo; Simizu, Mituru

    2011-01-01

    A method for identification of fish species using three different mitochondrial DNA regions, 16S rRNA, cytochrome b and cytochrome c gene fragments, was investigated. The combined use of all three regions enabled reliable species identification in not only raw fish, but also dried, seasoned and boiled fish, products. Furthermore, the method was applicable even to vomitus from a patient involved in a puffer fish poisoning incident. However, further improvement is necessary to discriminate between closely related species such as Takifugu rubripes and T. chinensis, because they showed close similarity in the nucleotide sequences in the three gene fragments analyzed in this study.

  14. 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.

  15. Iris recognition with compact zero-crossing-based coding

    NASA Astrophysics Data System (ADS)

    Czajka, Adam; Strzelczyk, Przemek

    2006-10-01

    We propose an iris recognition technique using transformation of the iris image into a binary sequence that represents zero-crossing points of the filtered image by way of Laplacian of Gaussians. Novel iris localization and occlusion detection methods are developed to transform the iris image into the sequence of 1D stripes. The proposed enrollment procedure includes an independent selection of iris stripes among a number of enrollment images to minimize the recognition errors. The eyeball spontaneous rotation is corrected at the verification stage. The methodology was tested with a local database of 180 different irises, revealing the EER (Equal Error Rate) at the level of 0.03%.

  16. 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.

  17. 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.

  18. Adjoint-based optimization of fish swimming gaits

    NASA Astrophysics Data System (ADS)

    Floryan, Daniel; Rowley, Clarence W.; Smits, Alexander J.

    2016-11-01

    We study a simplified model of fish swimming, namely a flat plate periodically pitching about its leading edge. Using gradient-based optimization, we seek periodic gaits that are optimal in regards to a particular objective (e.g. maximal thrust). The two-dimensional immersed boundary projection method is used to investigate the flow states, and its adjoint formulation is used to efficiently calculate the gradient of the objective function needed for optimization. The adjoint method also provides sensitivity information, which may be used to elucidate the physics responsible for optimality. Supported under ONR MURI Grants N00014-14-1-0533, Program Manager Bob Brizzolara.

  19. 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.

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

    PubMed

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

    2012-02-27

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

  1. 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 ...

  2. 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

  3. 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.

  4. Hand vein recognition based on the connection lines of reference point and feature point

    NASA Astrophysics Data System (ADS)

    Yun-peng, Hu; Zhi-yong, Wang; Xiao-ping, Yang; Yu-ming, Xue

    2014-01-01

    According to the essential characters of the image topology, a new hand vein recognition algorithm based on the connection lines of reference point and feature points is proposed. In this method, the intersection points and the endpoints of the vein image are used as feature points. After the intersection points and the endpoints selected as feature points, the reference point for image matching are extracted from these points. The relative distances between the reference point and the feature points and the angles between the adjacent connections of the reference point and feature points are calculated and used as recognition features. Finally these two features are combined for hand vein recognition. This method can effectively overcome the influence on the recognition results caused by image translation and rotation. Experimental results show that the proposed algorithm is able to achieve hand vein recognition reliably and quickly.

  5. A study on target recognition fusion algorithm based on fuzzy theory

    NASA Astrophysics Data System (ADS)

    Han, Feng; Yang, WanHai

    2008-03-01

    In the process of the multi-sensors target recognition fusion, focused on the problem that it is difficult to determine the reliability of each sensor and how the data measured by different sensors are fused, a multi-sensor target recognition fusion method based on fuzzy theory is proposed. The mutual supportability of multiple sensors is obtained from the correlation function. Then by the membership function, the reliability of information provide by each sensor is gained. Finally, the supposed fusion result of multi-sensors target recognition can be produced on the basis of fuzzy integration function. The method is simple computationally and can objectively reflect the reliability of each sensor and interrelationship between these sensors. By applying the method to the target recognition, the simulation experiment shows that it can identify the target accurately and is an effective and feasible multi-sensors target recognition fusion method.

  6. 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.

  7. 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.

  8. Knowledge-based automatic recognition technology of radome from infrared images

    NASA Astrophysics Data System (ADS)

    Wang, Xiao-jian; Ma, Ling; Fang, Xiao; Chen, Lei; Lu, Hong-bin

    2009-07-01

    In this paper, a kind of knowledge-based automatic target recognition (ATR) technology of radome from infrared image is studied. The circular imaging of radome is used as the characteristic distinguished from background to realize target recognition. For the characteristic of low contrast of infrared image, brightness transformation is used to preliminarily enhance the contrast of the original image. In the light of the fact that target background outline statistically takes on vertical and horizontal directivity, a kind of revised Sobel operator with direction of 45°and 135°is adopted to detect edge feature so that background noise is effectively suppressed. To reduce the error ratio of target recognition from single frame image, the method to inspect the relativity of target recognition results of successive frames is adopted. The performance of the algorithm is tested using actually taken infrared radome images, and the right recognition ratio is around 90%, which turns out that this technology is feasible.

  9. Molecular recognition-based detoxification of aluminum in human plasma.

    PubMed

    Demircelik, Ahmet H; Andac, Muge; Andac, Cenk A; Say, Ridvan; Denizli, Adil

    2009-01-01

    Molecular recognition-based Al(3+)-imprinted poly(hydroxyethyl methacrylate-N-methacryloyl-L-glutamic acid) (PHEMAGA-Al(3+)) beads were prepared to be used in selective removal of Al(3+) out of human plasma overdosed with Al(3+) cations. The PHEMAGA-Al(3+) beads were synthesized by suspension polymerization in the presence of a template-monomer complex (MAGA-Al(3+)). The specific surface area of PHEMAGA-Al(3+) beads was found to be 55.6 m(2)/g on the average. The MAGA content in the PHEMAGA-Al(3+) beads were found to be 640 micgomol/g polymer. The template Al(3+) cations could be reversibly detached from the matrix to form PHEMAGA-Al(3+) using a 50 mM solution of EDTA. The Al(3+)-free PHEMAGA-Al(3+) beads were then exposed to a selective separation procedure of Al(3+) out of human plasma, which was implemented in a continuous system by packing the beads into a separation column (10 cm long with an inner diameter of 0.9 cm) equipped with a water jacket to control the temperature. The Al(3+) adsorption capacity of the PHEMAGA-Al(3+) beads decreased drastically from 0.76 mg/g polymer to 0.22 mg/g polymer as the flow rate was increased from 0.3 ml/min to 1.5 ml/min. The relative selectivity coefficients of the PHEMAGA-Al(3+) beads for Al(3+)/Fe(3+), Al(3+)/Cu(2+) and Al(3+)/Zn(2+) were found to be 4.49, 8.95 and 32.44 times greater than those of the non-imprinted PHEMAGA beads, respectively. FT-IR analyses on the synthesized PHEMAGA-Al(3+) beads reveals monodentate and bidentate binding modes of Al(3+) in complex with the carboxylate groups of the glutamate residues. Density functional theory computations at the B3LYP/6-31G(d,p) basis set suggests that structured water molecules play essential role in the stability of the monodentate binding mode in 1:1 PHEMAGA-Al(3+) complexes. The PHEMAGA-Al(3+) beads were recovered and reused many times, with no significant decrease in their adsorption capacities.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Shape Recognition Using A CMAC Based Learning System

    NASA Astrophysics Data System (ADS)

    Glanz, F. H.; Miller, W. T.

    1988-02-01

    This paper discusses pattern recognition using a learning system which can learn an arbitrary function of the input and which has built-in generalization with the characteristic that similar inputs lead to similar outputs even for untrained inputs. The amount of similarity is controlled by a parameter of the program at compile time. Inputs and/or outputs may be vectors. The system is trained in a way similar to other pattern recognition systems using an LMS rule. Patterns in the input space are not separated by hyperplanes in the way they normally are using adaptive linear elements. As a result, linear separability is not the problem it is when using Perceptron or Adaline type elements. In fact, almost any shape category region is possible, and a region need not be simply connected nor convex. An example is given of geometric shape recognition using as features autoregressive model parameters representing the shape boundaries. These features are approximately independent of translation, rotation, and size of the shape. Results in the form of percent correct on test sets are given for eight different combinations of training and test sets derived from two groups of shapes.

  15. Design and implementation of knowledge-based framework for ground objects recognition in remote sensing images

    NASA Astrophysics Data System (ADS)

    Chen, Shaobin; Ding, Mingyue; Cai, Chao; Fu, Xiaowei; Sun, Yue; Chen, Duo

    2009-10-01

    The advance of image processing makes knowledge-based automatic image interpretation much more realistic than ever. In the domain of remote sensing image processing, the introduction of knowledge enhances the confidence of recognition of typical ground objects. There are mainly two approaches to employ knowledge: the first one is scattering knowledge in concrete program and relevant knowledge of ground objects are fixed by programming; the second is systematically storing knowledge in knowledge base to offer a unified instruction for each object recognition procedure. In this paper, a knowledge-based framework for ground objects recognition in remote sensing image is proposed. This framework takes the second means for using knowledge with a hierarchical architecture. The recognition of typical airport demonstrated the feasibility of the proposed framework.

  16. 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.

  17. Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

    PubMed

    Fu, H C; Xu, Y Y; Chang, H Y

    1999-12-01

    Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

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

    PubMed

    Maxcey, Ashleigh M

    2016-01-01

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

  19. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

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

  1. 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

  2. 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.

  3. 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.

  4. Real-time traffic sign recognition based on a general purpose GPU and deep-learning

    PubMed Central

    Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

    2017-01-01

    We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011

  5. 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.

  6. Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

    PubMed

    Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

    2017-01-01

    We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

  7. 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.

  8. Named Entity Recognition in a Hungarian NL Based QA System

    NASA Astrophysics Data System (ADS)

    Tikkl, Domonkos; Szidarovszky, P. Ferenc; Kardkovacs, Zsolt T.; Magyar, Gábor

    In WoW project our purpose is to create a complex search interface with the following features: search in the deep web content of contracted partners' databases, processing Hungarian natural language (NL) questions and transforming them to SQL queries for database access, image search supported by a visual thesaurus that describes in a structural form the visual content of images (also in Hungarian). This paper primarily focuses on a particular problem of question processing task: the entity recognition. Before going into details we give a short overview of the project's aims.

  9. 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.

  10. Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

    PubMed Central

    Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

    2017-01-01

    The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body. PMID:28300783

  11. Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

    PubMed

    Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

    2017-03-16

    The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

  12. 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.

  13. Attentional sequence-based recognition: Markovian and evidential reasoning.

    PubMed

    Soyer, C; Bozma, H I; Istefanopulos, Y

    2003-01-01

    Biological vision systems explore their environment via allocating their visual resources to only the interesting parts of a scene. This is achieved by a selective attention mechanism that controls eye movements. The data thus generated is a sequence of subimages of different locations and thus a sequence of features extracted from those images - referred to as attentional sequence. In higher level visual processing leading to scene cognition, it is hypothesized that the information contained in attentional sequences are combined and utilized by special mechanisms - although still poorly understood. However, developing models of such mechanisms prove out to be crucial - if we are to understand and mimic this behavior in robotic systems. In this paper, we consider the recognition problem and present two approaches to using attentional sequences for recognition: Markovian and evidential reasoning. Experimental results with our mobile robot APES reveal that simple shapes can be modeled and recognized by these methods - using as few as ten fixations and very simple features. For more complex scenes, longer attentional sequences or more sophisticated features may be required for cognition.

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. Optical correlator based target detection, recognition, classification, and tracking.

