Infant visual attention and object recognition.

Science.gov (United States)

Reynolds, Greg D

2015-05-15

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

Technical Reviews on Pattern Recognition in Process Analytical Technology

International Nuclear Information System (INIS)

Kim, Jong Yun; Choi, Yong Suk; Ji, Sun Kyung; Park, Yong Joon; Song, Kyu Seok; Jung, Sung Hee

2008-12-01

Pattern recognition is one of the first and the most widely adopted chemometric tools among many active research area in chemometrics such as design of experiment(DoE), pattern recognition, multivariate calibration, signal processing. Pattern recognition has been used to identify the origin of a wine and the time of year that the vine was grown by using chromatography, cause of fire by using GC/MS chromatography, detection of explosives and land mines, cargo and luggage inspection in seaports and airports by using a prompt gamma-ray activation analysis, and source apportionment of environmental pollutant by using a stable isotope ratio mass spectrometry. Recently, pattern recognition has been taken into account as a major chemometric tool in the so-called 'process analytical technology (PAT)', which is a newly-developed concept in the area of process analytics proposed by US Food and Drug Administration (US FDA). For instance, identification of raw material by using a pattern recognition analysis plays an important role for the effective quality control of the production process. Recently, pattern recognition technique has been used to identify the spatial distribution and uniformity of the active ingredients present in the product such as tablet by transforming the chemical data into the visual information

Hopfield's Model of Patterns Recognition and Laws of Artistic Perception

Science.gov (United States)

Yevin, Igor; Koblyakov, Alexander

The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’s model of patterns recognition.

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

Science.gov (United States)

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

[Pattern recognition of decorative papers with different visual characteristics using visible spectroscopy coupled with principal component analysis (PCA)].

Science.gov (United States)

Zhang, Mao-mao; Yang, Zhong; Lu, Bin; Liu, Ya-na; Sun, Xue-dong

2015-02-01

As one of the most important decorative materials for the modern household products, decorative papers impregnated with melamine not only have better decorative performance, but also could greatly improve the surface properties of materials. However, the appearance quality (such as color-difference evaluation and control) of decorative papers, as an important index for the surface quality of decorative paper, has been a puzzle for manufacturers and consumers. Nowadays, human eye is used to discriminate whether there exist color difference in the factory, which is not only of low efficiency but also prone to bring subjective error. Thus, it is of great significance to find an effective method in order to realize the fast recognition and classification of the decorative papers. In the present study, the visible spectroscopy coupled with principal component analysis (PCA) was used for the pattern recognition of decorative papers with different visual characteristics to investigate the feasibility of visible spectroscopy to rapidly recognize the types of decorative papers. The results showed that the correlation between visible spectroscopy and visual characteristics (L*, a* and b*) was significant, and the correlation coefficients wereup to 0.85 and some was even more than 0. 99, which might suggest that the visible spectroscopy reflected some information about visual characteristics on the surface of decorative papers. When using the visible spectroscopy coupled with PCA to recognize the types of decorative papers, the accuracy reached 94%-100%, which might suggest that the visible spectroscopy was a very potential new method for the rapid, objective and accurate recognition of decorative papers with different visual characteristics.

Syllabic Length Effect in Visual Word Recognition

Directory of Open Access Journals (Sweden)

Roya Ranjbar Mohammadi

2014-07-01

Full Text Available Studies on visual word recognition have resulted in different and sometimes contradictory proposals as Multi-Trace Memory Model (MTM, Dual-Route Cascaded Model (DRC, and Parallel Distribution Processing Model (PDP. The role of the number of syllables in word recognition was examined by the use of five groups of English words and non-words. The reaction time of the participants to these words was measured using reaction time measuring software. The results indicated that there was syllabic effect on recognition of both high and low frequency words. The pattern was incremental in terms of syllable number. This pattern prevailed in high and low frequency words and non-words except in one syllable words. In general, the results are in line with the PDP model which claims that a single processing mechanism is used in both words and non-words recognition. In other words, the findings suggest that lexical items are mainly processed via a lexical route.  A pedagogical implication of the findings would be that reading in English as a foreign language involves analytical processing of the syllable of the words.

Audio-Visual Speech Recognition Using MPEG-4 Compliant Visual Features

Directory of Open Access Journals (Sweden)

Petar S. Aleksic

2002-11-01

Full Text Available We describe an audio-visual automatic continuous speech recognition system, which significantly improves speech recognition performance over a wide range of acoustic noise levels, as well as under clean audio conditions. The system utilizes facial animation parameters (FAPs supported by the MPEG-4 standard for the visual representation of speech. We also describe a robust and automatic algorithm we have developed to extract FAPs from visual data, which does not require hand labeling or extensive training procedures. The principal component analysis (PCA was performed on the FAPs in order to decrease the dimensionality of the visual feature vectors, and the derived projection weights were used as visual features in the audio-visual automatic speech recognition (ASR experiments. Both single-stream and multistream hidden Markov models (HMMs were used to model the ASR system, integrate audio and visual information, and perform a relatively large vocabulary (approximately 1000 words speech recognition experiments. The experiments performed use clean audio data and audio data corrupted by stationary white Gaussian noise at various SNRs. The proposed system reduces the word error rate (WER by 20% to 23% relatively to audio-only speech recognition WERs, at various SNRs (0–30 dB with additive white Gaussian noise, and by 19% relatively to audio-only speech recognition WER under clean audio conditions.

Learning to Recognize Patterns: Changes in the Visual Field with Familiarity

Science.gov (United States)

Bebko, James M.; Uchikawa, Keiji; Saida, Shinya; Ikeda, Mitsuo

1995-01-01

Two studies were conducted to investigate changes which take place in the visual information processing of novel stimuli as they become familiar. Japanese writing characters (Hiragana and Kanji) which were unfamiliar to two native English speaking subjects were presented using a moving window technique to restrict their visual fields. Study time for visual recognition was recorded across repeated sessions, and with varying visual field restrictions. The critical visual field was defined as the size of the visual field beyond which further increases did not improve the speed of recognition performance. In the first study, when the Hiragana patterns were novel, subjects needed to see about half of the entire pattern simultaneously to maintain optimal performance. However, the critical visual field size decreased as familiarity with the patterns increased. These results were replicated in the second study with more complex Kanji characters. In addition, the critical field size decreased as pattern complexity decreased. We propose a three component model of pattern perception. In the first stage a representation of the stimulus must be constructed by the subject, and restricting of the visual field interferes dramatically with this component when stimuli are unfamiliar. With increased familiarity, subjects become able to reconstruct a previous representation from very small, unique segments of the pattern, analogous to the informativeness areas hypothesized by Loftus and Mackworth [J. Exp. Psychol., 4 (1978) 565].

Visual Object Pattern Separation Varies in Older Adults

Science.gov (United States)

Holden, Heather M.; Toner, Chelsea; Pirogovsky, Eva; Kirwan, C. Brock; Gilbert, Paul E.

2013-01-01

Young and nondemented older adults completed a visual object continuous recognition memory task in which some stimuli (lures) were similar but not identical to previously presented objects. The lures were hypothesized to result in increased interference and increased pattern separation demand. To examine variability in object pattern separation…

Cortical Networks for Visual Self-Recognition

Science.gov (United States)

Sugiura, Motoaki

This paper briefly reviews recent developments regarding the brain mechanisms of visual self-recognition. A special cognitive mechanism for visual self-recognition has been postulated based on behavioral and neuropsychological evidence, but its neural substrate remains controversial. Recent functional imaging studies suggest that multiple cortical mechanisms play self-specific roles during visual self-recognition, reconciling the existing controversy. Respective roles for the left occipitotemporal, right parietal, and frontal cortices in symbolic, visuospatial, and conceptual aspects of self-representation have been proposed.

Cortical networks for visual self-recognition

International Nuclear Information System (INIS)

Sugiura, Motoaki

2007-01-01

This paper briefly reviews recent developments regarding the brain mechanisms of visual self-recognition. A special cognitive mechanism for visual self-recognition has been postulated based on behavioral and neuropsychological evidence, but its neural substrate remains controversial. Recent functional imaging studies suggest that multiple cortical mechanisms play self-specific roles during visual self-recognition, reconciling the existing controversy. Respective roles for the left occipitotemporal, right parietal, and frontal cortices in symbolic, visuospatial, and conceptual aspects of self-representation have been proposed. (author)

Emotional Faces in Context: Age Differences in Recognition Accuracy and Scanning Patterns

Science.gov (United States)

Noh, Soo Rim; Isaacowitz, Derek M.

2014-01-01

While age-related declines in facial expression recognition are well documented, previous research relied mostly on isolated faces devoid of context. We investigated the effects of context on age differences in recognition of facial emotions and in visual scanning patterns of emotional faces. While their eye movements were monitored, younger and older participants viewed facial expressions (i.e., anger, disgust) in contexts that were emotionally congruent, incongruent, or neutral to the facial expression to be identified. Both age groups had highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were worst. These context effects were more pronounced for older adults. Compared to younger adults, older adults exhibited a greater benefit from congruent contextual information, regardless of facial expression. Context also influenced the pattern of visual scanning characteristics of emotional faces in a similar manner across age groups. In addition, older adults initially attended more to context overall. Our data highlight the importance of considering the role of context in understanding emotion recognition in adulthood. PMID:23163713

Exemplar Based Recognition of Visual Shapes

DEFF Research Database (Denmark)

Olsen, Søren I.

2005-01-01

This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoint....... The matching is insensitive to rotations, limited scalings and small deformations. The recognition is robust to noise, background clutter and partial occlusion. Recognition is possible from few training images and improve with the number of training images.......This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoints...

Auditory orientation in crickets: Pattern recognition controls reactive steering

Science.gov (United States)

Poulet, James F. A.; Hedwig, Berthold

2005-10-01

Many groups of insects are specialists in exploiting sensory cues to locate food resources or conspecifics. To achieve orientation, bees and ants analyze the polarization pattern of the sky, male moths orient along the females' odor plume, and cicadas, grasshoppers, and crickets use acoustic signals to locate singing conspecifics. In comparison with olfactory and visual orientation, where learning is involved, auditory processing underlying orientation in insects appears to be more hardwired and genetically determined. In each of these examples, however, orientation requires a recognition process identifying the crucial sensory pattern to interact with a localization process directing the animal's locomotor activity. Here, we characterize this interaction. Using a sensitive trackball system, we show that, during cricket auditory behavior, the recognition process that is tuned toward the species-specific song pattern controls the amplitude of auditory evoked steering responses. Females perform small reactive steering movements toward any sound patterns. Hearing the male's calling song increases the gain of auditory steering within 2-5 s, and the animals even steer toward nonattractive sound patterns inserted into the speciesspecific pattern. This gain control mechanism in the auditory-to-motor pathway allows crickets to pursue species-specific sound patterns temporarily corrupted by environmental factors and may reflect the organization of recognition and localization networks in insects. localization | phonotaxis

Suprasegmental lexical stress cues in visual speech can guide spoken-word recognition.

Science.gov (United States)

Jesse, Alexandra; McQueen, James M

2014-01-01

Visual cues to the individual segments of speech and to sentence prosody guide speech recognition. The present study tested whether visual suprasegmental cues to the stress patterns of words can also constrain recognition. Dutch listeners use acoustic suprasegmental cues to lexical stress (changes in duration, amplitude, and pitch) in spoken-word recognition. We asked here whether they can also use visual suprasegmental cues. In two categorization experiments, Dutch participants saw a speaker say fragments of word pairs that were segmentally identical but differed in their stress realization (e.g., 'ca-vi from cavia "guinea pig" vs. 'ka-vi from kaviaar "caviar"). Participants were able to distinguish between these pairs from seeing a speaker alone. Only the presence of primary stress in the fragment, not its absence, was informative. Participants were able to distinguish visually primary from secondary stress on first syllables, but only when the fragment-bearing target word carried phrase-level emphasis. Furthermore, participants distinguished fragments with primary stress on their second syllable from those with secondary stress on their first syllable (e.g., pro-'jec from projector "projector" vs. 'pro-jec from projectiel "projectile"), independently of phrase-level emphasis. Seeing a speaker thus contributes to spoken-word recognition by providing suprasegmental information about the presence of primary lexical stress.

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Temporal visual cues aid speech recognition

DEFF Research Database (Denmark)

Zhou, Xiang; Ross, Lars; Lehn-Schiøler, Tue

2006-01-01

of audio to generate an artificial talking-face video and measured word recognition performance on simple monosyllabic words. RESULTS: When presenting words together with the artificial video we find that word recognition is improved over purely auditory presentation. The effect is significant (p......BACKGROUND: It is well known that under noisy conditions, viewing a speaker's articulatory movement aids the recognition of spoken words. Conventionally it is thought that the visual input disambiguates otherwise confusing auditory input. HYPOTHESIS: In contrast we hypothesize...... that it is the temporal synchronicity of the visual input that aids parsing of the auditory stream. More specifically, we expected that purely temporal information, which does not convey information such as place of articulation may facility word recognition. METHODS: To test this prediction we used temporal features...

Spatial pattern recognition of seismic events in South West Colombia

Science.gov (United States)

Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

2013-09-01

Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

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

Science.gov (United States)

Kuwabara, Megumi; Smith, Linda B.

2016-01-01

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

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

Science.gov (United States)

Kuwabara, Megumi; Smith, Linda B

2016-07-01

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

Pattern activation/recognition theory of mind.

Science.gov (United States)

du Castel, Bertrand

2015-01-01

In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.

Visual Localization by Place Recognition Based on Multifeature (D-λLBP++HOG

Directory of Open Access Journals (Sweden)

Yongliang Qiao

2017-01-01

Full Text Available Visual localization is widely used in the autonomous navigation system and Advanced Driver Assistance Systems (ADAS. This paper presents a visual localization method based on multifeature fusion and disparity information using stereo images. We integrate disparity information into complete center-symmetric local binary patterns (CSLBP to obtain a robust global image description (D-CSLBP. In order to represent the scene in depth, multifeature fusion of D-CSLBP and HOG features provides valuable information and permits decreasing the effect of some typical problems in place recognition such as perceptual aliasing. It improves visual recognition performance by taking advantage of depth, texture, and shape information. In addition, for real-time visual localization, local sensitive hashing method (LSH was used to compress the high-dimensional multifeature into binary vectors. It can thus speed up the process of image matching. To show its effectiveness, the proposed method is tested and evaluated using real datasets acquired in outdoor environments. Given the obtained results, our approach allows more effective visual localization compared with the state-of-the-art method FAB-MAP.

The time course of individual face recognition: A pattern analysis of ERP signals.

Science.gov (United States)

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Deep Complementary Bottleneck Features for Visual Speech Recognition

NARCIS (Netherlands)

Petridis, Stavros; Pantic, Maja

Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

Pattern recognition and string matching

CERN Document Server

Cheng, Xiuzhen

2002-01-01

The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica­ tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro­ vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes student...

Data structures, computer graphics, and pattern recognition

CERN Document Server

Klinger, A; Kunii, T L

1977-01-01

Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr

Acoustic Pattern Recognition on Android Devices

DEFF Research Database (Denmark)

Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild

2013-01-01

an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

Science.gov (United States)

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

The nature of visual self-recognition.

Science.gov (United States)

Suddendorf, Thomas; Butler, David L

2013-03-01

Visual self-recognition is often controversially cited as an indicator of self-awareness and assessed with the mirror-mark test. Great apes and humans, unlike small apes and monkeys, have repeatedly passed mirror tests, suggesting that the underlying brain processes are homologous and evolved 14-18 million years ago. However, neuroscientific, developmental, and clinical dissociations show that the medium used for self-recognition (mirror vs photograph vs video) significantly alters behavioral and brain responses, likely due to perceptual differences among the different media and prior experience. On the basis of this evidence and evolutionary considerations, we argue that the visual self-recognition skills evident in humans and great apes are a byproduct of a general capacity to collate representations, and need not index other aspects of self-awareness. Copyright © 2013 Elsevier Ltd. All rights reserved.

Pattern recognition and classification an introduction

CERN Document Server

Dougherty, Geoff

2012-01-01

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

What are the visual features underlying rapid object recognition?

Directory of Open Access Journals (Sweden)

Sébastien M Crouzet

2011-11-01

Full Text Available Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically-plausible computational models of (bottom-up pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task.

Star pattern recognition algorithm aided by inertial information

Science.gov (United States)

Liu, Bao; Wang, Ke-dong; Zhang, Chao

2011-08-01

Star pattern recognition is one of the key problems of the celestial navigation. The traditional star pattern recognition approaches, such as the triangle algorithm and the star angular distance algorithm, are a kind of all-sky matching method whose recognition speed is slow and recognition success rate is not high. Therefore, the real time and reliability of CNS (Celestial Navigation System) is reduced to some extent, especially for the maneuvering spacecraft. However, if the direction of the camera optical axis can be estimated by other navigation systems such as INS (Inertial Navigation System), the star pattern recognition can be fulfilled in the vicinity of the estimated direction of the optical axis. The benefits of the INS-aided star pattern recognition algorithm include at least the improved matching speed and the improved success rate. In this paper, the direction of the camera optical axis, the local matching sky, and the projection of stars on the image plane are estimated by the aiding of INS firstly. Then, the local star catalog for the star pattern recognition is established in real time dynamically. The star images extracted in the camera plane are matched in the local sky. Compared to the traditional all-sky star pattern recognition algorithms, the memory of storing the star catalog is reduced significantly. Finally, the INS-aided star pattern recognition algorithm is validated by simulations. The results of simulations show that the algorithm's computation time is reduced sharply and its matching success rate is improved greatly.

Pattern Recognition by Humans and Machines

International Nuclear Information System (INIS)

Versino, C.; )

2015-01-01

Data visualization is centred on new ways of processing and displaying large data sets to support pattern recognition by humans rather than by machines. The motivation for approaches based on data visualization is to encourage data exploration and curiosity by analysts. They should help formulating the right question more than addressing specific predefined issues or expectations. Translated into IAEA's terms, they should help verify the completeness of information declared to the IAEA more than their correctness. Data visualization contrasts with traditional information retrieval where one needs first to formulate a query in order to get to a narrow slice of data. Using traditional information retrieval, no one knows what is missed out. The system may fail to recall relevant data due to the way the query was formulated, or the query itself may not be the most relevant one to be asked in the first place. Examples of data visualizations relevant to safeguards will be illustrated, including new approaches for the review of surveillance images and for trade analysis. Common to these examples is the attempt to enlarge the view of the analyst on a universe of data, where context or detailed data is presented on-demand and by levels of abstraction. The paper will make reference to ongoing research and to enabling information technologies. (author)

Adult Word Recognition and Visual Sequential Memory

Science.gov (United States)

Holmes, V. M.

2012-01-01

Two experiments were conducted investigating the role of visual sequential memory skill in the word recognition efficiency of undergraduate university students. Word recognition was assessed in a lexical decision task using regularly and strangely spelt words, and nonwords that were either standard orthographically legal strings or items made from…

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

Science.gov (United States)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

What Types of Visual Recognition Tasks Are Mediated by the Neural Subsystem that Subserves Face Recognition?

Science.gov (United States)

Brooks, Brian E.; Cooper, Eric E.

2006-01-01

Three divided visual field experiments tested current hypotheses about the types of visual shape representation tasks that recruit the cognitive and neural mechanisms underlying face recognition. Experiment 1 found a right hemisphere advantage for subordinate but not basic-level face recognition. Experiment 2 found a right hemisphere advantage for…

Fringe patterns generated by micro-optical sensors for pattern recognition.

Science.gov (United States)

Tamee, Kreangsak; Chaiwong, Khomyuth; Yothapakdee, Kriengsak; Yupapin, Preecha P

2015-01-01

We present a new result of pattern recognition generation scheme using a small-scale optical muscle sensing system, which consisted of an optical add-drop filter incorporating two nonlinear optical side ring resonators. When light from laser source enters into the system, the device is stimulated by an external physical parameter that introduces a change in the phase of light propagation within the sensing device, which can be formed by the interference fringe patterns. Results obtained have shown that the fringe patterns can be used to form the relationship between signal patterns and fringe pattern recognitions.

Pattern Recognition Control Design

Science.gov (United States)

Gambone, Elisabeth A.

2018-01-01

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

The processing of auditory and visual recognition of self-stimuli.

Science.gov (United States)

Hughes, Susan M; Nicholson, Shevon E

2010-12-01

This study examined self-recognition processing in both the auditory and visual modalities by determining how comparable hearing a recording of one's own voice was to seeing photograph of one's own face. We also investigated whether the simultaneous presentation of auditory and visual self-stimuli would either facilitate or inhibit self-identification. Ninety-one participants completed reaction-time tasks of self-recognition when presented with their own faces, own voices, and combinations of the two. Reaction time and errors made when responding with both the right and left hand were recorded to determine if there were lateralization effects on these tasks. Our findings showed that visual self-recognition for facial photographs appears to be superior to auditory self-recognition for voice recordings. Furthermore, a combined presentation of one's own face and voice appeared to inhibit rather than facilitate self-recognition and there was a left-hand advantage for reaction time on the combined-presentation tasks. Copyright © 2010 Elsevier Inc. All rights reserved.

Visual face-movement sensitive cortex is relevant for auditory-only speech recognition.

Science.gov (United States)

Riedel, Philipp; Ragert, Patrick; Schelinski, Stefanie; Kiebel, Stefan J; von Kriegstein, Katharina

2015-07-01

It is commonly assumed that the recruitment of visual areas during audition is not relevant for performing auditory tasks ('auditory-only view'). According to an alternative view, however, the recruitment of visual cortices is thought to optimize auditory-only task performance ('auditory-visual view'). This alternative view is based on functional magnetic resonance imaging (fMRI) studies. These studies have shown, for example, that even if there is only auditory input available, face-movement sensitive areas within the posterior superior temporal sulcus (pSTS) are involved in understanding what is said (auditory-only speech recognition). This is particularly the case when speakers are known audio-visually, that is, after brief voice-face learning. Here we tested whether the left pSTS involvement is causally related to performance in auditory-only speech recognition when speakers are known by face. To test this hypothesis, we applied cathodal transcranial direct current stimulation (tDCS) to the pSTS during (i) visual-only speech recognition of a speaker known only visually to participants and (ii) auditory-only speech recognition of speakers they learned by voice and face. We defined the cathode as active electrode to down-regulate cortical excitability by hyperpolarization of neurons. tDCS to the pSTS interfered with visual-only speech recognition performance compared to a control group without pSTS stimulation (tDCS to BA6/44 or sham). Critically, compared to controls, pSTS stimulation additionally decreased auditory-only speech recognition performance selectively for voice-face learned speakers. These results are important in two ways. First, they provide direct evidence that the pSTS is causally involved in visual-only speech recognition; this confirms a long-standing prediction of current face-processing models. Secondly, they show that visual face-sensitive pSTS is causally involved in optimizing auditory-only speech recognition. These results are in line

Emotion Recognition and Visual-Scan Paths in Fragile X Syndrome

Science.gov (United States)

Shaw, Tracey A.; Porter, Melanie A.

2013-01-01

This study investigated emotion recognition abilities and visual scanning of emotional faces in 16 Fragile X syndrome (FXS) individuals compared to 16 chronological-age and 16 mental-age matched controls. The relationships between emotion recognition, visual scan-paths and symptoms of social anxiety, schizotypy and autism were also explored.…

Face recognition system and method using face pattern words and face pattern bytes

Science.gov (United States)

Zheng, Yufeng

2014-12-23

The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

Effect of physical workload and modality of information presentation on pattern recognition and navigation task performance by high-fit young males.

Science.gov (United States)

Zahabi, Maryam; Zhang, Wenjuan; Pankok, Carl; Lau, Mei Ying; Shirley, James; Kaber, David

2017-11-01

Many occupations require both physical exertion and cognitive task performance. Knowledge of any interaction between physical demands and modalities of cognitive task information presentation can provide a basis for optimising performance. This study examined the effect of physical exertion and modality of information presentation on pattern recognition and navigation-related information processing. Results indicated males of equivalent high fitness, between the ages of 18 and 34, rely more on visual cues vs auditory or haptic for pattern recognition when exertion level is high. We found that navigation response time was shorter under low and medium exertion levels as compared to high intensity. Navigation accuracy was lower under high level exertion compared to medium and low levels. In general, findings indicated that use of the haptic modality for cognitive task cueing decreased accuracy in pattern recognition responses. Practitioner Summary: An examination was conducted on the effect of physical exertion and information presentation modality in pattern recognition and navigation. In occupations requiring information presentation to workers, who are simultaneously performing a physical task, the visual modality appears most effective under high level exertion while haptic cueing degrades performance.

Description and recognition of patterns in stochastic signals. [Electroencephalograms

Energy Technology Data Exchange (ETDEWEB)

Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

1975-10-01

A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

Improved pattern recognition systems by hybrid methods

International Nuclear Information System (INIS)

Duerr, B.; Haettich, W.; Tropf, H.; Winkler, G.; Fraunhofer-Gesellschaft zur Foerderung der Angewandten Forschung e.V., Karlsruhe

1978-12-01

This report describes a combination of statistical and syntactical pattern recongition methods. The hierarchically structured recognition system consists of a conventional statistical classifier, a structural classifier analysing the topological composition of the patterns, a stage reducing the number of hypotheses made by the first two stages, and a mixed stage based on a search for maximum similarity between syntactically generated prototypes and patterns. The stages work on different principles to avoid mistakes made in one stage in the other stages. This concept is applied to the recognition of numerals written without constraints. If no samples are rejected, a recognition rate of 99,5% is obtained. (orig.) [de

Degraded character recognition based on gradient pattern

Science.gov (United States)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Learned image representations for visual recognition

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo

This thesis addresses the problem of extracting image structures for representing images effectively in order to solve visual recognition tasks. Problems from diverse research areas (medical imaging, material science and food processing) have motivated large parts of the methodological development...

The effect of mood-context on visual recognition and recall memory.

Science.gov (United States)

Robinson, Sarita J; Rollings, Lucy J L

2011-01-01

Although it is widely known that memory is enhanced when encoding and retrieval occur in the same state, the impact of elevated stress/arousal is less understood. This study explores mood-dependent memory's effects on visual recognition and recall of material memorized either in a neutral mood or under higher stress/arousal levels. Participants' (N = 60) recognition and recall were assessed while they experienced either the same o a mismatched mood at retrieval. The results suggested that both visual recognition and recall memory were higher when participants experienced the same mood at encoding and retrieval compared with those who experienced a mismatch in mood context between encoding and retrieval. These findings offer support for a mood dependency effect on both the recognition and recall of visual information.

Pattern recognition in high energy physics

International Nuclear Information System (INIS)

Tenner, A.G.

1980-01-01

In high energy physics experiments tracks of elementary particles are recorded by different types of equipment. Coordinates of points of these tracks have to be measured for the geometrical reconstruction and the further analysis of the observed events. Pattern recognition methods may facilitate the detection of tracks or whole events and the separation of relevant from non-relevant information. They may also serve for the automation of measurement. Generally, all work is done by digital computation. In a bubble chamber tracks appear as strings of vapour bubbles that can be recorded photographically. Two methods of pattern recognition are discussed. The flying spot digitizer encodes the pattern on the photograph into point coordinates in the memory of a computer. The computer carries out the pattern recognition procedure entirely on the basis of the stored information. Cathode ray instruments scan the photograph by means of a computer steered optical device. Data acquisition from the film is performed in a feedback loop of the computation. In electronic experimental equipment tracks are defined by the spacial distribution of hits of counters (wire counters, scintillation counters, spark chambers). Pattern recognition is generally performed in various stages both by on-line and off-line equipment. Problems in the data handling arise both from the great abundance of data and from the time limits imposed on the on-line computation by high measuring rates. The on-line computation is carried out by hardwired logic, small computers, and to an increasing extent by microprocessors. (Auth.)

Pattern recognition methods in air pollution control

Energy Technology Data Exchange (ETDEWEB)

Tauber, S

1978-01-01

The use of pattern recognition methods for predicting air pollution developments is discussed. Computer analysis of historical pollution data allows comparison in graphical form. An example of crisis prediction for carbon monoxide concentrations, using the pattern recognition method of analysis, is presented. Results of the analysis agreed well with actual CO conditions. (6 graphs, 4 references, 1 table)

Designing visual recognition for the brand

NARCIS (Netherlands)

Karjalainen, T.M.; Snelders, H.M.J.J.

2010-01-01

The present paper examines how companies strategically employ design to create visual recognition of their brands' core values. To address this question, an explorative in-depth case study was carried out concerning the strategic design efforts of two companies: Nokia (mobile phones) and Volvo

Optical Pattern Recognition for Missile Guidance.

Science.gov (United States)

1982-11-15

directed to novel pattern recognition algo- rithms (that allow pattern recognition and object classification in the face of various geometrical and...I wats EF5 = 50) p.j/t’ni 2 (for btith image pat tern recognitio itas a preproicessing oiperatiton. Ini devices). TIhe rt’ad light intensity (0.33t mW...electrodes on its large faces . This Priz light modulator and the motivation for its devel- SLM is known as the Prom (Pockels real-time optical opment. In Sec

New Digital Approach to CNN On-chip Implementation for Pattern Recognition

OpenAIRE

Durackova, Daniela

2008-01-01

We developed a novel simulator for the CNN using the program tool Visual Basic for Application. Its algorithm is based on the same principle as the planned designed circuit. The network can process the patterns with 400 point recognition. The created universal simulator can change various simulation parameters. We found that the rounding at multiplication is not as important as we previously expected. On the basis of the simulations we designed a novel digital CNN cell implemented on a chip. ...

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

Science.gov (United States)

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Main Cognitive Model of Visual Recognition: Contour Recognition

OpenAIRE

Chen, YongHong

2017-01-01

In this paper, we will study the following pattern recognition problem: Every pattern is a 3-dimensional graph, its surface can be split up into some regions, every region is composed of the pixels with the approximately same colour value and the approximately same depth value that is distance to eyes, and there may also be some contours, e.g., literal contours, on a surface of every pattern. For this problem we reveal the inherent laws. Moreover, we establish a cognitive model to reflect the...

Evaluating structural pattern recognition for handwritten math via primitive label graphs

Science.gov (United States)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

The Pandora software development kit for pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Marshall, J.S.; Thomson, M.A. [University of Cambridge, Cavendish Laboratory, Cambridge (United Kingdom)

2015-09-15

The development of automated solutions to pattern recognition problems is important in many areas of scientific research and human endeavour. This paper describes the implementation of the Pandora software development kit, which aids the process of designing, implementing and running pattern recognition algorithms. The Pandora Application Programming Interfaces ensure simple specification of the building-blocks defining a pattern recognition problem. The logic required to solve the problem is implemented in algorithms. The algorithms request operations to create or modify data structures and the operations are performed by the Pandora framework. This design promotes an approach using many decoupled algorithms, each addressing specific topologies. Details of algorithms addressing two pattern recognition problems in High Energy Physics are presented: reconstruction of events at a high-energy e{sup +}e{sup -} linear collider and reconstruction of cosmic ray or neutrino events in a liquid argon time projection chamber. (orig.)