    PubMed

    Manzur, Tariq; Zeller, John; Serati, Steve

    2012-07-20

    A dedicated automatic target recognition and tracking optical correlator (OC) system using advanced processing technology has been developed. Rapidly cycling data-cubes with size, shape, and orientation are employed with software algorithms to isolate correlation peaks and enable tracking of targets in maritime environments with future track prediction. The method has been found superior to employing maximum average correlation height filters for which the correlation peak intensity drops off in proportion to the number of training images. The physical dimensions of the OC system may be reduced to as small as 2 in. × 2 in. × 3 in. (51 mm × 51 mm × 76 mm) by modifying and minimizing the OC components.

  20. 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.

  1. 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

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

    PubMed

    Pfeiffer, Lisa; Gratz, Trevor

    2016-03-08

    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.

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

    NASA Astrophysics Data System (ADS)

    Xie, Zhihua

    2014-07-01

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

  4. An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition.

    PubMed

    Shi, Baoguang; Bai, Xiang; Yao, Cong

    2016-12-29

    Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.

  5. Facial expression recognition in the wild based on multimodal texture features

    NASA Astrophysics Data System (ADS)

    Sun, Bo; Li, Liandong; Zhou, Guoyan; He, Jun

    2016-11-01

    Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%.

  6. 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.

  7. 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

  8. The neural correlates of gist-based true and false recognition

    PubMed Central

    Gutchess, Angela H.; Schacter, Daniel L.

    2012-01-01

    When information is thematically related to previously studied information, gist-based processes contribute to false recognition. Using functional MRI, we examined the neural correlates of gist-based recognition as a function of increasing numbers of studied exemplars. Sixteen participants incidentally encoded small, medium, and large sets of pictures, and we compared the neural response at recognition using parametric modulation analyses. For hits, regions in middle occipital, middle temporal, and posterior parietal cortex linearly modulated their activity according to the number of related encoded items. For false alarms, visual, parietal, and hippocampal regions were modulated as a function of the encoded set size. The present results are consistent with prior work in that the neural regions supporting veridical memory also contribute to false memory for related information. The results also reveal that these regions respond to the degree of relatedness among similar items, and implicate perceptual and constructive processes in gist-based false memory. PMID:22155331

  9. 2.5D Multi-View Gait Recognition Based on Point Cloud Registration

    PubMed Central

    Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

    2014-01-01

    This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. PMID:24686727

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-04-19

    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.

  12. 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…

  13. A kernel-based approach for biomedical named entity recognition.

    PubMed

    Patra, Rakesh; Saha, Sujan Kumar

    2013-01-01

    Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). The performance of the SVM classifier largely depends on the appropriateness of the kernel function. In the last few years a number of task-specific kernel functions have been proposed and used in various text processing tasks, for example, string kernel, graph kernel, tree kernel and so on. So far very few efforts have been devoted to the development of NER task specific kernel. In the literature we found that the tree kernel has been used in NER task only for entity boundary detection or reannotation. The conventional tree kernel is unable to execute the complete NER task on its own. In this paper we have proposed a kernel function, motivated by the tree kernel, which is able to perform the complete NER task. To examine the effectiveness of the proposed kernel, we have applied the kernel function on the openly available JNLPBA 2004 data. Our kernel executes the complete NER task and achieves reasonable accuracy.

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

    NASA Astrophysics Data System (ADS)

    Abolmaesumi, Purang; Jahed, M.

    1997-09-01

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

  15. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  17. A machine learning approach for body part recognition based on CT images

    NASA Astrophysics Data System (ADS)

    Nakamura, Keigo; Li, Yuanzhong; Ito, Wataru; Shimura, Kazuo

    2008-03-01

    Body part recognition based on CT slice images is very important for many applications in PACS and CAD systems. In this paper, we propose a novel approach that can recognize which body part a slice image belongs to robustly. We focus on how to effectively express and use the unique statistical information of the correlation between the CT value and the position information of each body part. We apply the machine learning method AdaBoost to express and use this statistical information. Our approach consists of a training process and a recognition process. In the training process, we first define the whole body using a set of specific classes to ensure that training images in the same class have a high similarity, and prepare a training image set (positive samples and negative samples) for each class. Second, the training images are normalized to a fixed size and rotation in each class. Third, features are calculated for each normalized training image. Finally, AdaBoosted histogram classifiers are trained. After the training process, each class has its own classifiers. In the recognition process, given a series of CT images, the scores of all classes for each slice image are calculated based on the classifiers obtained in the training process. Then, based on the scores of each slice and a simple model of body part sequence continuity, we use dynamic programming (DP) to eliminate false recognition results. Experimental results on 440 unknown series including lesions show that our approach has high a recognition rate.

  18. 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

  19. An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations

    PubMed Central

    Wang, Hanyu; Xu, Jiangtao; Gao, Zhiyuan; Lu, Chengye; Yao, Suying; Ma, Jianguo

    2016-01-01

    A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increased owing to the use of both ON and OFF events. AER data acquired by a dynamic vision senses (DVS) are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition. The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation. PMID:27867346

  20. An Event-Based Neurobiological Recognition System with Orientation Detector for Objects in Multiple Orientations.

    PubMed

    Wang, Hanyu; Xu, Jiangtao; Gao, Zhiyuan; Lu, Chengye; Yao, Suying; Ma, Jianguo

    2016-01-01

    A new multiple orientation event-based neurobiological recognition system is proposed by integrating recognition and tracking function in this paper, which is used for asynchronous address-event representation (AER) image sensors. The characteristic of this system has been enriched to recognize the objects in multiple orientations with only training samples moving in a single orientation. The system extracts multi-scale and multi-orientation line features inspired by models of the primate visual cortex. An orientation detector based on modified Gaussian blob tracking algorithm is introduced for object tracking and orientation detection. The orientation detector and feature extraction block work in simultaneous mode, without any increase in categorization time. An addresses lookup table (addresses LUT) is also presented to adjust the feature maps by addresses mapping and reordering, and they are categorized in the trained spiking neural network. This recognition system is evaluated with the MNIST dataset which have played important roles in the development of computer vision, and the accuracy is increased owing to the use of both ON and OFF events. AER data acquired by a dynamic vision senses (DVS) are also tested on the system, such as moving digits, pokers, and vehicles. The experimental results show that the proposed system can realize event-based multi-orientation recognition. The work presented in this paper makes a number of contributions to the event-based vision processing system for multi-orientation object recognition. It develops a new tracking-recognition architecture to feedforward categorization system and an address reorder approach to classify multi-orientation objects using event-based data. It provides a new way to recognize multiple orientation objects with only samples in single orientation.

  1. 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.

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

    PubMed

    Westhof, Eric; Yusupov, Marat; Yusupova, Gulnara

    2014-01-01

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

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

    PubMed

    Ar, Ilktan; Akgul, Yusuf Sinan

    2014-11-01

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

  4. A primitive-based 3D object recognition system

    NASA Technical Reports Server (NTRS)

    Dhawan, Atam P.

    1988-01-01

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

  5. 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...

  6. 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...

  7. 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...

  8. 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.

  9. 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

  10. Handwritten Chinese character recognition based on supervised competitive learning neural network and block-based relative fuzzy feature extraction

    NASA Astrophysics Data System (ADS)

    Sun, Limin; Wu, Shuanhu

    2005-02-01

    Offline handwritten chinese character recognition is still a difficult problem because of its large stroke changes, writing anomaly, and the difficulty for obtaining its stroke ranking information. Generally, offline handwritten chinese character can be divided into two procedures: feature extraction for capturing handwritten chinese character information and feature classifying for character recognition. In this paper, we proposed a new Chinese character recognition algorithm. In feature extraction part, we adopted elastic mesh dividing method for extracting the block features and its relative fuzzy features that utilized the relativities between different strokes and distribution probability of a stroke in its neighbor sub-blocks. In recognition part, we constructed a classifier based on a supervised competitive learning algorithm to train competitive learning neural network with the extracted features set. Experimental results show that the performance of our algorithm is encouraging and can be comparable to other algorithms.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  13. 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.

  14. A Method of Neighbor Classes Based SVM Classification for Optical Printed Chinese Character Recognition

    PubMed Central

    Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

    2013-01-01

    In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR. PMID:23536777

  15. A correlation-based algorithm for recognition and tracking of partially occluded objects

    NASA Astrophysics Data System (ADS)

    Ruchay, Alexey; Kober, Vitaly

    2016-09-01

    In this work, a correlation-based algorithm consisting of a set of adaptive filters for recognition of occluded objects in still and dynamic scenes in the presence of additive noise is proposed. The designed algorithm is adaptive to the input scene, which may contain different fragments of the target, false objects, and background to be rejected. The algorithm output is high correlation peaks corresponding to pieces of the target in scenes. The proposed algorithm uses a bank of composite optimum filters. The performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.

  16. A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

    PubMed

    Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

    2013-01-01

    In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

  17. Cascade fuzzy ART: a new extensible database for model-based object recognition

    NASA Astrophysics Data System (ADS)

    Hung, Hai-Lung; Liao, Hong-Yuan M.; Lin, Shing-Jong; Lin, Wei-Chung; Fan, Kuo-Chin

    1996-02-01

    In this paper, we propose a cascade fuzzy ART (CFART) neural network which can be used as an extensible database in a model-based object recognition system. The proposed CFART networks can accept both binary and continuous inputs. Besides, it preserves the prominent characteristics of a fuzzy ART network and extends the fuzzy ART's capability toward a hierarchical class representation of input patterns. The learning processes of the proposed network are unsupervised and self-organizing, which include coupled top-down searching and bottom-up learning processes. In addition, a global searching tree is built to speed up the learning and recognition processes.

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

    PubMed

    Ding, Shuai; Wang, Liang

    2016-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  20. 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.

  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. The Development of Adaptive Decision Making: Recognition-Based Inference in Children and Adolescents

    ERIC Educational Resources Information Center

    Horn, Sebastian S.; Ruggeri, Azzurra; Pachur, Thorsten

    2016-01-01

    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,…

  3. [Research on finger key-press gesture recognition based on surface electromyographic signals].

    PubMed

    Cheng, Juan; Chen, Xiang; Lu, Zhiyuan; Zhang, Xu; Zhao, Zhangyan

    2011-04-01

    This article reported researches on the pattern recognition of finger key-press gestures based on surface electromyographic (SEMG) signals. All the gestures were defined referring to the PC standard keyboard, and totally 16 sorts of key-press gestures relating to the right hand were defined. The SEMG signals were collected from the forearm of the subjects by 4 sensors. And two kinds of pattern recognition experiments were designed and implemented for exploring the feasibility and repeatability of the key-press gesture recognition based on SEMG signals. The results from 6 subjects showed, by using the same-day templates, that the average classification rates of 16 defined key-press gestures reached above 75.8%. Moreover, when the training samples added up to 5 days, the recognition accuracies approached those obtained with the same-day templates. The experimental results confirm the feasibility and repeatability of SEMG-based key-press gestures classification, which is meaningful for the implementation of myoelectric control-based virtual keyboard interaction.

  4. Base motif recognition and design of DNA templates for fluorescent silver clusters by machine learning.

    PubMed

    Copp, Stacy M; Bogdanov, Petko; Debord, Mark; Singh, Ambuj; Gwinn, Elisabeth

    2014-09-03

    Discriminative base motifs within DNA templates for fluorescent silver clusters are identified using methods that combine large experimental data sets with machine learning tools for pattern recognition. Combining the discovery of certain multibase motifs important for determining fluorescence brightness with a generative algorithm, the probability of selecting DNA templates that stabilize fluorescent silver clusters is increased by a factor of >3.

  5. 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...

  6. 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...

  7. 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...

  8. 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...

  9. 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...

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

    ERIC Educational Resources Information Center

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

    2005-01-01

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

  12. 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...