Pattern recognition applied to uranium prospecting

Energy Technology Data Exchange (ETDEWEB)

Briggs, P L; Press, F [Massachusetts Inst. of Tech., Cambridge (USA). Dept. of Earth and Planetary Sciences

1977-07-14

It is stated that pattern recognition techniques provide one way of combining quantitative and descriptive geological data for mineral prospecting. A quantified decision process using computer-selected patterns of geological data has the potential for selecting areas with undiscovered deposits of uranium or other minerals. When a natural resource is mined more rapidly than it is discovered, its continued production becomes increasingly difficult, and it has been noted that, although a considerable uranium reserve may remain in the U.S.A., the discovery rate for uranium is decreasing exponentially with cumulative exploration footage drilled. Pattern recognition methods of organising geological information for prospecting may provide new predictive power, as well as insight into the occurrence of uranium ore deposits. Often the task of prospecting consists of three stages of information processing: (1) collection of data on known ore deposits; (2) noting any regularities common to the known examples of an ore; (3) selection of new exploration targets based on the results of the second stage. A logical pattern recognition algorithm is here described that implements this geological procedure to demonstrate the possibility of building a quantified uranium prospecting guide from diverse geologic data.

Application Of t-Cherry Junction Trees in Pattern Recognition

Directory of Open Access Journals (Sweden)

Edith Kovacs

2010-06-01

Full Text Available Pattern recognition aims to classify data (patterns based ei-
ther on a priori knowledge or on statistical information extracted from the data. In this paper we will concentrate on statistical pattern recognition using a new probabilistic approach which makes possible to select the so called 'informative' features. We develop a pattern recognition algorithm which is based on the conditional independence structure underlying the statistical data. Our method was succesfully applied on a real problem of recognizing Parkinson's disease on the basis of voice disorders.

Verifying visual properties in sentence verification facilitates picture recognition memory.

Science.gov (United States)

Pecher, Diane; Zanolie, Kiki; Zeelenberg, René

2007-01-01

According to the perceptual symbols theory (Barsalou, 1999), sensorimotor simulations underlie the representation of concepts. We investigated whether recognition memory for pictures of concepts was facilitated by earlier representation of visual properties of those concepts. During study, concept names (e.g., apple) were presented in a property verification task with a visual property (e.g., shiny) or with a nonvisual property (e.g., tart). Delayed picture recognition memory was better if the concept name had been presented with a visual property than if it had been presented with a nonvisual property. These results indicate that modality-specific simulations are used for concept representation.

Towards The Deep Model : Understanding Visual Recognition Through Computational Models

OpenAIRE

Wang, Panqu

2017-01-01

Understanding how visual recognition is achieved in the human brain is one of the most fundamental questions in vision research. In this thesis I seek to tackle this problem from a neurocomputational modeling perspective. More specifically, I build machine learning-based models to simulate and explain cognitive phenomena related to human visual recognition, and I improve computational models using brain-inspired principles to excel at computer vision tasks.I first describe how a neurocomputat...

Evidence for Separate Contributions of High and Low Spatial Frequencies during Visual Word Recognition.

Science.gov (United States)

Winsler, Kurt; Holcomb, Phillip J; Midgley, Katherine J; Grainger, Jonathan

2017-01-01

Previous studies have shown that different spatial frequency information processing streams interact during the recognition of visual stimuli. However, it is a matter of debate as to the contributions of high and low spatial frequency (HSF and LSF) information for visual word recognition. This study examined the role of different spatial frequencies in visual word recognition using event-related potential (ERP) masked priming. EEG was recorded from 32 scalp sites in 30 English-speaking adults in a go/no-go semantic categorization task. Stimuli were white characters on a neutral gray background. Targets were uppercase five letter words preceded by a forward-mask (#######) and a 50 ms lowercase prime. Primes were either the same word (repeated) or a different word (un-repeated) than the subsequent target and either contained only high, only low, or full spatial frequency information. Additionally within each condition, half of the prime-target pairs were high lexical frequency, and half were low. In the full spatial frequency condition, typical ERP masked priming effects were found with an attenuated N250 (sub-lexical) and N400 (lexical-semantic) for repeated compared to un-repeated primes. For HSF primes there was a weaker N250 effect which interacted with lexical frequency, a significant reversal of the effect around 300 ms, and an N400-like effect for only high lexical frequency word pairs. LSF primes did not produce any of the classic ERP repetition priming effects, however they did elicit a distinct early effect around 200 ms in the opposite direction of typical repetition effects. HSF information accounted for many of the masked repetition priming ERP effects and therefore suggests that HSFs are more crucial for word recognition. However, LSFs did produce their own pattern of priming effects indicating that larger scale information may still play a role in word recognition.

Data analysis and pattern recognition in multiple databases

CERN Document Server

Adhikari, Animesh; Pedrycz, Witold

2014-01-01

Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different...

Visual object recognition and category-specificity

DEFF Research Database (Denmark)

Gerlach, Christian

This thesis is based on seven published papers. The majority of the papers address two topics in visual object recognition: (i) category-effects at pre-semantic stages, and (ii) the integration of visual elements into elaborate shape descriptions corresponding to whole objects or large object parts...... (shape configuration). In the early writings these two topics were examined more or less independently. In later works, findings concerning category-effects and shape configuration merge into an integrated model, termed RACE, advanced to explain category-effects arising at pre-semantic stages in visual...... in visual long-term memory. In the thesis it is described how this simple model can account for a wide range of findings on category-specificity in both patients with brain damage and normal subjects. Finally, two hypotheses regarding the neural substrates of the model's components - and how activation...

Chess players' eye movements reveal rapid recognition of complex visual patterns: Evidence from a chess-related visual search task.

Science.gov (United States)

Sheridan, Heather; Reingold, Eyal M

2017-03-01

To explore the perceptual component of chess expertise, we monitored the eye movements of expert and novice chess players during a chess-related visual search task that tested anecdotal reports that a key differentiator of chess skill is the ability to visualize the complex moves of the knight piece. Specifically, chess players viewed an array of four minimized chessboards, and they rapidly searched for the target board that allowed a knight piece to reach a target square in three moves. On each trial, there was only one target board (i.e., the "Yes" board), and for the remaining "lure" boards, the knight's path was blocked on either the first move (the "Easy No" board) or the second move (i.e., "the Difficult No" board). As evidence that chess experts can rapidly differentiate complex chess-related visual patterns, the experts (but not the novices) showed longer first-fixation durations on the "Yes" board relative to the "Difficult No" board. Moreover, as hypothesized, the task strongly differentiated chess skill: Reaction times were more than four times faster for the experts relative to novices, and reaction times were correlated with within-group measures of expertise (i.e., official chess ratings, number of hours of practice). These results indicate that a key component of chess expertise is the ability to rapidly recognize complex visual patterns.

Pattern recognition application for surveillance of abnormal conditions in a nuclear reactor

International Nuclear Information System (INIS)

Pepelyshev, Yu.N.; Dzwinel, W.

1990-01-01

The system to monitor abnormal conditions in a nuclear reactor, based on the noise analysis of the reactor basic parameters such as power, temperature and coolant flow rate, has been developed. The pattern recognition techniques such as clustering, cluster analysis, feature selection and clusters visualization methods form the basis of the software. Apart from non-hierarchical clustering procedures applied earlier, the hierarchical one is recommended. The system application for IBR-2 Dubna reactor diagnostics is shown. 10 refs.; 6 figs

Environmental Sound Recognition Using Time-Frequency Intersection Patterns

Directory of Open Access Journals (Sweden)

Xuan Guo

2012-01-01

Full Text Available Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we use a multistage perceptron neural network system for environmental sound recognition. The input data is a combination of time-variance pattern of instantaneous powers and frequency-variance pattern with instantaneous spectrum at the power peak, referred to as a time-frequency intersection pattern. Spectra of many environmental sounds change more slowly than those of speech or voice, so the intersectional time-frequency pattern will preserve the major features of environmental sounds but with drastically reduced data requirements. Two experiments were conducted using an original database and an open database created by the RWCP project. The recognition rate for 20 kinds of environmental sounds was 92%. The recognition rate of the new method was about 12% higher than methods using only an instantaneous spectrum. The results are also comparable with HMM-based methods, although those methods need to treat the time variance of an input vector series with more complicated computations.

Category-specificity in visual object recognition

DEFF Research Database (Denmark)

Gerlach, Christian

2009-01-01

Are all categories of objects recognized in the same manner visually? Evidence from neuropsychology suggests they are not: some brain damaged patients are more impaired in recognizing natural objects than artefacts whereas others show the opposite impairment. Category-effects have also been...... demonstrated in neurologically intact subjects, but the findings are contradictory and there is no agreement as to why category-effects arise. This article presents a Pre-semantic Account of Category Effects (PACE) in visual object recognition. PACE assumes two processing stages: shape configuration (the...... binding of shape elements into elaborate shape descriptions) and selection (among competing representations in visual long-term memory), which are held to be differentially affected by the structural similarity between objects. Drawing on evidence from clinical studies, experimental studies...

Type-2 fuzzy graphical models for pattern recognition

CERN Document Server

Zeng, Jia

2015-01-01

This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

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

Science.gov (United States)

Poth, Christian H; Schneider, Werner X

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Christian H. Poth

2016-09-01

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

Algorithms for adaptive nonlinear pattern recognition

Science.gov (United States)

Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

2011-09-01

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral

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

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

1998-05-01

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

Behavioral model of visual perception and recognition

Science.gov (United States)

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

1993-09-01

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

fMRI characterization of visual working memory recognition.

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Rahm, Benjamin; Kaiser, Jochen; Unterrainer, Josef M; Simon, Juliane; Bledowski, Christoph

2014-04-15

Encoding and maintenance of information in visual working memory have been extensively studied, highlighting the crucial and capacity-limiting role of fronto-parietal regions. In contrast, the neural basis of recognition in visual working memory has remained largely unspecified. Cognitive models suggest that recognition relies on a matching process that compares sensory information with the mental representations held in memory. To characterize the neural basis of recognition we varied both the need for recognition and the degree of similarity between the probe item and the memory contents, while independently manipulating memory load to produce load-related fronto-parietal activations. fMRI revealed a fractionation of working memory functions across four distributed networks. First, fronto-parietal regions were activated independent of the need for recognition. Second, anterior parts of load-related parietal regions contributed to recognition but their activations were independent of the difficulty of matching in terms of sample-probe similarity. These results argue against a key role of the fronto-parietal attention network in recognition. Rather the third group of regions including bilateral temporo-parietal junction, posterior cingulate cortex and superior frontal sulcus reflected demands on matching both in terms of sample-probe-similarity and the number of items to be compared. Also, fourth, bilateral motor regions and right superior parietal cortex showed higher activation when matching provided clear evidence for a decision. Together, the segregation between the well-known fronto-parietal activations attributed to attentional operations in working memory from those regions involved in matching supports the theoretical view of separable attentional and mnemonic contributions to working memory. Yet, the close theoretical and empirical correspondence to perceptual decision making may call for an explicit consideration of decision making mechanisms in

The Functional Architecture of Visual Object Recognition

Science.gov (United States)

1991-07-01

different forms of agnosia can provide clues to the representations underlying normal object recognition (Farah, 1990). For example, the pair-wise...patterns of deficit and sparing occur. In a review of 99 published cases of agnosia , the observed patterns of co- occurrence implicated two underlying

The principles of the pattern recognition of skeletal structures

International Nuclear Information System (INIS)

Motto, J.A.

2006-01-01

Request of the skeletal system form a lage proportion of plain film radiographic examinations. A sound knowledge of normal radiographic appearances is vital if abnormal patterns are to be recognized.The ABCS, SPACED and SASNOES methods of applying pattern recognition to plain radiographers of bones and joints will be presented in an attempt to make pattern recognition and offer an opinion constitutes role extension of radiographers

Visual discrimination and short-term memory for random patterns in patients with a focal cortical lesion

NARCIS (Netherlands)

Greenlee, MW; Koessler, M; Cornelissen, FW; Mergner, T

1997-01-01

Visual discrimination and short-term recognition memory for computer-generated random patterns were explored in 23 patients with a postsurgical lesion in one of the cortical hemispheres. Their results are compared with those of 23 age-matched volunteers. In a same-different forced-choice

The neural correlates of visual self-recognition.

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Devue, Christel; Brédart, Serge

2011-03-01

This paper presents a review of studies that were aimed at determining which brain regions are recruited during visual self-recognition, with a particular focus on self-face recognition. A complex bilateral network, involving frontal, parietal and occipital areas, appears to be associated with self-face recognition, with a particularly high implication of the right hemisphere. Results indicate that it remains difficult to determine which specific cognitive operation is reflected by each recruited brain area, in part due to the variability of used control stimuli and experimental tasks. A synthesis of the interpretations provided by previous studies is presented. The relevance of using self-recognition as an indicator of self-awareness is discussed. We argue that a major aim of future research in the field should be to identify more clearly the cognitive operations induced by the perception of the self-face, and search for dissociations between neural correlates and cognitive components. Copyright © 2010 Elsevier Inc. All rights reserved.

Applications of chaotic neurodynamics in pattern recognition

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Baird, Bill; Freeman, Walter J.; Eeckman, Frank H.; Yao, Yong

1991-08-01

Network algorithms and architectures for pattern recognition derived from neural models of the olfactory system are reviewed. These span a range from highly abstract to physiologically detailed, and employ the kind of dynamical complexity observed in olfactory cortex, ranging from oscillation to chaos. A simple architecture and algorithm for analytically guaranteed associative memory storage of analog patterns, continuous sequences, and chaotic attractors in the same network is described. A matrix inversion determines network weights, given prototype patterns to be stored. There are N units of capacity in an N node network with 3N2 weights. It costs one unit per static attractor, two per Fourier component of each sequence, and three to four per chaotic attractor. There are no spurious attractors, and for sequences there is a Liapunov function in a special coordinate system which governs the approach of transient states to stored trajectories. Unsupervised or supervised incremental learning algorithms for pattern classification, such as competitive learning or bootstrap Widrow-Hoff can easily be implemented. The architecture can be ''folded'' into a recurrent network with higher order weights that can be used as a model of cortex that stores oscillatory and chaotic attractors by a Hebb rule. Network performance is demonstrated by application to the problem of real-time handwritten digit recognition. An effective system with on-line learning has been written by Eeckman and Baird for the Macintosh. It utilizes static, oscillatory, and/or chaotic attractors of two kinds--Lorenze attractors, or attractors resulting from chaotically interacting oscillatory modes. The successful application to an industrial pattern recognition problem of a network architecture of considerable physiological and dynamical complexity, developed by Freeman and Yao, is described. The data sets of the problem come in three classes of difficulty, and performance of the biological network is

Pattern recognition of neurotransmitters using multimode sensing.

Science.gov (United States)

Stefan-van Staden, Raluca-Ioana; Moldoveanu, Iuliana; van Staden, Jacobus Frederick

2014-05-30

Pattern recognition is essential in chemical analysis of biological fluids. Reliable and sensitive methods for neurotransmitters analysis are needed. Therefore, we developed for pattern recognition of neurotransmitters: dopamine, epinephrine, norepinephrine a method based on multimode sensing. Multimode sensing was performed using microsensors based on diamond paste modified with 5,10,15,20-tetraphenyl-21H,23H-porphyrine, hemin and protoporphyrin IX in stochastic and differential pulse voltammetry modes. Optimized working conditions: phosphate buffer solution of pH 3.01 and KCl 0.1mol/L (as electrolyte support), were determined using cyclic voltammetry and used in all measurements. The lowest limits of quantification were: 10(-10)mol/L for dopamine and epinephrine, and 10(-11)mol/L for norepinephrine. The multimode microsensors were selective over ascorbic and uric acids and the method facilitated reliable assay of neurotransmitters in urine samples, and therefore, the pattern recognition showed high reliability (RSDneurotransmitters on biological fluids at a lower determination level than chromatographic methods. The sampling of the biological fluids referees only to the buffering (1:1, v/v) with a phosphate buffer pH 3.01, while for chromatographic methods the sampling is laborious. Accordingly with the statistic evaluation of the results at 99.00% confidence level, both modes can be used for pattern recognition and quantification of neurotransmitters with high reliability. The best multimode microsensor was the one based on diamond paste modified with protoporphyrin IX. Copyright © 2014 Elsevier B.V. All rights reserved.

Running Improves Pattern Separation during Novel Object Recognition.

Science.gov (United States)

Bolz, Leoni; Heigele, Stefanie; Bischofberger, Josef

2015-10-09

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

Automated pattern recognition system for noise analysis

International Nuclear Information System (INIS)

Sides, W.H. Jr.; Piety, K.R.

1980-01-01

A pattern recognition system was developed at ORNL for on-line monitoring of noise signals from sensors in a nuclear power plant. The system continuousy measures the power spectral density (PSD) values of the signals and the statistical characteristics of the PSDs in unattended operation. Through statistical comparison of current with past PSDs (pattern recognition), the system detects changes in the noise signals. Because the noise signals contain information about the current operational condition of the plant, a change in these signals could indicate a change, either normal or abnormal, in the operational condition

A pattern recognition account of decision making.

Science.gov (United States)

Massaro, D W

1994-09-01

In the domain of pattern recognition, experiments have shown that perceivers integrate multiple sources of information in an optimal manner. In contrast, other research has been interpreted to mean that decision making is nonoptimal. As an example, Tversky and Kahneman (1983) have shown that subjects commit a conjunction fallacy because they judge it more likely that a fictitious person named Linda is a bank teller and a feminist than just a bank teller. This judgment supposedly violates probability theory, because the probability of two events can never be greater than the probability of either event alone. The present research tests the hypothesis that subjects interpret this judgment task as a pattern recognition task. If this hypothesis is correct, subjects' judgments should be described accurately by the fuzzy logical model of perception (FLMP)--a successful model of pattern recognition. In the first experiment, the Linda task was extended to an expanded factorial design with five vocations and five avocations. The probability ratings were described well by the FLMP and described poorly by a simple probability model. The second experiment included (1) two fictitious people, Linda and Joan, as response alternatives and (2) both ratings and categorization judgments. Although the ratings were accurately described by both the FLMP and an averaging of the sources of information, the categorization judgments were described better by the FLMP. These results reveal important similarities in recognizing patterns and in decision making. Given that the FLMP is an optimal method for combining multiple sources of information, the probability judgments appear to be optimal in the same manner as pattern-recognition judgments.

Subspace methods for pattern recognition in intelligent environment

CERN Document Server

Jain, Lakhmi

2014-01-01

This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

MCAW-DB: A glycan profile database capturing the ambiguity of glycan recognition patterns.

Science.gov (United States)

Hosoda, Masae; Takahashi, Yushi; Shiota, Masaaki; Shinmachi, Daisuke; Inomoto, Renji; Higashimoto, Shinichi; Aoki-Kinoshita, Kiyoko F

2018-05-11

Glycan-binding protein (GBP) interaction experiments, such as glycan microarrays, are often used to understand glycan recognition patterns. However, oftentimes the interpretation of glycan array experimental data makes it difficult to identify discrete GBP binding patterns due to their ambiguity. It is known that lectins, for example, are non-specific in their binding affinities; the same lectin can bind to different monosaccharides or even different glycan structures. In bioinformatics, several tools to mine the data generated from these sorts of experiments have been developed. These tools take a library of predefined motifs, which are commonly-found glycan patterns such as sialyl-Lewis X, and attempt to identify the motif(s) that are specific to the GBP being analyzed. In our previous work, as opposed to using predefined motifs, we developed the Multiple Carbohydrate Alignment with Weights (MCAW) tool to visualize the state of the glycans being recognized by the GBP under analysis. We previously reported on the effectiveness of our tool and algorithm by analyzing several glycan array datasets from the Consortium of Functional Glycomics (CFG). In this work, we report on our analysis of 1081 data sets which we collected from the CFG, the results of which we have made publicly and freely available as a database called MCAW-DB. We introduce this database, its usage and describe several analysis results. We show how MCAW-DB can be used to analyze glycan-binding patterns of GBPs amidst their ambiguity. For example, the visualization of glycan-binding patterns in MCAW-DB show how they correlate with the concentrations of the samples used in the array experiments. Using MCAW-DB, the patterns of glycans found to bind to various GBP-glycan binding proteins are visualized, indicating the binding "environment" of the glycans. Thus, the ambiguity of glycan recognition is numerically represented, along with the patterns of monosaccharides surrounding the binding region. The

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

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Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Infant Visual Recognition Memory: Independent Contributions of Speed and Attention.

Science.gov (United States)

Rose, Susan A.; Feldman, Judith F.; Jankowski, Jeffery J.

2003-01-01

Examined contributions of cognitive processing speed, short-term memory capacity, and attention to infant visual recognition memory. Found that infants who showed better attention and faster processing had better recognition memory. Contributions of attention and processing speed were independent of one another and similar at all ages studied--5,…

Two processes support visual recognition memory in rhesus monkeys.

Science.gov (United States)

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-11-29

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans.

Emotion recognition abilities across stimulus modalities in schizophrenia and the role of visual attention.

Science.gov (United States)

Simpson, Claire; Pinkham, Amy E; Kelsven, Skylar; Sasson, Noah J

2013-12-01

Emotion can be expressed by both the voice and face, and previous work suggests that presentation modality may impact emotion recognition performance in individuals with schizophrenia. We investigated the effect of stimulus modality on emotion recognition accuracy and the potential role of visual attention to faces in emotion recognition abilities. Thirty-one patients who met DSM-IV criteria for schizophrenia (n=8) or schizoaffective disorder (n=23) and 30 non-clinical control individuals participated. Both groups identified emotional expressions in three different conditions: audio only, visual only, combined audiovisual. In the visual only and combined conditions, time spent visually fixating salient features of the face were recorded. Patients were significantly less accurate than controls in emotion recognition during both the audio and visual only conditions but did not differ from controls on the combined condition. Analysis of visual scanning behaviors demonstrated that patients attended less than healthy individuals to the mouth in the visual condition but did not differ in visual attention to salient facial features in the combined condition, which may in part explain the absence of a deficit for patients in this condition. Collectively, these findings demonstrate that patients benefit from multimodal stimulus presentations of emotion and support hypotheses that visual attention to salient facial features may serve as a mechanism for accurate emotion identification. © 2013.

End-to-end visual speech recognition with LSTMS

NARCIS (Netherlands)

Petridis, Stavros; Li, Zuwei; Pantic, Maja

2017-01-01

Traditional visual speech recognition systems consist of two stages, feature extraction and classification. Recently, several deep learning approaches have been presented which automatically extract features from the mouth images and aim to replace the feature extraction stage. However, research on

Visual recognition of permuted words

Science.gov (United States)

Rashid, Sheikh Faisal; Shafait, Faisal; Breuel, Thomas M.

2010-02-01

In current study we examine how letter permutation affects in visual recognition of words for two orthographically dissimilar languages, Urdu and German. We present the hypothesis that recognition or reading of permuted and non-permuted words are two distinct mental level processes, and that people use different strategies in handling permuted words as compared to normal words. A comparison between reading behavior of people in these languages is also presented. We present our study in context of dual route theories of reading and it is observed that the dual-route theory is consistent with explanation of our hypothesis of distinction in underlying cognitive behavior for reading permuted and non-permuted words. We conducted three experiments in lexical decision tasks to analyze how reading is degraded or affected by letter permutation. We performed analysis of variance (ANOVA), distribution free rank test, and t-test to determine the significance differences in response time latencies for two classes of data. Results showed that the recognition accuracy for permuted words is decreased 31% in case of Urdu and 11% in case of German language. We also found a considerable difference in reading behavior for cursive and alphabetic languages and it is observed that reading of Urdu is comparatively slower than reading of German due to characteristics of cursive script.

The what, when, where, and how of visual word recognition.

Science.gov (United States)

Carreiras, Manuel; Armstrong, Blair C; Perea, Manuel; Frost, Ram

2014-02-01

A long-standing debate in reading research is whether printed words are perceived in a feedforward manner on the basis of orthographic information, with other representations such as semantics and phonology activated subsequently, or whether the system is fully interactive and feedback from these representations shapes early visual word recognition. We review recent evidence from behavioral, functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and biologically plausible connectionist modeling approaches, focusing on how each approach provides insight into the temporal flow of information in the lexical system. We conclude that, consistent with interactive accounts, higher-order linguistic representations modulate early orthographic processing. We also discuss how biologically plausible interactive frameworks and coordinated empirical and computational work can advance theories of visual word recognition and other domains (e.g., object recognition). Copyright © 2013 Elsevier Ltd. All rights reserved.

Pattern recognition and modelling of earthquake registrations with interactive computer support

International Nuclear Information System (INIS)

Manova, Katarina S.

2004-01-01

The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

Reader error, object recognition, and visual search

Science.gov (United States)

Kundel, Harold L.

2004-05-01

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

Effects of cholinergic deafferentation of the rhinal cortex on visual recognition memory in monkeys.

Science.gov (United States)

Turchi, Janita; Saunders, Richard C; Mishkin, Mortimer

2005-02-08

Excitotoxic lesion studies have confirmed that the rhinal cortex is essential for visual recognition ability in monkeys. To evaluate the mnemonic role of cholinergic inputs to this cortical region, we compared the visual recognition performance of monkeys given rhinal cortex infusions of a selective cholinergic immunotoxin, ME20.4-SAP, with the performance of monkeys given control infusions into this same tissue. The immunotoxin, which leads to selective cholinergic deafferentation of the infused cortex, yielded recognition deficits of the same magnitude as those produced by excitotoxic lesions of this region, providing the most direct demonstration to date that cholinergic activation of the rhinal cortex is essential for storing the representations of new visual stimuli and thereby enabling their later recognition.

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

Science.gov (United States)

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

2005-01-01

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

The role of long-term and short-term familiarity in visual and haptic face recognition.

Science.gov (United States)

Casey, Sarah J; Newell, Fiona N

2005-10-01

Recent studies have suggested that the familiarity of a face leads to more robust recognition, at least within the visual domain. The aim of our study was to investigate whether face familiarity resulted in a representation of faces that was easily shared across the sensory modalities. In Experiment 1, we tested whether haptic recognition of a highly familiar face (one's own face) was as efficient as visual recognition. Our observers were unable to recognise their own face models from tactile memory alone but were able to recognise their faces visually. However, haptic recognition improved when participants were primed by their own live face. In Experiment 2, we found that short-term familiarisation with a set of previously unfamiliar face stimuli improved crossmodal recognition relative to the recognition of unfamiliar faces. Our findings suggest that familiarisation provides a strong representation of faces but that the nature of the information encoded during learning is critical for efficient crossmodal recognition.

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Chen, H; Tan, J; Kavanaugh, J; Dolly, S; Gay, H; Thorstad, W; Anastasio, M; Altman, M; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States)

2014-06-15

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding

Structural pattern recognition methods based on string comparison for fusion databases

International Nuclear Information System (INIS)

Dormido-Canto, S.; Farias, G.; Dormido, R.; Vega, J.; Sanchez, J.; Duro, N.; Vargas, H.; Ratta, G.; Pereira, A.; Portas, A.

2008-01-01

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed

Structural pattern recognition methods based on string comparison for fusion databases

Energy Technology Data Exchange (ETDEWEB)

Dormido-Canto, S. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain)], E-mail: sebas@dia.uned.es; Farias, G.; Dormido, R. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain); Sanchez, J.; Duro, N.; Vargas, H. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Ratta, G.; Pereira, A.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain)

2008-04-15

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed.

Recognition of visual stimuli and memory for spatial context in schizophrenic patients and healthy volunteers.

Science.gov (United States)

Brébion, Gildas; David, Anthony S; Pilowsky, Lyn S; Jones, Hugh

2004-11-01

Verbal and visual recognition tasks were administered to 40 patients with schizophrenia and 40 healthy comparison subjects. The verbal recognition task consisted of discriminating between 16 target words and 16 new words. The visual recognition task consisted of discriminating between 16 target pictures (8 black-and-white and 8 color) and 16 new pictures (8 black-and-white and 8 color). Visual recognition was followed by a spatial context discrimination task in which subjects were required to remember the spatial location of the target pictures at encoding. Results showed that recognition deficit in patients was similar for verbal and visual material. In both schizophrenic and healthy groups, men, but not women, obtained better recognition scores for the colored than for the black-and-white pictures. However, men and women similarly benefited from color to reduce spatial context discrimination errors. Patients showed a significant deficit in remembering the spatial location of the pictures, independently of accuracy in remembering the pictures themselves. These data suggest that patients are impaired in the amount of visual information that they can encode. With regards to the perceptual attributes of the stimuli, memory for spatial information appears to be affected, but not processing of color information.

Reactor noise analysis by statistical pattern recognition methods

International Nuclear Information System (INIS)

Howington, L.C.; Gonzalez, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis is presented. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, updating, and data compacting capabilities. System design emphasizes control of the false-alarm rate. Its abilities to learn normal patterns, to recognize deviations from these patterns, and to reduce the dimensionality of data with minimum error were evaluated by experiments at the Oak Ridge National Laboratory (ORNL) High-Flux Isotope Reactor. Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the pattern recognition system

Two Challenges of Correct Validation in Pattern Recognition

Directory of Open Access Journals (Sweden)

Thomas eNowotny

2014-09-01

Full Text Available Supervised pattern recognition is the process of mapping patterns to class labelsthat define their meaning. The core methods for pattern recognitionhave been developed by machine learning experts but due to their broadsuccess an increasing number of non-experts are now employing andrefining them. In this perspective I will discuss the challenge ofcorrect validation of supervised pattern recognition systems, in particular whenemployed by non-experts. To illustrate the problem I will give threeexamples of common errors that I have encountered in the lastyear. Much of this challenge can be addressed by strict procedure invalidation but there are remaining problems of correctlyinterpreting comparative work on exemplary data sets, which I willelucidate on the example of the well-used MNIST data set of handwrittendigits.

The Pattern Recognition in Cattle Brand using Bag of Visual Words and Support Vector Machines Multi-Class

Directory of Open Access Journals (Sweden)

Carlos Silva, Mr

2018-03-01

Full Text Available The recognition images of cattle brand in an automatic way is a necessity to governmental organs responsible for this activity. To help this process, this work presents a method that consists in using Bag of Visual Words for extracting of characteristics from images of cattle brand and Support Vector Machines Multi-Class for classification. This method consists of six stages: a select database of images; b extract points of interest (SURF; c create vocabulary (K-means; d create vector of image characteristics (visual words; e train and sort images (SVM; f evaluate the classification results. The accuracy of the method was tested on database of municipal city hall, where it achieved satisfactory results, reporting 86.02% of accuracy and 56.705 seconds of processing time, respectively.

Pattern recognition methods for acoustic emission analysis

International Nuclear Information System (INIS)

Doctor, P.G.; Harrington, T.P.; Hutton, P.H.

1979-07-01

Models have been developed that relate the rate of acoustic emissions to structural integrity. The implementation of these techniques in the field has been hindered by the noisy environment in which the data must be taken. Acoustic emissions from noncritical sources are recorded in addition to those produced by critical sources, such as flaws. A technique is discussed for prescreening acoustic events and filtering out those that are produced by noncritical sources. The methodology that was investigated is pattern recognition. Three different pattern recognition techniques were applied to a data set that consisted of acoustic emissions caused by crack growth and acoustic signals caused by extraneous noise sources. Examination of the acoustic emission data presented has uncovered several features of the data that can provide a reasonable filter. Two of the most valuable features are the frequency of maximum response and the autocorrelation coefficient at Lag 13. When these two features and several others were combined with a least squares decision algorithm, 90% of the acoustic emissions in the data set were correctly classified. It appears possible to design filters that eliminate extraneous noise sources from flaw-growth acoustic emissions using pattern recognition techniques

Data complexity in pattern recognition

CERN Document Server

Kam Ho Tin

2006-01-01

Machines capable of automatic pattern recognition have many fascinating uses. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. This book looks at data complexity and its role in shaping the theories and techniques in different disciplines

Applications of evolutionary computation in image processing and pattern recognition

CERN Document Server

Cuevas, Erik; Perez-Cisneros, Marco

2016-01-01

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an...

Association of impaired facial affect recognition with basic facial and visual processing deficits in schizophrenia.

Science.gov (United States)

Norton, Daniel; McBain, Ryan; Holt, Daphne J; Ongur, Dost; Chen, Yue

2009-06-15

Impaired emotion recognition has been reported in schizophrenia, yet the nature of this impairment is not completely understood. Recognition of facial emotion depends on processing affective and nonaffective facial signals, as well as basic visual attributes. We examined whether and how poor facial emotion recognition in schizophrenia is related to basic visual processing and nonaffective face recognition. Schizophrenia patients (n = 32) and healthy control subjects (n = 29) performed emotion discrimination, identity discrimination, and visual contrast detection tasks, where the emotionality, distinctiveness of identity, or visual contrast was systematically manipulated. Subjects determined which of two presentations in a trial contained the target: the emotional face for emotion discrimination, a specific individual for identity discrimination, and a sinusoidal grating for contrast detection. Patients had significantly higher thresholds (worse performance) than control subjects for discriminating both fearful and happy faces. Furthermore, patients' poor performance in fear discrimination was predicted by performance in visual detection and face identity discrimination. Schizophrenia patients require greater emotional signal strength to discriminate fearful or happy face images from neutral ones. Deficient emotion recognition in schizophrenia does not appear to be determined solely by affective processing but is also linked to the processing of basic visual and facial information.

Toward unsupervised outbreak detection through visual perception of new patterns

Directory of Open Access Journals (Sweden)

Lévy Pierre P

2009-06-01

Full Text Available Abstract Background Statistical algorithms are routinely used to detect outbreaks of well-defined syndromes, such as influenza-like illness. These methods cannot be applied to the detection of emerging diseases for which no preexisting information is available. This paper presents a method aimed at facilitating the detection of outbreaks, when there is no a priori knowledge of the clinical presentation of cases. Methods The method uses a visual representation of the symptoms and diseases coded during a patient consultation according to the International Classification of Primary Care 2nd version (ICPC-2. The surveillance data are transformed into color-coded cells, ranging from white to red, reflecting the increasing frequency of observed signs. They are placed in a graphic reference frame mimicking body anatomy. Simple visual observation of color-change patterns over time, concerning a single code or a combination of codes, enables detection in the setting of interest. Results The method is demonstrated through retrospective analyses of two data sets: description of the patients referred to the hospital by their general practitioners (GPs participating in the French Sentinel Network and description of patients directly consulting at a hospital emergency department (HED. Informative image color-change alert patterns emerged in both cases: the health consequences of the August 2003 heat wave were visualized with GPs' data (but passed unnoticed with conventional surveillance systems, and the flu epidemics, which are routinely detected by standard statistical techniques, were recognized visually with HED data. Conclusion Using human visual pattern-recognition capacities to detect the onset of unexpected health events implies a convenient image representation of epidemiological surveillance and well-trained "epidemiology watchers". Once these two conditions are met, one could imagine that the epidemiology watchers could signal epidemiological alerts

Instruction of pattern recognition by MATLAB practice 1

International Nuclear Information System (INIS)

1999-06-01

This book describes the pattern recognition by MATLAB practice. It includes possibility and limit of AI, introduction of pattern recognition a vector and matrix, basic status and a probability theory, a random variable and probability distribution, statistical decision theory, data-mining, gaussian mixture model, a nerve cell modeling such as Hebb's learning rule, LMS learning rule, genetic algorithm, dynamic programming and DTW, HMN on Markov model and HMM's three problems and solution, introduction of SVM with KKT condition and margin optimum, kernel trick and MATLAB practice.

Biometric verification based on grip-pattern recognition

NARCIS (Netherlands)

Veldhuis, Raymond N.J.; Bazen, A.M.; Kauffman, J.A.; Hartel, Pieter H.; Delp, Edward J.; Wong, Ping W.

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 x 44 piezoresistive elements is used to measure the grip pattern. An interface has been

Biometric verification based on grip-pattern recognition

NARCIS (Netherlands)

Veldhuis, Raymond N.J.; Bazen, A.M.; Kauffman, J.A.; Hartel, Pieter H.

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

Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

DEFF Research Database (Denmark)

Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.

2018-01-01

, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data......PAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance...... recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate....

Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering

DEFF Research Database (Denmark)

Busk, Peter Kamp

2017-01-01

Large collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited...... number of sequences as input. I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than...... the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer. Peptide Pattern Recognition...

Association of auditory-verbal and visual hallucinations with impaired and improved recognition of colored pictures.

Science.gov (United States)

Brébion, Gildas; Stephan-Otto, Christian; Usall, Judith; Huerta-Ramos, Elena; Perez del Olmo, Mireia; Cuevas-Esteban, Jorge; Haro, Josep Maria; Ochoa, Susana

2015-09-01

A number of cognitive underpinnings of auditory hallucinations have been established in schizophrenia patients, but few have, as yet, been uncovered for visual hallucinations. In previous research, we unexpectedly observed that auditory hallucinations were associated with poor recognition of color, but not black-and-white (b/w), pictures. In this study, we attempted to replicate and explain this finding. Potential associations with visual hallucinations were explored. B/w and color pictures were presented to 50 schizophrenia patients and 45 healthy individuals under 2 conditions of visual context presentation corresponding to 2 levels of visual encoding complexity. Then, participants had to recognize the target pictures among distractors. Auditory-verbal hallucinations were inversely associated with the recognition of the color pictures presented under the most effortful encoding condition. This association was fully mediated by working-memory span. Visual hallucinations were associated with improved recognition of the color pictures presented under the less effortful condition. Patients suffering from visual hallucinations were not impaired, relative to the healthy participants, in the recognition of these pictures. Decreased working-memory span in patients with auditory-verbal hallucinations might impede the effortful encoding of stimuli. Visual hallucinations might be associated with facilitation in the visual encoding of natural scenes, or with enhanced color perception abilities. (c) 2015 APA, all rights reserved).

COSFIRE : A Brain-Inspired Approach to Visual Pattern Recognition

NARCIS (Netherlands)

Azzopardi, G.; Petkov, N.

2014-01-01

The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a

COSFIRE : A brain-inspired approach to visual pattern recognition

NARCIS (Netherlands)

Azzopardi, George; Petkov, Nicolai; Grandinetti, Lucio; Lippert, Thomas; Petkov, Nicolai

2014-01-01

The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a

Does viotin activate violin more than viocin? On the use of visual cues during visual-word recognition.

Science.gov (United States)

Perea, Manuel; Panadero, Victoria

2014-01-01

The vast majority of neural and computational models of visual-word recognition assume that lexical access is achieved via the activation of abstract letter identities. Thus, a word's overall shape should play no role in this process. In the present lexical decision experiment, we compared word-like pseudowords like viotín (same shape as its base word: violín) vs. viocín (different shape) in mature (college-aged skilled readers), immature (normally reading children), and immature/impaired (young readers with developmental dyslexia) word-recognition systems. Results revealed similar response times (and error rates) to consistent-shape and inconsistent-shape pseudowords for both adult skilled readers and normally reading children - this is consistent with current models of visual-word recognition. In contrast, young readers with developmental dyslexia made significantly more errors to viotín-like pseudowords than to viocín-like pseudowords. Thus, unlike normally reading children, young readers with developmental dyslexia are sensitive to a word's visual cues, presumably because of poor letter representations.

Attentional cueing by cross-modal congruency produces both facilitation and inhibition on short-term visual recognition.

Science.gov (United States)

Makovac, Elena; Kwok, Sze Chai; Gerbino, Walter

2014-10-01

The attentional modulation of performance in a memory task, comparable to the one obtained in a perceptual task, is at the focus of contemporary research. We hypothesized that a biphasic effect (namely, facilitation followed by inhibition) can be obtained in visual working memory when attention is cued towards one item of the memorandum and participants must recognize a delayed probe as being identical to any item of the memorandum. In every trial, a delayed spiky/curvy probe appeared centrally, to be matched with the same-category shape maintained in visual working memory which could be either physically identical (positive trials) or only categorically similar (negative trials). To orient the participant's attention towards a selected portion of a two-item memorandum, a (tzk/wow) sound was played simultaneously with two lateral visual shapes (one spiky and one curved). Our results indicate that an exogenous attentional shift during perception of the memorandum, induced by a congruent audio-visual pairing, first facilitates and then inhibits the recognition of a cued item (but not of a non-cued item) stored in visual working memory. A coherent pattern of individual differences emerged, indicating that the amount of early facilitation in congruent-sound trials was negatively correlated with recognition sensitivity in no-sound trials (suggesting that the inverse effectiveness rule may also apply to memory) and positively correlated with later inhibition, as well as with the self-reported susceptibility to memory failures. Copyright © 2014 Elsevier B.V. All rights reserved.

Sparse representation, modeling and learning in visual recognition theory, algorithms and applications

CERN Document Server

Cheng, Hong

2015-01-01

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen

ANALYSIS OF MULTIMODAL FUSION TECHNIQUES FOR AUDIO-VISUAL SPEECH RECOGNITION

Directory of Open Access Journals (Sweden)

D.V. Ivanko

2016-05-01

Full Text Available The paper deals with analytical review, covering the latest achievements in the field of audio-visual (AV fusion (integration of multimodal information. We discuss the main challenges and report on approaches to address them. One of the most important tasks of the AV integration is to understand how the modalities interact and influence each other. The paper addresses this problem in the context of AV speech processing and speech recognition. In the first part of the review we set out the basic principles of AV speech recognition and give the classification of audio and visual features of speech. Special attention is paid to the systematization of the existing techniques and the AV data fusion methods. In the second part we provide a consolidated list of tasks and applications that use the AV fusion based on carried out analysis of research area. We also indicate used methods, techniques, audio and video features. We propose classification of the AV integration, and discuss the advantages and disadvantages of different approaches. We draw conclusions and offer our assessment of the future in the field of AV fusion. In the further research we plan to implement a system of audio-visual Russian continuous speech recognition using advanced methods of multimodal fusion.

Insensitivity of visual short-term memory to irrelevant visual information.

Science.gov (United States)

Andrade, Jackie; Kemps, Eva; Werniers, Yves; May, Jon; Szmalec, Arnaud

2002-07-01

Several authors have hypothesized that visuo-spatial working memory is functionally analogous to verbal working memory. Irrelevant background speech impairs verbal short-term memory. We investigated whether irrelevant visual information has an analogous effect on visual short-term memory, using a dynamic visual noise (DVN) technique known to disrupt visual imagery (Quinn & McConnell, 1996b). Experiment I replicated the effect of DVN on pegword imagery. Experiments 2 and 3 showed no effect of DVN on recall of static matrix patterns, despite a significant effect of a concurrent spatial tapping task. Experiment 4 showed no effect of DVN on encoding or maintenance of arrays of matrix patterns, despite testing memory by a recognition procedure to encourage visual rather than spatial processing. Serial position curves showed a one-item recency effect typical of visual short-term memory. Experiment 5 showed no effect of DVN on short-term recognition of Chinese characters, despite effects of visual similarity and a concurrent colour memory task that confirmed visual processing of the characters. We conclude that irrelevant visual noise does not impair visual short-term memory. Visual working memory may not be functionally analogous to verbal working memory, and different cognitive processes may underlie visual short-term memory and visual imagery.

Generation of oculomotor images during tasks requiring visual recognition of polygons.

Science.gov (United States)

Olivier, G; de Mendoza, J L

2001-06-01

This paper concerns the contribution of mentally simulated ocular exploration to generation of a visual mental image. In Exp. 1, repeated exploration of the outlines of an irregular decagon allowed an incidental learning of the shape. Analyses showed subjects memorized their ocular movements rather than the polygon. In Exp. 2, exploration of a reversible figure such as a Necker cube varied in opposite directions. Then, both perspective possibilities are presented. The perspective the subjects recognized depended on the way they explored the ambiguous figure. In both experiments, during recognition the subjects recalled a visual mental image of the polygon they compared with the different polygons proposed for recognition. To interpret the data, hypotheses concerning common processes underlying both motor intention of ocular movements and generation of a visual image are suggested.

Fuzzy tree automata and syntactic pattern recognition.

Science.gov (United States)

Lee, E T

1982-04-01

An approach of representing patterns by trees and processing these trees by fuzzy tree automata is described. Fuzzy tree automata are defined and investigated. The results include that the class of fuzzy root-to-frontier recognizable ¿-trees is closed under intersection, union, and complementation. Thus, the class of fuzzy root-to-frontier recognizable ¿-trees forms a Boolean algebra. Fuzzy tree automata are applied to processing fuzzy tree representation of patterns based on syntactic pattern recognition. The grade of acceptance is defined and investigated. Quantitative measures of ``approximate isosceles triangle,'' ``approximate elongated isosceles triangle,'' ``approximate rectangle,'' and ``approximate cross'' are defined and used in the illustrative examples of this approach. By using these quantitative measures, a house, a house with high roof, and a church are also presented as illustrative examples. In addition, three fuzzy tree automata are constructed which have the capability of processing the fuzzy tree representations of ``fuzzy houses,'' ``houses with high roofs,'' and ``fuzzy churches,'' respectively. The results may have useful applications in pattern recognition, image processing, artificial intelligence, pattern database design and processing, image science, and pictorial information systems.

A Cooking Recipe Recommendation System with Visual Recognition of Food Ingredients

Directory of Open Access Journals (Sweden)

Keiji Yanai

2014-04-01

Full Text Available In this paper, we propose a cooking recipe recommendation system which runs on a consumer smartphone as an interactive mobile application. The proposed system employs real-time visual object recognition of food ingredients, and recommends cooking recipes related to the recognized food ingredients. Because of visual recognition, by only pointing a built-in camera on a smartphone to food ingredients, a user can get to know a related cooking recipes instantly. The objective of the proposed system is to assist people who cook to decide a cooking recipe at grocery stores or at a kitchen. In the current implementation, the system can recognize 30 kinds of food ingredient in 0.15 seconds, and it has achieved the 83.93% recognition rate within the top six candidates. By the user study, we confirmed the effectiveness of the proposed system.

Functional architecture of visual emotion recognition ability: A latent variable approach.

Science.gov (United States)

Lewis, Gary J; Lefevre, Carmen E; Young, Andrew W

2016-05-01

Emotion recognition has been a focus of considerable attention for several decades. However, despite this interest, the underlying structure of individual differences in emotion recognition ability has been largely overlooked and thus is poorly understood. For example, limited knowledge exists concerning whether recognition ability for one emotion (e.g., disgust) generalizes to other emotions (e.g., anger, fear). Furthermore, it is unclear whether emotion recognition ability generalizes across modalities, such that those who are good at recognizing emotions from the face, for example, are also good at identifying emotions from nonfacial cues (such as cues conveyed via the body). The primary goal of the current set of studies was to address these questions through establishing the structure of individual differences in visual emotion recognition ability. In three independent samples (Study 1: n = 640; Study 2: n = 389; Study 3: n = 303), we observed that the ability to recognize visually presented emotions is based on different sources of variation: a supramodal emotion-general factor, supramodal emotion-specific factors, and face- and within-modality emotion-specific factors. In addition, we found evidence that general intelligence and alexithymia were associated with supramodal emotion recognition ability. Autism-like traits, empathic concern, and alexithymia were independently associated with face-specific emotion recognition ability. These results (a) provide a platform for further individual differences research on emotion recognition ability, (b) indicate that differentiating levels within the architecture of emotion recognition ability is of high importance, and (c) show that the capacity to understand expressions of emotion in others is linked to broader affective and cognitive processes. (c) 2016 APA, all rights reserved).

Visualizing Dynamic Bitcoin Transaction Patterns.

Science.gov (United States)

McGinn, Dan; Birch, David; Akroyd, David; Molina-Solana, Miguel; Guo, Yike; Knottenbelt, William J

2016-06-01

This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.

Visualizing Dynamic Bitcoin Transaction Patterns

Science.gov (United States)

McGinn, Dan; Birch, David; Akroyd, David; Molina-Solana, Miguel; Guo, Yike; Knottenbelt, William J.

2016-01-01

Abstract This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network. PMID:27441715

The Role of Binocular Disparity in Rapid Scene and Pattern Recognition

Directory of Open Access Journals (Sweden)

Matteo Valsecchi

2013-04-01

Full Text Available We investigated the contribution of binocular disparity to the rapid recognition of scenes and simpler spatial patterns using a paradigm combining backward masked stimulus presentation and short-term match-to-sample recognition. First, we showed that binocular disparity did not contribute significantly to the recognition of briefly presented natural and artificial scenes, even when the availability of monocular cues was reduced. Subsequently, using dense random dot stereograms as stimuli, we showed that observers were in principle able to extract spatial patterns defined only by disparity under brief, masked presentations. Comparing our results with the predictions from a cue-summation model, we showed that combining disparity with luminance did not per se disrupt the processing of disparity. Our results suggest that the rapid recognition of scenes is mediated mostly by a monocular comparison of the images, although we can rely on stereo in fast pattern recognition.

Pattern recognition approach to nondestructive evaluation of materials

International Nuclear Information System (INIS)

Chen, C.H.

1987-01-01

In this paper, a pattern recognition approach to the ultrasonic nondestructive evaluation of materials is examined. Emphasis is placed on identifying effective features from time and frequency domains, correlation functions and impulse responses to classify aluminum plate specimens into three major defect geometry categories: flat, angular cut and circular hole defects. A multi-stage classification procedure is developed which can further determine the angles and sizes for defect characterization and classification. The research clearly demonstrates that the pattern recognition approach can significantly improve the nondestructive material evaluation capability of the ultrasonic methods without resorting to the solution of highly complex mathematical inverse problems

Application of pattern recognition techniques to crime analysis

Energy Technology Data Exchange (ETDEWEB)

Bender, C.F.; Cox, L.A. Jr.; Chappell, G.A.

1976-08-15

The initial goal was to evaluate the capabilities of current pattern recognition techniques when applied to existing computerized crime data. Performance was to be evaluated both in terms of the system's capability to predict crimes and to optimize police manpower allocation. A relation was sought to predict the crime's susceptibility to solution, based on knowledge of the crime type, location, time, etc. The preliminary results of this work are discussed. They indicate that automatic crime analysis involving pattern recognition techniques is feasible, and that efforts to determine optimum variables and techniques are warranted. 47 figures (RWR)

Quantum pattern recognition with multi-neuron interactions

Science.gov (United States)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

Optimal pattern synthesis for speech recognition based on principal component analysis

Science.gov (United States)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Dentate gyrus supports slope recognition memory, shades of grey-context pattern separation and recognition memory, and CA3 supports pattern completion for object memory.

Science.gov (United States)

Kesner, Raymond P; Kirk, Ryan A; Yu, Zhenghui; Polansky, Caitlin; Musso, Nick D

2016-03-01

In order to examine the role of the dorsal dentate gyrus (dDG) in slope (vertical space) recognition and possible pattern separation, various slope (vertical space) degrees were used in a novel exploratory paradigm to measure novelty detection for changes in slope (vertical space) recognition memory and slope memory pattern separation in Experiment 1. The results of the experiment indicate that control rats displayed a slope recognition memory function with a pattern separation process for slope memory that is dependent upon the magnitude of change in slope between study and test phases. In contrast, the dDG lesioned rats displayed an impairment in slope recognition memory, though because there was no significant interaction between the two groups and slope memory, a reliable pattern separation impairment for slope could not be firmly established in the DG lesioned rats. In Experiment 2, in order to determine whether, the dDG plays a role in shades of grey spatial context recognition and possible pattern separation, shades of grey were used in a novel exploratory paradigm to measure novelty detection for changes in the shades of grey context environment. The results of the experiment indicate that control rats displayed a shades of grey-context pattern separation effect across levels of separation of context (shades of grey). In contrast, the DG lesioned rats displayed a significant interaction between the two groups and levels of shades of grey suggesting impairment in a pattern separation function for levels of shades of grey. In Experiment 3 in order to determine whether the dorsal CA3 (dCA3) plays a role in object pattern completion, a new task requiring less training and using a choice that was based on choosing the correct set of objects on a two-choice discrimination task was used. The results indicated that control rats displayed a pattern completion function based on the availability of one, two, three or four cues. In contrast, the dCA3 lesioned rats

Consistent melanophore spot patterns allow long-term individual recognition of Atlantic salmon Salmo salar.

Science.gov (United States)

Stien, L H; Nilsson, J; Bui, S; Fosseidengen, J E; Kristiansen, T S; Øverli, Ø; Folkedal, O

2017-12-01

The present study shows that permanent melanophore spot patterns in Atlantic salmon Salmo salar make it possible to use images of the operculum to keep track of individual fish over extended periods of their life history. Post-smolt S. salar (n = 246) were initially photographed at an average mass of 98 g and again 10 months later after rearing in a sea cage, at an average mass of 3088 g. Spots that were present initially remained and were the most overt (largest) 10 months later, while new and less overt spots had developed. Visual recognition of spot size and position showed that fish with at least four initial spots were relatively easy to identify, while identifying fish with less than four spots could be challenging. An automatic image analysis method was developed and shows potential for fast match processing of large numbers of fish. The current findings promote visual recognition of opercular spots as a welfare-friendly alternative to tagging in experiments involving salmonid fishes. © The Authors. Journal of Fish Biology published by John Wiley & Sons Ltd on behalf of The Fisheries Society of the British Isles.

Optical character recognition reading aid for the visually impaired.

Science.gov (United States)

Grandin, Juan Carlos; Cremaschi, Fabian; Lombardo, Elva; Vitu, Ed; Dujovny, Manuel

2008-06-01

An optical character recognition (OCR) reading machine is a significant help for visually impaired patients. An OCR reading machine is used. This instrument can provide a significant help in order to improve the quality of life of patients with low vision or blindness.

An inverse problem approach to pattern recognition in industry

Directory of Open Access Journals (Sweden)

Ali Sever

2015-01-01

Full Text Available Many works have shown strong connections between learning and regularization techniques for ill-posed inverse problems. A careful analysis shows that a rigorous connection between learning and regularization for inverse problem is not straightforward. In this study, pattern recognition will be viewed as an ill-posed inverse problem and applications of methods from the theory of inverse problems to pattern recognition are studied. A new learning algorithm derived from a well-known regularization model is generated and applied to the task of reconstruction of an inhomogeneous object as pattern recognition. Particularly, it is demonstrated that pattern recognition can be reformulated in terms of inverse problems defined by a Riesz-type kernel. This reformulation can be employed to design a learning algorithm based on a numerical solution of a system of linear equations. Finally, numerical experiments have been carried out with synthetic experimental data considering a reasonable level of noise. Good recoveries have been achieved with this methodology, and the results of these simulations are compatible with the existing methods. The comparison results show that the Regularization-based learning algorithm (RBA obtains a promising performance on the majority of the test problems. In prospects, this method can be used for the creation of automated systems for diagnostics, testing, and control in various fields of scientific and applied research, as well as in industry.

Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats

Directory of Open Access Journals (Sweden)

Federica Bianca Rosselli

2015-03-01

Full Text Available In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness. In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: i smaller and more scattered; ii only partially preserved across object views; and iii only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning.

Common constraints limit Korean and English character recognition in peripheral vision.

Science.gov (United States)

He, Yingchen; Kwon, MiYoung; Legge, Gordon E

2018-01-01

The visual span refers to the number of adjacent characters that can be recognized in a single glance. It is viewed as a sensory bottleneck in reading for both normal and clinical populations. In peripheral vision, the visual span for English characters can be enlarged after training with a letter-recognition task. Here, we examined the transfer of training from Korean to English characters for a group of bilingual Korean native speakers. In the pre- and posttests, we measured visual spans for Korean characters and English letters. Training (1.5 hours × 4 days) consisted of repetitive visual-span measurements for Korean trigrams (strings of three characters). Our training enlarged the visual spans for Korean single characters and trigrams, and the benefit transferred to untrained English symbols. The improvement was largely due to a reduction of within-character and between-character crowding in Korean recognition, as well as between-letter crowding in English recognition. We also found a negative correlation between the size of the visual span and the average pattern complexity of the symbol set. Together, our results showed that the visual span is limited by common sensory (crowding) and physical (pattern complexity) factors regardless of the language script, providing evidence that the visual span reflects a universal bottleneck for text recognition.

Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

CERN Document Server

Melin, Patricia

2012-01-01

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

Audio-Visual Tibetan Speech Recognition Based on a Deep Dynamic Bayesian Network for Natural Human Robot Interaction

Directory of Open Access Journals (Sweden)

Yue Zhao

2012-12-01

Full Text Available Audio-visual speech recognition is a natural and robust approach to improving human-robot interaction in noisy environments. Although multi-stream Dynamic Bayesian Network and coupled HMM are widely used for audio-visual speech recognition, they fail to learn the shared features between modalities and ignore the dependency of features among the frames within each discrete state. In this paper, we propose a Deep Dynamic Bayesian Network (DDBN to perform unsupervised extraction of spatial-temporal multimodal features from Tibetan audio-visual speech data and build an accurate audio-visual speech recognition model under a no frame-independency assumption. The experiment results on Tibetan speech data from some real-world environments showed the proposed DDBN outperforms the state-of-art methods in word recognition accuracy.

Applications of pattern recognition theory in diagnostics of nuclear power plants

International Nuclear Information System (INIS)

Cech, J.

1982-01-01

The questions are discussed of the application of the theory of pattern recognition in the diagnostics of nuclear power plants. For the future use of recognition systems in the diagnostics of nuclear power plants it is obvious that like with other complex systems, optimal models will have to be used which will organize the optimal recognition algorithm. The conclusion is presented that for the needs of nuclear power plants special systems will be more suitable for pattern recognition than digital computers which are flexible and adaptible but have a lower decision rate, an insufficient working memory, complicated programs, etc. (Z.M.)

Visual recognition and inference using dynamic overcomplete sparse learning.

Science.gov (United States)

Murray, Joseph F; Kreutz-Delgado, Kenneth

2007-09-01

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

Sub-pattern based multi-manifold discriminant analysis for face recognition

Science.gov (United States)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Distorted Pattern Recognition and Analysis with the Help of IEf Graph Representation

Directory of Open Access Journals (Sweden)

Adam Sedziwy

2002-01-01

Full Text Available An algorithm for distorted pattern recognition is presented. lt's generalization of M Flasinski results (Pattern Recognition, 27, 1-16, 1992. A new formalism allows to make both qualitative and quantitive distortion analysis. It also enlarges parser flexibility by extending the set of patterns which may be recognized.

Patterns recognition of electric brain activity using artificial neural networks

Science.gov (United States)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Individual Differences in Visual Word Recognition: Insights from the English Lexicon Project

Science.gov (United States)

Yap, Melvin J.; Balota, David A.; Sibley, Daragh E.; Ratcliff, Roger

2012-01-01

Empirical work and models of visual word recognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates word recognition performance. In the present study, we examined differences among individuals who contributed to the English…

Deep Learning For Sequential Pattern Recognition

OpenAIRE

Safari, Pooyan

2013-01-01

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM) In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with ...

How cortical neurons help us see: visual recognition in the human brain

OpenAIRE

Blumberg, Julie; Kreiman, Gabriel

2010-01-01

Through a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us under...

Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

Directory of Open Access Journals (Sweden)

Koji Iwano

2007-03-01

Full Text Available This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments. Our proposed method assumes that lip images can be captured using a small camera installed in a handset. Two different kinds of lip features, lip-contour geometric features and lip-motion velocity features, are used individually or jointly, in combination with audio features. Phoneme HMMs modeling the audio and visual features are built based on the multistream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions. These visual features were confirmed to be effective even when the audio HMM was adapted to noise by the MLLR method.

How cortical neurons help us see: visual recognition in the human brain

Science.gov (United States)

Blumberg, Julie; Kreiman, Gabriel

2010-01-01

Through a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us understand the computations performed by visual cortex. PMID:20811161

Improving user-friendliness by using visually supported speech recognition

NARCIS (Netherlands)

Waals, J.A.J.S.; Kooi, F.L.; Kriekaard, J.J.

2002-01-01

While speech recognition in principle may be one of the most natural interfaces, in practice it is not due to the lack of user-friendliness. Words are regularly interpreted wrong, and subjects tend to articulate in an exaggerated manner. We explored the potential of visually supported error

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

Science.gov (United States)

Tachibana, Ryosuke; Namba, Yuri; Noguchi, Yasuki

2014-01-01

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

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

Science.gov (United States)

Swartz, R. Andrew

2013-01-01

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

Directory of Open Access Journals (Sweden)

Wenjia Liu

2013-01-01

Full Text Available This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate.

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

Science.gov (United States)

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

2017-10-01

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

Threats of Password Pattern Leakage Using Smartwatch Motion Recognition Sensors

Directory of Open Access Journals (Sweden)

Jihun Kim

2017-06-01

Full Text Available Thanks to the development of Internet of Things (IoT technologies, wearable markets have been growing rapidly. Smartwatches can be said to be the most representative product in wearable markets, and involve various hardware technologies in order to overcome the limitations of small hardware. Motion recognition sensors are a representative example of those hardware technologies. However, smartwatches and motion recognition sensors that can be worn by users may pose security threats of password pattern leakage. In the present paper, passwords are inferred through experiments to obtain password patterns inputted by users using motion recognition sensors, and verification of the results and the accuracy of the results is shown.

Individual Differences in Visual Self-Recognition as a Function of Mother-Infant Attachment Relationship.

Science.gov (United States)

Lewis, Michael; And Others

1985-01-01

Compares attachment relationships of infants at 12 months to their visual self-recognition at both 18 and 24 months. Individual differences in early attachment relations were related to later self-recognition. In particular, insecurely attached infants showed a trend toward earlier self-recognition than did securely attached infants. (Author/NH)

Modality effects in delayed free recall and recognition: visual is better than auditory.

Science.gov (United States)

Penney, C G

1989-08-01

During presentation of auditory and visual lists of words, different groups of subjects generated words that either rhymed with the presented words or that were associates. Immediately after list presentation, subjects recalled either the presented or the generated words. After presentation and test of all lists, a final free recall test and a recognition test were given. Visual presentation generally produced higher recall and recognition than did auditory presentation for both encoding conditions. The results are not consistent with explanations of modality effects in terms of echoic memory or greater temporal distinctiveness of auditory items. The results are more in line with the separate-streams hypothesis, which argues for different kinds of input processing for auditory and visual items.

Grip-pattern recognition: Applied to a smart gun

NARCIS (Netherlands)

Shang, X.

2008-01-01

In our work the verification performance of a biometric recognition system based on grip patterns, as part of a smart gun for use by the police ocers, has been investigated. The biometric features are extracted from a two-dimensional pattern of the pressure, exerted on the grip of a gun by the hand

Computing with Connections in Visual Recognition of Origami Objects.