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

    PubMed

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

    2015-01-01

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

  14. 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…

  15. 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...

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

    ERIC Educational Resources Information Center

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

    2016-01-01

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

  17. 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.

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

    PubMed

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

    2015-07-14

    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.

  19. Pattern recognition based on the correlated intensity fluctuations of thermal light.

    PubMed

    Liu, Yi-Kuo; Wang, Ying; Cao, De-Zhong; Zhang, Su-Heng

    2014-07-01

    Here we present a pattern recognition scheme based on the intensity correlation of thermal light. We prove theoretically that under spatially incoherent illumination the matched filtering technique can be realized in the ghost imaging field. Using the matched filtering technique, it is possible to distinguish an object from a preestablished set of objects through their ghost images, which are extracted by means of intensity correlation measurement. According to the pattern recognition scheme, we present a numerical simulation in which we can easily identify the character inserted into the object arm from a set of two characters through the position of the autocorrelation peak. This pattern recognition scheme opens up the possibility of performing coherent optical processing under spatially incoherent illumination.

  20. Hybrid Feature Extraction-based Approach for Facial Parts Representation and Recognition

    NASA Astrophysics Data System (ADS)

    Rouabhia, C.; Tebbikh, H.

    2008-06-01

    Face recognition is a specialized image processing which has attracted a considerable attention in computer vision. In this article, we develop a new facial recognition system from video sequences images dedicated to person identification whose face is partly occulted. This system is based on a hybrid image feature extraction technique called ACPDL2D (Rouabhia et al. 2007), it combines two-dimensional principal component analysis and two-dimensional linear discriminant analysis with neural network. We performed the feature extraction task on the eyes and the nose images separately then a Multi-Layers Perceptron classifier is used. Compared to the whole face, the results of simulation are in favor of the facial parts in terms of memory capacity and recognition (99.41% for the eyes part, 98.16% for the nose part and 97.25 % for the whole face).

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

    PubMed

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

    2008-10-01

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

  2. 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.

  3. 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.

  4. 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

  5. High-voltage cable insulation online monitoring in coal mine based on pattern recognition

    NASA Astrophysics Data System (ADS)

    Zhao, Yongmei; Li, Junfeng; Wu, Lingjie; Wang, Yanwen

    2017-03-01

    The single-phase grounding fault is the main electrical fault types of the mine power grid. A new cable insulation online monitoring based on pattern recognition is proposed, in case single-phase grounding fault in coal mine. Firstly, using the pattern recognition method, the insulation state of the cable is divided into three types: "good insulation" and "insulation decline symmetrically" and "insulation decline asymmetrically". Then the cables with "insulation decline asymmetrically" can be further analysed and calculated and its insulation parameter value can be determined. The algorithm is simulated and verified. Simulation result shows that: The zero-sequence voltage and each phase voltage and the zero-sequence current of each cable are taken in the coal mine high-voltage system, and the insulation parameter value of each cable can be calculated accurately by using the pattern recognition method.

  6. 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

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

    NASA Astrophysics Data System (ADS)

    Rak, Steven J.; Kolodzy, Paul J.

    1991-08-01

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

  8. 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

  9. 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.

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

    PubMed

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

    2016-01-01

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

  11. Status recognition of isolator based on SmartGuard

    NASA Astrophysics Data System (ADS)

    Wang, Wanguo; Wang, Binhai; Wang, Zhenli; Li, Li; Zhang, Jingjing; Li, Yibin

    2013-07-01

    This paper concerns the method for checking the status of isolators and is applied in the sequence control in smart substation based on SmartGuard--a mobile inspection robot for substations. It can recognize the status of an isolator through analyzing its feature. We could get the homography matrix by using the SIFT feature between the template image and new acquired image, then get the range of isolator, finally recognize the status of isolator by image processing. The experiment of results proved that the method could recognize isolator status effectively. The substation realizes one key sequence control system through this technology based SmartGuard.

  12. 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 order to…

  13. 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)

  14. 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…

  15. Physiologically based modeling of hepatic and gastrointestinal biotransformation in fish

    EPA Science Inventory

    In fish, as in mammals, the liver generally viewed as the principal site of chemical biotransformation. For waterborne exposures, such as those conducted in support of standardized BCF testing, the effects of hepatic metabolism on chemical accumulation can be simulated using rela...

  16. Effects of sampling methodology on fish based IBI metrics

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  17. Fish Acoustics: Physics-Based Modeling and Measurement

    DTIC Science & Technology

    2011-01-01

    physical scattering mechanisms. To demonstrate this point, the target strength of a canonical gas-filled sphere is computed using a standard...high-frequency sound scattering by swimbladdered fish,” Journal of the Acoustical Society of America, Vol. 78, pp. 688-700 (1985). 9. Gauss , R. C

  18. Differential Effects of Stress-induced Cortisol Responses on Recollection and Familiarity-based Recognition Memory

    PubMed Central

    McCullough, Andrew M.; Ritchey, Maureen; Ranganath, Charan; Yonelinas, Andrew

    2015-01-01

    Stress-induced changes in cortisol can impact memory in various ways. However, the precise relationship between cortisol and recognition memory is still poorly understood. For instance, there is reason to believe that stress could differentially affect recollection-based memory, which depends on the hippocampus, and familiarity-based recognition, which can be supported by neocortical areas alone. Accordingly, in the current study we examined the effects of stress-related changes in cortisol on the processes underlying recognition memory. Stress was induced with a cold-pressor test after incidental encoding of emotional and neutral pictures, and recollection and familiarity-based recognition memory were measured one day later. The relationship between stress-induced cortisol responses and recollection was non-monotonic, such that subjects with moderate stress-related increases in cortisol had the highest levels of recollection. In contrast, stress-related cortisol responses were linearly related to increases in familiarity. In addition, measures of cortisol taken at the onset of the experiment showed that individuals with higher levels of pre-learning cortisol had lower levels of both recollection and familiarity. The results are consistent with the proposition that hippocampal-dependent memory processes such as recollection function optimally under moderate levels of stress, whereas more cortically-based processes such as familiarity are enhanced even with higher levels of stress. These results indicate that whether post-encoding stress improves or disrupts recognition memory depends on the specific memory process examined as well as the magnitude of the stress-induced cortisol response. PMID:25930175

  19. Differential effects of stress-induced cortisol responses on recollection and familiarity-based recognition memory.

    PubMed

    McCullough, Andrew M; Ritchey, Maureen; Ranganath, Charan; Yonelinas, Andrew

    2015-09-01

    Stress-induced changes in cortisol can impact memory in various ways. However, the precise relationship between cortisol and recognition memory is still poorly understood. For instance, there is reason to believe that stress could differentially affect recollection-based memory, which depends on the hippocampus, and familiarity-based recognition, which can be supported by neocortical areas alone. Accordingly, in the current study we examined the effects of stress-related changes in cortisol on the processes underlying recognition memory. Stress was induced with a cold-pressor test after incidental encoding of emotional and neutral pictures, and recollection and familiarity-based recognition memory were measured one day later. The relationship between stress-induced cortisol responses and recollection was non-monotonic, such that subjects with moderate stress-related increases in cortisol had the highest levels of recollection. In contrast, stress-related cortisol responses were linearly related to increases in familiarity. In addition, measures of cortisol taken at the onset of the experiment showed that individuals with higher levels of pre-learning cortisol had lower levels of both recollection and familiarity. The results are consistent with the proposition that hippocampal-dependent memory processes such as recollection function optimally under moderate levels of stress, whereas more cortically-based processes such as familiarity are enhanced even with higher levels of stress. These results indicate that whether post-encoding stress improves or disrupts recognition memory depends on the specific memory process examined as well as the magnitude of the stress-induced cortisol response.

  20. 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.

  1. Road shape recognition based on scene self-similarity

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

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

    EPA Science Inventory

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

  3. 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...

  4. 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.

  5. How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach.

    PubMed

    Horn, Sebastian S; Pachur, Thorsten; Mata, Rui

    2015-01-01

    The recognition heuristic (RH) is a simple strategy for probabilistic inference according to which recognized objects are judged to score higher on a criterion than unrecognized objects. In this article, a hierarchical Bayesian extension of the multinomial r-model is applied to measure use of the RH on the individual participant level and to re-evaluate differences between younger and older adults' strategy reliance across environments. Further, it is explored how individual r-model parameters relate to alternative measures of the use of recognition and other knowledge, such as adherence rates and indices from signal-detection theory (SDT). Both younger and older adults used the RH substantially more often in an environment with high than low recognition validity, reflecting adaptivity in strategy use across environments. In extension of previous analyses (based on adherence rates), hierarchical modeling revealed that in an environment with low recognition validity, (a) older adults had a stronger tendency than younger adults to rely on the RH and (b) variability in RH use between individuals was larger than in an environment with high recognition validity; variability did not differ between age groups. Further, the r-model parameters correlated moderately with an SDT measure expressing how well people can discriminate cases where the RH leads to a correct vs. incorrect inference; this suggests that the r-model and the SDT measures may offer complementary insights into the use of recognition in decision making. In conclusion, younger and older adults are largely adaptive in their application of the RH, but cognitive aging may be associated with an increased tendency to rely on this strategy.

  6. 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.

  7. 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

  8. An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine.

    PubMed

    Huang, Zhiyong; Yu, Yuanlong; Gu, Jason; Liu, Huaping

    2017-04-01

    This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.

  9. 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.

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

    PubMed

    McCullough, Andrew M; Yonelinas, Andrew P

    2013-11-01

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

  11. 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.

  12. 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

  13. 2D Log-Gabor Wavelet Based Action Recognition

    NASA Astrophysics Data System (ADS)

    Li, Ning; Xu, De

    The frequency response of log-Gabor function matches well the frequency response of primate visual neurons. In this letter, motion-salient regions are extracted based on the 2D log-Gabor wavelet transform of the spatio-temporal form of actions. A supervised classification technique is then used to classify the actions. The proposed method is robust to the irregular segmentation of actors. Moreover, the 2D log-Gabor wavelet permits more compact representation of actions than the recent neurobiological models using Gabor wavelet.

  14. 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.

  15. Neural dynamics based on the recognition of neural fingerprints

    PubMed Central

    Carrillo-Medina, José Luis; Latorre, Roberto

    2015-01-01

    Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. PMID:25852531

  16. 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.

  17. 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.

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

    NASA Astrophysics Data System (ADS)

    Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

    2007-12-01

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

  19. Sensor-independent approach to recognition: the object-based approach

    NASA Astrophysics Data System (ADS)

    Morrow, Jim C.; Hossain, Sqama

    1994-03-01

    This paper introduces a fundamentally different approach to recognition -- the object-based approach -- which is inherently knowledge-based and sensor independent. The paper begins with a description of an object-based recognition system, contrasting it with the image-based approach. Next, the multilevel stage of the system, incorporating several sensor data sources is described. From these sources elements of the situation hypothesis are generated as directed by the recognition goal. Depending on the degree of correspondence between the sensor-fed elements and the object-model-fed elements, a hypothetical element is created. The hypothetical element is further employed to develop evidence for the sensor-fed element through the inclusion of secondary sensor outputs. The sensor-fed element is thus modeled in more detail, and further evidence is added to the hypothetical element. Several levels of reasoning and data integration are involved in this overall process; further, a self-adjusting correction mechanism is included through the feedback from the hypothetical element to the sensors, thus defining secondary output connections to the sensor-fed element. Some preliminary work based on this approach has been carried out and initial results show improvements over the conventional image-based approach.

  20. Power independent EMG based gesture recognition for robotics.

    PubMed

    Li, Ling; Looney, David; Park, Cheolsoo; Rehman, Naveed U; Mandic, Danilo P

    2011-01-01

    A novel method for detecting muscle contraction is presented. This method is further developed for identifying four different gestures to facilitate a hand gesture controlled robot system. It is achieved based on surface Electromyograph (EMG) measurements of groups of arm muscles. The cross-information is preserved through a simultaneous processing of EMG channels using a recent multivariate extension of Empirical Mode Decomposition (EMD). Next, phase synchrony measures are employed to make the system robust to different power levels due to electrode placements and impedances. The multiple pairwise muscle synchronies are used as features of a discrete gesture space comprising four gestures (flexion, extension, pronation, supination). Simulations on real-time robot control illustrate the enhanced accuracy and robustness of the proposed methodology.

  1. Selective recognition of americium by peptide-based reagents.

    PubMed

    Özçubukçu, Salih; Mandal, Kalyanaswer; Wegner, Seraphine; Jensen, Mark P; He, Chuan

    2011-09-05

    The separation of lanthanides from minor actinides such as americium and curium is an important step during the recycling process in the treatment of nuclear waste. However, the similar chemistry and ionic size of lanthanide and actinide ions make the separation challenging. Here, we report that a peptide-based reagent can selectively bind trivalent actinides over trivalent lanthanides by means of introducing soft-donor atoms into a peptide known as a lanthanide-binding tag (LBT). Fluorescence spectroscopy has been used to measure the dissociation constant of each metal/peptide complex. A 10-fold selectivity was obtained for Am(3+) over the similarly sized lanthanide cation, Nd(3+), when the asparagine on the fifth position of a LBT was mutated to a cysteine and further functionalized by a pyridine moiety.

  2. 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

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

    PubMed

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

    2012-01-01

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

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

    PubMed

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

    2016-01-20

    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.

  5. Better image texture recognition based on SVM classification

    NASA Astrophysics Data System (ADS)

    Liu, Kuan; Lu, Bin; Wei, Yaxun

    2013-10-01

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

  6. 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

  7. 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.

  8. 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.

  9. Handwritten Chinese character recognition based on SVM with hybrid kernel function

    NASA Astrophysics Data System (ADS)

    Sun, Limin

    2005-10-01

    Offline handwritten chinese character recognition (HCCR) is one of means for quick text input and it has a great demand in the area of file recognition, form processing, machine translation and office automation. However it still is a difficult task for handwritten chinese character recognition to put into practical use because of its large stroke change, writing anomaly, and no stroke ranking information can get, etc. al. An efficient classifier occupies very important position for increasing offline HCCR ratio. Support vector machines offer a theoretically well-founded approach to automated learning of pattern classifiers for mining labeled data sets. But as we know, the performance of SVMs largely depend on the kernel function, different kernel function will produce different SVMs, and may result in different performance. However, there are no theories concerning how to choose good kernel functions based on practical using problem. In this paper we make use of the basic properties of Mercer kernel to construct a hybrid kernel from the existing common kernel, and to find the unknown parameters of the hybrid kernel in data-dependent way by minimizing the upper bound of the VC dimension of the set of functions. Our experiment results show that the proposed method is efficient compared with other classifier for handwritten Chinese character recognition.

  10. Motorcycle Start-stop System based on Intelligent Biometric Voice Recognition

    NASA Astrophysics Data System (ADS)

    Winda, A.; Byan, W. R. E.; Sofyan; Armansyah; Zariantin, D. L.; Josep, B. G.

    2017-03-01

    Current mechanical key in the motorcycle is prone to bulgary, being stolen or misplaced. Intelligent biometric voice recognition as means to replace this mechanism is proposed as an alternative. The proposed system will decide whether the voice is belong to the user or not and the word utter by the user is ‘On’ or ‘Off’. The decision voice will be sent to Arduino in order to start or stop the engine. The recorded voice is processed in order to get some features which later be used as input to the proposed system. The Mel-Frequency Ceptral Coefficient (MFCC) is adopted as a feature extraction technique. The extracted feature is the used as input to the SVM-based identifier. Experimental results confirm the effectiveness of the proposed intelligent voice recognition and word recognition system. It show that the proposed method produces a good training and testing accuracy, 99.31% and 99.43%, respectively. Moreover, the proposed system shows the performance of false rejection rate (FRR) and false acceptance rate (FAR) accuracy of 0.18% and 17.58%, respectively. In the intelligent word recognition shows that the training and testing accuracy are 100% and 96.3%, respectively.

  11. 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

  12. 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.

  13. 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.

  14. Modeling optical pattern recognition algorithms for object tracking based on nonlinear equivalent models and subtraction of frames

    NASA Astrophysics Data System (ADS)

    Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Lazarev, Alexander A.

    2015-12-01

    We have proposed and discussed optical pattern recognition algorithms for object tracking based on nonlinear equivalent models and subtraction of frames. Experimental results of suggested algorithms in Mathcad and LabVIEW are shown. Application of equivalent functions and difference of frames gives good results for recognition and tracking moving objects.

  15. 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

  16. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation.

    PubMed

    Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Geng, Weidong

    2017-02-24

    High-density surface electromyography (HD-sEMG) is to record muscles' electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition.

  17. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

    PubMed Central

    Du, Yu; Jin, Wenguang; Wei, Wentao; Hu, Yu; Geng, Weidong

    2017-01-01

    High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition. PMID:28245586

  18. 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.

  19. 3D face recognition based on the hierarchical score-level fusion classifiers

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  20. 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.

  1. RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition.

    PubMed

    Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

    2017-03-24

    Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today's electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

  2. 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.

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

    DTIC Science & Technology

    1985-01-01

    Game Testing Softwiare Tools 1S *AhCZ XPP .. w o Awex &w awe *a% -This report documents a computer-based gaming sytem for uassesing recog- nition...then, pro- vides a set of software tools which can be used by others who want to assess recognition performance. The software for the complete gaming...this set of software tools for either research, development, or operational implementation will have an easier time comprehending the modularity of

  4. 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.

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

    PubMed Central

    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

  6. 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.

  7. 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).

  8. Case-Based Behavior Recognition in Beyond Visual Range Air Combat

    DTIC Science & Technology

    2015-05-01

    Case-Based Behavior Recognition in Beyond Visual Range Air Combat Hayley Borck 1 , Justin Karneeb 1 , Ron Alford 2 & David W. Aha 3 1Knexus...understanding the behaviors of hostile agents, which is challenging in partially observable environments such as the one we study. In particular, unobserved...hostile behaviors in our domain may alter the world state. To effectively counter hostile behaviors , they need to be recognized and predicted. We

  9. 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

  10. 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.

  11. Ecosystem-based fisheries management requires a change to the selective fishing philosophy.

    PubMed

    Zhou, Shijie; Smith, Anthony D M; Punt, André E; Richardson, Anthony J; Gibbs, Mark; Fulton, Elizabeth A; Pascoe, Sean; Bulman, Catherine; Bayliss, Peter; Sainsbury, Keith

    2010-05-25

    Globally, many fish species are overexploited, and many stocks have collapsed. This crisis, along with increasing concerns over flow-on effects on ecosystems, has caused a reevaluation of traditional fisheries management practices, and a new ecosystem-based fisheries management (EBFM) paradigm has emerged. As part of this approach, selective fishing is widely encouraged in the belief that nonselective fishing has many adverse impacts. In particular, incidental bycatch is seen as wasteful and a negative feature of fishing, and methods to reduce bycatch are implemented in many fisheries. However, recent advances in fishery science and ecology suggest that a selective approach may also result in undesirable impacts both to fisheries and marine ecosystems. Selective fishing applies one or more of the "6-S" selections: species, stock, size, sex, season, and space. However, selective fishing alters biodiversity, which in turn changes ecosystem functioning and may affect fisheries production, hindering rather than helping achieve the goals of EBFM. We argue here that a "balanced exploitation" approach might alleviate many of the ecological effects of fishing by avoiding intensive removal of particular components of the ecosystem, while still supporting sustainable fisheries. This concept may require reducing exploitation rates on certain target species or groups to protect vulnerable components of the ecosystem. Benefits to society could be maintained or even increased because a greater proportion of the entire suite of harvested species is used.

  12. Ecosystem-based fisheries management requires a change to the selective fishing philosophy

    PubMed Central

    Zhou, Shijie; Smith, Anthony D. M.; Punt, André E.; Richardson, Anthony J.; Gibbs, Mark; Fulton, Elizabeth A.; Pascoe, Sean; Bulman, Catherine; Bayliss, Peter; Sainsbury, Keith

    2010-01-01

    Globally, many fish species are overexploited, and many stocks have collapsed. This crisis, along with increasing concerns over flow-on effects on ecosystems, has caused a reevaluation of traditional fisheries management practices, and a new ecosystem-based fisheries management (EBFM) paradigm has emerged. As part of this approach, selective fishing is widely encouraged in the belief that nonselective fishing has many adverse impacts. In particular, incidental bycatch is seen as wasteful and a negative feature of fishing, and methods to reduce bycatch are implemented in many fisheries. However, recent advances in fishery science and ecology suggest that a selective approach may also result in undesirable impacts both to fisheries and marine ecosystems. Selective fishing applies one or more of the “6-S” selections: species, stock, size, sex, season, and space. However, selective fishing alters biodiversity, which in turn changes ecosystem functioning and may affect fisheries production, hindering rather than helping achieve the goals of EBFM. We argue here that a “balanced exploitation” approach might alleviate many of the ecological effects of fishing by avoiding intensive removal of particular components of the ecosystem, while still supporting sustainable fisheries. This concept may require reducing exploitation rates on certain target species or groups to protect vulnerable components of the ecosystem. Benefits to society could be maintained or even increased because a greater proportion of the entire suite of harvested species is used. PMID:20435916

  13. 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.

  14. 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

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

    PubMed Central

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

    2014-01-01

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

  16. Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

    NASA Astrophysics Data System (ADS)

    Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

    2011-01-01

    One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

  17. Dynamic Context-Aware Event Recognition Based on Markov Logic Networks

    PubMed Central

    Liu, Fagui; Deng, Dacheng; Li, Ping

    2017-01-01

    Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data. PMID:28257113

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

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

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

    PubMed

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

    2014-07-10

    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.

  20. Lunar phase-dependent expression of cryptochrome and a photoperiodic mechanism for lunar phase-recognition in a reef fish, goldlined spinefoot.

    PubMed

    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.

  1. 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.

  2. 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.

  3. 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.

  4. Vibrodiagnostics of gearboxes using NBV-based classifier: A pattern recognition approach

    NASA Astrophysics Data System (ADS)

    Dybała, Jacek

    2013-07-01

    Gearbox faults are one of the major factors causing breakdown of industrial machinery and gearbox diagnosing is one of the most important topics in machine condition monitoring. This paper presents a new pattern recognition approach to the condition monitoring of technical objects working under time varying load. The approach shows potential for the fault detection of the high-power planetary gearbox used in bucket wheel excavators. In the presented pattern recognition approach, relations between spectral components of the gearbox vibration signal were investigated in the full range of gearbox operating conditions. A novel Noise-Assisted Feature Subset Evaluation (NAFSE) method addressed for the extraction of diagnostic parameters was introduced. The NAFSE method integrates the feature subset evaluation with the NBV-based classifier and extracts the diagnostic parameter set useful for this classifier. The NBV-based classifier conducted the final recognition of the gearbox condition on the basis of the diagnostic parameters obtained from the NAFSE method. The NBV-based classifier is, in its essence, the condensed 1-NN classifier based on Nearest Boundary Vector algorithm. The elaborated algorithms for determining basic and supplemental boundary vectors together with the original editing procedure of the training set reduction create the original hybrid prototype selection method. The effectiveness of this method has been confirmed in the classification task of the benchmark dataset. In contrast to the traditional hard classifier that assigns only a single-value class label to an investigated pattern, the NBV-based classifier enables the semi-soft classification which offers the possibility of evaluating classification certainty. The offered possibility of evaluating classification certainty has a significant diagnostic meaning. In diagnostic practice it is often not enough merely to recognize the object's condition, but the information about the certainty of the

  5. [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%.