Science.gov (United States)

Sabbah, Daniel

1985-01-01

Summarizes an initial foray in tackling artificial intelligence problems using a connectionist approach. The task chosen is visual recognition of Origami objects, and the questions answered are how to construct a connectionist network to represent and recognize projected Origami line drawings and the advantages such an approach would have. (30…

Visualizing Data Patterns with Micromaps

CERN Document Server

Carr, Daniel B

2010-01-01

After more than 15 years of development drawing on research in cognitive psychology, statistical graphics, computer science, and cartography, micromap designs are becoming part of mainstream statistical visualizations. Bringing together the research of two leaders in this field, "Visualizing Data Patterns with Micromaps" presents the many design variations and applications of micromaps, which link statistical information to an organized set of small maps. This full-color book helps readers simultaneously explore the statistical and geographic patterns in their data. After illustratin

Neural Correlates of Individual Differences in Infant Visual Attention and Recognition Memory

Science.gov (United States)

Reynolds, Greg D.; Guy, Maggie W.; Zhang, Dantong

2011-01-01

Past studies have identified individual differences in infant visual attention based upon peak look duration during initial exposure to a stimulus. Colombo and colleagues found that infants that demonstrate brief visual fixations (i.e., short lookers) during familiarization are more likely to demonstrate evidence of recognition memory during…

Recognition without Awareness: Encoding and Retrieval Factors

Science.gov (United States)

Craik, Fergus I. M.; Rose, Nathan S.; Gopie, Nigel

2015-01-01

The article reports 4 experiments that explore the notion of recognition without awareness using words as the material. Previous work by Voss and associates has shown that complex visual patterns were correctly selected as targets in a 2-alternative forced-choice (2-AFC) recognition test although participants reported that they were guessing. The…

Weighted Local Active Pixel Pattern (WLAPP for Face Recognition in Parallel Computation Environment

Directory of Open Access Journals (Sweden)

Gundavarapu Mallikarjuna Rao

2013-10-01

Full Text Available Abstract  - The availability of multi-core technology resulted totally new computational era. Researchers are keen to explore available potential in state of art-machines for breaking the bearer imposed by serial computation. Face Recognition is one of the challenging applications on so ever computational environment. The main difficulty of traditional Face Recognition algorithms is lack of the scalability. In this paper Weighted Local Active Pixel Pattern (WLAPP, a new scalable Face Recognition Algorithm suitable for parallel environment is proposed.  Local Active Pixel Pattern (LAPP is found to be simple and computational inexpensive compare to Local Binary Patterns (LBP. WLAPP is developed based on concept of LAPP. The experimentation is performed on FG-Net Aging Database with deliberately introduced 20% distortion and the results are encouraging. Keywords — Active pixels, Face Recognition, Local Binary Pattern (LBP, Local Active Pixel Pattern (LAPP, Pattern computing, parallel workers, template, weight computation.  

Cotinine improves visual recognition memory and decreases cortical Tau phosphorylation in the Tg6799 mice.

Science.gov (United States)

Grizzell, J Alex; Patel, Sagar; Barreto, George E; Echeverria, Valentina

2017-08-01

Alzheimer's disease (AD) is associated with the progressive aggregation of hyperphosphorylated forms of the microtubule associated protein Tau in the central nervous system. Cotinine, the main metabolite of nicotine, reduced working memory deficits, synaptic loss, and amyloid β peptide aggregation into oligomers and plaques as well as inhibited the cerebral Tau kinase, glycogen synthase 3β (GSK3β) in the transgenic (Tg)6799 (5XFAD) mice. In this study, the effect of cotinine on visual recognition memory and cortical Tau phosphorylation at the GSK3β sites Serine (Ser)-396/Ser-404 and phospho-CREB were investigated in the Tg6799 and non-transgenic (NT) littermate mice. Tg mice showed short-term visual recognition memory impairment in the novel object recognition test, and higher levels of Tau phosphorylation when compared to NT mice. Cotinine significantly improved visual recognition memory performance increased CREB phosphorylation and reduced cortical Tau phosphorylation. Potential mechanisms underlying theses beneficial effects are discussed. Copyright © 2017. Published by Elsevier Inc.

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

Science.gov (United States)

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.

Artificial intelligence tools for pattern recognition

Science.gov (United States)

Acevedo, Elena; Acevedo, Antonio; Felipe, Federico; Avilés, Pedro

2017-06-01

In this work, we present a system for pattern recognition that combines the power of genetic algorithms for solving problems and the efficiency of the morphological associative memories. We use a set of 48 tire prints divided into 8 brands of tires. The images have dimensions of 200 x 200 pixels. We applied Hough transform to obtain lines as main features. The number of lines obtained is 449. The genetic algorithm reduces the number of features to ten suitable lines that give thus the 100% of recognition. Morphological associative memories were used as evaluation function. The selection algorithms were Tournament and Roulette wheel. For reproduction, we applied one-point, two-point and uniform crossover.

Pattern recognition of state variables by neural networks

International Nuclear Information System (INIS)

Faria, Eduardo Fernandes; Pereira, Claubia

1996-01-01

An artificial intelligence system based on artificial neural networks can be used to classify predefined events and emergency procedures. These systems are being used in different areas. In the nuclear reactors safety, the goal is the classification of events whose data can be processed and recognized by neural networks. In this works we present a preliminary simple system, using neural networks in the recognition of patterns the recognition of variables which define a situation. (author)

Granular neural networks, pattern recognition and bioinformatics

CERN Document Server

Pal, Sankar K; Ganivada, Avatharam

2017-01-01

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

A new pattern associative memory model for image recognition based on Hebb rules and dot product

Science.gov (United States)

Gao, Mingyue; Deng, Limiao; Wang, Yanjiang

2018-04-01

A great number of associative memory models have been proposed to realize information storage and retrieval inspired by human brain in the last few years. However, there is still much room for improvement for those models. In this paper, we extend a binary pattern associative memory model to accomplish real-world image recognition. The learning process is based on the fundamental Hebb rules and the retrieval is implemented by a normalized dot product operation. Our proposed model can not only fulfill rapid memory storage and retrieval for visual information but also have the ability on incremental learning without destroying the previous learned information. Experimental results demonstrate that our model outperforms the existing Self-Organizing Incremental Neural Network (SOINN) and Back Propagation Neuron Network (BPNN) on recognition accuracy and time efficiency.

State Recognition and Visualization of Hoisting Motor of Quayside Container Crane Based on SOFM

Science.gov (United States)

Yang, Z. Q.; He, P.; Tang, G.; Hu, X.

2017-07-01

The neural network structure and algorithm of self-organizing feature map (SOFM) are researched and analysed. The method is applied to state recognition and visualization of the quayside container crane hoisting motor. By using SOFM, the clustering and visualization of attribute reduction of data are carried out, and three kinds motor states are obtained with Root Mean Square(RMS), Impulse Index and Margin Index, and the simulation visualization interface is realized by MATLAB. Through the processing of the sample data, it can realize the accurate identification of the motor state, thus provide better monitoring of the quayside container crane hoisting motor and a new way for the mechanical state recognition.

The Influence of Semantic Neighbours on Visual Word Recognition

Science.gov (United States)

Yates, Mark

2012-01-01

Although it is assumed that semantics is a critical component of visual word recognition, there is still much that we do not understand. One recent way of studying semantic processing has been in terms of semantic neighbourhood (SN) density, and this research has shown that semantic neighbours facilitate lexical decisions. However, it is not clear…

Using Pattern Classification and Recognition Techniques for Diagnostic and Prediction

Directory of Open Access Journals (Sweden)

MORARIU, N.

2007-04-01

Full Text Available The paper presents some aspects regarding the joint use of classification and recognition techniques for the activity evolution diagnostication and prediction by means of a set of indexes. Starting from the indexes set there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indexes set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostication and prediction the following tools are used: pattern recognition and multilayer perceptron. The data set used in experiments describes the pollution due to CO2 emission from the consumption of fuels in Europe. The paper also presents the REFORME software written by the authors and the results of the experiment obtained with this software.

Pattern Recognition of the Multiple Sclerosis Syndrome

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Stewart, Renee; Healey, Kathleen M.

2017-01-01

During recent decades, the autoimmune disease neuromyelitis optica spectrum disorder (NMOSD), once broadly classified under the umbrella of multiple sclerosis (MS), has been extended to include autoimmune inflammatory conditions of the central nervous system (CNS), which are now diagnosable with serum serological tests. These antibody-mediated inflammatory diseases of the CNS share a clinical presentation to MS. A number of practical learning points emerge in this review, which is geared toward the pattern recognition of optic neuritis, transverse myelitis, brainstem/cerebellar and hemispheric tumefactive demyelinating lesion (TDL)-associated MS, aquaporin-4-antibody and myelin oligodendrocyte glycoprotein (MOG)-antibody NMOSD, overlap syndrome, and some yet-to-be-defined/classified demyelinating disease, all unspecifically labeled under MS syndrome. The goal of this review is to increase clinicians’ awareness of the clinical nuances of the autoimmune conditions for MS and NMSOD, and to highlight highly suggestive patterns of clinical, paraclinical or imaging presentations in order to improve differentiation. With overlay in clinical manifestations between MS and NMOSD, magnetic resonance imaging (MRI) of the brain, orbits and spinal cord, serology, and most importantly, high index of suspicion based on pattern recognition, will help lead to the final diagnosis. PMID:29064441

Surveillance of a nuclear reactor core by use of a pattern recognition method

International Nuclear Information System (INIS)

Invernizzi, Michel.

1982-07-01

A pattern recognition system is described for the surveillance of a PWR reactor. This report contains four chapters. The first one succinctly deals with statistical pattern recognition principles. In the second chapter we show how a surveillance problem may be treated by pattern recognition and we present methods for surveillances (detection of abnormalities), controls (kind of running recognition) and diagnotics (kind of abnormality recognition). The third chapter shows a surveillance method of a nuclear plant. The signals used are the neutron noise observations made by the ionization chambers inserted in the reactor. Abnormality is defined in opposition with the training set witch is supposed to be an exhaustive summary of normality. In the fourth chapter we propose a scheme for an adaptative recognition and a method based on classes modelisations by hyper-spheres. This method has been tested on simulated training sets in two-dimensional feature spaces. It gives solutions to problems of non-linear separability [fr

Action recognition and movement direction discrimination tasks are associated with different adaptation patterns

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Stephan eDe La Rosa

2016-02-01

Full Text Available The ability to discriminate between different actions is essential for action recognition and social interaction. Surprisingly previous research has often probed action recognition mechanisms with tasks that did not require participants to discriminate between actions, e.g. left-right direction discrimination tasks. It is not known to what degree visual processes in direction discrimination tasks are also involved in the discrimination of actions, e.g. when telling apart a handshake from a high-five. Here, we examined whether action discrimination is influenced by movement direction and whether direction discrimination depends on the type of action. We used an action adaptation paradigm to target action and direction discrimination specific visual processes. In separate conditions participants visually adapted to forward and backward moving handshake and high-five actions. Participants subsequently either categorized the action or the movement direction of an ambiguous action. The results showed that direction discrimination adaptation effects were modulated by the type of action but action discrimination adaptation effects were unaffected by movement direction. These results suggest that action discrimination and direction categorization rely on partly different visual information. We propose that action discrimination tasks should be considered for the exploration of visual action recognition mechanisms.

How a hobby can shape cognition: visual word recognition in competitive Scrabble players.

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Hargreaves, Ian S; Pexman, Penny M; Zdrazilova, Lenka; Sargious, Peter

2012-01-01

Competitive Scrabble is an activity that involves extraordinary word recognition experience. We investigated whether that experience is associated with exceptional behavior in the laboratory in a classic visual word recognition paradigm: the lexical decision task (LDT). We used a version of the LDT that involved horizontal and vertical presentation and a concreteness manipulation. In Experiment 1, we presented this task to a group of undergraduates, as these participants are the typical sample in word recognition studies. In Experiment 2, we compared the performance of a group of competitive Scrabble players with a group of age-matched nonexpert control participants. The results of a series of cognitive assessments showed that the Scrabble players and control participants differed only in Scrabble-specific skills (e.g., anagramming). Scrabble expertise was associated with two specific effects (as compared to controls): vertical fluency (relatively less difficulty judging lexicality for words presented in the vertical orientation) and semantic deemphasis (smaller concreteness effects for word responses). These results suggest that visual word recognition is shaped by experience, and that with experience there are efficiencies to be had even in the adult word recognition system.

Characterizing synchrony patterns across cognitive task stages of associative recognition memory.

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Portoles, Oscar; Borst, Jelmer P; van Vugt, Marieke K

2017-12-28

Numerous studies seek to understand the role of oscillatory synchronization in cognition. This problem is particularly challenging in the context of complex cognitive behavior, which consists of a sequence of processing steps with uncertain duration. In this study, we analyzed oscillatory connectivity measures in time windows that previous computational models had associated with a specific sequence of processing steps in an associative memory recognition task (visual encoding, familiarity, memory retrieval, decision making, and motor response). The timing of these processing steps was estimated on a single-trial basis with a novel hidden semi-Markov model multivariate pattern analysis (HSMM-MVPA) method. We show that different processing stages are associated with specific patterns of oscillatory connectivity. Visual encoding is characterized by a dense network connecting frontal, posterior, and temporal areas as well as frontal and occipital phase locking in the 4-9 Hz theta band. Familiarity is associated with frontal phase locking in the 9-14 Hz alpha band. Decision making is associated with frontal and temporo-central interhemispheric connections in the alpha band. During decision making, a second network in the theta band that connects left-temporal, central, and occipital areas bears similarity to the neural signature for preparing a motor response. A similar theta band network is also present during the motor response, with additionally alpha band connectivity between right-temporal and posterior areas. This demonstrates that the processing stages discovered with the HSMM-MVPA method are indeed linked to distinct synchronization patterns, leading to a closer understanding of the functional role of oscillations in cognition. © 2017 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

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Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

2016-11-01

Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

Comparison of Object Recognition Behavior in Human and Monkey

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Rajalingham, Rishi; Schmidt, Kailyn

2015-01-01

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

Repetition suppression and multi-voxel pattern similarity differentially track implicit and explicit visual memory.

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Ward, Emily J; Chun, Marvin M; Kuhl, Brice A

2013-09-11

Repeated exposure to a visual stimulus is associated with corresponding reductions in neural activity, particularly within visual cortical areas. It has been argued that this phenomenon of repetition suppression is related to increases in processing fluency or implicit memory. However, repetition of a visual stimulus can also be considered in terms of the similarity of the pattern of neural activity elicited at each exposure--a measure that has recently been linked to explicit memory. Despite the popularity of each of these measures, direct comparisons between the two have been limited, and the extent to which they differentially (or similarly) relate to behavioral measures of memory has not been clearly established. In the present study, we compared repetition suppression and pattern similarity as predictors of both implicit and explicit memory. Using functional magnetic resonance imaging, we scanned 20 participants while they viewed and categorized repeated presentations of scenes. Repetition priming (facilitated categorization across repetitions) was used as a measure of implicit memory, and subsequent scene recognition was used as a measure of explicit memory. We found that repetition priming was predicted by repetition suppression in prefrontal, parietal, and occipitotemporal regions; however, repetition priming was not predicted by pattern similarity. In contrast, subsequent explicit memory was predicted by pattern similarity (across repetitions) in some of the same occipitotemporal regions that exhibited a relationship between priming and repetition suppression; however, explicit memory was not related to repetition suppression. This striking double dissociation indicates that repetition suppression and pattern similarity differentially track implicit and explicit learning.

Pattern Recognition as a Human Centered non-Euclidean Problem

NARCIS (Netherlands)

Duin, R.P.W.

2010-01-01

Regularities in the world are human defined. Patterns in the observed phenomena are there because we define and recognize them as such. Automatic pattern recognition tries to bridge the gap between human judgment and measurements made by artificial sensors. This is done in two steps: representation

The Effect of Mood-Context on Visual Recognition and Recall Memory

OpenAIRE

Robinson, Sarita Jane; Rollings, Lucy J. L.

2010-01-01

Although it is widely known that memory is enhanced when encoding and retrieval occur in the same state, the impact of elevated stress/arousal is less understood. This study explores mood-dependent memory's effects on visual recognition and recall of material memorized either in a neutral mood or under higher stress/arousal levels. Participants’ (N = 60) recognition and recall were assessed while they experienced either the same or a mismatched mood at retrieval. The results suggested that bo...

Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition

Directory of Open Access Journals (Sweden)

Bineng Zhong

2008-06-01

Full Text Available This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP, which is based on Daugman’s method for iris recognition and the local XOR pattern (LXP operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.

Neural activation patterns and connectivity in visual attention during Number and Non-number processing: An ERP study using the Ishihara pseudoisochromatic plates.

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Al-Marri, Faraj; Reza, Faruque; Begum, Tahamina; Hitam, Wan Hazabbah Wan; Jin, Goh Khean; Xiang, Jing

2017-10-25

and connectivity patterns revealed that Number recognition occurred in the occipital and left frontal areas where as the consequence was limited to the occipital area during the Non-number plate processing. Finally, the results displayed that the visual recognition of Numbers dissociates from the recognition of Non-numbers at the level of defined neural networks. Number recognition was not only a process of visual perception and attention, but it was also related to a higher level of cognitive function, that of language.

Rough-fuzzy pattern recognition applications in bioinformatics and medical imaging

CERN Document Server

Maji, Pradipta

2012-01-01

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems dev

Morphological characterization of Mycobacterium tuberculosis in a MODS culture for an automatic diagnostics through pattern recognition.

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Alicia Alva

Full Text Available Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7-10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii. The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.

A shared representation of order between encoding and recognition in visual short-term memory.

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Kalm, Kristjan; Norris, Dennis

2017-07-15

Many complex tasks require people to bind individual events into a sequence that can be held in short term memory (STM). For this purpose information about the order of the individual events in the sequence needs to be maintained in an active and accessible form in STM over a period of few seconds. Here we investigated how the temporal order information is shared between the presentation and response phases of an STM task. We trained a classification algorithm on the fMRI activity patterns from the presentation phase of the STM task to predict the order of the items during the subsequent recognition phase. While voxels in a number of brain regions represented positional information during either presentation and recognition phases, only voxels in the lateral prefrontal cortex (PFC) and the anterior temporal lobe (ATL) represented position consistently across task phases. A shared positional code in the ATL might reflect verbal recoding of visual sequences to facilitate the maintenance of order information over several seconds. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

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

Energy Technology Data Exchange (ETDEWEB)

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

2010-10-01

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

Robust audio-visual speech recognition under noisy audio-video conditions.

Science.gov (United States)

Stewart, Darryl; Seymour, Rowan; Pass, Adrian; Ming, Ji

2014-02-01

This paper presents the maximum weighted stream posterior (MWSP) model as a robust and efficient stream integration method for audio-visual speech recognition in environments, where the audio or video streams may be subjected to unknown and time-varying corruption. A significant advantage of MWSP is that it does not require any specific measurements of the signal in either stream to calculate appropriate stream weights during recognition, and as such it is modality-independent. This also means that MWSP complements and can be used alongside many of the other approaches that have been proposed in the literature for this problem. For evaluation we used the large XM2VTS database for speaker-independent audio-visual speech recognition. The extensive tests include both clean and corrupted utterances with corruption added in either/both the video and audio streams using a variety of types (e.g., MPEG-4 video compression) and levels of noise. The experiments show that this approach gives excellent performance in comparison to another well-known dynamic stream weighting approach and also compared to any fixed-weighted integration approach in both clean conditions or when noise is added to either stream. Furthermore, our experiments show that the MWSP approach dynamically selects suitable integration weights on a frame-by-frame basis according to the level of noise in the streams and also according to the naturally fluctuating relative reliability of the modalities even in clean conditions. The MWSP approach is shown to maintain robust recognition performance in all tested conditions, while requiring no prior knowledge about the type or level of noise.

Short-Term and Long-Term Effects on Visual Word Recognition

Science.gov (United States)

Protopapas, Athanassios; Kapnoula, Efthymia C.

2016-01-01

Effects of lexical and sublexical variables on visual word recognition are often treated as homogeneous across participants and stable over time. In this study, we examine the modulation of frequency, length, syllable and bigram frequency, orthographic neighborhood, and graphophonemic consistency effects by (a) individual differences, and (b) item…

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

Directory of Open Access Journals (Sweden)

Yasmine Probst

2015-07-01

Full Text Available Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT, local binary patterns (LBP, and colour are used for describing food images. The popular bag-of-words (BoW model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

Sonographic Diagnosis of Tubal Cancer with IOTA Simple Rules Plus Pattern Recognition

Science.gov (United States)

Tongsong, Theera; Wanapirak, Chanane; Tantipalakorn, Charuwan; Tinnangwattana, Dangcheewan

2017-11-26

Objective: To evaluate diagnostic performance of IOTA simple rules plus pattern recognition in predicting tubal cancer. Methods: Secondary analysis was performed on prospective database of our IOTA project. The patients recruited in the project were those who were scheduled for pelvic surgery due to adnexal masses. The patients underwent ultrasound examinations within 24 hours before surgery. On ultrasound examination, the masses were evaluated using the well-established IOTA simple rules plus pattern recognition (sausage-shaped appearance, incomplete septum, visible ipsilateral ovaries) to predict tubal cancer. The gold standard diagnosis was based on histological findings or operative findings. Results: A total of 482 patients, including 15 cases of tubal cancer, were evaluated by ultrasound preoperatively. The IOTA simple rules plus pattern recognition gave a sensitivity of 86.7% (13 in 15) and specificity of 97.4%. Sausage-shaped appearance was identified in nearly all cases (14 in 15). Incomplete septa and normal ovaries could be identified in 33.3% and 40%, respectively. Conclusion: IOTA simple rules plus pattern recognition is relatively effective in predicting tubal cancer. Thus, we propose the simple scheme in diagnosis of tubal cancer as follows. First of all, the adnexal masses are evaluated with IOTA simple rules. If the B-rules could be applied, tubal cancer is reliably excluded. If the M-rules could be applied or the result is inconclusive, careful delineation of the mass with pattern recognition should be performed. Creative Commons Attribution License

Lexical decisions in adults with low and high susceptibility to pattern-related visual stress: a preliminary investigation.

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James M Gilchrist

2015-04-01

Full Text Available Pattern-related visual stress (PRVS is a form of sensory hypersensitivity that some people experience when viewing high contrast repeating patterns, notably alternating dark and light stripes. Those susceptible to PRVS typically have a strong aversion to such stimuli, and this is often accompanied by experiences of visual discomfort and disturbance. The patterns most likely to elicit symptoms of PRVS have a square-wave grating configuration of spatial frequency ~ 3 cycles/degree. Such stimuli are characteristic of printed text in which lines of words and the spaces between them present a high contrast grating-like stimulus. Consequently, much printed reading material has the potential to elicit PRVS that may impair reading performance, and this problem is now thought to be common in individuals with reading difficulties including dyslexia. However, the manner in which PRVS affects reading ability is unknown. One possibility is that the early sensory visual stress may interfere with the later cognitive word recognition stage of the reading process, resulting in reading performance that is slower and/or less accurate.To explore the association of PRVS with word recognition ability, lexical decision performance (speed and accuracy to words and pronounceable non-words was measured in two groups of adults, having low and high susceptibility to PRVS. Results showed that lexical decisions were generally faster but less accurate in high-PRVS, and also that high-PRVS participants made decisions significantly faster for words than for non-words, revealing a strong lexicality effect that was not present in low-PRVS. These findings are novel and, as yet, unconfirmed by other studies. We consider possible interpretation in terms of cortical activity and perceptual mechanisms.

RECOG-ORNL, Pattern Recognition Data Analysis

International Nuclear Information System (INIS)

Begovich, C.L.; Larson, N.M.

2000-01-01

Description of program or function: RECOG-ORNL, a general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by auto-calling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

Science.gov (United States)

Imamoglu, Nevrez; Dorronzoro, Enrique; Wei, Zhixuan; Shi, Huangjun; González, José; Gu, Dongyun; Yu, Wenwei

2014-01-01

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy. PMID:25587560

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

Directory of Open Access Journals (Sweden)

Nevrez Imamoglu

2014-01-01

Full Text Available Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy.

Development of robust behaviour recognition for an at-home biomonitoring robot with assistance of subject localization and enhanced visual tracking.

Science.gov (United States)

Imamoglu, Nevrez; Dorronzoro, Enrique; Wei, Zhixuan; Shi, Huangjun; Sekine, Masashi; González, José; Gu, Dongyun; Chen, Weidong; Yu, Wenwei

2014-01-01

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy.

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition.

Directory of Open Access Journals (Sweden)

Na Shu

Full Text Available Humans can easily understand other people's actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1, and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model.

Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

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Md. Abdullah-al-mamun

2015-08-01

Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

An ERP investigation of visual word recognition in syllabary scripts.

Science.gov (United States)

Okano, Kana; Grainger, Jonathan; Holcomb, Phillip J

2013-06-01

The bimodal interactive-activation model has been successfully applied to understanding the neurocognitive processes involved in reading words in alphabetic scripts, as reflected in the modulation of ERP components in masked repetition priming. In order to test the generalizability of this approach, in the present study we examined word recognition in a different writing system, the Japanese syllabary scripts hiragana and katakana. Native Japanese participants were presented with repeated or unrelated pairs of Japanese words in which the prime and target words were both in the same script (within-script priming, Exp. 1) or were in the opposite script (cross-script priming, Exp. 2). As in previous studies with alphabetic scripts, in both experiments the N250 (sublexical processing) and N400 (lexical-semantic processing) components were modulated by priming, although the time course was somewhat delayed. The earlier N/P150 effect (visual feature processing) was present only in "Experiment 1: Within-script priming", in which the prime and target words shared visual features. Overall, the results provide support for the hypothesis that visual word recognition involves a generalizable set of neurocognitive processes that operate in similar manners across different writing systems and languages, as well as pointing to the viability of the bimodal interactive-activation framework for modeling such processes.

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

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Smith, Linda B

2013-11-01

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

Pattern Recognition-Based Analysis of COPD in CT

DEFF Research Database (Denmark)

Sørensen, Lauge Emil Borch Laurs

recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we...... will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually...... annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema...

The impact of task demand on visual word recognition.

Science.gov (United States)

Yang, J; Zevin, J

2014-07-11

The left occipitotemporal cortex has been found sensitive to the hierarchy of increasingly complex features in visually presented words, from individual letters to bigrams and morphemes. However, whether this sensitivity is a stable property of the brain regions engaged by word recognition is still unclear. To address the issue, the current study investigated whether different task demands modify this sensitivity. Participants viewed real English words and stimuli with hierarchical word-likeness while performing a lexical decision task (i.e., to decide whether each presented stimulus is a real word) and a symbol detection task. General linear model and independent component analysis indicated strong activation in the fronto-parietal and temporal regions during the two tasks. Furthermore, the bilateral inferior frontal gyrus and insula showed significant interaction effects between task demand and stimulus type in the pseudoword condition. The occipitotemporal cortex showed strong main effects for task demand and stimulus type, but no sensitivity to the hierarchical word-likeness was found. These results suggest that different task demands on semantic, phonological and orthographic processes can influence the involvement of the relevant regions during visual word recognition. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Nonlinear dynamics of pattern formation and pattern recognition in the rabbit olfactory bulb

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Baird, Bill

1986-10-01

A mathematical model of the process of pattern recognition in the first olfactory sensory cortex of the rabbit is presented. It explains the formation and alteration of spatial patterns in neural activity observed experimentally during classical Pavlovian conditioning. On each inspiration of the animal, a surge of receptor input enters the olfactory bulb. EEG activity recorded at the surface of the bulb undergoes a transition from a low amplitude background state of temporal disorder to coherent oscillation. There is a distinctive spatial pattern of rms amplitude in this oscillation which changes reliably to a second pattern during each successful recognition by the animal of a conditioned stimulus odor. When a new odor is paired as conditioned stimulus, these patterns are replaced by new patterns that stabilize as the animal adapts to the new environment. I will argue that a unification of the theories of pattern formation and associative memory is required to account for these observations. This is achieved in a model of the bulb as a discrete excitable medium with spatially inhomogeneous coupling expressed by a connection matrix. The theory of multiple Hopf bifurcations is employed to find coupled equations for the amplitudes of competing unstable oscillatory modes. These may be created in the system by proper coupling and selectively evoked by specific classes of inputs. This allows a view of limit cycle attractors as “stored” fixed points of a gradient vector field and thereby recovers the more familiar dynamical systems picture of associative memory.

A Dynamic Interval-Valued Intuitionistic Fuzzy Sets Applied to Pattern Recognition

Directory of Open Access Journals (Sweden)

Zhenhua Zhang

2013-01-01

Full Text Available We present dynamic interval-valued intuitionistic fuzzy sets (DIVIFS, which can improve the recognition accuracy when they are applied to pattern recognition. By analyzing the degree of hesitancy, we propose some DIVIFS models from intuitionistic fuzzy sets (IFS and interval-valued IFS (IVIFS. And then we present a novel ranking condition on the distance of IFS and IVIFS and introduce some distance measures of DIVIFS satisfying the ranking condition. Finally, a pattern recognition example applied to medical diagnosis decision making is given to demonstrate the application of DIVIFS and its distances. The simulation results show that the DIVIFS method is more comprehensive and flexible than the IFS method and the IVIFS method.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

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Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

2018-01-01

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

Do pattern recognition skills transfer across sports? A preliminary analysis.

Science.gov (United States)

Smeeton, Nicholas J; Ward, Paul; Williams, A Mark

2004-02-01

The ability to recognize patterns of play is fundamental to performance in team sports. While typically assumed to be domain-specific, pattern recognition skills may transfer from one sport to another if similarities exist in the perceptual features and their relations and/or the strategies used to encode and retrieve relevant information. A transfer paradigm was employed to compare skilled and less skilled soccer, field hockey and volleyball players' pattern recognition skills. Participants viewed structured and unstructured action sequences from each sport, half of which were randomly represented with clips not previously seen. The task was to identify previously viewed action sequences quickly and accurately. Transfer of pattern recognition skill was dependent on the participant's skill, sport practised, nature of the task and degree of structure. The skilled soccer and hockey players were quicker than the skilled volleyball players at recognizing structured soccer and hockey action sequences. Performance differences were not observed on the structured volleyball trials between the skilled soccer, field hockey and volleyball players. The skilled field hockey and soccer players were able to transfer perceptual information or strategies between their respective sports. The less skilled participants' results were less clear. Implications for domain-specific expertise, transfer and diversity across domains are discussed.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

Science.gov (United States)

Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

2018-03-12

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation

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Perception of pathogen-associated molecular patterns (PAMPs) by surface-localised pattern-recognition receptors (PRRs) is a key component of plant innate immunity. Most known plant PRRs are receptor kinases and initiation of PAMP-triggered immunity (PTI) signalling requires phosphorylation of the PR...

Quantitative EEG Applying the Statistical Recognition Pattern Method

DEFF Research Database (Denmark)

Engedal, Knut; Snaedal, Jon; Hoegh, Peter

2015-01-01

BACKGROUND/AIM: The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. METHODS...

Multi-script handwritten character recognition : Using feature descriptors and machine learning

NARCIS (Netherlands)

Surinta, Olarik

2016-01-01

Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and

Face Recognition Using Local Quantized Patterns and Gabor Filters

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Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

2015-05-01

The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

DNA pattern recognition using canonical correlation algorithm.

Science.gov (United States)

Sarkar, B K; Chakraborty, Chiranjib

2015-10-01

We performed canonical correlation analysis as an unsupervised statistical tool to describe related views of the same semantic object for identifying patterns. A pattern recognition technique based on canonical correlation analysis (CCA) was proposed for finding required genetic code in the DNA sequence. Two related but different objects were considered: one was a particular pattern, and other was test DNA sequence. CCA found correlations between two observations of the same semantic pattern and test sequence. It is concluded that the relationship possesses maximum value in the position where the pattern exists. As a case study, the potential of CCA was demonstrated on the sequence found from HIV-1 preferred integration sites. The subsequences on the left and right flanking from the integration site were considered as the two views, and statistically significant relationships were established between these two views to elucidate the viral preference as an important factor for the correlation.

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

Science.gov (United States)

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

2011-05-01

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

Stage-specific sampling by pattern recognition receptors during Candida albicans phagocytosis.