  6. 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%.

  7. Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

    PubMed Central

    Kang, Miao; Ji, Kefeng; Leng, Xiangguang; Xing, Xiangwei; Zou, Huanxin

    2017-01-01

    Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm. PMID:28117689

  8. 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.

  9. 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.

  10. ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering.

    PubMed

    Ren, Xiang; El-Kishky, Ahmed; Wang, Chi; Tao, Fangbo; Voss, Clare R; Ji, Heng; Han, Jiawei

    2015-08-01

    Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres-news, Yelp reviews and tweets-demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method.

  11. 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.

  12. 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

  13. 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.

  14. Infrared face recognition based on intensity of local micropattern-weighted local binary pattern

    NASA Astrophysics Data System (ADS)

    Xie, Zhihua; Liu, Guodong

    2011-07-01

    The traditional local binary pattern (LBP) histogram representation extracts the local micropatterns and assigns the same weight to all local micropatterns. To combine the different contributions of local micropatterns to face recognition, this paper proposes a weighted LBP histogram based on Weber's law. First, inspired by psychological Weber's law, intensity of local micropattern is defined by the ratio between two terms: one is relative intensity differences of a central pixel against its neighbors and the other is intensity of local central pixel. Second, regarding the intensity of local micropattern as its weight, the weighted LBP histogram is constructed with the defined weight. Finally, to make full use of the space location information and lessen the complexity of recognition, the partitioning and locality preserving projection are applied to get final features. The proposed method is tested on our infrared face databases and yields the recognition rate of 99.2% for same-session situation and 96.4% for elapsed-time situation compared to the 97.6 and 92.1% produced by the method based on traditional LBP.

  15. 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.

  16. 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.

  17. Static and Dynamic Features for Improved HMM based Visual Speech Recognition

    NASA Astrophysics Data System (ADS)

    Rajavel, R.; Sathidevi, P. S.

    Visual speech recognition refers to the identification of utterances through the movements of lips, tongue, teeth, and other facial muscles of the speaker without using the acoustic signal. This work shows the relative benefits of both static and dynamic visual speech features for improved visual speech recognition. Two approaches for visual feature extraction have been considered: (1) an image transform based static feature approach in which Discrete Cosine Transform (DCT) is applied to each video frame and 6×6 triangle region coefficients are considered as features. Principal Component Analysis (PCA) is applied over all 60 features corresponding to the video frame to reduce the redundancy; the resultant 21 coefficients are taken as the static visual features. (2) Motion segmentation based dynamic feature approach in which the facial movements are segmented from the video file using motion history images (MHI). DCT is applied to the MHI and triangle region coefficients are taken as the dynamic visual features. Two types of experiments were done one with concatenated features and another with dimension reduced feature by using PCA to identify the utterances. The left-right continuous HMMs are used as visual speech classifier to classify nine MPEG-4 standard viseme consonants. The experimental result shows that the concatenated as well as dimension reduced features improve te visual speech recognition with a high accuracy of 92.45% and 92.15% respectively.

  18. 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.

  19. 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.

  20. Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

    PubMed Central

    García-Garrido, Miguel A.; Ocaña, Manuel; Llorca, David F.; Arroyo, Estefanía; Pozuelo, Jorge; Gavilán, Miguel

    2012-01-01

    This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance. PMID:22438704

  1. Speech recognition method based on genetic vector quantization and BP neural network

    NASA Astrophysics Data System (ADS)

    Gao, Li'ai; Li, Lihua; Zhou, Jian; Zhao, Qiuxia

    2009-07-01

    Vector Quantization is one of popular codebook design methods for speech recognition at present. In the process of codebook design, traditional LBG algorithm owns the advantage of fast convergence, but it is easy to get the local optimal result and be influenced by initial codebook. According to the understanding that Genetic Algorithm has the capability of getting the global optimal result, this paper proposes a hybrid clustering method GA-L based on Genetic Algorithm and LBG algorithm to improve the codebook.. Then using genetic neural networks for speech recognition. consequently search a global optimization codebook of the training vector space. The experiments show that neural network identification method based on genetic algorithm can extricate from its local maximum value and the initial restrictions, it can show superior to the standard genetic algorithm and BP neural network algorithm from various sources, and the genetic BP neural networks has a higher recognition rate and the unique application advantages than the general BP neural network in the same GA-VQ codebook, it can achieve a win-win situation in the time and efficiency.

  2. Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder.

    PubMed

    Kang, Miao; Ji, Kefeng; Leng, Xiangguang; Xing, Xiangwei; Zou, Huanxin

    2017-01-20

    Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.

  3. ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering

    PubMed Central

    Ren, Xiang; El-Kishky, Ahmed; Wang, Chi; Tao, Fangbo; Voss, Clare R.; Ji, Heng; Han, Jiawei

    2015-01-01

    Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres—news, Yelp reviews and tweets—demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method. PMID:26705503

  4. 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.

  5. Tank target recognition used in infrared imaging fuze based on FPGA

    NASA Astrophysics Data System (ADS)

    Chen, Ming; Wang, Ke-yong; Song, Cheng-tian; Jiang, Yi-Ming

    2009-07-01

    Infrared imaging fuze is invulnerable to the electromagnetic interference, and it has the ability to recognize the local image of the target. At present, the infrared imaging fuze technology has become one of the key technologies which perform the target detection and the ignition of the warhead in the complex tactical environment. According to the scanning mechanism of the infrared imaging fuze, based on the analysis of features of the infrared image of tank target, this paper presents a feature extraction method based on knowledge to recognize infrared gray image. The geometric features and gray level features are extracted. The geometric features include the corner features and angular features. The corners of the image are extracted through the SUSAN corner detection principle,the angular feature is extracted by Freeman chain code. The hot-zone gray feature is extracted by the template matching and image binarization principle. In order to realize real-time recognition, this paper uses FPGA technology to achieve recognition circuit. The experiments show that the recognition method has a certain anti-interference ability.

  6. 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

  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. Data Recognition and Filtering Based on Efficient RFID Data Processing Control Schemes

    NASA Astrophysics Data System (ADS)

    Kung, Hsu-Yang; Kuo, Chiung-Wen; Tsai, Ching-Ping

    Radio Frequency Identification (RFID) applications have changed gradually from a single vendor and single application to being integrated into applications for supply chains. The primary function of RFID middleware is to process large amounts of data within a short period. High performance and efficiency are difficult to achieve in a RFID data processing control scheme when the volume of RFID data is large. This work is designed the core functions of RFID middleware and developed data processing control scheme that includes data recognition, data filtering and data searching processes. The control scheme for RFID data recognition is used to identify data with false positives and then to obtain corrected data objects. The data filtering control scheme is used to solve problems associated with RFID expansion under a large amount of work and data. The proposed data searching method is based on the EPC (Electronic Product Code) and uses the Hash to accelerate information filtering efficiency.

  9. Preparation and characterization of novel molecularly imprinted polymers based on thiourea receptors for nitrocompounds recognition.

    PubMed

    Athikomrattanakul, Umporn; Katterle, Martin; Gajovic-Eichelmann, Nenad; Scheller, Frieder W

    2011-04-15

    Molecularly imprinted polymers (MIPs) for the recognition of nitro derivatives are prepared from three different (thio)urea-bearing functional monomers. The binding capability of the polymers is characterized by a batch binding experiment. The imprinting factors and affinity constants (K) of the imprinted polymers exhibit the same tendency as the binding constants (K(a)) of the functional monomers to the target substance in solution. Not only nitrofurantoin is efficiently bound by these MIPs but also a broad spectrum of other nitro compounds is bound with at the intermediate level, addressing that these (thio)urea-based monomers can be utilized to prepare a family of MIPs for various nitro compounds, which can be applied as recognition elements in separation and analytical application.

  10. 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.

  11. [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.

  12. 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.

  13. Contour-based automatic crater recognition using digital elevation models from Chang'E missions

    NASA Astrophysics Data System (ADS)

    Zuo, Wei; Zhang, Zhoubin; Li, Chunlai; Wang, Rongwu; Yu, Linjie; Geng, Liang

    2016-12-01

    In order to provide fundamental information for exploration and related scientific research on the Moon and other planets, we propose a new automatic method to recognize craters on the lunar surface based on contour data extracted from a digital elevation model (DEM). Through DEM and image processing, this method can be used to reconstruct contour surfaces, extract and combine contour lines, set the characteristic parameters of crater morphology, and establish a crater pattern recognition program. The method has been tested and verified with DEM data from Chang'E-1 (CE-1) and Chang'E-2 (CE-2), showing a strong crater recognition ability with high detection rate, high robustness, and good adaptation to recognize various craters with different diameter and morphology. The method has been used to identify craters with high precision and accuracy on the Moon. The results meet requirements for supporting exploration and related scientific research for the Moon and planets.

  14. Fuzzy-based latent-dynamic conditional random fields for continuous gesture recognition

    NASA Astrophysics Data System (ADS)

    Zhang, Shengjun; He, Xiaohai; Teng, Qizhi

    2012-06-01

    We show an original method for automatic hand gesture recognition that makes use of fuzzified latent-dynamic conditional random fields (LDCRF). In this method, fuzzy linguistic variables are used to model the features of hand gestures and then to modify the potential function in LDCRFs. By combining LDCRFs and fuzzy sets, these fuzzy-based LDCRFs (FLDCRF) have the advantages of LDCRFs in sequence labeling along with the advantage of retaining the imprecise character of gestures. The efficiency of the proposed method was tested with unsegmented gesture sequences in three different hand gesture data sets. The experimental results demonstrate that FLDCRFs compare favorably with support vector machines, hidden conditional random fields, and LDCRFs on hand gesture recognition tasks.

  15. Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people.

    PubMed

    Chernbumroong, Saisakul; Cang, Shuang; Yu, Hongnian

    2015-01-01

    Activity recognition of an elderly person can be used to provide information and intelligent services to health care professionals, carers, elderly people, and their families so that the elderly people can remain at homes independently. This study investigates the use and contribution of wrist-worn multisensors for activity recognition. We found that accelerometers are the most important sensors and heart rate data can be used to boost classification of activities with diverse heart rates. We propose a genetic algorithm-based fusion weight selection (GAFW) approach which utilizes GA to find fusion weights. For all possible classifier combinations and fusion methods, the study shows that 98% of times GAFW can achieve equal or higher accuracy than the best classifier within the group.

  16. 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.

  17. 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

  18. 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

  19. RNA-based recognition and targeting: sowing the seeds of specificity.

    PubMed

    Gorski, Stanislaw A; Vogel, Jörg; Doudna, Jennifer A

    2017-04-01

    RNA is involved in the regulation of multiple cellular processes, often by forming sequence-specific base pairs with cellular RNA or DNA targets that must be identified among the large number of nucleic acids in a cell. Several RNA-based regulatory systems in eukaryotes, bacteria and archaea, including microRNAs (miRNAs), small interfering RNAs (siRNAs), CRISPR RNAs (crRNAs) and small RNAs (sRNAs) that are dependent on the RNA chaperone protein Hfq, achieve specificity using similar strategies. Central to their function is the presentation of short 'seed sequences' within a ribonucleoprotein complex to facilitate the search for and recognition of targets.