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Sigrid E M Heinsbroek

2008-11-01

Full Text Available Candida albicans is a medically important pathogen, and recognition by innate immune cells is critical for its clearance. Although a number of pattern recognition receptors have been shown to be involved in recognition and phagocytosis of this fungus, the relative role of these receptors has not been formally examined. In this paper, we have investigated the contribution of the mannose receptor, Dectin-1, and complement receptor 3; and we have demonstrated that Dectin-1 is the main non-opsonic receptor involved in fungal uptake. However, both Dectin-1 and complement receptor 3 were found to accumulate at the site of uptake, while mannose receptor accumulated on C. albicans phagosomes at later stages. These results suggest a potential role for MR in phagosome sampling; and, accordingly, MR deficiency led to a reduction in TNF-alpha and MCP-1 production in response to C. albicans uptake. Our data suggest that pattern recognition receptors sample the fungal phagosome in a sequential fashion.

Reading laterally: the cerebral hemispheric use of spatial frequencies in visual word recognition.

Science.gov (United States)

Tadros, Karine; Dupuis-Roy, Nicolas; Fiset, Daniel; Arguin, Martin; Gosselin, Frédéric

2013-01-04

It is generally accepted that the left hemisphere (LH) is more capable for reading than the right hemisphere (RH). Left hemifield presentations (initially processed by the RH) lead to a globally higher error rate, slower word identification, and a significantly stronger word length effect (i.e., slower reaction times for longer words). Because the visuo-perceptual mechanisms of the brain for word recognition are primarily localized in the LH (Cohen et al., 2003), it is possible that this part of the brain possesses better spatial frequency (SF) tuning for processing the visual properties of words than the RH. The main objective of this study is to determine the SF tuning functions of the LH and RH for word recognition. Each word image was randomly sampled in the SF domain using the SF bubbles method (Willenbockel et al., 2010) and was presented laterally to the left or right visual hemifield. As expected, the LH requires less visual information than the RH to reach the same level of performance, illustrating the well-known LH advantage for word recognition. Globally, the SF tuning of both hemispheres is similar. However, these seemingly identical tuning functions hide important differences. Most importantly, we argue that the RH requires higher SFs to identify longer words because of crowding.

Enhanced recognition memory in grapheme-color synaesthesia for different categories of visual stimuli.

Science.gov (United States)

Ward, Jamie; Hovard, Peter; Jones, Alicia; Rothen, Nicolas

2013-01-01

Memory has been shown to be enhanced in grapheme-color synaesthesia, and this enhancement extends to certain visual stimuli (that don't induce synaesthesia) as well as stimuli comprised of graphemes (which do). Previous studies have used a variety of testing procedures to assess memory in synaesthesia (e.g., free recall, recognition, associative learning) making it hard to know the extent to which memory benefits are attributable to the stimulus properties themselves, the testing method, participant strategies, or some combination of these factors. In the first experiment, we use the same testing procedure (recognition memory) for a variety of stimuli (written words, non-words, scenes, and fractals) and also check which memorization strategies were used. We demonstrate that grapheme-color synaesthetes show enhanced memory across all these stimuli, but this is not found for a non-visual type of synaesthesia (lexical-gustatory). In the second experiment, the memory advantage for scenes is explored further by manipulating the properties of the old and new images (changing color, orientation, or object presence). Again, grapheme-color synaesthetes show a memory advantage for scenes across all manipulations. Although recognition memory is generally enhanced in this study, the largest effects were found for abstract visual images (fractals) and scenes for which color can be used to discriminate old/new status.

Macrophage pattern recognition receptors in immunity, homeostasis and self tolerance.

Science.gov (United States)

Mukhopadhyay, Subhankar; Plüddemann, Annette; Gordon, Siamon

2009-01-01

Macrophages, a major component of innate immune defence, express a large repertoire of different classes of pattern recognition receptors and other surface antigens which determine the immunologic and homeostatic potential of these versatile cells. In the light of present knowledge ofmacrophage surface antigens, we discuss self versus nonself recognition, microbicidal effector functions and self tolerance in the innate immune system.

Conditional Random Fields for Pattern Recognition Applied to Structured Data

Directory of Open Access Journals (Sweden)

Tom Burr

2015-07-01

Full Text Available Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building or “natural” (such as a tree. Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs model structured data using the conditional distribution P(Y|X = x, without specifying a model for P(X, and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches in the output domain. Second, we identify research topics and present numerical examples.

Deep neural networks rival the representation of primate IT cortex for core visual object recognition.

Directory of Open Access Journals (Sweden)

Charles F Cadieu

2014-12-01

Full Text Available The primate visual system achieves remarkable visual object recognition performance even in brief presentations, and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition. This remarkable performance is mediated by the representation formed in inferior temporal (IT cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs. It remains unclear, however, whether the representational performance of DNNs rivals that of the brain. To accurately produce such a comparison, a major difficulty has been a unifying metric that accounts for experimental limitations, such as the amount of noise, the number of neural recording sites, and the number of trials, and computational limitations, such as the complexity of the decoding classifier and the number of classifier training examples. In this work, we perform a direct comparison that corrects for these experimental limitations and computational considerations. As part of our methodology, we propose an extension of "kernel analysis" that measures the generalization accuracy as a function of representational complexity. Our evaluations show that, unlike previous bio-inspired models, the latest DNNs rival the representational performance of IT cortex on this visual object recognition task. Furthermore, we show that models that perform well on measures of representational performance also perform well on measures of representational similarity to IT, and on measures of predicting individual IT multi-unit responses. Whether these DNNs rely on computational mechanisms similar to the primate visual system is yet to be determined, but, unlike all previous bio-inspired models, that possibility cannot be ruled out merely on representational performance grounds.

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

International Nuclear Information System (INIS)

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

2011-01-01

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

Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

International Nuclear Information System (INIS)

Gu Yu; Li Qiang

2014-01-01

We present a new pattern recognition system based on moving average and linear discriminant analysis (LDA), which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

The integration of visual context information in facial emotion recognition in 5- to 15-year-olds.

Science.gov (United States)

Theurel, Anne; Witt, Arnaud; Malsert, Jennifer; Lejeune, Fleur; Fiorentini, Chiara; Barisnikov, Koviljka; Gentaz, Edouard

2016-10-01

The current study investigated the role of congruent visual context information in the recognition of facial emotional expression in 190 participants from 5 to 15years of age. Children performed a matching task that presented pictures with different facial emotional expressions (anger, disgust, happiness, fear, and sadness) in two conditions: with and without a visual context. The results showed that emotions presented with visual context information were recognized more accurately than those presented in the absence of visual context. The context effect remained steady with age but varied according to the emotion presented and the gender of participants. The findings demonstrated for the first time that children from the age of 5years are able to integrate facial expression and visual context information, and this integration improves facial emotion recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Biometric recognition via texture features of eye movement trajectories in a visual searching task.

Science.gov (United States)

Li, Chunyong; Xue, Jiguo; Quan, Cheng; Yue, Jingwei; Zhang, Chenggang

2018-01-01

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers' temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases.

Comparisons of memory for nonverbal auditory and visual sequential stimuli.

Science.gov (United States)

McFarland, D J; Cacace, A T

1995-01-01

Properties of auditory and visual sensory memory were compared by examining subjects' recognition performance of randomly generated binary auditory sequential frequency patterns and binary visual sequential color patterns within a forced-choice paradigm. Experiment 1 demonstrated serial-position effects in auditory and visual modalities consisting of both primacy and recency effects. Experiment 2 found that retention of auditory and visual information was remarkably similar when assessed across a 10s interval. Experiments 3 and 4, taken together, showed that the recency effect in sensory memory is affected more by the type of response required (recognition vs. reproduction) than by the sensory modality employed. These studies suggest that auditory and visual sensory memory stores for nonverbal stimuli share similar properties with respect to serial-position effects and persistence over time.

Hardware processors for pattern recognition tasks in experiments with wire chambers

International Nuclear Information System (INIS)

Verkerk, C.

1975-01-01

Hardware processors for pattern recognition tasks in experiments with multiwire proportional chambers or drift chambers are described. They vary from simple ones used for deciding in real time if particle trajectories are straight to complex ones for recognition of curved tracks. Schematics and block-diagrams of different processors are shown

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

Science.gov (United States)

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

2014-09-01

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

A new concept of vertically integrated pattern recognition associative memory

International Nuclear Information System (INIS)

Liu, Ted; Hoff, Jim; Deptuch, Grzegorz; Yarema, Ray

2011-01-01

Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing fast pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Scaling of current technologies is unlikely to satisfy the scientific needs of the future, and investments in transformational new technologies need to be made. In this paper, we will discuss a new concept of using the emerging 3D vertical integration technology to significantly advance the state-of-the-art for fast pattern recognition within and outside HEP. A generic R and D proposal based on this new concept, with a few institutions involved, has recently been submitted to DOE with the goal to design and perform the ASIC engineering necessary to realize a prototype device. The progress of this R and D project will be reported in the future. Here we will only focus on the concept of this new approach.

Wavelet-based moment invariants for pattern recognition

Science.gov (United States)

Chen, Guangyi; Xie, Wenfang

2011-07-01

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

Learning and Recognition of a Non-conscious Sequence of Events in Human Primary Visual Cortex.

Science.gov (United States)

Rosenthal, Clive R; Andrews, Samantha K; Antoniades, Chrystalina A; Kennard, Christopher; Soto, David

2016-03-21

Human primary visual cortex (V1) has long been associated with learning simple low-level visual discriminations [1] and is classically considered outside of neural systems that support high-level cognitive behavior in contexts that differ from the original conditions of learning, such as recognition memory [2, 3]. Here, we used a novel fMRI-based dichoptic masking protocol-designed to induce activity in V1, without modulation from visual awareness-to test whether human V1 is implicated in human observers rapidly learning and then later (15-20 min) recognizing a non-conscious and complex (second-order) visuospatial sequence. Learning was associated with a change in V1 activity, as part of a temporo-occipital and basal ganglia network, which is at variance with the cortico-cerebellar network identified in prior studies of "implicit" sequence learning that involved motor responses and visible stimuli (e.g., [4]). Recognition memory was associated with V1 activity, as part of a temporo-occipital network involving the hippocampus, under conditions that were not imputable to mechanisms associated with conscious retrieval. Notably, the V1 responses during learning and recognition separately predicted non-conscious recognition memory, and functional coupling between V1 and the hippocampus was enhanced for old retrieval cues. The results provide a basis for novel hypotheses about the signals that can drive recognition memory, because these data (1) identify human V1 with a memory network that can code complex associative serial visuospatial information and support later non-conscious recognition memory-guided behavior (cf. [5]) and (2) align with mouse models of experience-dependent V1 plasticity in learning and memory [6]. Copyright © 2016 Elsevier Ltd. All rights reserved.

Recent progress in invariant pattern recognition

Science.gov (United States)

Arsenault, Henri H.; Chang, S.; Gagne, Philippe; Gualdron Gonzalez, Oscar

1996-12-01

We present some recent results in invariant pattern recognition, including methods that are invariant under two or more distortions of position, orientation and scale. There are now a few methods that yield good results under changes of both rotation and scale. Some new methods are introduced. These include locally adaptive nonlinear matched filters, scale-adapted wavelet transforms and invariant filters for disjoint noise. Methods using neural networks will also be discussed, including an optical method that allows simultaneous classification of multiple targets.

Similarity-based pattern analysis and recognition

CERN Document Server

Pelillo, Marcello

2013-01-01

This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification alg

A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

Directory of Open Access Journals (Sweden)

He-Yuan Lin

2008-03-01

Full Text Available A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

Directory of Open Access Journals (Sweden)

Li Hsin-Te

2008-01-01

Full Text Available Abstract A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

Transfer of L1 Visual Word Recognition Strategies during Early Stages of L2 Learning: Evidence from Hebrew Learners Whose First Language Is Either Semitic or Indo-European

Science.gov (United States)

Norman, Tal; Degani, Tamar; Peleg, Orna

2016-01-01

The present study examined visual word recognition processes in Hebrew (a Semitic language) among beginning learners whose first language (L1) was either Semitic (Arabic) or Indo-European (e.g. English). To examine if learners, like native Hebrew speakers, exhibit morphological sensitivity to root and word-pattern morphemes, learners made an…

Pattern Recognition

Directory of Open Access Journals (Sweden)

Aleš Procházka

2018-05-01

Full Text Available Multimodal signal analysis based on sophisticated sensors, efficient communicationsystems and fast parallel processing methods has a rapidly increasing range of multidisciplinaryapplications. The present paper is devoted to pattern recognition, machine learning, and the analysisof sleep stages in the detection of sleep disorders using polysomnography (PSG data, includingelectroencephalography (EEG, breathing (Flow, and electro-oculogram (EOG signals. The proposedmethod is based on the classification of selected features by a neural network system with sigmoidaland softmax transfer functions using Bayesian methods for the evaluation of the probabilities of theseparate classes. The application is devoted to the analysis of the sleep stages of 184 individualswith different diagnoses, using EEG and further PSG signals. Data analysis points to an averageincrease of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of theRapid Eye Movement (REM stages by 0.8% per 10 years. The mean classification accuracy for givensets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1 and89.6% (STD:1.9, respectively. The proposed methods enable the use of adaptive learning processesfor the detection and classification of health disorders based on prior specialist experience andman–machine interaction.

Effects of facial emotion recognition remediation on visual scanning of novel face stimuli.

Science.gov (United States)

Marsh, Pamela J; Luckett, Gemma; Russell, Tamara; Coltheart, Max; Green, Melissa J

2012-11-01

Previous research shows that emotion recognition in schizophrenia can be improved with targeted remediation that draws attention to important facial features (eyes, nose, mouth). Moreover, the effects of training have been shown to last for up to one month after training. The aim of this study was to investigate whether improved emotion recognition of novel faces is associated with concomitant changes in visual scanning of these same novel facial expressions. Thirty-nine participants with schizophrenia received emotion recognition training using Ekman's Micro-Expression Training Tool (METT), with emotion recognition and visual scanpath (VSP) recordings to face stimuli collected simultaneously. Baseline ratings of interpersonal and cognitive functioning were also collected from all participants. Post-METT training, participants showed changes in foveal attention to the features of facial expressions of emotion not used in METT training, which were generally consistent with the information about important features from the METT. In particular, there were changes in how participants looked at the features of facial expressions of emotion surprise, disgust, fear, happiness, and neutral, demonstrating that improved emotion recognition is paralleled by changes in the way participants with schizophrenia viewed novel facial expressions of emotion. However, there were overall decreases in foveal attention to sad and neutral faces that indicate more intensive instruction might be needed for these faces during training. Most importantly, the evidence shows that participant gender may affect training outcomes. Copyright © 2012 Elsevier B.V. All rights reserved.

Listening for recollection: a multi-voxel pattern analysis of recognition memory retrieval strategies

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Joel R Quamme

2010-08-01

Full Text Available Recent studies of recognition memory indicate that subjects can strategically vary how much they rely on recollection of specific details vs. feelings of familiarity when making recognition judgments. One possible explanation of these results is that subjects can establish an internally-directed attentional state (listening for recollection that enhances retrieval of studied details; fluctuations in this attentional state over time should be associated with fluctuations in subjects' recognition behavior. In this study, we used multi-voxel pattern analysis of fMRI data to identify brain regions that are involved in listening for recollection. Specifically, we looked for brain regions that met the following criteria: 1 Distinct neural patterns should be present when subjects are instructed to rely on recollection vs. familiarity, and 2 fluctuations in these neural patterns should be related to recognition behavior in the manner predicted by dual-process theories of recognition: Specifically, the presence of the recollection pattern during the pre-stimulus interval (indicating that subjects are listening for recollection at that moment should be associated with a selective decrease in false alarms to related lures. We found that pre-stimulus activity in the right supramarginal gyrus met all of these criteria, suggesting that this region proactively establishes an internally-directed attentional state that fosters recollection. We also found other regions (e.g., left middle temporal gyrus where the pattern of neural activity was related to subjects’ responding to related lures after stimulus onset (but not before, suggesting that these regions implement processes that are engaged in a reactive fashion to boost recollection.

Enhanced Recognition Memory in Grapheme-Colour Synaesthesia for Different Categories of Visual Stimuli

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Jamie eWard

2013-10-01

Full Text Available Memory has been shown to be enhanced in grapheme-colour synaesthesia, and this enhancement extends to certain visual stimuli (that don’t induce synaesthesia as well as stimuli comprised of graphemes (which do. Previous studies have used a variety of testing procedures to assess memory in synaesthesia (e.g. free recall, recognition, associative learning making it hard to know the extent to which memory benefits are attributable to the stimulus properties themselves, the testing method, participant strategies, or some combination of these factors. In the first experiment, we use the same testing procedure (recognition memory for a variety of stimuli (written words, nonwords, scenes, and fractals and also check which memorisation strategies were used. We demonstrate that grapheme-colour synaesthetes show enhanced memory across all these stimuli, but this is not found for a non-visual type of synaesthesia (lexical-gustatory. In the second experiment, the memory advantage for scenes is explored further by manipulating the properties of the old and new images (changing colour, orientation, or object presence. Again, grapheme-colour synaesthetes show a memory advantage for scenes across all manipulations. Although recognition memory is generally enhanced in this study, the largest effects were found for abstract visual images (fractals and scenes for which colour can be used to discriminate old/new status.

Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

DEFF Research Database (Denmark)

Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.

2018-01-01

, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data...... recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate....

Recognition of neural brain activity patterns correlated with complex motor activity

Science.gov (United States)

Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

2018-04-01

In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information

OpenAIRE

Wei-Jong Yang; Wei-Hau Du; Pau-Choo Chang; Jar-Ferr Yang; Pi-Hsia Hung

2017-01-01

The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an importan...

Pattern recognition receptors and the inflammasome in kidney disease

NARCIS (Netherlands)

Leemans, Jaklien C.; Kors, Lotte; Anders, Hans-Joachim; Florquin, Sandrine

2014-01-01

Toll-like receptors (TLRs) and nucleotide-binding oligomerization domain receptors (NLRs) are families of pattern recognition receptors that, together with inflammasomes, sense and respond to highly conserved pathogen motifs and endogenous molecules released upon cell damage or stress. Evidence

Sparsity-regularized HMAX for visual recognition.

Directory of Open Access Journals (Sweden)

Xiaolin Hu

Full Text Available About ten years ago, HMAX was proposed as a simple and biologically feasible model for object recognition, based on how the visual cortex processes information. However, the model does not encompass sparse firing, which is a hallmark of neurons at all stages of the visual pathway. The current paper presents an improved model, called sparse HMAX, which integrates sparse firing. This model is able to learn higher-level features of objects on unlabeled training images. Unlike most other deep learning models that explicitly address global structure of images in every layer, sparse HMAX addresses local to global structure gradually along the hierarchy by applying patch-based learning to the output of the previous layer. As a consequence, the learning method can be standard sparse coding (SSC or independent component analysis (ICA, two techniques deeply rooted in neuroscience. What makes SSC and ICA applicable at higher levels is the introduction of linear higher-order statistical regularities by max pooling. After training, high-level units display sparse, invariant selectivity for particular individuals or for image categories like those observed in human inferior temporal cortex (ITC and medial temporal lobe (MTL. Finally, on an image classification benchmark, sparse HMAX outperforms the original HMAX by a large margin, suggesting its great potential for computer vision.

Do subitizing deficits in developmental dyscalculia involve pattern recognition weakness?

Science.gov (United States)

Ashkenazi, Sarit; Mark-Zigdon, Nitza; Henik, Avishai

2013-01-01

The abilities of children diagnosed with developmental dyscalculia (DD) were examined in two types of object enumeration: subitizing, and small estimation (5-9 dots). Subitizing is usually defined as a fast and accurate assessment of a number of small dots (range 1 to 4 dots), and estimation is an imprecise process to assess a large number of items (range 5 dots or more). Based on reaction time (RT) and accuracy analysis, our results indicated a deficit in the subitizing and small estimation range among DD participants in relation to controls. There are indications that subitizing is based on pattern recognition, thus presenting dots in a canonical shape in the estimation range should result in a subitizing-like pattern. In line with this theory, our control group presented a subitizing-like pattern in the small estimation range for canonically arranged dots, whereas the DD participants presented a deficit in the estimation of canonically arranged dots. The present finding indicates that pattern recognition difficulties may play a significant role in both subitizing and subitizing deficits among those with DD. © 2012 Blackwell Publishing Ltd.

Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

Directory of Open Access Journals (Sweden)

Chia-Hung Lin

2010-01-01

Full Text Available This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD from a two-dimensional (2D image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Visual information constrains early and late stages of spoken-word recognition in sentence context.

Science.gov (United States)

Brunellière, Angèle; Sánchez-García, Carolina; Ikumi, Nara; Soto-Faraco, Salvador

2013-07-01

Audiovisual speech perception has been frequently studied considering phoneme, syllable and word processing levels. Here, we examined the constraints that visual speech information might exert during the recognition of words embedded in a natural sentence context. We recorded event-related potentials (ERPs) to words that could be either strongly or weakly predictable on the basis of the prior semantic sentential context and, whose initial phoneme varied in the degree of visual saliency from lip movements. When the sentences were presented audio-visually (Experiment 1), words weakly predicted from semantic context elicited a larger long-lasting N400, compared to strongly predictable words. This semantic effect interacted with the degree of visual saliency over a late part of the N400. When comparing audio-visual versus auditory alone presentation (Experiment 2), the typical amplitude-reduction effect over the auditory-evoked N100 response was observed in the audiovisual modality. Interestingly, a specific benefit of high- versus low-visual saliency constraints occurred over the early N100 response and at the late N400 time window, confirming the result of Experiment 1. Taken together, our results indicate that the saliency of visual speech can exert an influence over both auditory processing and word recognition at relatively late stages, and thus suggest strong interactivity between audio-visual integration and other (arguably higher) stages of information processing during natural speech comprehension. Copyright © 2013 Elsevier B.V. All rights reserved.

The role of native-language phonology in the auditory word identification and visual word recognition of Russian-English bilinguals.

Science.gov (United States)

Shafiro, Valeriy; Kharkhurin, Anatoliy V

2009-03-01

Does native language phonology influence visual word processing in a second language? This question was investigated in two experiments with two groups of Russian-English bilinguals, differing in their English experience, and a monolingual English control group. Experiment 1 tested visual word recognition following semantic categorization of words containing four phonological vowel contrasts (/i/-/u/,/I/-/A/,/i/-/I/,/epsilon/-/ae/). Experiment 2 assessed auditory identification accuracy of words containing these four contrasts. Both bilingual groups demonstrated reduced accuracy in auditory identification of two English vowel contrasts absent in their native phonology (/i/-/I/,epsilon/-/ae/). For late- bilinguals, auditory identification difficulty was accompanied by poor visual word recognition for one difficult contrast (/i/-/I/). Bilinguals' visual word recognition moderately correlated with their auditory identification of difficult contrasts. These results indicate that native language phonology can play a role in visual processing of second language words. However, this effect may be considerably constrained by orthographic systems of specific languages.

Statistical pattern recognition for automatic writer identification and verification

NARCIS (Netherlands)

Bulacu, Marius Lucian

2007-01-01

The thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document

Transformation-tolerant object recognition in rats revealed by visual priming.

Science.gov (United States)

Tafazoli, Sina; Di Filippo, Alessandro; Zoccolan, Davide

2012-01-04

Successful use of rodents as models for studying object vision crucially depends on the ability of their visual system to construct representations of visual objects that tolerate (i.e., remain relatively unchanged with respect to) the tremendous changes in object appearance produced, for instance, by size and viewpoint variation. Whether this is the case is still controversial, despite some recent demonstration of transformation-tolerant object recognition in rats. In fact, it remains unknown to what extent such a tolerant recognition has a spontaneous, perceptual basis, or, alternatively, mainly reflects learning of arbitrary associative relations among trained object appearances. In this study, we addressed this question by training rats to categorize a continuum of morph objects resulting from blending two object prototypes. The resulting psychometric curve (reporting the proportion of responses to one prototype along the morph line) served as a reference when, in a second phase of the experiment, either prototype was briefly presented as a prime, immediately before a test morph object. The resulting shift of the psychometric curve showed that recognition became biased toward the identity of the prime. Critically, this bias was observed also when the primes were transformed along a variety of dimensions (i.e., size, position, viewpoint, and their combination) that the animals had never experienced before. These results indicate that rats spontaneously perceive different views/appearances of an object as similar (i.e., as instances of the same object) and argue for the existence of neuronal substrates underlying formation of transformation-tolerant object representations in rats.

Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments.

Science.gov (United States)

Ouerghi, Safa; Boutteau, Rémi; Savatier, Xavier; Tlili, Fethi

2018-03-22

In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context.

[Representation of letter position in visual word recognition process].

Science.gov (United States)

Makioka, S

1994-08-01

Two experiments investigated the representation of letter position in visual word recognition process. In Experiment 1, subjects (12 undergraduates and graduates) were asked to detect a target word in a briefly-presented probe. Probes consisted of two kanji words. The latters which formed targets (critical letters) were always contained in probes. (e.g. target: [symbol: see text] probe: [symbol: see text]) High false alarm rate was observed when critical letters occupied the same within-word relative position (left or right within the word) in the probe words as in the target word. In Experiment 2 (subject were ten undergraduates and graduates), spaces adjacent to probe words were replaced by randomly chosen hiragana letters (e.g. [symbol: see text]), because spaces are not used to separate words in regular Japanese sentences. In addition to the effect of within-word relative position as in Experiment 1, the effect of between-word relative position (left or right across the probe words) was observed. These results suggest that information about within-word relative position of a letter is used in word recognition process. The effect of within-word relative position was explained by a connectionist model of word recognition.

Sequential pattern recognition by maximum conditional informativity

Czech Academy of Sciences Publication Activity Database

Grim, Jiří

2014-01-01

Roč. 45, č. 1 (2014), s. 39-45 ISSN 0167-8655 R&D Projects: GA ČR(CZ) GA14-02652S; GA ČR(CZ) GA14-10911S Keywords : Multivariate statistics * Statistical pattern recognition * Sequential decision making * Product mixtures * EM algorithm * Shannon information Subject RIV: IN - Informatics, Computer Sci ence Impact factor: 1.551, year: 2014 http://library.utia.cas.cz/separaty/2014/RO/grim-0428565.pdf

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

OpenAIRE

Iddamalgoda, Lahiru; Das, Partha S.; Aponso, Achala; Sundararajan, Vijayaraghava S.; Suravajhala, Prashanth; Valadi, Jayaraman K.

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited ...

The activation of visual face memory and explicit face recognition are delayed in developmental prosopagnosia.

Science.gov (United States)

Parketny, Joanna; Towler, John; Eimer, Martin

2015-08-01

Individuals with developmental prosopagnosia (DP) are strongly impaired in recognizing faces, but the causes of this deficit are not well understood. We employed event-related brain potentials (ERPs) to study the time-course of neural processes involved in the recognition of previously unfamiliar faces in DPs and in age-matched control participants with normal face recognition abilities. Faces of different individuals were presented sequentially in one of three possible views, and participants had to detect a specific Target Face ("Joe"). EEG was recorded during task performance to Target Faces, Nontarget Faces, or the participants' Own Face (which had to be ignored). The N250 component was measured as a marker of the match between a seen face and a stored representation in visual face memory. The subsequent P600f was measured as an index of attentional processes associated with the conscious awareness and recognition of a particular face. Target Faces elicited reliable N250 and P600f in the DP group, but both of these components emerged later in DPs than in control participants. This shows that the activation of visual face memory for previously unknown learned faces and the subsequent attentional processing and conscious recognition of these faces are delayed in DP. N250 and P600f components to Own Faces did not differ between the two groups, indicating that the processing of long-term familiar faces is less affected in DP. However, P600f components to Own Faces were absent in two participants with DP who failed to recognize their Own Face during the experiment. These results provide new evidence that face recognition deficits in DP may be linked to a delayed activation of visual face memory and explicit identity recognition mechanisms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Patterns of perceptual performance in Developmental Prosopagnosia

DEFF Research Database (Denmark)

Starrfelt, Randi; Petersen, Anders; Gerlach, Christian

and Methods. 10 DPs and 20 matched controls participated in experimental tasks measuring recognition of faces, objects, letters, words, and scenes, as well as text reading, global-local processing (Navon’s paradigm), and visual attention / short-term memory. Performance was compared using both group...... and single case statistics. 3. Results. We find an intriguing pattern of preserved and impaired functions in DP: Letter and word recognition, text reading, and visual attention / short-term memory were normal. For geographical scenes, simultaneous matching was normal, while there was a group difference......1. Objective Developmental prosopagnosia (DP) is a life-long deficit in face recognition. The deficit is thought to be selective for faces, a hypothesis we tested by investigating patterns of associated and dissociated visuoperceptual and -attentional functions in a group of DPs. 2. Participants...

Exploring How User Routine Affects the Recognition Performance of a Lock Pattern

NARCIS (Netherlands)

de Wide, Lisa; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

2015-01-01

To protect an Android smartphone against attackers, a lock pattern can be used. Nevertheless, shoulder-surfing and smudge attacks can be used to get access despite of this protection. To combat these attacks, biometric recognition can be added to the lock pattern, such that the lock-pattern

Automated target recognition and tracking using an optical pattern recognition neural network

Science.gov (United States)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Decoding complex flow-field patterns in visual working memory.

Science.gov (United States)

Christophel, Thomas B; Haynes, John-Dylan

2014-05-01

There has been a long history of research on visual working memory. Whereas early studies have focused on the role of lateral prefrontal cortex in the storage of sensory information, this has been challenged by research in humans that has directly assessed the encoding of perceptual contents, pointing towards a role of visual and parietal regions during storage. In a previous study we used pattern classification to investigate the storage of complex visual color patterns across delay periods. This revealed coding of such contents in early visual and parietal brain regions. Here we aim to investigate whether the involvement of visual and parietal cortex is also observable for other types of complex, visuo-spatial pattern stimuli. Specifically, we used a combination of fMRI and multivariate classification to investigate the retention of complex flow-field stimuli defined by the spatial patterning of motion trajectories of random dots. Subjects were trained to memorize the precise spatial layout of these stimuli and to retain this information during an extended delay. We used a multivariate decoding approach to identify brain regions where spatial patterns of activity encoded the memorized stimuli. Content-specific memory signals were observable in motion sensitive visual area MT+ and in posterior parietal cortex that might encode spatial information in a modality independent manner. Interestingly, we also found information about the memorized visual stimulus in somatosensory cortex, suggesting a potential crossmodal contribution to memory. Our findings thus indicate that working memory storage of visual percepts might be distributed across unimodal, multimodal and even crossmodal brain regions. Copyright © 2014 Elsevier Inc. All rights reserved.

Visual Analytics for Pattern Discovery in Home Care

Science.gov (United States)

Monsen, Karen A.; Bae, Sung-Heui; Zhang, Wenhui

2016-01-01

Summary Background Visualization can reduce the cognitive load of information, allowing users to easily interpret and assess large amounts of data. The purpose of our study was to examine home health data using visual analysis techniques to discover clinically salient associations between patient characteristics with problem-oriented health outcomes of older adult home health patients during the home health service period. Methods Knowledge, Behavior and Status ratings at discharge as well as change from admission to discharge that was coded using the Omaha System was collected from a dataset on 988 de-identified patient data from 15 home health agencies. SPSS Visualization Designer v1.0 was used to visually analyze patterns between independent and outcome variables using heat maps and histograms. Visualizations suggesting clinical salience were tested for significance using correlation analysis. Results The mean age of the patients was 80 years, with the majority female (66%). Of the 150 visualizations, 69 potentially meaningful patterns were statistically evaluated through bivariate associations, revealing 21 significant associations. Further, 14 associations between episode length and Charlson co-morbidity index mainly with urinary related diagnoses and problems remained significant after adjustment analyses. Through visual analysis, the adverse association of the longer home health episode length and higher Charlson co-morbidity index with behavior or status outcomes for patients with impaired urinary function was revealed. Conclusions We have demonstrated the use of visual analysis to discover novel patterns that described high-needs subgroups among the older home health patient population. The effective presentation of these data patterns can allow clinicians to identify areas of patient improvement, and time periods that are most effective for implementing home health interventions to improve patient outcomes. PMID:27466053

Infants’ Visual Recognition Memory for a Series of Categorically Related Items

Science.gov (United States)

Oakes, Lisa M.; Kovack-Lesh, Kristine A.

2013-01-01

Six-month-old infants' ("N" = 168) memory for individual items in a categorized list (e.g., images of dogs or cats) was examined to investigate the interactions between visual recognition memory, working memory, and categorization. In Experiments 1 and 2, infants were familiarized with six different cats or dogs, presented one at a time…

Sizing up the competition: quantifying the influence of the mental lexicon on auditory and visual spoken word recognition.

Science.gov (United States)

Strand, Julia F; Sommers, Mitchell S

2011-09-01

Much research has explored how spoken word recognition is influenced by the architecture and dynamics of the mental lexicon (e.g., Luce and Pisoni, 1998; McClelland and Elman, 1986). A more recent question is whether the processes underlying word recognition are unique to the auditory domain, or whether visually perceived (lipread) speech may also be sensitive to the structure of the mental lexicon (Auer, 2002; Mattys, Bernstein, and Auer, 2002). The current research was designed to test the hypothesis that both aurally and visually perceived spoken words are isolated in the mental lexicon as a function of their modality-specific perceptual similarity to other words. Lexical competition (the extent to which perceptually similar words influence recognition of a stimulus word) was quantified using metrics that are well-established in the literature, as well as a statistical method for calculating perceptual confusability based on the phi-square statistic. Both auditory and visual spoken word recognition were influenced by modality-specific lexical competition as well as stimulus word frequency. These findings extend the scope of activation-competition models of spoken word recognition and reinforce the hypothesis (Auer, 2002; Mattys et al., 2002) that perceptual and cognitive properties underlying spoken word recognition are not specific to the auditory domain. In addition, the results support the use of the phi-square statistic as a better predictor of lexical competition than metrics currently used in models of spoken word recognition. © 2011 Acoustical Society of America

LOCAL LINE BINARY PATTERN FOR FEATURE EXTRACTION ON PALM VEIN RECOGNITION

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Jayanti Yusmah Sari

2015-08-01

Full Text Available In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris. Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP, a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is needed

The Effects of Static and Dynamic Visual Representations as Aids for Primary School Children in Tasks of Auditory Discrimination of Sound Patterns. An Intervention-based Study.

Directory of Open Access Journals (Sweden)

Jesus Tejada

2018-02-01

Full Text Available It has been proposed that non-conventional presentations of visual information could be very useful as a scaffolding strategy in the learning of Western music notation. As a result, this study has attempted to determine if there is any effect of static and dynamic presentation modes of visual information in the recognition of sound patterns. An intervention-based quasi-experimental design was adopted with two groups of fifth-grade students in a Spanish city. Students did tasks involving discrimination, auditory recognition and symbolic association of the sound patterns with non-musical representations, either static images (S group, or dynamic images (D group. The results showed neither statistically significant differences in the scores of D and S, nor influence of the covariates on the dependent variable, although statistically significant intra-group differences were found for both groups. This suggests that both types of graphic formats could be effective as digital learning mediators in the learning of Western musical notation.

Is Syntactic-Category Processing Obligatory in Visual Word Recognition? Evidence from Chinese

Science.gov (United States)

Wong, Andus Wing-Kuen; Chen, Hsuan-Chih

2012-01-01

Three experiments were conducted to investigate how syntactic-category and semantic information is processed in visual word recognition. The stimuli were two-character Chinese words in which semantic and syntactic-category ambiguities were factorially manipulated. A lexical decision task was employed in Experiment 1, whereas a semantic relatedness…

Distinct spatio-temporal profiles of beta-oscillations within visual and sensorimotor areas during action recognition as revealed by MEG.

Science.gov (United States)

Pavlidou, Anastasia; Schnitzler, Alfons; Lange, Joachim

2014-05-01

The neural correlates of action recognition have been widely studied in visual and sensorimotor areas of the human brain. However, the role of neuronal oscillations involved during the process of action recognition remains unclear. Here, we were interested in how the plausibility of an action modulates neuronal oscillations in visual and sensorimotor areas. Subjects viewed point-light displays (PLDs) of biomechanically plausible and implausible versions of the same actions. Using magnetoencephalography (MEG), we examined dynamic changes of oscillatory activity during these action recognition processes. While both actions elicited oscillatory activity in visual and sensorimotor areas in several frequency bands, a significant difference was confined to the beta-band (∼20 Hz). An increase of power for plausible actions was observed in left temporal, parieto-occipital and sensorimotor areas of the brain, in the beta-band in successive order between 1650 and 2650 msec. These distinct spatio-temporal beta-band profiles suggest that the action recognition process is modulated by the degree of biomechanical plausibility of the action, and that spectral power in the beta-band may provide a functional interaction between visual and sensorimotor areas in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.

Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

Science.gov (United States)

Chen, Bo; Zang, Chuanzhi

2009-03-01

This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

Science.gov (United States)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

Natural Cytotoxicity Receptors: Pattern Recognition and Involvement of Carbohydrates

Directory of Open Access Journals (Sweden)

Angel Porgador

2005-01-01

Full Text Available Natural cytotoxicity receptors (NCRs, expressed by natural killer (NK cells, trigger NK lysis of tumor and virus-infected cells on interaction with cell-surface ligands of these target cells. We have determined that viral hemagglutinins expressed on the surface of virus-infected cells are involved in the recognition by the NCRs, NKp44 and NKp46. Recognition of tumor cells by the NCRs NKp30 and NKp46 involves heparan sulfate epitopes expressed on the tumor cell membrane. Our studies provide new evidence for the identity of the ligands for NCRs and indicate that a broader definition should be applied to pathological patterns recognized by innate immune receptors. Since nonmicrobial endogenous carbohydrate structures contribute significantly to this recognition, there is an imperative need to develop appropriate tools for the facile sequencing of carbohydrate moieties.

Introduction of pattern recognition by MATLAB practice 2

International Nuclear Information System (INIS)

1999-06-01

The contents of this book starts introduction and examples of pattern recognition. This book describes a vector and matrix, basic statistics and a probability distribution, statistical decision theory and probability density function, liner shunt, vector quantizing and clustering GMM, PCA and KL conversion, LDA, ID 3, a nerve cell modeling, HMM, SVM and Ada boost. It has direction of MATLAB in the appendix.

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

Science.gov (United States)

Biederman, Irving; Cooper, Eric E.

1991-01-01

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

Simultaneous pattern recognition and track fitting by the Kalman filtering method

International Nuclear Information System (INIS)

Billoir, P.

1990-01-01

A progressive pattern recognition algorithm based on the Kalman filtering method has been tested. The algorithm starts from a small track segment or from a fitted track of a neighbouring detector, then extends the candidate tracks by adding measured points one by one. The fitted parameters and weight matrix of the candidate track are updated when adding a point, and give an increasing precision on prediction of the next point. Thus, pattern recognition and track fitting can be accomplished simultaneously. The method has been implemented and tested for track reconstruction for the vertex detector of the ZEUS experiment at DESY. Detailed procedures of the method and its performance are presented. Its flexibility is described as well. (orig.)

Pattern visual evoked potentials in malingering.

Science.gov (United States)

Nakamura, A; Akio, T; Matsuda, E; Wakami, Y

2001-03-01

We previously developed a new method for estimating objective visual acuity by means of pattern visual evoked potentials (PVEP). In this study, this method was applied to the diagnosis of malingering. Six patients ranging in age from 40 to 54 years (mean 47 years) with suspected malingering were evaluated by means of the visual evoked potential test, optokinetic nystagmus (OKN) inhibition test, and the visual field test. In the PVEP study, the stimulus consisted of black and white checkerboards (39', 26', 15', and 9') with a visual angle of 8 degrees, contrast level of 15%, and a frequency of 0.7 Hz. One hundred PVEP responses were averaged per session. Routine ophthalmic examinations were normal in all patients. Five patients had a tubularly constricted visual field, and the remaining patient had a normal visual field. The objective visual acuities of the six patients estimated from PVEP were better than their subjective visual acuities estimated with Landolt rings. Among a variety of psychophysical and electrophysiologic ancillary tests, we consider our PVEP method a useful method for objectively determining visual acuity in a patient with signs of ocular malingering.

Functions of graphemic and phonemic codes in visual word-recognition.

Science.gov (United States)

Meyer, D E; Schvaneveldt, R W; Ruddy, M G

1974-03-01

Previous investigators have argued that printed words are recognized directly from visual representations and/or phonological representations obtained through phonemic recoding. The present research tested these hypotheses by manipulating graphemic and phonemic relations within various pairs of letter strings. Ss in two experiments classified the pairs as words or nonwords. Reaction times and error rates were relatively small for word pairs (e.g., BRIBE-TRIBE) that were both graphemically, and phonemically similar. Graphemic similarity alone inhibited performance on other word pairs (e.g., COUCH-TOUCH). These and other results suggest that phonological representations play a significant role in visual word recognition and that there is a dependence between successive phonemic-encoding operations. An encoding-bias model is proposed to explain the data.

Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

Directory of Open Access Journals (Sweden)

Tomasz Ciszewski

2006-01-01

Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

Human hippocampal and parahippocampal activity during visual associative recognition memory for spatial and nonspatial stimulus configurations.

Science.gov (United States)

Düzel, Emrah; Habib, Reza; Rotte, Michael; Guderian, Sebastian; Tulving, Endel; Heinze, Hans-Jochen

2003-10-15

Evidence from animal studies points to the importance of the parahippocampal region (PHR) [including entorhinal, perirhinal, and parahippocampal (PHC) cortices] for recognition of visual stimuli. Recent findings in animals suggest that PHR may also be involved in visual associative recognition memory for configurations of stimuli. Thus far, however, such involvement has not been demonstrated in humans. In fact, it has been argued that associative recognition in humans is critically dependent on the hippocampal formation (HF). To better understand the division of function between HF and PHR during recognition memory in humans, we measured the activity of both areas in healthy young adults during an associative recognition memory task using functional magnetic resonance imaging. To more precisely characterize the nature of the associations that might be coded by the HF and PHR during recognition, subjects were required to learn and were later tested for associations based on either the spatial arrangements of two stimuli or the identity of two stimuli (a face and a tool). An area in the PHC was found to be more active for recognized old configurations than new configurations in both the spatial and identity conditions. The HF, on the other hand, was more active for recognition of new configurations than old configurations and also more active in the spatial than the identity condition. These data highlight the involvement of PHR in the long-term coding of associative relationships between stimuli and help to clarify the nature of its functional distinction from the HF.

Where vision meets memory: prefrontal-posterior networks for visual object constancy during categorization and recognition.

Science.gov (United States)

Schendan, Haline E; Stern, Chantal E

2008-07-01

Objects seen from unusual relative to more canonical views require more time to categorize and recognize, and, according to object model verification theories, additionally recruit prefrontal processes for cognitive control that interact with parietal processes for mental rotation. To test this using functional magnetic resonance imaging, people categorized and recognized known objects from unusual and canonical views. Canonical views activated some components of a default network more on categorization than recognition. Activation to unusual views showed that both ventral and dorsal visual pathways, and prefrontal cortex, have key roles in visual object constancy. Unusual views activated object-sensitive and mental rotation (and not saccade) regions in ventrocaudal intraparietal, transverse occipital, and inferotemporal sulci, and ventral premotor cortex for verification processes of model testing on any task. A collateral-lingual sulci "place" area activated for mental rotation, working memory, and unusual views on correct recognition and categorization trials to accomplish detailed spatial matching. Ventrolateral prefrontal cortex and object-sensitive lateral occipital sulcus activated for mental rotation and unusual views on categorization more than recognition, supporting verification processes of model prediction. This visual knowledge framework integrates vision and memory theories to explain how distinct prefrontal-posterior networks enable meaningful interactions with objects in diverse situations.

Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

Energy Technology Data Exchange (ETDEWEB)

Lu, Wei; Dong, Xiao [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Qiu, Lili, E-mail: qiulili@bit.edu.cn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Yan, Zequn [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Meng, Zihui, E-mail: m_zihui@yahoo.com [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); Xue, Min [School of Chemical Engineering and the Environment, Beijing Institute of Technology, Beijing, 100081 (China); He, Xuan; Liu, Xueyong [Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900 (China)

2017-03-15

Graphical abstract: A colorimetric sensor array based on four kinds molecularly imprinted photonic crystal (MIPC) was explored for the selective visual detection of TNT, 2,6-DNT, 2,4-DNT and 4-MNT. The color of individual sensor changed with the increasing concentration of the analytes, and a cross-responsive platform was evaluated by a “radar” pattern. With the assistance of principal component analysis (PCA), a separate response region contained 95.25% of significant characteristics for the detection of nitroaromatics was generated, which also promised high potential for the customized visual detection system of other harmful chemicals. - Highlights: • Nitroaromatics were visually detected by molecularly imprinted photonic crystal. • The adsorption capacity was calculated. • The cross responsive platform of sensor array was established and discussed. • The discrimination capability was promoted by principal component analysis. • This system had high potential to be used in other customed visual detection. - Abstract: This research demonstrated that, in a colorimetric sensor array, 2,4,6-trinitrotoluene (TNT), 2,6-dinitrotoluene (2,6-DNT), 2,4-dinitrotoluene (2,4-DNT) and 4-nitrotoluene (4-MNT) were identifiable through a unique pattern in a qualitative and semi-quantitative manner. The adsorption capacity of the molecularly imprinted colloidal particles (MICs) for their corresponding templates was 0.27 mmol TNT/g, 0.22 mmol 2,6-DNT/g, 0.31 mmol 2,4-DNT/g and 0.16 mmol 4-MNT/g, respectively. Every optical sensor utilized in the arrays contained three-dimensional molecularly imprinted photonic crystal (MIPC) sensor with different imprinted templates. The intelligent materials can display different colors from green to red to 20 mM corresponding nitroaromatics with varying diffraction red shifts of 84 nm (TNT), 46 nm (2,6-DNT), 54 nm (2,4-DNT) and 35 nm (4-MNT), respectively. With the assistance of principal component analysis (PCA) and rational design

Colorimetric sensor arrays based on pattern recognition for the detection of nitroaromatic molecules

International Nuclear Information System (INIS)

Lu, Wei; Dong, Xiao; Qiu, Lili; Yan, Zequn; Meng, Zihui; Xue, Min; He, Xuan; Liu, Xueyong

2017-01-01

Graphical abstract: A colorimetric sensor array based on four kinds molecularly imprinted photonic crystal (MIPC) was explored for the selective visual detection of TNT, 2,6-DNT, 2,4-DNT and 4-MNT. The color of individual sensor changed with the increasing concentration of the analytes, and a cross-responsive platform was evaluated by a “radar” pattern. With the assistance of principal component analysis (PCA), a separate response region contained 95.25% of significant characteristics for the detection of nitroaromatics was generated, which also promised high potential for the customized visual detection system of other harmful chemicals. - Highlights: • Nitroaromatics were visually detected by molecularly imprinted photonic crystal. • The adsorption capacity was calculated. • The cross responsive platform of sensor array was established and discussed. • The discrimination capability was promoted by principal component analysis. • This system had high potential to be used in other customed visual detection. - Abstract: This research demonstrated that, in a colorimetric sensor array, 2,4,6-trinitrotoluene (TNT), 2,6-dinitrotoluene (2,6-DNT), 2,4-dinitrotoluene (2,4-DNT) and 4-nitrotoluene (4-MNT) were identifiable through a unique pattern in a qualitative and semi-quantitative manner. The adsorption capacity of the molecularly imprinted colloidal particles (MICs) for their corresponding templates was 0.27 mmol TNT/g, 0.22 mmol 2,6-DNT/g, 0.31 mmol 2,4-DNT/g and 0.16 mmol 4-MNT/g, respectively. Every optical sensor utilized in the arrays contained three-dimensional molecularly imprinted photonic crystal (MIPC) sensor with different imprinted templates. The intelligent materials can display different colors from green to red to 20 mM corresponding nitroaromatics with varying diffraction red shifts of 84 nm (TNT), 46 nm (2,6-DNT), 54 nm (2,4-DNT) and 35 nm (4-MNT), respectively. With the assistance of principal component analysis (PCA) and rational design

Ficolins and FIBCD1: Soluble and membrane bound pattern recognition molecules with acetyl group selectivity

DEFF Research Database (Denmark)

Thomsen, Theresa; Schlosser, Anders; Holmskov, Uffe

2011-01-01

as pattern recognition molecules. Ficolins are soluble oligomeric proteins composed of trimeric collagen-like regions linked to fibrinogen-related domains (FReDs) that have the ability to sense molecular patterns on both pathogens and apoptotic cell surfaces and activate the complement system. The ficolins......D-containing molecules, and discusses structural resemblance but also diversity in recognition of acetylated ligands....

Adaptive pattern recognition in real-time video-based soccer analysis

DEFF Research Database (Denmark)

Schlipsing, Marc; Salmen, Jan; Tschentscher, Marc

2017-01-01

are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online......Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results......, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data...

Neurocomputing methods for pattern recognition in nuclear physics

Energy Technology Data Exchange (ETDEWEB)

Gyulassy, M.; Dong, D.; Harlander, M. [Lawrence Berkeley Lab., CA (United States)

1991-12-31

We review recent progress on the development and applications of novel neurocomputing techniques for pattern recognition problems of relevance to RHIC experiments. The Elastic Tracking algorithm is shown to achieve sub-pad two track resolution without preprocessing. A high pass neural filter is developed for jet analysis and singular deconvolution methods are shown to recover the primordial jet distribution to a surprising high degree of accuracy.

Pattern recognition as a method of data analysis

Energy Technology Data Exchange (ETDEWEB)

Caputo, M.

1978-11-15

The method of pattern recognition has been used in biological and social sciences and has been recently introduced for the solution of geological and geophysical problems such as oil and ore prospecting and seismological prediction. The method is briefly illustrated by an application to earthquake prediction in Italy in which topographic and geologic maps are used in conjunction with earthquake catalogs. 3 figures, 1 table.

Do handwritten words magnify lexical effects in visual word recognition?

Science.gov (United States)

Perea, Manuel; Gil-López, Cristina; Beléndez, Victoria; Carreiras, Manuel

2016-01-01

An examination of how the word recognition system is able to process handwritten words is fundamental to formulate a comprehensive model of visual word recognition. Previous research has revealed that the magnitude of lexical effects (e.g., the word-frequency effect) is greater with handwritten words than with printed words. In the present lexical decision experiments, we examined whether the quality of handwritten words moderates the recruitment of top-down feedback, as reflected in word-frequency effects. Results showed a reading cost for difficult-to-read and easy-to-read handwritten words relative to printed words. But the critical finding was that difficult-to-read handwritten words, but not easy-to-read handwritten words, showed a greater word-frequency effect than printed words. Therefore, the inherent physical variability of handwritten words does not necessarily boost the magnitude of lexical effects.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

Science.gov (United States)

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Developing Visual Editors for High-Resolution Haptic Patterns

DEFF Research Database (Denmark)

Cuartielles, David; Göransson, Andreas; Olsson, Tony

2012-01-01

In this article we give an overview of our iterative work in developing visual editors for creating high resolution haptic patterns to be used in wearable, haptic feedback devices. During the past four years we have found the need to address the question of how to represent, construct and edit high...... resolution haptic patterns so that they translate naturally to the user’s haptic experience. To solve this question we have developed and tested several visual editors...

Differential effects of m1 and m2 receptor antagonists in perirhinal cortex on visual recognition memory in monkeys.

Science.gov (United States)

Wu, Wei; Saunders, Richard C; Mishkin, Mortimer; Turchi, Janita

2012-07-01

Microinfusions of the nonselective muscarinic antagonist scopolamine into perirhinal cortex impairs performance on visual recognition tasks, indicating that muscarinic receptors in this region play a pivotal role in recognition memory. To assess the mnemonic effects of selective blockade in perirhinal cortex of muscarinic receptor subtypes, we locally infused either the m1-selective antagonist pirenzepine or the m2-selective antagonist methoctramine in animals performing one-trial visual recognition, and compared these scores with those following infusions of equivalent volumes of saline. Compared to these control infusions, injections of pirenzepine, but not of methoctramine, significantly impaired recognition accuracy. Further, similar doses of scopolamine and pirenzepine yielded similar deficits, suggesting that the deficits obtained earlier with scopolamine were due mainly, if not exclusively, to blockade of m1 receptors. The present findings indicate that m1 and m2 receptors have functionally dissociable roles, and that the formation of new visual memories is critically dependent on the cholinergic activation of m1 receptors located on perirhinal cells. Published by Elsevier Inc.

Definition of new 3D invariants. Applications to pattern recognition problems with neural networks

International Nuclear Information System (INIS)

Proriol, J.

1996-01-01

We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the defined 3D invariants which can be used with neural networks to solve pattern recognition problems. We describe some methods to use the 2 D approximants. This work is an extension of previous 3D invariants used to solve some high energy physics problems. (author)

Brain dynamics of upstream perceptual processes leading to visual object recognition: a high density ERP topographic mapping study.

Science.gov (United States)

Schettino, Antonio; Loeys, Tom; Delplanque, Sylvain; Pourtois, Gilles

2011-04-01

Recent studies suggest that visual object recognition is a proactive process through which perceptual evidence accumulates over time before a decision can be made about the object. However, the exact electrophysiological correlates and time-course of this complex process remain unclear. In addition, the potential influence of emotion on this process has not been investigated yet. We recorded high density EEG in healthy adult participants performing a novel perceptual recognition task. For each trial, an initial blurred visual scene was first shown, before the actual content of the stimulus was gradually revealed by progressively adding diagnostic high spatial frequency information. Participants were asked to stop this stimulus sequence as soon as they could correctly perform an animacy judgment task. Behavioral results showed that participants reliably gathered perceptual evidence before recognition. Furthermore, prolonged exploration times were observed for pleasant, relative to either neutral or unpleasant scenes. ERP results showed distinct effects starting at 280 ms post-stimulus onset in distant brain regions during stimulus processing, mainly characterized by: (i) a monotonic accumulation of evidence, involving regions of the posterior cingulate cortex/parahippocampal gyrus, and (ii) true categorical recognition effects in medial frontal regions, including the dorsal anterior cingulate cortex. These findings provide evidence for the early involvement, following stimulus onset, of non-overlapping brain networks during proactive processes eventually leading to visual object recognition. Copyright © 2011 Elsevier Inc. All rights reserved.

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

Science.gov (United States)

Juday, Richard D. (Editor)

1988-01-01

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

A study on the extraction of feature variables for the pattern recognition for welding flaws

International Nuclear Information System (INIS)

Kim, J. Y.; Kim, C. H.; Kim, B. H.

1996-01-01

In this study, the researches classifying the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing, feature extraction, feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function classifier, the empirical Bayesian classifier. Also, the pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack, lack of penetration, lack of fusion, porosity, and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately teamed the neural network classifier is better than stastical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition.

Science.gov (United States)

Hansen, Mirko; Zahari, Finn; Ziegler, Martin; Kohlstedt, Hermann

2017-01-01

The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al 2 O 3 /Nb x O y /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al 2 O 3 tunnel barrier and a 2.5 mm thick Nb x O y memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strong I - V non-linearity might avoid the need for selector devices in crossbar array structures.

Atypical evening cortisol profile induces visual recognition memory deficit in healthy human subjects

Directory of Open Access Journals (Sweden)

Gilpin Heather

2008-08-01

Full Text Available Abstract Background Diurnal rhythm-mediated endogenous cortisol levels in humans are characterised by a peak in secretion after awakening that declines throughout the day to an evening trough. However, a significant proportion of the population exhibits an atypical cycle of diurnal cortisol due to shift work, jet-lag, aging, and mental illness. Results The present study has demonstrated a correlation between elevation of cortisol in the evening and deterioration of visual object recognition memory. However, high evening cortisol levels have no effect on spatial memory. Conclusion This study suggests that atypical evening salivary cortisol levels have an important role in the early deterioration of recognition memory. The loss of recognition memory, which is vital for everyday life, is a major symptom of the amnesic syndrome and early stages of Alzheimer's disease. Therefore, this study will promote a potential physiologic marker of early deterioration of recognition memory and a possible diagnostic strategy for Alzheimer's disease.

Oxidation-specific epitopes are danger-associated molecular patterns recognized by pattern recognition receptors of innate immunity

DEFF Research Database (Denmark)

Miller, Yury I; Choi, Soo-Ho; Wiesner, Philipp

2011-01-01

are a major target of innate immunity, recognized by a variety of "pattern recognition receptors" (PRRs). By analogy with microbial "pathogen-associated molecular patterns" (PAMPs), we postulate that host-derived, oxidation-specific epitopes can be considered to represent "danger (or damage......)-associated molecular patterns" (DAMPs). We also argue that oxidation-specific epitopes present on apoptotic cells and their cellular debris provided the primary evolutionary pressure for the selection of such PRRs. Furthermore, because many PAMPs on microbes share molecular identity and/or mimicry with oxidation...

DEVELOPING VISUAL NOVEL GAME WITH SPEECH-RECOGNITION INTERACTIVITY TO ENHANCE STUDENTS’ MASTERY ON ENGLISH EXPRESSIONS

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Elizabeth Anggraeni Amalo

2017-11-01

Full Text Available The teaching of English-expressions has always been done through conversation samples in form of written texts, audio recordings, and videos. In the meantime, the development of computer-aided learning technology has made autonomous language learning possible. Game, as one of computer-aided learning technology products, can serve as a medium to provide educational contents like that of language teaching and learning. Visual Novel is considered as a conversational game that is suitable to be combined with English-expressions material. Unlike the other click-based interaction Visual Novel Games, the visual novel game in this research implements speech recognition as the interaction trigger. Hence, this paper aims at elaborating how visual novel games are utilized to deliver English-expressions with speech recognition command for the interaction. This research used Research and Development (R&D method with Experimental design through control and experimental groups to measure its effectiveness in enhancing students’ English-expressions mastery. ANOVA was utilized to prove the significant differences between the control and experimental groups. It is expected that the result of this development and experiment can devote benefits to the English teaching and learning, especially on English-expressions.

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1975-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

Directory of Open Access Journals (Sweden)

Zhaoqin Peng

2013-01-01

Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

Pattern recognition approach to quantify the atomic structure of graphene

DEFF Research Database (Denmark)

Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm

2014-01-01

We report a pattern recognition approach to detect the atomic structure in high-resolution transmission electron microscopy images of graphene. The approach provides quantitative information such as carbon-carbon bond lengths and bond length variations on a global and local scale alike. © 2014...

The colorful brain: Visualization of EEG background patterns

NARCIS (Netherlands)

van Putten, Michel Johannes Antonius Maria

2008-01-01

This article presents a method to transform routine clinical EEG recordings to an alternative visual domain. The method is intended to support the classic visual interpretation of the EEG background pattern and to facilitate communication about relevant EEG characteristics. In addition, it provides

New pattern recognition system in the e-nose for Chinese spirit identification

International Nuclear Information System (INIS)

Zeng Hui; Li Qiang; Gu Yu

2016-01-01

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance (QCM) principle, and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an 8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value (A), root-mean-square value (RMS), shape factor value (S f ), crest factor value (C f ), impulse factor value (I f ), clearance factor value (CL f ), kurtosis factor value (K v ) are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis (PCA) method. Finally the back propagation (BP) neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively. (paper)

A model for size- and rotation-invariant pattern processing in the visual system.

Science.gov (United States)

Reitboeck, H J; Altmann, J

1984-01-01

The mapping of retinal space onto the striate cortex of some mammals can be approximated by a log-polar function. It has been proposed that this mapping is of functional importance for scale- and rotation-invariant pattern recognition in the visual system. An exact log-polar transform converts centered scaling and rotation into translations. A subsequent translation-invariant transform, such as the absolute value of the Fourier transform, thus generates overall size- and rotation-invariance. In our model, the translation-invariance is realized via the R-transform. This transform can be executed by simple neural networks, and it does not require the complex computations of the Fourier transform, used in Mellin-transform size-invariance models. The logarithmic space distortion and differentiation in the first processing stage of the model is realized via "Mexican hat" filters whose diameter increases linearly with eccentricity, similar to the characteristics of the receptive fields of retinal ganglion cells. Except for some special cases, the model can explain object recognition independent of size, orientation and position. Some general problems of Mellin-type size-invariance models-that also apply to our model-are discussed.

Got Rhythm...For Better and for Worse. Cross-Modal Effects of Auditory Rhythm on Visual Word Recognition

Science.gov (United States)

Brochard, Renaud; Tassin, Maxime; Zagar, Daniel

2013-01-01

The present research aimed to investigate whether, as previously observed with pictures, background auditory rhythm would also influence visual word recognition. In a lexical decision task, participants were presented with bisyllabic visual words, segmented into two successive groups of letters, while an irrelevant strongly metric auditory…

Interfamily transfer of a plant pattern-recognition receptor confers broad-spectrum bacterial resistance

NARCIS (Netherlands)

Lacombe, S.; Rougon-Cardoso, A.; Sherwood, E.; Peeters, N.; Dahlbeck, D.; Esse, van H.P.; Smoker, M.; Rallapalli, G.; Thomma, B.P.H.J.; Staskawicz, B.; Jones, J.D.G.; Zipfel, C.

2010-01-01

Plant diseases cause massive losses in agriculture. Increasing the natural defenses of plants may reduce the impact of phytopathogens on agricultural productivity. Pattern-recognition receptors (PRRs) detect microbes by recognizing conserved pathogen-associated molecular patterns (PAMPs)1, 2, 3.

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

Directory of Open Access Journals (Sweden)

Shouyi Yin

2015-01-01

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

Neural correlates of auditory recognition memory in the primate dorsal temporal pole

Science.gov (United States)

Ng, Chi-Wing; Plakke, Bethany

2013-01-01

Temporal pole (TP) cortex is associated with higher-order sensory perception and/or recognition memory, as human patients with damage in this region show impaired performance during some tasks requiring recognition memory (Olson et al. 2007). The underlying mechanisms of TP processing are largely based on examination of the visual nervous system in humans and monkeys, while little is known about neuronal activity patterns in the auditory portion of this region, dorsal TP (dTP; Poremba et al. 2003). The present study examines single-unit activity of dTP in rhesus monkeys performing a delayed matching-to-sample task utilizing auditory stimuli, wherein two sounds are determined to be the same or different. Neurons of dTP encode several task-relevant events during the delayed matching-to-sample task, and encoding of auditory cues in this region is associated with accurate recognition performance. Population activity in dTP shows a match suppression mechanism to identical, repeated sound stimuli similar to that observed in the visual object identification pathway located ventral to dTP (Desimone 1996; Nakamura and Kubota 1996). However, in contrast to sustained visual delay-related activity in nearby analogous regions, auditory delay-related activity in dTP is transient and limited. Neurons in dTP respond selectively to different sound stimuli and often change their sound response preferences between experimental contexts. Current findings suggest a significant role for dTP in auditory recognition memory similar in many respects to the visual nervous system, while delay memory firing patterns are not prominent, which may relate to monkeys' shorter forgetting thresholds for auditory vs. visual objects. PMID:24198324

Neural correlates of auditory recognition memory in the primate dorsal temporal pole.