  20. Recognition of blurred license plate of vehicle based on natural image matting

    NASA Astrophysics Data System (ADS)

    Liang, Fangfang; Liu, Yong; Yao, Gang

    2009-10-01

    Car's license plate identification system based on image processing is one of the key technologies of intelligent transportation system. In most cases, license plate numbers can be accurately recognized by the generic identification systems while the image is clear. However, sometime the license plate are seriously blurred by some dunghill such as mud or water smoke before camera lens, which is hard to be identified. In order to get higher recognition rate of blurred license plate, an approach based on natural image matting is proposed in this paper.

  1. 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

  2. FAST TRACK COMMUNICATION: Modulation of electronic structures of bases through DNA recognition of protein

    NASA Astrophysics Data System (ADS)

    Hagiwara, Yohsuke; Kino, Hiori; Tateno, Masaru

    2010-04-01

    The effects of environmental structures on the electronic states of functional regions in a fully solvated \\mathrm {DNA}{\\bullet }\\mathrm {protein} complex were investigated using combined ab initio quantum mechanics/molecular mechanics calculations. A complex of a transcriptional factor, PU.1, and the target DNA was used for the calculations. The effects of solvent on the energies of molecular orbitals (MOs) of some DNA bases strongly correlate with the magnitude of masking of the DNA bases from the solvent by the protein. In the complex, PU.1 causes a variation in the magnitude among DNA bases by means of directly recognizing the DNA bases through hydrogen bonds and inducing structural changes of the DNA structure from the canonical one. Thus, the strong correlation found in this study is the first evidence showing the close quantitative relationship between recognition modes of DNA bases and the energy levels of the corresponding MOs. Thus, it has been revealed that the electronic state of each base is highly regulated and organized by the DNA recognition of the protein. Other biological macromolecular systems can be expected to also possess similar modulation mechanisms, suggesting that this finding provides a novel basis for the understanding for the regulation functions of biological macromolecular systems.

  3. Nonspecific base recognition mediated by water bridges and hydrophobic stacking in ribonuclease I from Escherichia coli

    PubMed Central

    Rodriguez, Sergio Martinez; Panjikar, Santosh; Van Belle, Karolien; Wyns, Lode; Messens, Joris; Loris, Remy

    2008-01-01

    The crystal structure of Escherichia coli ribonuclease I (EcRNase I) reveals an RNase T2-type fold consisting of a conserved core of six β-strands and three α-helices. The overall architecture of the catalytic residues is very similar to the plant and fungal RNase T2 family members, but the perimeter surrounding the active site is characterized by structural elements specific for E. coli. In the structure of EcRNase I in complex with a substrate-mimicking decadeoxynucleotide d(CGCGATCGCG), we observe a cytosine bound in the B2 base binding site and mixed binding of thymine and guanine in the B1 base binding site. The active site residues His55, His133, and Glu129 interact with the phosphodiester linkage only through a set of water molecules. Residues forming the B2 base recognition site are well conserved among bacterial homologs and may generate limited base specificity. On the other hand, the B1 binding cleft acquires true base aspecificity by combining hydrophobic van der Waals contacts at its sides with a water-mediated hydrogen-bonding network at the bottom. This B1 base recognition site is highly variable among bacterial sequences and the observed interactions are unique to EcRNaseI and a few close relatives. PMID:18305191

  4. [Recognition of water-injected meat based on visible/near-infrared spectrum and sparse representation].

    PubMed

    Hao, Dong-mei; Zhou, Ya-nan; Wang, Yu; Zhang, Song; Yang, Yi-min; Lin, Ling; Li, Gang; Wang, Xiu-li

    2015-01-01

    The present paper proposed a new nondestructive method based on visible/near infrared spectrum (Vis/NIRS) and sparse representation to rapidly and accurately discriminate between raw meat and water-injected meat. Water-injected meat model was built by injecting water into non-destructed meat samples comprising pigskin, fat layer and muscle layer. Vis/NIRS data were collected from raw meat and six scales of water-injected meat with spectrometers. To reduce the redundant information in the spectrum and improve the difference between the samples,. some preprocessing steps were performed for the spectral data, including light modulation and normalization. Effective spectral bands were extracted from the preprocessed spectral data. The meat samples were classified as raw meat and water-injected meat, and further, water-injected meat with different water injection rates. All the training samples were used to compose an atom dictionary, and test samples were represented by the sparsest linear combinations of these atoms via l1-minimization. Projection errors of test samples with respect to each category were calculated. A test sample was classified to the category with the minimum projection error, and leave-one-out cross-validation was conducted. The recognition performance from sparse representation was compared with that from support vector machine (SVM).. Experimental results showed that the overall recognition accuracy of sparse representation for raw meat and water-injected meat was more than 90%, which was higher than that of SVM. For water-injected meat samples with different water injection rates, the recognition accuracy presented a positive correlation with the water injection rate difference. Spare representation-based classifier eliminates the need for the training and feature extraction steps required by conventional pattern recognition models, and is suitable for processing data of high dimensionality and small sample size. Furthermore, it has a low

  5. Zone Based Hybrid Feature Extraction Algorithm for Handwritten Numeral Recognition of South Indian Scripts

    NASA Astrophysics Data System (ADS)

    Rajashekararadhya, S. V.; Ranjan, P. Vanaja

    India is a multi-lingual multi script country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose a zone based hybrid feature extraction algorithm scheme towards the recognition of off-line handwritten numerals of south Indian scripts. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance and Average angle from the character centroid to the pixels present in the zone are computed (two features). Similarly zone centroid is computed (two features). This procedure is repeated sequentially for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the value of that particular zone image value in the feature vector is zero. Finally 4*n such features are extracted. Nearest neighbor classifier is used for subsequent classification and recognition purpose. We obtained 97.55 %, 94 %, 92.5% and 95.2 % recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.

  6. Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

    PubMed Central

    Wei, Leyi; Zou, Quan

    2016-01-01

    Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive. Thus, developing computational prediction methods that can automatically, rapidly, and accurately classify unknown protein sequences into specific fold categories is urgently needed. Computational recognition of protein folds has been a recent research hotspot in bioinformatics and computational biology. Many computational efforts have been made, generating a variety of computational prediction methods. In this review, we conduct a comprehensive survey of recent computational methods, especially machine learning-based methods, for protein fold recognition. This review is anticipated to assist researchers in their pursuit to systematically understand the computational recognition of protein folds. PMID:27999256

  7. Computational and performance aspects of PCA-based face-recognition algorithms.

    PubMed

    Moon, H; Phillips, P J

    2001-01-01

    Algorithms based on principal component analysis (PCA) form the basis of numerous studies in the psychological and algorithmic face-recognition literature. PCA is a statistical technique and its incorporation into a face-recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions, including those not documented in the literature. We experimented with different implementations of each module, and evaluated the different implementations using the September 1996 FERET evaluation protocol (the de facto standard for evaluating face-recognition algorithms). We experimented with (i) changing the illumination normalization procedure; (ii) studying effects on algorithm performance of compressing images with JPEG and wavelet compression algorithms; (iii) varying the number of eigenvectors in the representation; and (iv) changing the similarity measure in the classification process. We performed two experiments. In the first experiment, we obtained performance results on the standard September 1996 FERET large-gallery image sets. In the second experiment, we examined the variability in algorithm performance on different sets of facial images. The study was performed on 100 randomly generated image sets (galleries) of the same size. Our two most significant results are (i) changing the similarity measure produced the greatest change in performance, and (ii) that difference in performance of +/- 10% is needed to distinguish between algorithms.

  8. 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.

  9. 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.

  10. Optical correlation recognition of infrared target based on wavelet multi-scale product

    NASA Astrophysics Data System (ADS)

    Chen, Fang-han; Wang, Wen-sheng

    2011-06-01

    As one of the most successful optical correlation recognizers, hybrid optoelectronic joint transform correlator (HOJTC) has received more and more attraction than the purely electronic way in the field of target detection and recognition. It primarily because that HOJTC has the advantages of optics as well as those of electronics. This kind of combination determines that the performance of HOJTC is closely related to optical configuration of system and digital image processing technology. For the stability of optical part, a lot of efforts concerning image processing methods have been made in recent years for improving the power of recognition of HOJTC. Edge contours play a decisive role in target detection. In order to obtain adequate contour feature of target, the solution of edge extraction based on wavelet multi-scale product is proposed. Normalized maximum and argument of each point could be defined utilizing wavelet coefficient of image. Both of them contain the relation of coefficient product between each scale. Edge points synthesized the information of multi-scale are extracted by searching local maxima along the direction of gradient. The way adopted fully exploited the character of multi-resolution of wavelet. Simulation experiments and optical experiments indicate that the energy of correlation peaks is obviously enhanced after the original image is processed by wavelet multi-scale product, and it successfully realizes detection and recognition of infrared target.

  11. Dynamically reconfigurable multiprocessor system for high-order-bidirectional-associative-memory-based image recognition

    NASA Astrophysics Data System (ADS)

    Wu, Chwan-Hwa; Roland, David A.

    1991-08-01

    In this paper a high-order bidirectional associative memory (HOBAM) based image recognition system and a dynamically reconfigurable multiprocessor system that achieves real- time response are reported. The HOBAM has been utilized to recognize corrupted images of human faces (with hats, glasses, masks, and slight translation and scaling effects). In addition, the HOBAM, incorporated with edge detection techniques, has been used to recognize isolated objects within multiple-object images. Successful recognition rates have been achieved. A dynamically reconfigurable multiprocessor system and parallel software have been developed to achieve real-time response for image recognition. The system consists of Inmos transputers and crossbar switches (IMS C004). The communication links can be dynamically connected by circuit switching. This is the first time and the transputers and crossbar switches are reported to form a low-cost multiprocessor system connected by a switching network. Moreover, the switching network simplifies the design of the communication in parallel software without handling the message routing. Although the HOBAM is a fully connected network, the algorithm minimizes the amount of information that needs to be exchanged between processors using a data compression technique. The detailed design of both hardware and software are discussed in the paper. Significant speedup through parallel processing is accomplished. The architecture of the experimental system is a cost-effective design for an embedded system for neural network applications on computer vision.

  12. 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%.

  13. 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

  14. A simulation-based approach towards automatic target recognition of high resolution space borne radar signatures

    NASA Astrophysics Data System (ADS)

    Anglberger, H.; Kempf, T.

    2016-10-01

    Specific imaging effects that are caused mainly by the range measurement principle of a radar device, its much lower frequency range as compared to the optical spectrum, the slanted imaging geometry and certainly the limited spatial resolution complicates the interpretation of radar signatures decisively. Especially the coherent image formation which causes unwanted speckle noise aggravates the problem of visually recognizing target objects. Fully automatic approaches with acceptable false alarm rates are therefore an even harder challenge. At the Microwaves and Radar Institute of the German Aerospace Center (DLR) the development of methods to implement a robust overall processing workflow for automatic target recognition (ATR) out of high resolution synthetic aperture radar (SAR) image data is under progress. The heart of the general approach is to use time series exploitation for the former detection step and simulation-based signature matching for the subsequent recognition. This paper will show the overall ATR chain as a proof of concept for the special case of airplane recognition on image data from the space borne SAR sensor TerraSAR-X.

  15. An improved finger-vein recognition algorithm based on template matching

    NASA Astrophysics Data System (ADS)

    Liu, Yueyue; Di, Si; Jin, Jian; Huang, Daoping

    2016-10-01

    Finger-vein recognition has became the most popular biometric identify methods. The investigation on the recognition algorithms always is the key point in this field. So far, there are many applicable algorithms have been developed. However, there are still some problems in practice, such as the variance of the finger position which may lead to the image distortion and shifting; during the identification process, some matching parameters determined according to experience may also reduce the adaptability of algorithm. Focus on above mentioned problems, this paper proposes an improved finger-vein recognition algorithm based on template matching. In order to enhance the robustness of the algorithm for the image distortion, the least squares error method is adopted to correct the oblique finger. During the feature extraction, local adaptive threshold method is adopted. As regard as the matching scores, we optimized the translation preferences as well as matching distance between the input images and register images on the basis of Naoto Miura algorithm. Experimental results indicate that the proposed method can improve the robustness effectively under the finger shifting and rotation conditions.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Active destabilization of base pairs by a DNA glycosylase wedge initiates damage recognition.

    PubMed

    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.

  1. Enantiospecific recognition of DNA sequences by a proflavine Tröger base.

    PubMed

    Bailly, C; Laine, W; Demeunynck, M; Lhomme, J

    2000-07-05

    The DNA interaction of a chiral Tröger base derived from proflavine was investigated by DNA melting temperature measurements and complementary biochemical assays. DNase I footprinting experiments demonstrate that the binding of the proflavine-based Tröger base is both enantio- and sequence-specific. The (+)-isomer poorly interacts with DNA in a non-sequence-selective fashion. In sharp contrast, the corresponding (-)-isomer recognizes preferentially certain DNA sequences containing both A. T and G. C base pairs, such as the motifs 5'-GTT. AAC and 5'-ATGA. TCAT. This is the first experimental demonstration that acridine-type Tröger bases can be used for enantiospecific recognition of DNA sequences.

  2. Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

    PubMed

    Shin, Young Hoon; Seo, Jiwon

    2016-10-29

    People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

  3. Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

    PubMed Central

    Shin, Young Hoon; Seo, Jiwon

    2016-01-01

    People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

  4. Knowledge-based goal-driven approach for information extraction and decision making for target recognition

    NASA Astrophysics Data System (ADS)

    Wilson, Roderick D.; Wilson, Anitra C.

    1996-06-01

    This paper presents a novel goal-driven approach for designing a knowledge-based system for information extraction and decision-making for target recognition. The underlying goal-driven model uses a goal frame tree schema for target organization, a hybrid rule-based pattern- directed formalism for target structural encoding, and a goal-driven inferential control strategy. The knowledge-base consists of three basic structures for the organization and control of target information: goals, target parameters, and an object-rulebase. Goal frames represent target recognition tasks as goals and subgoals in the knowledge base. Target parameters represent characteristic attributes of targets that are encoded as information atoms. Information atoms may have one or more assigned values and are used for information extraction. The object-rulebase consists of pattern/action assertional implications that describe the logical relationships existing between target parameter values. A goal realization process formulates symbolic patten expressions whose atomic values map to target parameters contained a priori in a hierarchical database of target state information. Symbolic pattern expression creation is accomplished via the application of a novel goal-driven inference strategy that logically prunes an AND/OR tree constructed object-rulebase. Similarity analysis is performed via pattern matching of query symbolic patterns and a priori instantiated target parameters.

  5. Tensor-based AAM with continuous variation estimation: application to variation-robust face recognition.

    PubMed

    Lee, Hyung-Soo; Kim, Daijin

    2009-06-01

    The Active appearance model (AAM) is a well-known model that can represent a non-rigid object effectively. However, the fitting result is often unsatisfactory when an input image deviates from the training images due to its fixed shape and appearance model. To obtain more robust AAM fitting, we propose a tensor-based AAM that can handle a variety of subjects, poses, expressions, and illuminations in the tensor algebra framework, which consists of an image tensor and a model tensor. The image tensor estimates image variations such as pose, expression, and illumination of the input image using two different variation estimation techniques: discrete and continuous variation estimation. The model tensor generates variation-specific AAM basis vectors from the estimated image variations, which leads to more accurate fitting results. To validate the usefulness of the tensor-based AAM, we performed variation-robust face recognition using the tensor-based AAM fitting results. To do, we propose indirect AAM feature transformation. Experimental results show that tensor-based AAM with continuous variation estimation outperforms that with discrete variation estimation and conventional AAM in terms of the average fitting error and the face recognition rate.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. Recognition of extraversion level based on handwriting and support vector machines.

    PubMed

    Górska, Zuzanna; Janicki, Artur

    2012-06-01

    This study investigated whether it is possible to train a machine to discriminate levels of extraversion based on handwriting variables. Support vector machines (SVMs) were used as a learning algorithm. Handwriting of 883 people (404 men, 479 women) was examined. Extraversion was measured using the Polish version of the NEO-Five Factor Inventory. The handwriting samples were described by 48 variables. The support vector machines were separately trained and tested for each sex, using 10-fold cross-validation. Good recognition accuracy (around .7) was achieved for 10 handwriting variables, different for men and women. The results suggest the existence of a relationship between handwriting elements and extraversion.

  11. 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.

  12. 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.

  13. 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.

  14. A Knowledge-Based System For The Recognition Of Roads On SPOT Satellite Images

    NASA Astrophysics Data System (ADS)

    van Cleynenbreugel, J.; Suetens, Paul; Fierens, F.; Wambacq, Patrick; Oosterlinck, Andre J.

    1989-09-01

    Due to the resolution of current satellite imagery (e.g. SPOT), the extraction of roads and linear networks from satellite data has become a feasible - although labour-intensive - task for a human expert. This interpretation problem relies on structural image recognition as well as on expertise in combining data sources external to the image data (e.g. topography, landcover classification). In this paper different knowledge sources employed by human interpreters are discussed. Ways to implement these sources using current knowledge-based tools are suggested. A practical case study of knowledge integration is described.

  15. Recognition Of Partially Occluded Workpieces By A Knowledge-Based System

    NASA Astrophysics Data System (ADS)

    Serpico, S. B.; Vernazza, G.; Dellepiane, S.; Angela, P.

    1987-01-01

    A knowledge-based system is presented that is oriented toward partially occluded 2-D workpiece recognition in TV camera images. The generalized Hough transform is employed to extract elementary edge patterns. Intrinsic and relational information regarding elementary patterns is computed and then stored inside a net of frames. A similar net of frames is employed for workpiece model representation, for an easy matching with the previous net. A set of production rules provide the heuristics to find hints for locating focus-of-attention regions, while other production rules specify modalities for applying a hypothesis-generation-and-test process. Experimental results on a set of 20 workpieces are reported.

  16. 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.

  17. Molecular Recognition of DNA. Synthesis of Novel Bases for Triple Helix Formation

    DTIC Science & Technology

    1991-01-01

    to the purine strand in the major groove of the Watson - Crick double helical DNA (TAT, C+GC triplets). Purine oligonucleotides bind antiparallel to...R&T Code 4135018 S MAy 05 199411 "Molecular Recognition of DNA . Synthesis of Novel Bases for Triple Helix Formation" Peter B. Dervan cv _California...035 T"IQA""D PART I A) Completed work (1988-91) Triple Helix Formation by Oligonucleotides on DNA Extended to the Physiological pH Range. T. J. Povsic

  18. A RFID authentication protocol based on infinite dimension pseudo random number generator for image recognition system

    NASA Astrophysics Data System (ADS)

    Tong, Qiaoling; Zou, Xuecheng; Tong, Hengqing

    2009-10-01

    Radio Frequency Identification (RFID) technology has been widely used in the image recognition system. However, the feature of the RFID system may bring out security threatens. In this paper, we analyze the existing RFID authentication protocols and state an infinite dimension pseudo random number generator to strengthen the protocol security. Then an authentication protocol based on infinite dimension pseudo random number generator is proposed. Compared to the traditional protocols, our method could resist various attack approaches, and protect the tag information and the location privacy of the tag holder efficiently.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors

    PubMed Central

    Liu, Xinhua; Mei, Huafeng; Lu, Huachang; Kuang, Hailan; Ma, Xiaolin

    2017-01-01

    Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver’s safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle’s angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers. PMID:28335540

  4. 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

  5. A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors

    PubMed Central

    Wu, Minglin; Zhang, Sheng; Dong, Yuhan

    2016-01-01

    In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution) and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden) can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects. PMID:27775625

  6. A Vehicle Steering Recognition System Based on Low-Cost Smartphone Sensors.

    PubMed

    Liu, Xinhua; Mei, Huafeng; Lu, Huachang; Kuang, Hailan; Ma, Xiaolin

    2017-03-20

    Recognizing how a vehicle is steered and then alerting drivers in real time is of utmost importance to the vehicle and driver's safety, since fatal accidents are often caused by dangerous vehicle maneuvers, such as rapid turns, fast lane-changes, etc. Existing solutions using video or in-vehicle sensors have been employed to identify dangerous vehicle maneuvers, but these methods are subject to the effects of the environmental elements or the hardware is very costly. In the mobile computing era, smartphones have become key tools to develop innovative mobile context-aware systems. In this paper, we present a recognition system for dangerous vehicle steering based on the low-cost sensors found in a smartphone: i.e., the gyroscope and the accelerometer. To identify vehicle steering maneuvers, we focus on the vehicle's angular velocity, which is characterized by gyroscope data from a smartphone mounted in the vehicle. Three steering maneuvers including turns, lane-changes and U-turns are defined, and a vehicle angular velocity matching algorithm based on Fast Dynamic Time Warping (FastDTW) is adopted to recognize the vehicle steering. The results of extensive experiments show that the average accuracy rate of the presented recognition reaches 95%, which implies that the proposed smartphone-based method is suitable for recognizing dangerous vehicle steering maneuvers.

  7. 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.

  8. A Novel Model-Based Driving Behavior Recognition System Using Motion Sensors.

    PubMed

    Wu, Minglin; Zhang, Sheng; Dong, Yuhan

    2016-10-20

    In this article, a novel driving behavior recognition system based on a specific physical model and motion sensory data is developed to promote traffic safety. Based on the theory of rigid body kinematics, we build a specific physical model to reveal the data change rule during the vehicle moving process. In this work, we adopt a nine-axis motion sensor including a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and apply a Kalman filter for noise elimination and an adaptive time window for data extraction. Based on the feature extraction guided by the built physical model, various classifiers are accomplished to recognize different driving behaviors. Leveraging the system, normal driving behaviors (such as accelerating, braking, lane changing and turning with caution) and aggressive driving behaviors (such as accelerating, braking, lane changing and turning with a sudden) can be classified with a high accuracy of 93.25%. Compared with traditional driving behavior recognition methods using machine learning only, the proposed system possesses a solid theoretical basis, performs better and has good prospects.

  9. 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.

  10. The response of fish to immunostimulant diets.

    PubMed

    Vallejos-Vidal, Eva; Reyes-López, Felipe; Teles, Mariana; MacKenzie, Simon

    2016-09-01

    In order to maintain fish health and to improve performance immunostimulants have been used as dietary additives to improve weight gain, feed efficiency, and/or disease resistance in cultured fish. In aquaculture, non-specific immunostimulants have been widely used probably due to the limited knowledge of the immune response in fish and the ease of their application. Many studies have been carried out to assess the effect of dietary immunostimulants in fish including algal derivatives, herb and plant extract containing diets using a wide range of downstream analytical techniques. Many immunostimulants are based upon tradition and folklore transferred through generations and specific to certain geographical regions rather than known biological properties. However, there are studies in which it is possible to observe a clear and direct dose-dependent stimulatory effect upon the immune system. Other dietary supplements used contain PAMPs (Pathogen Associated Molecular Patterns) as immunostimulants whose recognition depends upon PRR (pathogen recognition receptor) interactions including the TLRs (Toll-like receptor). Despite the growing interest in the use of immunostimulants across the aquaculture industry the underlying mechanisms of ligand recognition, extract composition and activation of the fish immune response remains fragmented. In this review we focus upon the last 15 years of studies addressing the assessment of: (1) plant, herb and algae extracts; and (2) PAMPs, upon non-specific immune parameters of activation and immunostimulant diet efficacy.