Science.gov (United States)

Ng, Chi-Wing; Plakke, Bethany; Poremba, Amy

2014-02-01

Temporal pole (TP) cortex is associated with higher-order sensory perception and/or recognition memory, as human patients with damage in this region show impaired performance during some tasks requiring recognition memory (Olson et al. 2007). The underlying mechanisms of TP processing are largely based on examination of the visual nervous system in humans and monkeys, while little is known about neuronal activity patterns in the auditory portion of this region, dorsal TP (dTP; Poremba et al. 2003). The present study examines single-unit activity of dTP in rhesus monkeys performing a delayed matching-to-sample task utilizing auditory stimuli, wherein two sounds are determined to be the same or different. Neurons of dTP encode several task-relevant events during the delayed matching-to-sample task, and encoding of auditory cues in this region is associated with accurate recognition performance. Population activity in dTP shows a match suppression mechanism to identical, repeated sound stimuli similar to that observed in the visual object identification pathway located ventral to dTP (Desimone 1996; Nakamura and Kubota 1996). However, in contrast to sustained visual delay-related activity in nearby analogous regions, auditory delay-related activity in dTP is transient and limited. Neurons in dTP respond selectively to different sound stimuli and often change their sound response preferences between experimental contexts. Current findings suggest a significant role for dTP in auditory recognition memory similar in many respects to the visual nervous system, while delay memory firing patterns are not prominent, which may relate to monkeys' shorter forgetting thresholds for auditory vs. visual objects.

Learning through hand- or typewriting influences visual recognition of new graphic shapes: behavioral and functional imaging evidence.

Science.gov (United States)

Longcamp, Marieke; Boucard, Céline; Gilhodes, Jean-Claude; Anton, Jean-Luc; Roth, Muriel; Nazarian, Bruno; Velay, Jean-Luc

2008-05-01

Fast and accurate visual recognition of single characters is crucial for efficient reading. We explored the possible contribution of writing memory to character recognition processes. We evaluated the ability of adults to discriminate new characters from their mirror images after being taught how to produce the characters either by traditional pen-and-paper writing or with a computer keyboard. After training, we found stronger and longer lasting (several weeks) facilitation in recognizing the orientation of characters that had been written by hand compared to those typed. Functional magnetic resonance imaging recordings indicated that the response mode during learning is associated with distinct pathways during recognition of graphic shapes. Greater activity related to handwriting learning and normal letter identification was observed in several brain regions known to be involved in the execution, imagery, and observation of actions, in particular, the left Broca's area and bilateral inferior parietal lobules. Taken together, these results provide strong arguments in favor of the view that the specific movements memorized when learning how to write participate in the visual recognition of graphic shapes and letters.

A survey of visual preprocessing and shape representation techniques

Science.gov (United States)

Olshausen, Bruno A.

1988-01-01

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

Science.gov (United States)

Lhamon, Michael Earl

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase

Stereotyped Visual Symbols as a Mean of Public Consolidation in Context Of International Genocide Recognition

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Elena Anatolievna Ivanova

2017-12-01

Full Text Available This article presents the results of the study devoted to stereotyped visual symbols as a part of the corporate identity complex of anti-genocide organizations aimed to reach an international genocide recognition as a part of their strategies. The relevance of the stud y is justified with the similarity of modern tools for visualizing the unique characteristics of organizations and centuries-old practice of opponents opposing each other, what was discovered in the investigation process. The effectiveness of the usage of stereotyped visual symbols as the means of public consolidation in combating the genocide, which is the purpose of this study, is proved. Using the method of structural and semiotic analysis, the authors studied visual symbols used as the means of broadcasting the public opinion coded into a key message within the framework of the anti-genocide organizations’ activities. The studied visual symbols were identified as the means of stereotyped influence aimed on the mass audience, which allowed us to conclude about the effectiveness of such symbols in solving problems in mass communications. During the generalization and systematization of the data obtained, the most frequently used symbols which enclose the codes of certain cultures were identified, which led us to the conclusion that such symbols are stereotypically used in the context of combating genocide and bringing the public forward the recognition of such conflicts.

Correlation of pattern reversal visual evoked potential parameters with the pattern standard deviation in primary open angle glaucoma.

Science.gov (United States)

Kothari, Ruchi; Bokariya, Pradeep; Singh, Ramji; Singh, Smita; Narang, Purvasha

2014-01-01

To evaluate whether glaucomatous visual field defect particularly the pattern standard deviation (PSD) of Humphrey visual field could be associated with visual evoked potential (VEP) parameters of patients having primary open angle glaucoma (POAG). Visual field by Humphrey perimetry and simultaneous recordings of pattern reversal visual evoked potential (PRVEP) were assessed in 100 patients with POAG. The stimulus configuration for VEP recordings consisted of the transient pattern reversal method in which a black and white checker board pattern was generated (full field) and displayed on VEP monitor (colour 14″) by an electronic pattern regenerator inbuilt in an evoked potential recorder (RMS EMG EP MARK II). The results of our study indicate that there is a highly significant (P<0.001) negative correlation of P100 amplitude and a statistically significant (P<0.05) positive correlation of N70 latency, P100 latency and N155 latency with the PSD of Humphrey visual field in the subjects of POAG in various age groups as evaluated by Student's t-test. Prolongation of VEP latencies were mirrored by a corresponding increase of PSD values. Conversely, as PSD increases the magnitude of VEP excursions were found to be diminished.

Pattern recognition in spectra

International Nuclear Information System (INIS)

Gebran, M; Paletou, F

2017-01-01

We present a new automated procedure that simultaneously derives the effective temperature T eff , surface gravity log g , metallicity [ Fe/H ], and equatorial projected rotational velocity v e sin i for stars. The procedure is inspired by the well-known PCA-based inversion of spectropolarimetric full-Stokes solar data, which was used both for Zeeman and Hanle effects. The efficiency and accuracy of this procedure have been proven for FGK, A, and late type dwarf stars of K and M spectral types. Learning databases are generated from the Elodie stellar spectra library using observed spectra for which fundamental parameters were already evaluated or with synthetic data. The synthetic spectra are calculated using ATLAS9 model atmospheres. This technique helped us to detect many peculiar stars such as Am, Ap, HgMn, SiEuCr and binaries. This fast and efficient technique could be used every time a pattern recognition is needed. One important application is the understanding of the physical properties of planetary surfaces by comparing aboard instrument data to synthetic ones. (paper)

Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

DEFF Research Database (Denmark)

Garrido, Jesús A.; Luque, Niceto R.; Tolu, Silvia

2016-01-01

The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly...... and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity...... effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input...

Pattern-recognition software detecting the onset of failures in complex systems

International Nuclear Information System (INIS)

Mott, J.; King, R.

1987-01-01

A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself

Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations

International Nuclear Information System (INIS)

Shah, Syed Islamuddin; Nandipati, Giridhar; Kara, Abdelkader; Rahman, Talat S

2012-01-01

We report the development of a pattern recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes, including those such as shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern recognition scheme to the self-diffusion of 9-atom islands (M 9 ) on M(111), where M = Cu, Ag or Ni.

Foundations for a syntatic pattern recognition system for genomic DNA sequences

Energy Technology Data Exchange (ETDEWEB)

Searles, D.B.

1993-03-01

The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

Automatic micropropagation of plants--the vision-system: graph rewriting as pattern recognition

Science.gov (United States)

Schwanke, Joerg; Megnet, Roland; Jensch, Peter F.

1993-03-01

The automation of plant-micropropagation is necessary to produce high amounts of biomass. Plants have to be dissected on particular cutting-points. A vision-system is needed for the recognition of the cutting-points on the plants. With this background, this contribution is directed to the underlying formalism to determine cutting-points on abstract-plant models. We show the usefulness of pattern recognition by graph-rewriting along with some examples in this context.

Tracking the emergence of the consonant bias in visual-word recognition: evidence with developing readers.

Science.gov (United States)

Soares, Ana Paula; Perea, Manuel; Comesaña, Montserrat

2014-01-01

Recent research with skilled adult readers has consistently revealed an advantage of consonants over vowels in visual-word recognition (i.e., the so-called "consonant bias"). Nevertheless, little is known about how early in development the consonant bias emerges. This work aims to address this issue by studying the relative contribution of consonants and vowels at the early stages of visual-word recognition in developing readers (2(nd) and 4(th) Grade children) and skilled adult readers (college students) using a masked priming lexical decision task. Target words starting either with a consonant or a vowel were preceded by a briefly presented masked prime (50 ms) that could be the same as the target (e.g., pirata-PIRATA [pirate-PIRATE]), a consonant-preserving prime (e.g., pureto-PIRATA), a vowel-preserving prime (e.g., gicala-PIRATA), or an unrelated prime (e.g., bocelo -PIRATA). Results revealed significant priming effects for the identity and consonant-preserving conditions in adult readers and 4(th) Grade children, whereas 2(nd) graders only showed priming for the identity condition. In adult readers, the advantage of consonants was observed both for words starting with a consonant or a vowel, while in 4(th) graders this advantage was restricted to words with an initial consonant. Thus, the present findings suggest that a Consonant/Vowel skeleton should be included in future (developmental) models of visual-word recognition and reading.

The role of pattern recognition in creative problem solving: a case study in search of new mathematics for biology.

Science.gov (United States)

Hong, Felix T

2013-09-01

Rosen classified sciences into two categories: formalizable and unformalizable. Whereas formalizable sciences expressed in terms of mathematical theories were highly valued by Rutherford, Hutchins pointed out that unformalizable parts of soft sciences are of genuine interest and importance. Attempts to build mathematical theories for biology in the past century was met with modest and sporadic successes, and only in simple systems. In this article, a qualitative model of humans' high creativity is presented as a starting point to consider whether the gap between soft and hard sciences is bridgeable. Simonton's chance-configuration theory, which mimics the process of evolution, was modified and improved. By treating problem solving as a process of pattern recognition, the known dichotomy of visual thinking vs. verbal thinking can be recast in terms of analog pattern recognition (non-algorithmic process) and digital pattern recognition (algorithmic process), respectively. Additional concepts commonly encountered in computer science, operations research and artificial intelligence were also invoked: heuristic searching, parallel and sequential processing. The refurbished chance-configuration model is now capable of explaining several long-standing puzzles in human cognition: a) why novel discoveries often came without prior warning, b) why some creators had no ideas about the source of inspiration even after the fact, c) why some creators were consistently luckier than others, and, last but not least, d) why it was so difficult to explain what intuition, inspiration, insight, hunch, serendipity, etc. are all about. The predictive power of the present model was tested by means of resolving Zeno's paradox of Achilles and the Tortoise after one deliberately invoked visual thinking. Additional evidence of its predictive power must await future large-scale field studies. The analysis was further generalized to constructions of scientific theories in general. This approach

Mobile Visual Recognition on Smartphones

Directory of Open Access Journals (Sweden)

Zhenwen Gui

2013-01-01

Full Text Available This paper addresses the recognition of large-scale outdoor scenes on smartphones by fusing outputs of inertial sensors and computer vision techniques. The main contributions can be summarized as follows. Firstly, we propose an ORD (overlap region divide method to plot image position area, which is fast enough to find the nearest visiting area and can also reduce the search range compared with the traditional approaches. Secondly, the vocabulary tree-based approach is improved by introducing GAGCC (gravity-aligned geometric consistency constraint. Our method involves no operation in the high-dimensional feature space and does not assume a global transform between a pair of images. Thus, it substantially reduces the computational complexity and memory usage, which makes the city scale image recognition feasible on the smartphone. Experiments on a collected database including 0.16 million images show that the proposed method demonstrates excellent recognition performance, while maintaining the average recognition time about 1 s.

Interactions of the humoral pattern recognition molecule PTX3 with the complement system

DEFF Research Database (Denmark)

Doni, Andrea; Garlanda, Cecilia; Bottazzi, Barbara

2012-01-01

The innate immune system comprises a cellular and a humoral arm. The long pentraxin PTX3 is a fluid phase pattern recognition molecule, which acts as an essential component of the humoral arm of innate immunity. PTX3 has antibody-like properties including interactions with complement components....... PTX3 interacts with C1q, ficolin-1 and ficolin-2 as well as mannose-binding lectin, recognition molecules in the classical and lectin complement pathways. The formation of these heterocomplexes results in cooperative pathogen recognition and complement activation. Interactions with C4b binding protein...

A Pilot Study of a Test for Visual Recognition Memory in Adults with Moderate to Severe Intellectual Disability

Science.gov (United States)

Pyo, Geunyeong; Ala, Tom; Kyrouac, Gregory A.; Verhulst, Steven J.

2010-01-01

Objective assessment of memory functioning is an important part of evaluation for Dementia of Alzheimer Type (DAT). The revised Picture Recognition Memory Test (r-PRMT) is a test for visual recognition memory to assess memory functioning of persons with intellectual disabilities (ID), specifically targeting moderate to severe ID. A pilot study was…

Correlation of pattern reversal visual evoked potential parameters with the pattern standard deviation in primary open angle glaucoma

Directory of Open Access Journals (Sweden)

Ruchi Kothari

2014-04-01

Full Text Available AIM:To evaluate whether glaucomatous visual field defect particularly the pattern standard deviation (PSD of Humphrey visual field could be associated with visual evoked potential (VEP parameters of patients having primary open angle glaucoma (POAG.METHODS:Visual field by Humphrey perimetry and simultaneous recordings of pattern reversal visual evoked potential (PRVEP were assessed in 100 patients with POAG. The stimulus configuration for VEP recordings consisted of the transient pattern reversal method in which a black and white checker board pattern was generated (full field and displayed on VEP monitor (colour 14” by an electronic pattern regenerator inbuilt in an evoked potential recorder (RMS EMG EP MARK II.RESULTS:The results of our study indicate that there is a highly significant (P<0.001 negative correlation of P100 amplitude and a statistically significant (P<0.05 positive correlation of N70 latency, P100 latency and N155 latency with the PSD of Humphrey visual field in the subjects of POAG in various age groups as evaluated by Student’s t-test.CONCLUSION:Prolongation of VEP latencies were mirrored by a corresponding increase of PSD values. Conversely, as PSD increases the magnitude of VEP excursions were found to be diminished.

Differential patterns of prefrontal MEG activation during verbal & visual encoding and retrieval.

Science.gov (United States)

Prendergast, Garreth; Limbrick-Oldfield, Eve; Ingamells, Ed; Gathercole, Susan; Baddeley, Alan; Green, Gary G R

2013-01-01

The spatiotemporal profile of activation of the prefrontal cortex in verbal and non-verbal recognition memory was examined using magnetoencephalography (MEG). Sixteen neurologically healthy right-handed participants were scanned whilst carrying out a modified version of the Doors and People Test of recognition memory. A pattern of significant prefrontal activity was found for non-verbal and verbal encoding and recognition. During the encoding, verbal stimuli activated an area in the left ventromedial prefrontal cortex, and non-verbal stimuli activated an area in the right. A region in the left dorsolateral prefrontal cortex also showed significant activation during the encoding of non-verbal stimuli. Both verbal and non-verbal stimuli significantly activated an area in the right dorsomedial prefrontal cortex and the right anterior prefrontal cortex during successful recognition, however these areas showed temporally distinct activation dependent on material, with non-verbal showing activation earlier than verbal stimuli. Additionally, non-verbal material activated an area in the left anterior prefrontal cortex during recognition. These findings suggest a material-specific laterality in the ventromedial prefrontal cortex during encoding for verbal and non-verbal but also support the HERA model for verbal material. The discovery of two process dependent areas during recognition that showed patterns of temporal activation dependent on material demonstrates the need for the application of more temporally sensitive techniques to the involvement of the prefrontal cortex in recognition memory.

Widespread correlation patterns of fMRI signal across visual cortex reflect eccentricity organization

Science.gov (United States)

Arcaro, Michael J; Honey, Christopher J; Mruczek, Ryan EB; Kastner, Sabine; Hasson, Uri

2015-01-01

The human visual system can be divided into over two-dozen distinct areas, each of which contains a topographic map of the visual field. A fundamental question in vision neuroscience is how the visual system integrates information from the environment across different areas. Using neuroimaging, we investigated the spatial pattern of correlated BOLD signal across eight visual areas on data collected during rest conditions and during naturalistic movie viewing. The correlation pattern between areas reflected the underlying receptive field organization with higher correlations between cortical sites containing overlapping representations of visual space. In addition, the correlation pattern reflected the underlying widespread eccentricity organization of visual cortex, in which the highest correlations were observed for cortical sites with iso-eccentricity representations including regions with non-overlapping representations of visual space. This eccentricity-based correlation pattern appears to be part of an intrinsic functional architecture that supports the integration of information across functionally specialized visual areas. DOI: http://dx.doi.org/10.7554/eLife.03952.001 PMID:25695154

The barista on the bus: cellular and synaptic mechanisms for visual recognition memory.

Science.gov (United States)

Barth, Alison L; Wheeler, Mark E

2008-04-24

Our ability to recognize that something is familiar, often referred to as visual recognition memory, has been correlated with a reduction in neural activity in the perirhinal cortex. In this issue of Neuron, Griffiths et al. now provide evidence that this form of memory requires AMPA receptor endocytosis and long-term depression of excitatory synapses in this brain area.

Neurobiological correlates of visual and olfactory recognition in sheep.

Science.gov (United States)

Kendrick, K M

1994-12-01

This paper describes neurophysiological and behavioural experiments which investigate the ability of sheep to recognise different individuals using visual and olfactory cues. Behavioural experiments using Y-mazes with back-projected images of faces have shown that sheep can distinguish between the faces of sheep and humans when the faces are presented in a frontal view although they have more difficulty in doing so if the faces are presented in profile, upside down or with the eyes obscured. Single-cell electrophysiological recordings made from neurones in the temporal cortex have shown that sheep, like non-human primates, have cells in this region that code preferentially for facial stimuli and that their responses are also diminished or abolished if the faces are presented upside-down, in profile, or with the eyes obscured. Different sub-populations of cells code for faces of similar social and emotional significance. Thus one population of cells codes for faces with horns and their responses are also modulated by the size of the horns, another population codes for faces of animals of the same breed, and particularly familiar animals, and a final population codes for faces of humans and dogs. Visual cues from body shape and posture are also important for recognition of different classes of individual. Field studies have shown that sheep find it difficult to recognise humans approaching them if they change their posture to quadrupedal as opposed to a bipedal one. Electrophysiological studies have also demonstrated the presence of cells in the temporal cortex which respond preferentially to the sight of a human body shape and their activity is influenced by body orientation, posture and direction of movement. In some cases alterations to the human's appearance can also influence their activity. Olfactory recognition studies have used electrophysiological, in vivo sampling and behavioural analyses to establish the mechanisms whereby a maternal ewe develops the

Image processing and recognition for biological images.

Science.gov (United States)

Uchida, Seiichi

2013-05-01

This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

Directory of Open Access Journals (Sweden)

Gaojing Wang

2018-06-01

Full Text Available Human activity recognition (HAR is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

Science.gov (United States)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

Scanning patterns of faces do not explain impaired emotion recognition in Huntington Disease: Evidence for a high level mechanism

Directory of Open Access Journals (Sweden)

Marieke evan Asselen

2012-02-01

Full Text Available Previous studies in patients with amygdala lesions suggested that deficits in emotion recognition might be mediated by impaired scanning patterns of faces. Here we investigated whether scanning patterns also contribute to the selective impairment in recognition of disgust in Huntington disease (HD. To achieve this goal, we recorded eye movements during a two-alternative forced choice emotion recognition task. HD patients in presymptomatic (n=16 and symptomatic (n=9 disease stages were tested and their performance was compared to a control group (n=22. In our emotion recognition task, participants had to indicate whether a face reflected one of six basic emotions. In addition, and in order to define whether emotion recognition was altered when the participants were forced to look at a specific component of the face, we used a second task where only limited facial information was provided (eyes/mouth in partially masked faces. Behavioural results showed no differences in the ability to recognize emotions between presymptomatic gene carriers and controls. However, an emotion recognition deficit was found for all 6 basic emotion categories in early stage HD. Analysis of eye movement patterns showed that patient and controls used similar scanning strategies. Patterns of deficits were similar regardless of whether parts of the faces were masked or not, thereby confirming that selective attention to particular face parts is not underlying the deficits. These results suggest that the emotion recognition deficits in symptomatic HD patients cannot be explained by impaired scanning patterns of faces. Furthermore, no selective deficit for recognition of disgust was found in presymptomatic HD patients.

Application of ann-based decision making pattern recognition to fishing operations

Energy Technology Data Exchange (ETDEWEB)

Akhlaghinia, M.; Torabi, F.; Wilton, R.R. [University of Regina, Saskatchewan (Canada). Faculty of Engineering. Dept. of Petroleum Engineering], e-mail: Farshid.Torabi@uregina.ca

2010-10-15

Decision making is a crucial part of fishing operations. Proper decisions should be made to prevent wasted time and associated costs on unsuccessful operations. This paper presents a novel model to help drilling managers decide when to commence and when to quit a fishing operation. A decision making model based on Artificial Neural Network (ANN) has been developed that utilizes Pattern Recognition based on 181 fishing incidents from one of the most fish-prone fields of the southwest of Iran. All parameters chosen to train the ANN-Based Pattern Recognition Tool are assumed to play a role in the success of the fishing operation and are therefore used to decide whether a fishing operation should be performed or not. If the tool deems the operation suitable for consideration, a cost analysis of the fishing operation can then be performed to justify its overall cost. (author)

Object Recognition in Clutter: Cortical Responses Depend on the Type of Learning

Directory of Open Access Journals (Sweden)

Jay eHegdé

2012-06-01

Full Text Available Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects’ recognition performance for both sets of objects in strong clutter. We found many brain regions that were differentially responsive to objects during object recognition depending on whether they were learned in strong or weak clutter. In particular, the responses of the left fusiform gyrus reliably reflected, on a trial-to-trial basis, subjects’ object recognition performance for objects learned in the presence of strong clutter. These results indicate that the visual system does not use a single, general-purpose mechanism to cope with clutter. Instead, there are two distinct spatial patterns of activation whose responses are attributable not to the visual context in which the objects were seen, but to the context in which the objects were learned.

Looking at My Own Face: Visual Processing Strategies in Self–Other Face Recognition

Directory of Open Access Journals (Sweden)

Anya Chakraborty

2018-02-01

Full Text Available We live in an age of ‘selfies.’ Yet, how we look at our own faces has seldom been systematically investigated. In this study we test if the visual processing of the highly familiar self-face is different from other faces, using psychophysics and eye-tracking. This paradigm also enabled us to test the association between the psychophysical properties of self-face representation and visual processing strategies involved in self-face recognition. Thirty-three adults performed a self-face recognition task from a series of self-other face morphs with simultaneous eye-tracking. Participants were found to look longer at the lower part of the face for self-face compared to other-face. Participants with a more distinct self-face representation, as indexed by a steeper slope of the psychometric response curve for self-face recognition, were found to look longer at upper part of the faces identified as ‘self’ vs. those identified as ‘other’. This result indicates that self-face representation can influence where we look when we process our own vs. others’ faces. We also investigated the association of autism-related traits with self-face processing metrics since autism has previously been associated with atypical self-processing. The study did not find any self-face specific association with autistic traits, suggesting that autism-related features may be related to self-processing in a domain specific manner.

Pattern recognition techniques for horizontal and vertically upward multiphase flow measurement

Science.gov (United States)

Arubi, Tesi I. M.; Yeung, Hoi

2012-03-01

The oil and gas industry need for high performing and low cost multiphase meters is ever more justified given the rapid depletion of conventional oil reserves that has led oil companies to develop smaller and marginal fields and reservoirs in remote locations and deep offshore, thereby placing great demands for compact and more cost effective solutions of on-line continuous multiphase flow measurement for well testing, production monitoring, production optimisation, process control and automation. The pattern recognition approach for clamp-on multiphase measurement employed in this study provides one means for meeting this need. High speed caesium-137 radioisotope-based densitometers were installed vertically at the top of a 50.8mm and 101.6mm riser as well as horizontally at the riser base in the Cranfield University multiphase flow test facility. A comprehensive experimental campaign comprising flow conditions typical of operating conditions found in the Petroleum Industry was conducted. The application of a single gamma densitometer unit, in conjunction with pattern recognition techniques to determine both the phase volume fractions and velocities to yield the individual phase flow rates of horizontal and vertically upward multiphase flows was investigated. The pattern recognition systems were trained to map the temporal fluctuations in the multiphase mixture density with the individual phase flow rates using statistical features extracted from the gamma counts signals as their inputs. Initial results yielded individual phase flow rate predictions to within ±5% relative error for the two phase airwater flows and ±10% for three phase air-oil-water flows data.

Fractal Analysis of Radiologists Visual Scanning Pattern in Screening Mammography

Energy Technology Data Exchange (ETDEWEB)

Alamudun, Folami T [ORNL; Yoon, Hong-Jun [ORNL; Hudson, Kathy [University of Tennessee, Knoxville (UTK); Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL

2015-01-01

Several investigators have investigated radiologists visual scanning patterns with respect to features such as total time examining a case, time to initially hit true lesions, number of hits, etc. The purpose of this study was to examine the complexity of the radiologists visual scanning pattern when viewing 4-view mammographic cases, as they typically do in clinical practice. Gaze data were collected from 10 readers (3 breast imaging experts and 7 radiology residents) while reviewing 100 screening mammograms (24 normal, 26 benign, 50 malignant). The radiologists scanpaths across the 4 mammographic views were mapped to a single 2-D image plane. Then, fractal analysis was applied on the derived scanpaths using the box counting method. For each case, the complexity of each radiologist s scanpath was estimated using fractal dimension. The association between gaze complexity, case pathology, case density, and radiologist experience was evaluated using 3 factor fixed effects ANOVA. ANOVA showed that case pathology, breast density, and experience level are all independent predictors of the visual scanning pattern complexity. Visual scanning patterns are significantly different for benign and malignant cases than for normal cases as well as when breast parenchyma density changes.

Linear Programming and Its Application to Pattern Recognition Problems

Science.gov (United States)

Omalley, M. J.

1973-01-01

Linear programming and linear programming like techniques as applied to pattern recognition problems are discussed. Three relatively recent research articles on such applications are summarized. The main results of each paper are described, indicating the theoretical tools needed to obtain them. A synopsis of the author's comments is presented with regard to the applicability or non-applicability of his methods to particular problems, including computational results wherever given.

The Ebb and Flow of Infant Attentional Preferences: Evidence for Long-Term Recognition Memory in 3-Month-Olds.

Science.gov (United States)

Courage, Mary L.; Howe, Mark L.

1998-01-01

Two experiments used paired-comparisons to investigate 3-month olds' recognition of dynamic visual events after various retention intervals. Results indicated a changing pattern of attentional preferences over time consistent with models of infant recognition memory in which novelty, familiarity, and null preferences are considered conjointly and…

Binary pattern flavored feature extractors for Facial Expression Recognition: An overview

DEFF Research Database (Denmark)

Kristensen, Rasmus Lyngby; Tan, Zheng-Hua; Ma, Zhanyu

2015-01-01

This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression Recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER...

Mechanisms of Expression and Internalisation of FIBCD1; a novel Pattern Recognition Receptor in the Gut Mucosa

DEFF Research Database (Denmark)

Hammond, Mark; Schlosser, Anders; Dubey, Lalit Kumar

2012-01-01

is a carbohydrate recognition domain also expressed by the ficolins, which are pattern recognition molecules that activate the complement system via the lectin pathway. Chitin is a highly ace¬tylated homopolymer of β-1,4-N-acetyl-glucosamine carbohydrate found abundantly in nature in organisms such as fungi...... pattern recognition receptor that binds chitin and directs acetylated structures for de¬gradation in the endosome via clathrin-mediated endocytosis. The localisation of FIBCD1 in the intestinal mucosal epithelia points towards a functional role in innate immunity and/or gut homeostasis....

Identification of natural metabolites in mixture: a pattern recognition strategy based on (13)C NMR.

Science.gov (United States)

Hubert, Jane; Nuzillard, Jean-Marc; Purson, Sylvain; Hamzaoui, Mahmoud; Borie, Nicolas; Reynaud, Romain; Renault, Jean-Hugues

2014-03-18

Because of their highly complex metabolite profile, the chemical characterization of bioactive natural extracts usually requires time-consuming multistep purification procedures to achieve the structural elucidation of pure individual metabolites. The aim of the present work was to develop a dereplication strategy for the identification of natural metabolites directly within mixtures. Exploiting the polarity range of metabolites, the principle was to rapidly fractionate a multigram quantity of a crude extract by centrifugal partition extraction (CPE). The obtained fractions of simplified chemical composition were subsequently analyzed by (13)C NMR. After automatic collection and alignment of (13)C signals across spectra, hierarchical clustering analysis (HCA) was performed for pattern recognition. As a result, strong correlations between (13)C signals of a single structure within the mixtures of the fraction series were visualized as chemical shift clusters. Each cluster was finally assigned to a molecular structure with the help of a locally built (13)C NMR chemical shift database. The proof of principle of this strategy was achieved on a simple model mixture of commercially available plant secondary metabolites and then applied to a bark extract of the African tree Anogeissus leiocarpus Guill. & Perr. (Combretaceae). Starting from 5 g of this genuine extract, the fraction series was generated by CPE in only 95 min. (13)C NMR analyses of all fractions followed by pattern recognition of (13)C chemical shifts resulted in the unambiguous identification of seven major compounds, namely, sericoside, trachelosperogenin E, ellagic acid, an epimer mixture of (+)-gallocatechin and (-)-epigallocatechin, 3,3'-di-O-methylellagic acid 4'-O-xylopyranoside, and 3,4,3'-tri-O-methylflavellagic acid 4'-O-glucopyranoside.

Topology of digital images visual pattern discovery in proximity spaces

CERN Document Server

Peters, James F

2014-01-01

This book carries forward recent work on visual patterns and structures in digital images and introduces a near set-based a topology of digital images. Visual patterns arise naturally in digital images viewed as sets of non-abstract points endowed with some form of proximity (nearness) relation. Proximity relations make it possible to construct uniform topolo- gies on the sets of points that constitute a digital image. In keeping with an interest in gaining an understanding of digital images themselves as a rich source of patterns, this book introduces the basics of digital images from a computer vision perspective. In parallel with a computer vision perspective on digital images, this book also introduces the basics of prox- imity spaces. Not only the traditional view of spatial proximity relations but also the more recent descriptive proximity relations are considered. The beauty of the descriptive proximity approach is that it is possible to discover visual set patterns among sets that are non-overlapping ...

The dynamics of visual experience, an EEG study of subjective pattern formation.

Directory of Open Access Journals (Sweden)

Mark A Elliott

Full Text Available BACKGROUND: Since the origin of psychological science a number of studies have reported visual pattern formation in the absence of either physiological stimulation or direct visual-spatial references. Subjective patterns range from simple phosphenes to complex patterns but are highly specific and reported reliably across studies. METHODOLOGY/PRINCIPAL FINDINGS: Using independent-component analysis (ICA we report a reduction in amplitude variance consistent with subjective-pattern formation in ventral posterior areas of the electroencephalogram (EEG. The EEG exhibits significantly increased power at delta/theta and gamma-frequencies (point and circle patterns or a series of high-frequency harmonics of a delta oscillation (spiral patterns. CONCLUSIONS/SIGNIFICANCE: Subjective-pattern formation may be described in a way entirely consistent with identical pattern formation in fluids or granular flows. In this manner, we propose subjective-pattern structure to be represented within a spatio-temporal lattice of harmonic oscillations which bind topographically organized visual-neuronal assemblies by virtue of low frequency modulation.