  11. 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.

  12. In-treatment 4D cone-beam CT with image-based respiratory phase recognition.

    PubMed

    Kida, Satoshi; Masutani, Yoshitaka; Yamashita, Hideomi; Imae, Toshikazu; Matsuura, Taeko; Saotome, Naoya; Ohtomo, Kuni; Nakagawa, Keiichi; Haga, Akihiro

    2012-07-01

    The use of respiration-correlated cone-beam computed tomography (4D-CBCT) appears to be crucial for implementing precise radiation therapy of lung cancer patients. The reconstruction of 4D-CBCT images requires a respiratory phase. In this paper, we propose a novel method based on an image-based phase recognition technique using normalized cross correlation (NCC). We constructed the respiratory phase by searching for a region in an adjacent projection that achieves the maximum correlation with a region in a reference projection along the cranio-caudal direction. The data on 12 lung cancer patients acquired just prior to treatment and on 3 lung cancer patients acquired during volumetric modulated arc therapy treatment were analyzed in the search for the effective area of cone-beam projection images for performing NCC with 12 combinations of registration area and segment size. The evaluation was done by a "recognition rate" defined as the ratio of the number of peak inhales detected with our method to that detected by eye (manual tracking). The average recognition rate of peak inhale with the most efficient area in the present method was 96.4%. The present method was feasible even when the diaphragm was outside the field of view. With the most efficient area, we reconstructed in-treatment 4D-CBCT by dividing the breathing signal into four phase bins; peak exhale, peak inhale, and two intermediate phases. With in-treatment 4D-CBCT images, it was possible to identify the tumor position and the tumor size in moments of inspiration and expiration, in contrast to in-treatment CBCT reconstructed with all projections.

  13. 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.

  14. Odor-based recognition of familiar and related conspecifics: a first test conducted on captive Humboldt penguins (Spheniscus humboldti).

    PubMed

    Coffin, Heather R; Watters, Jason V; Mateo, Jill M

    2011-01-01

    Studies of kin recognition in birds have largely focused on parent-offspring recognition using auditory or visual discrimination. Recent studies indicate that birds use odors during social and familial interactions and possibly for mate choice, suggesting olfactory cues may mediate kin recognition as well. Here, we show that Humboldt penguins (Spheniscus humboldti), a natally philopatric species with lifetime monogamy, discriminate between familiar and unfamiliar non-kin odors (using prior association) and between unfamiliar kin and non-kin odors (using phenotype matching). Penguins preferred familiar non-kin odors, which may be associated with the recognition of nest mates and colony mates and with locating burrows at night after foraging. In tests of kin recognition, penguins preferred unfamiliar non-kin odors. Penguins may have perceived non-kin odors as novel because they did not match the birds' recognition templates. Phenotype matching is likely the primary mechanism for kin recognition within the colony to avoid inbreeding. To our knowledge this is the first study to provide evidence of odor-based kin discrimination in a bird.

  15. 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

  16. 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

  17. 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.

  18. Emergency power for fish produced in intensive, pond-based systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Power failure in a heavily stocked and fed pond-based culture system can result in massive fish losses within minutes. Even in a conventional pond with a stand-by tractor powered aerator, the shock of a sudden loss of power can dramatically affect production resulting in mortalities and reduced perf...

  19. A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Genrang; Lin, ZhengChun

    The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

  20. 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.

  1. [Identification of fish species based on ribosomal DNA ITS2 locus].

    PubMed

    Yuan, Wan-An

    2010-04-01

    To prevent illegal fishing and sale, the most difficult problem is identification of marketed fish species, especially the parts that are difficult to be differentiated with morphological method (e.g., larval, eggs, scales, meat, products etc. To assist conservation and management of fishery resources, this paper reported a molecular genetic approach based on ribosomal internal transcribed spacer 2 locus. The method includes two steps: (1) the order general primers were designed according to the conservative nature of 5.8SrRAN and 28SrRNA genes within an order, and the DNA ribosomal internal transcribed spacer 2 locus fragment were then amplified and sequenced. (2) The species-specific ladders and the species-specific primers for each species were designed according to the sequencing results. The map of molecular taxonomy was constructed. This approach employs multiplex PCR that is formatted for fish species identification. We tested 210 single-species samples and 40 mix-species samples from different regions of China. The approach distinguished accurately and sensitively samples from each of the five species. This genetic and molecular approach will be useful for fish conservation, assessment, management and exploitation, strengthen in law enforcement of fishery manager, combat rare and endangered fish smuggling, and prevent commercial fraud and biological invasion by harmful nonnative species.

  2. Toxicity and effects of a glyphosate-based herbicide on the Neotropical fish Prochilodus lineatus.

    PubMed

    Langiano, Vivian do Carmo; Martinez, Cláudia B R

    2008-03-01

    The toxicity of Roundup, a glyphosate-based herbicide widely used in agriculture, was determined for the Neotropical fish Prochilodus lineatus. The 96 h-LC(50) of Roundup was 13.69 mg L(-1), indicating that this fish is more sensitive to Roundup than rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar). These differences should be considered when establishing criteria for water quality and animal well-being in the Neotropical region. Short-term (6, 24 and 96 h) toxicity tests were then performed to evaluate the effects of sub-lethal concentrations of the herbicide (7.5 and 10 mg L(-1)) to P. lineatus. Roundup did not interfere with the maintenance of the ionic balance and there was no significant alteration in plasma cortisol levels in Roundup-exposed fish. However an increase in plasma glucose was noted in fish exposed to 10 mg L(-1) of the herbicide, indicating a typical stress response. Catalase liver activity also showed an increase in fish exposed to 10 mg L(-1) of the herbicide, suggesting the activation of antioxidant defenses after Roundup exposure. In addition, Roundup induced several liver histological alterations that might impair normal organ functioning. Therefore, short-term exposure to Roundup at subletal concentrations induced biochemical, physiological and histological alterations in P. lineatus.

  3. [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

  4. RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

    PubMed Central

    Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

    2017-01-01

    Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance. PMID:28338069

  5. 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.

  6. 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

  7. Robust Nuclear Norm-based Matrix Regression with Applications to Robust Face Recognition.

    PubMed

    Xie, Jianchun; Yang, Jian; Qian, Jianjun; Tai, Ying; Zhang, Hengmin

    2017-02-01

    Face recognition (FR) via regression analysis based classification has been widely studied in the past several years. Most existing regression analysis methods characterize the pixelwise representation error via l1-norm or l2-norm, which overlook the two-dimensional structure of the error image. Recently, the nuclear norm based matrix regression (NMR) model is proposed to characterize low-rank structure of the error image. However, the nuclear norm cannot accurately describe the lowrank structural noise when the incoherence assumptions on the singular values does not hold, since it over-penalizes several much larger singular values. To address this problem, this paper presents the robust nuclear norm to characterize the structural error image and then extends it to deal with the mixed noise. The majorization-minimization (MM) method is applied to derive a iterative scheme for minimization of the robust nuclear norm optimization problem. Then, an efficiently alternating direction method of multipliers (ADMM) method is used to solve the proposed models. We use weighted nuclear norm as classification criterion to obtain the final recognition results. Experiments on several public face databases demonstrate the effectiveness of our models in handling with variations of structural noise (occlusion, illumination, etc.) and mixed noise.

  8. Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks.

    PubMed

    Luque, Joaquín; Larios, Diego F; Personal, Enrique; Barbancho, Julio; León, Carlos

    2016-05-18

    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.

  9. Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

    PubMed Central

    Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

    2016-01-01

    Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

  10. A food recognition system for diabetic patients based on an optimized bag-of-features model.

    PubMed

    Anthimopoulos, Marios M; Gianola, Lauro; Scarnato, Luca; Diem, Peter; Mougiakakou, Stavroula G

    2014-07-01

    Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

  11. 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

  12. RRAM-based parallel computing architecture using k-nearest neighbor classification for pattern recognition

    NASA Astrophysics Data System (ADS)

    Jiang, Yuning; Kang, Jinfeng; Wang, Xinan

    2017-03-01

    Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today’s electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementing k-nearest neighbor classification on metal-oxide RRAM crossbar arrays. Metal-oxide RRAM with gradual RESET behaviors is chosen as both the storage and computing components. The proposed architecture is tested by the MNIST database. High speed (~100 ns per example) and high recognition accuracy (97.05%) are obtained. The influence of several non-ideal device properties is also discussed, and it turns out that the proposed architecture shows great tolerance to device variations. This work paves a new way to achieve RRAM-based parallel computing hardware systems with high performance.

  13. 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.

  14. 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

  15. 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.

  16. Challenges Associated with Providing Speech Recognition User Interfaces for Computer-Based Educational Systems.

    ERIC Educational Resources Information Center

    Bergeron, Bryan

    1991-01-01

    Discussion of speech recognition technology and its use in computer-assisted instruction focuses on prototype systems designed for medical education. Commercial speech recognition systems are described, hardware and software requirements are examined, and the use of a speech recognition system to streamline an existing user interface is discussed.…

  17. 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

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

    PubMed

    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.

  19. A preliminary study on improving the recognition of esophageal speech using a hybrid system based on statistical voice conversion.

    PubMed

    Lachhab, Othman; Di Martino, Joseph; Elhaj, Elhassane Ibn; Hammouch, Ahmed

    2015-01-01

    In this paper, we propose a hybrid system based on a modified statistical GMM voice conversion algorithm for improving the recognition of esophageal speech. This hybrid system aims to compensate for the distorted information present in the esophageal acoustic features by using a voice conversion method. The esophageal speech is converted into a "target" laryngeal speech using an iterative statistical estimation of a transformation function. We did not apply a speech synthesizer for reconstructing the converted speech signal, given that the converted Mel cepstral vectors are used directly as input of our speech recognition system. Furthermore the feature vectors are linearly transformed by the HLDA (heteroscedastic linear discriminant analysis) method to reduce their size in a smaller space having good discriminative properties. The experimental results demonstrate that our proposed system provides an improvement of the phone recognition accuracy with an absolute increase of 3.40 % when compared with the phone recognition accuracy obtained with neither HLDA nor voice conversion.

  20. Fluorescent determination of Hg2+ in water and fish samples using a chemodosimeter based in a Rhodamine 6G derivative and a portable fiber-optic spectrofluorimeter.

    PubMed

    Bohoyo Gil, Diego; Rodriguez-Cáceres, María Isabel; Hurtado-Sánchez, María del Carmen; Muñoz de la Peña, Arsenio

    2010-05-01

    A fluorimetric chemodosimeter (FC1), based on a Rhodamine 6G derivative, is proposed for the recognition of Hg(2+) ions in water and fish samples. The reagent shows a highly selective and sensitive reaction with Hg(2+), giving rise to strong fluorescence emission at 555 nm. The obvious color change of the solution from colorless to pink upon the addition of Hg(2+) demonstrates that FC1 can be used for "naked-eye" detection of Hg(2+) in water effluents. The fluorescence intensity is proportional to the amount of Hg(2+) at ng mL(-1) levels, and it is capable of distinguishing between safe and toxic levels of inorganic mercury in drinking water and fish samples. The procedure has been implemented in a portable instrument composed of a 515 nm light-emitting diode (LED) excitation source, two fiber optics, and a charge-coupled device (CCD) camera as detector, connected to a portable computer for data acquisition and analysis, intended for in situ determination of mercury, offering a viable alternative to a conventional spectrofluorimeter. The proposed method has been applied to different water and fish samples with satisfactory results.