Two-phase flow patterns recognition and parameters estimation through natural circulation test loop image analysis

Energy Technology Data Exchange (ETDEWEB)

Mesquita, R.N.; Libardi, R.M.P.; Masotti, P.H.F.; Sabundjian, G.; Andrade, D.A.; Umbehaun, P.E.; Torres, W.M.; Conti, T.N.; Macedo, L.A. [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil). Nuclear Engineering Center], e-mail: rnavarro@ipen.br

2009-07-01

Visualization of natural circulation test loop cycles is used to study two-phase flow patterns associated with phase transients and static instabilities of flow. Experimental studies on natural circulation flow were originally related to accidents and transient simulations relative to nuclear reactor systems with light water refrigeration. In this regime, fluid circulation is mainly caused by a driving force ('thermal head') which arises from density differences due to temperature gradient. Natural circulation phenomenon has been important to provide residual heat removal in cases of 'loss of pump power' or plant shutdown in nuclear power plant accidents. The new generation of compact nuclear reactors includes natural circulation of their refrigerant fluid as a security mechanism in their projects. Two-phase flow patterns have been studied for many decades, and the related instabilities have been object of special attention recently. Experimental facility is an all glass-made cylindrical tubes loop which contains about twelve demineralized water liters, a heat source by an electrical resistor immersion heater controlled by a Variac, and a helicoidal heat exchanger working as cold source. Data is obtained through thermo-pairs distributed over the loop and CCD cameras. Artificial intelligence based algorithms are used to improve (bubble) border detection and patterns recognition, in order to estimate and characterize, phase transitions patterns and correlate them with the periodic static instability (chugging) cycle observed in this circuit. Most of initial results show good agreement with previous numerical studies in this same facility. (author)

Two-phase flow patterns recognition and parameters estimation through natural circulation test loop image analysis

International Nuclear Information System (INIS)

Mesquita, R.N.; Libardi, R.M.P.; Masotti, P.H.F.; Sabundjian, G.; Andrade, D.A.; Umbehaun, P.E.; Torres, W.M.; Conti, T.N.; Macedo, L.A.

2009-01-01

Visualization of natural circulation test loop cycles is used to study two-phase flow patterns associated with phase transients and static instabilities of flow. Experimental studies on natural circulation flow were originally related to accidents and transient simulations relative to nuclear reactor systems with light water refrigeration. In this regime, fluid circulation is mainly caused by a driving force ('thermal head') which arises from density differences due to temperature gradient. Natural circulation phenomenon has been important to provide residual heat removal in cases of 'loss of pump power' or plant shutdown in nuclear power plant accidents. The new generation of compact nuclear reactors includes natural circulation of their refrigerant fluid as a security mechanism in their projects. Two-phase flow patterns have been studied for many decades, and the related instabilities have been object of special attention recently. Experimental facility is an all glass-made cylindrical tubes loop which contains about twelve demineralized water liters, a heat source by an electrical resistor immersion heater controlled by a Variac, and a helicoidal heat exchanger working as cold source. Data is obtained through thermo-pairs distributed over the loop and CCD cameras. Artificial intelligence based algorithms are used to improve (bubble) border detection and patterns recognition, in order to estimate and characterize, phase transitions patterns and correlate them with the periodic static instability (chugging) cycle observed in this circuit. Most of initial results show good agreement with previous numerical studies in this same facility. (author)

Differential patterns of prefrontal MEG activation during verbal & visual encoding and retrieval.

Directory of Open Access Journals (Sweden)

Garreth Prendergast

Full Text Available The spatiotemporal profile of activation of the prefrontal cortex in verbal and non-verbal recognition memory was examined using magnetoencephalography (MEG. Sixteen neurologically healthy right-handed participants were scanned whilst carrying out a modified version of the Doors and People Test of recognition memory. A pattern of significant prefrontal activity was found for non-verbal and verbal encoding and recognition. During the encoding, verbal stimuli activated an area in the left ventromedial prefrontal cortex, and non-verbal stimuli activated an area in the right. A region in the left dorsolateral prefrontal cortex also showed significant activation during the encoding of non-verbal stimuli. Both verbal and non-verbal stimuli significantly activated an area in the right dorsomedial prefrontal cortex and the right anterior prefrontal cortex during successful recognition, however these areas showed temporally distinct activation dependent on material, with non-verbal showing activation earlier than verbal stimuli. Additionally, non-verbal material activated an area in the left anterior prefrontal cortex during recognition. These findings suggest a material-specific laterality in the ventromedial prefrontal cortex during encoding for verbal and non-verbal but also support the HERA model for verbal material. The discovery of two process dependent areas during recognition that showed patterns of temporal activation dependent on material demonstrates the need for the application of more temporally sensitive techniques to the involvement of the prefrontal cortex in recognition memory.

Influence of visual angle on pattern reversal visual evoked potentials

Directory of Open Access Journals (Sweden)

Ruchi Kothari

2014-01-01

Full Text Available Purpose: The aim of this study was to find whether the visual evoked potential (VEP latencies and amplitude are altered with different visual angles in healthy adult volunteers or not and to determine the visual angle which is the optimum and most appropriate among a wide range of check sizes for the reliable interpretation of pattern reversal VEPs (PRVEPs. Materials and Methods: The present study was conducted on 40 healthy volunteers. The subjects were divided into two groups. One group consisted of 20 individuals (nine males and 11 females in the age range of 25-57 years and they were exposed to checks subtending a visual angle of 90, 120, and 180 minutes of arc. Another group comprised of 20 individuals (10 males and 10 females in the age range of 36-60 years and they were subjected to checks subtending a visual angle of 15, 30, and 120 minutes of arc. The stimulus configuration comprised of the transient pattern reversal method in which a black and white checker board is generated (full field on a VEP Monitor by an Evoked Potential Recorder (RMS EMG. EPMARK II. The statistical analysis was done by One Way Analysis of Variance (ANOVA using EPI INFO 6. Results: In Group I, the maximum (max. P100 latency of 98.8 ± 4.7 and the max. P100 amplitude of 10.05 ± 3.1 μV was obtained with checks of 90 minutes. In Group II, the max. P100 latency of 105.19 ± 4.75 msec as well as the max. P100 amplitude of 8.23 ± 3.30 μV was obtained with 15 minutes. The min. P100 latency in both the groups was obtained with checks of 120 minutes while the min. P100 amplitude was obtained with 180 minutes. A statistically significant difference was derived between means of P100 latency for 15 and 30 minutes with reference to its value for 120 minutes and between the mean value of P100 amplitude for 120 minutes and that of 90 and 180 minutes. Conclusion: Altering the size of stimulus (visual angle has an effect on the PRVEP parameters. Our study found that the 120

A pattern recognition approach to transistor array parameter variance

Science.gov (United States)

da F. Costa, Luciano; Silva, Filipi N.; Comin, Cesar H.

2018-06-01

The properties of semiconductor devices, including bipolar junction transistors (BJTs), are known to vary substantially in terms of their parameters. In this work, an experimental approach, including pattern recognition concepts and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), was used to experimentally investigate the variation among BJTs belonging to integrated circuits known as transistor arrays. It was shown that a good deal of the devices variance can be captured using only two PCA axes. It was also verified that, though substantially small variation of parameters is observed for BJT from the same array, larger variation arises between BJTs from distinct arrays, suggesting the consideration of device characteristics in more critical analog designs. As a consequence of its supervised nature, LDA was able to provide a substantial separation of the BJT into clusters, corresponding to each transistor array. In addition, the LDA mapping into two dimensions revealed a clear relationship between the considered measurements. Interestingly, a specific mapping suggested by the PCA, involving the total harmonic distortion variation expressed in terms of the average voltage gain, yielded an even better separation between the transistor array clusters. All in all, this work yielded interesting results from both semiconductor engineering and pattern recognition perspectives.

Knowledge fusion: An approach to time series model selection followed by pattern recognition

International Nuclear Information System (INIS)

Bleasdale, S.A.; Burr, T.L.; Scovel, J.C.; Strittmatter, R.B.

1996-03-01

This report describes work done during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security, Knowledge Fusion Project. The project team selected satellite sensor data to use as the one main example for the application of its analysis algorithms. The specific sensor-fusion problem has many generic features, which make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series that define a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. An accompanying report (Ref 1) describes the implementation and application of this 2-step process for separating events from unusual background and applies a suite of forecasting methods followed by a suite of pattern recognition methods. This report goes into more detail about one of the forecasting methods and one of the pattern recognition methods and is applied to the same kind of satellite-sensor data that is described in Ref. 1

Comparison of eye imaging pattern recognition using neural network

Science.gov (United States)

Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

2015-05-01

The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

CNNs flag recognition preprocessing scheme based on gray scale stretching and local binary pattern

Science.gov (United States)

Gong, Qian; Qu, Zhiyi; Hao, Kun

2017-07-01

Flag is a rather special recognition target in image recognition because of its non-rigid features with the location, scale and rotation characteristics. The location change can be handled well by the depth learning algorithm Convolutional Neural Networks (CNNs), but the scale and rotation changes are quite a challenge for CNNs. Since it has good rotation and gray scale invariance, the local binary pattern (LBP) is combined with grayscale stretching and CNNs to make LBP and grayscale stretching as CNNs pretreatment, which can not only significantly improve the efficiency of flag recognition, but can also evaluate the recognition effect through ROC, accuracy, MSE and quality factor.

Compact holographic optical neural network system for real-time pattern recognition

Science.gov (United States)

Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

1996-08-01

One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

Category-Specific Visual Recognition and Aging from the PACE Theory Perspective: Evidence for a Presemantic Deficit in Aging Object Recognition

DEFF Research Database (Denmark)

Bordaberry, Pierre; Gerlach, Christian; Lenoble, Quentin

2016-01-01

Background/Study Context: The objective of this study was to investigate the object recognition deficit in aging. Age-related declines were examined from the presemantic account of category effects (PACE) theory perspective (Gerlach, 2009, Cognition, 111, 281–301). This view assumes that the stru......Background/Study Context: The objective of this study was to investigate the object recognition deficit in aging. Age-related declines were examined from the presemantic account of category effects (PACE) theory perspective (Gerlach, 2009, Cognition, 111, 281–301). This view assumes...... that the structural similarity/dissimilarity inherent in living and nonliving objects, respectively, can account for a wide range of category-specific effects. Methods: In two experiments on object recognition, young (36 participants, 18–27 years) and older (36 participants, 53–69 years) adult participants...... in the selection stage of the PACE theory (visual long-term memory matching) could be responsible for these impairments. Indeed, the older group showed a deficit when this stage was most relevant. This article emphasize on the critical need for taking into account structural component of the stimuli and type...

Complement activating soluble pattern recognition molecules with collagen-like regions, mannan-binding lectin, ficolins and associated proteins

DEFF Research Database (Denmark)

Thiel, Steffen

2007-01-01

Mannan-binding lectin (MBL), L-ficolin, M-ficolin and H-ficolin are all complement activating soluble pattern recognition molecules with recognition domains linked to collagen-like regions. All four may form complexes with four structurally related proteins, the three MBL-associated serine...... proteases (MASPs), MASP-1, MASP-2 and MASP-3, and a smaller MBL-associated protein (MAp19). The four recognition molecules recognize patterns of carbohydrate or acetyl-group containing ligands. After binding to the relevant targets all four are able to activate the complement system. We thus have a system...... where four different and/or overlapping patterns of microbial origin or patterns of altered-self may be recognized, but in all cases the signalling molecules, the MASPs, are shared. MASP-1 and MASP-3 are formed from one gene, MASP1/3, by alternative splicing generating two different mRNAs from a single...

Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition

NARCIS (Netherlands)

Leon Rincon, Carlos; Moreno, José Fernando; Cely, Jorge

2017-01-01

The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

Science.gov (United States)

Dewar, Alex D M; Wystrach, Antoine; Philippides, Andrew; Graham, Paul

2017-10-01

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

Ultrasonic pattern recognition study of feedwater nozzle inner radius indication

International Nuclear Information System (INIS)

Yoneyama, H.; Takama, S.; Kishigami, M.; Sasahara, T.; Ando, H.

1983-01-01

A study was made to distinguish defects on feed-water nozzle inner radius from noise echo caused by stainless steel cladding by using ultrasonic pattern recognition method with frequency analysis technique. Experiment has been successfully performed on flat clad plates and nozzle mock-up containing fatigue cracks and the following results which shows the high capability of frequency analysis technique are obtained

An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive

Directory of Open Access Journals (Sweden)

Xiao-Dong Zhang

2011-12-01

Full Text Available For controlling the prosthetic hand by only electroencephalogram (EEG, it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open. Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.

EBR-II [Experimental Breeder Reactor-II] system surveillance using pattern recognition software

International Nuclear Information System (INIS)

Mott, J.E.; Radtke, W.H.; King, R.W.

1986-02-01

The problem of most accurately determining the Experimental Breeder Reactor-II (EBR-II) reactor outlet temperature from currently available plant signals is investigated. Historically, the reactor outlet pipe was originally instrumented with 8 temperature sensors but, during 22 years of operation, all these instruments have failed except for one remaining thermocouple, and its output had recently become suspect. Using pattern recognition methods to compare values of 129 plant signals for similarities over a 7 month period spanning reconfiguration of the core and recalibration of many plant signals, it was determined that the remaining reactor outlet pipe thermocouple is still useful as an indicator of true mixed mean reactor outlet temperature. Application of this methodology to investigate one specific signal has automatically validated the vast majority of the 129 signals used for pattern recognition and also highlighted a few inconsistent signals for further investigation

ALBEDO PATTERN RECOGNITION AND TIME-SERIES ANALYSES IN MALAYSIA

Directory of Open Access Journals (Sweden)

S. A. Salleh

2012-07-01

Full Text Available Pattern recognition and time-series analyses will enable one to evaluate and generate predictions of specific phenomena. The albedo pattern and time-series analyses are very much useful especially in relation to climate condition monitoring. This study is conducted to seek for Malaysia albedo pattern changes. The pattern recognition and changes will be useful for variety of environmental and climate monitoring researches such as carbon budgeting and aerosol mapping. The 10 years (2000–2009 MODIS satellite images were used for the analyses and interpretation. These images were being processed using ERDAS Imagine remote sensing software, ArcGIS 9.3, the 6S code for atmospherical calibration and several MODIS tools (MRT, HDF2GIS, Albedo tools. There are several methods for time-series analyses were explored, this paper demonstrates trends and seasonal time-series analyses using converted HDF format MODIS MCD43A3 albedo land product. The results revealed significance changes of albedo percentages over the past 10 years and the pattern with regards to Malaysia's nebulosity index (NI and aerosol optical depth (AOD. There is noticeable trend can be identified with regards to its maximum and minimum value of the albedo. The rise and fall of the line graph show a similar trend with regards to its daily observation. The different can be identified in term of the value or percentage of rises and falls of albedo. Thus, it can be concludes that the temporal behavior of land surface albedo in Malaysia have a uniform behaviours and effects with regards to the local monsoons. However, although the average albedo shows linear trend with nebulosity index, the pattern changes of albedo with respects to the nebulosity index indicates that there are external factors that implicates the albedo values, as the sky conditions and its diffusion plotted does not have uniform trend over the years, especially when the trend of 5 years interval is examined, 2000 shows high

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

Directory of Open Access Journals (Sweden)

Andre F Marquand

Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

Visual Sharpness Contingency in Recognition Memory for Orientation: Mnemonic Illusion Suppressed by Sensory Signature

Science.gov (United States)

Martin, Maryanne; Jones, Gregory V.

2006-01-01

A striking finding about human memory is that people's level of accuracy in remembering the orientation of heads on coins is often not simply at the chance level but significantly below it. However, S. W. Kelly, A. M. Burton, T. Kato, and S. Akamatsu (2001) reported that this is not so when two-alternative forced-choice visual recognition is…

Implicit recognition based on lateralized perceptual fluency.

Science.gov (United States)

Vargas, Iliana M; Voss, Joel L; Paller, Ken A

2012-02-06

In some circumstances, accurate recognition of repeated images in an explicit memory test is driven by implicit memory. We propose that this "implicit recognition" results from perceptual fluency that influences responding without awareness of memory retrieval. Here we examined whether recognition would vary if images appeared in the same or different visual hemifield during learning and testing. Kaleidoscope images were briefly presented left or right of fixation during divided-attention encoding. Presentation in the same visual hemifield at test produced higher recognition accuracy than presentation in the opposite visual hemifield, but only for guess responses. These correct guesses likely reflect a contribution from implicit recognition, given that when the stimulated visual hemifield was the same at study and test, recognition accuracy was higher for guess responses than for responses with any level of confidence. The dramatic difference in guessing accuracy as a function of lateralized perceptual overlap between study and test suggests that implicit recognition arises from memory storage in visual cortical networks that mediate repetition-induced fluency increments.

Morphological Processing during Visual Word Recognition in Hebrew as a First and a Second Language

Science.gov (United States)

Norman, Tal; Degani, Tamar; Peleg, Orna

2017-01-01

The present study examined whether sublexical morphological processing takes place during visual word-recognition in Hebrew, and whether morphological decomposition of written words depends on lexical activation of the complete word. Furthermore, it examined whether morphological processing is similar when reading Hebrew as a first language (L1)…

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

Science.gov (United States)

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

Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

Science.gov (United States)

Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

2017-01-01

In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

A New Functional Classification of Glucuronoyl Esterases by Peptide Pattern Recognition

DEFF Research Database (Denmark)

Wittrup Agger, Jane; Busk, Peter Kamp; Pilgaard, Bo

2017-01-01

of characterized enzymes exist and the exact activity is still uncertain. Here peptide pattern recognition is used as a bioinformatic tool to identify and group new CE15 proteins that are likely to have glucuronoyl esterase activity. 1024 CE15-like sequences were drawn from GenBank and grouped into 24 groups...

A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades

Directory of Open Access Journals (Sweden)

Jialin Tang

2017-11-01

Full Text Available The identification of particular types of damage in wind turbine blades using acoustic emission (AE techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency−frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency−MARSE, and average frequency−peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope. The results show that these parameters are representative for the classification of the failure modes.

A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades.

Science.gov (United States)

Tang, Jialin; Soua, Slim; Mares, Cristinel; Gan, Tat-Hean

2017-11-01

The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency-frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency-MARSE, and average frequency-peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes.

Pattern recognition for cache management in distributed medical imaging environments.

Science.gov (United States)

Viana-Ferreira, Carlos; Ribeiro, Luís; Matos, Sérgio; Costa, Carlos

2016-02-01

Traditionally, medical imaging repositories have been supported by indoor infrastructures with huge operational costs. This paradigm is changing thanks to cloud outsourcing which not only brings technological advantages but also facilitates inter-institutional workflows. However, communication latency is one main problem in this kind of approaches, since we are dealing with tremendous volumes of data. To minimize the impact of this issue, cache and prefetching are commonly used. The effectiveness of these mechanisms is highly dependent on their capability of accurately selecting the objects that will be needed soon. This paper describes a pattern recognition system based on artificial neural networks with incremental learning to evaluate, from a set of usage pattern, which one fits the user behavior at a given time. The accuracy of the pattern recognition model in distinct training conditions was also evaluated. The solution was tested with a real-world dataset and a synthesized dataset, showing that incremental learning is advantageous. Even with very immature initial models, trained with just 1 week of data samples, the overall accuracy was very similar to the value obtained when using 75% of the long-term data for training the models. Preliminary results demonstrate an effective reduction in communication latency when using the proposed solution to feed a prefetching mechanism. The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.

Extending models of visual-word recognition to semicursive scripts: Evidence from masked priming in Uyghur.

Science.gov (United States)

Yakup, Mahire; Abliz, Wayit; Sereno, Joan; Perea, Manuel

2015-12-01

One basic feature of the Arabic script is its semicursive style: some letters are connected to the next, but others are not, as in the Uyghur word [see text]/ya xʃi/ ("good"). None of the current orthographic coding schemes in models of visual-word recognition, which were created for the Roman script, assign a differential role to the coding of within letter "chunks" and between letter "chunks" in words in the Arabic script. To examine how letter identity/position is coded at the earliest stages of word processing in the Arabic script, we conducted 2 masked priming lexical decision experiments in Uyghur, an agglutinative Turkic language. The target word was preceded by an identical prime, by a transposed-letter nonword prime (that either kept the ligation pattern or did not), or by a 2-letter replacement nonword prime. Transposed-letter primes were as effective as identity primes when the letter transposition in the prime kept the same ligation pattern as the target word (e.g., [see text]/inta_jin/-/itna_jin/), but not when the transposed-letter prime didn't keep the ligation pattern (e.g., [see text]/so_w_ʁa_t/-/so_ʁw_a_t/). Furthermore, replacement-letter primes were more effective when they kept the ligation pattern of the target word than when they did not (e.g., [see text]/so_d_ʧa_t/-/so_w_ʁa_t/ faster than [see text]/so_ʧd_a_t/-/so_w_ʁa_t/). We examined how input coding schemes could be extended to deal with the intricacies of semicursive scripts. (c) 2015 APA, all rights reserved).

The Impact of Orthographic Connectivity on Visual Word Recognition in Arabic: A Cross-Sectional Study

Science.gov (United States)

Khateb, Asaid; Khateb-Abdelgani, Manal; Taha, Haitham Y.; Ibrahim, Raphiq

2014-01-01

This study aimed at assessing the effects of letters' connectivity in Arabic on visual word recognition. For this purpose, reaction times (RTs) and accuracy scores were collected from ninety-third, sixth and ninth grade native Arabic speakers during a lexical decision task, using fully connected (Cw), partially connected (PCw) and…

Perceptual and affective mechanisms in facial expression recognition: An integrative review.

Science.gov (United States)

Calvo, Manuel G; Nummenmaa, Lauri

2016-09-01

Facial expressions of emotion involve a physical component of morphological changes in a face and an affective component conveying information about the expresser's internal feelings. It remains unresolved how much recognition and discrimination of expressions rely on the perception of morphological patterns or the processing of affective content. This review of research on the role of visual and emotional factors in expression recognition reached three major conclusions. First, behavioral, neurophysiological, and computational measures indicate that basic expressions are reliably recognized and discriminated from one another, albeit the effect may be inflated by the use of prototypical expression stimuli and forced-choice responses. Second, affective content along the dimensions of valence and arousal is extracted early from facial expressions, although this coarse affective representation contributes minimally to categorical recognition of specific expressions. Third, the physical configuration and visual saliency of facial features contribute significantly to expression recognition, with "emotionless" computational models being able to reproduce some of the basic phenomena demonstrated in human observers. We conclude that facial expression recognition, as it has been investigated in conventional laboratory tasks, depends to a greater extent on perceptual than affective information and mechanisms.

Visual language recognition with a feed-forward network of spiking neurons

Energy Technology Data Exchange (ETDEWEB)

Rasmussen, Craig E [Los Alamos National Laboratory; Garrett, Kenyan [Los Alamos National Laboratory; Sottile, Matthew [GALOIS; Shreyas, Ns [INDIANA UNIV.

2010-01-01

An analogy is made and exploited between the recognition of visual objects and language parsing. A subset of regular languages is used to define a one-dimensional 'visual' language, in which the words are translational and scale invariant. This allows an exploration of the viewpoint invariant languages that can be solved by a network of concurrent, hierarchically connected processors. A language family is defined that is hierarchically tiling system recognizable (HREC). As inspired by nature, an algorithm is presented that constructs a cellular automaton that recognizes strings from a language in the HREC family. It is demonstrated how a language recognizer can be implemented from the cellular automaton using a feed-forward network of spiking neurons. This parser recognizes fixed-length strings from the language in parallel and as the computation is pipelined, a new string can be parsed in each new interval of time. The analogy with formal language theory allows inferences to be drawn regarding what class of objects can be recognized by visual cortex operating in purely feed-forward fashion and what class of objects requires a more complicated network architecture.

Performance Study of the First 2D Prototype of Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

Energy Technology Data Exchange (ETDEWEB)

Deptuch, Gregory [Fermilab; Hoff, James [Fermilab; Jindariani, Sergo [Fermilab; Liu, Tiehui [Fermilab; Olsen, Jamieson [Fermilab; Tran, Nhan [Fermilab; Joshi, Siddhartha [Northwestern U.; Li, Dawei [Northwestern U.; Ogrenci-Memik, Seda [Northwestern U.

2017-09-24

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.

Pattern recognition algorithms for data mining scalability, knowledge discovery and soft granular computing

CERN Document Server

Pal, Sankar K

2004-01-01

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

Science.gov (United States)

Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala; Sundararajan, Vijayaraghava S; Suravajhala, Prashanth; Valadi, Jayaraman K

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

Finger crease pattern recognition using Legendre moments and principal component analysis

Science.gov (United States)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

Pattern recognition applied to infrared images for early alerts in fog

Science.gov (United States)

Boucher, Vincent; Marchetti, Mario; Dumoulin, Jean; Cord, Aurélien

2014-09-01

Fog conditions are the cause of severe car accidents in western countries because of the poor induced visibility. Its forecast and intensity are still very difficult to predict by weather services. Infrared cameras allow to detect and to identify objects in fog while visibility is too low for eye detection. Over the past years, the implementation of cost effective infrared cameras on some vehicles has enabled such detection. On the other hand pattern recognition algorithms based on Canny filters and Hough transformation are a common tool applied to images. Based on these facts, a joint research program between IFSTTAR and Cerema has been developed to study the benefit of infrared images obtained in a fog tunnel during its natural dissipation. Pattern recognition algorithms have been applied, specifically on road signs which shape is usually associated to a specific meaning (circular for a speed limit, triangle for an alert, …). It has been shown that road signs were detected early enough in images, with respect to images in the visible spectrum, to trigger useful alerts for Advanced Driver Assistance Systems.

The study of infrared target recognition at sea background based on visual attention computational model

Science.gov (United States)

Wang, Deng-wei; Zhang, Tian-xu; Shi, Wen-jun; Wei, Long-sheng; Wang, Xiao-ping; Ao, Guo-qing

2009-07-01

Infrared images at sea background are notorious for the low signal-to-noise ratio, therefore, the target recognition of infrared image through traditional methods is very difficult. In this paper, we present a novel target recognition method based on the integration of visual attention computational model and conventional approach (selective filtering and segmentation). The two distinct techniques for image processing are combined in a manner to utilize the strengths of both. The visual attention algorithm searches the salient regions automatically, and represented them by a set of winner points, at the same time, demonstrated the salient regions in terms of circles centered at these winner points. This provides a priori knowledge for the filtering and segmentation process. Based on the winner point, we construct a rectangular region to facilitate the filtering and segmentation, then the labeling operation will be added selectively by requirement. Making use of the labeled information, from the final segmentation result we obtain the positional information of the interested region, label the centroid on the corresponding original image, and finish the localization for the target. The cost time does not depend on the size of the image but the salient regions, therefore the consumed time is greatly reduced. The method is used in the recognition of several kinds of real infrared images, and the experimental results reveal the effectiveness of the algorithm presented in this paper.

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

Science.gov (United States)

Adewuyi, Adenike A; Hargrove, Levi J; Kuiken, Todd A

2016-04-01

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

Assay for the pattern recognition molecule collectin liver 1 (CL-L1)

DEFF Research Database (Denmark)

Axelgaard, Esben; Jensenius, Jens Christian; Thiel, Steffen

Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA...... to co-purify with MASPs, possibly rendering it a role in complement. CL-L1 showed binding activity towards mannose-TSK beads in a Ca2+-dependent manner. This binding could be inhibited by mannose and glucose, but not by galactose, indicating that CL-L1 binds via its carbohydrate-recognition domain (CRD)....

Transformations of visual memory induced by implied motions of pattern elements.

Science.gov (United States)

Finke, R A; Freyd, J J

1985-10-01

Four experiments measured distortions in short-term visual memory induced by displays depicting independent translations of the elements of a pattern. In each experiment, observers saw a sequence of 4 dot patterns and were instructed to remember the third pattern and to compare it with the fourth. The first three patterns depicted translations of the dots in consistent, but separate directions. Error rates and reaction times for rejecting the fourth pattern as different from the third were substantially higher when the dots in that pattern were displaced slightly forward, in the same directions as the implied motions, compared with when the dots were displaced in the opposite, backward directions. These effects showed little variation across interstimulus intervals ranging from 250 to 2,000 ms, and did not depend on whether the displays gave rise to visual apparent motion. However, they were eliminated when the dots in the fourth pattern were displaced by larger amounts in each direction, corresponding to the dot positions in the next and previous patterns in the same inducing sequence. These findings extend our initial report of the phenomenon of "representational momentum" (Freyd & Finke, 1984a), and help to rule out alternatives to the proposal that visual memories tend to undergo, at least to some extent, the transformations implied by a prior sequence of observed events.

Implicit Recognition Based on Lateralized Perceptual Fluency

Directory of Open Access Journals (Sweden)

Iliana M. Vargas

2012-02-01

Full Text Available In some circumstances, accurate recognition of repeated images in an explicit memory test is driven by implicit memory. We propose that this “implicit recognition” results from perceptual fluency that influences responding without awareness of memory retrieval. Here we examined whether recognition would vary if images appeared in the same or different visual hemifield during learning and testing. Kaleidoscope images were briefly presented left or right of fixation during divided-attention encoding. Presentation in the same visual hemifield at test produced higher recognition accuracy than presentation in the opposite visual hemifield, but only for guess responses. These correct guesses likely reflect a contribution from implicit recognition, given that when the stimulated visual hemifield was the same at study and test, recognition accuracy was higher for guess responses than for responses with any level of confidence. The dramatic difference in guessing accuracy as a function of lateralized perceptual overlap between study and test suggests that implicit recognition arises from memory storage in visual cortical networks that mediate repetition-induced fluency increments.

Pattern recognition in menstrual bleeding diaries by statistical cluster analysis

Directory of Open Access Journals (Sweden)

Wessel Jens

2009-07-01

Full Text Available Abstract Background The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs. Methods We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstrual bleeding diaries. The optimal number of clusters was determined using the semi-partial R2, the cubic cluster criterion, the pseudo-F- and the pseudo-t2-statistic. Finally, the interpretability of the results from a gynecological point of view was assessed. Results The method of Ward yielded distinct clusters of the bleeding diaries. The other methods successively chained the observations into one cluster. The optimal number of distinctive bleeding patterns was six. We found two desirable and four undesirable bleeding patterns. Cyclic and non cyclic bleeding patterns were well separated. Conclusion Using this cluster analysis with the method of Ward medications and devices having an impact on bleeding can be easily compared and categorized.

Facial and prosodic emotion recognition in social anxiety disorder.

Science.gov (United States)

Tseng, Huai-Hsuan; Huang, Yu-Lien; Chen, Jian-Ting; Liang, Kuei-Yu; Lin, Chao-Cheng; Chen, Sue-Huei

2017-07-01

Patients with social anxiety disorder (SAD) have a cognitive preference to negatively evaluate emotional information. In particular, the preferential biases in prosodic emotion recognition in SAD have been much less explored. The present study aims to investigate whether SAD patients retain negative evaluation biases across visual and auditory modalities when given sufficient response time to recognise emotions. Thirty-one SAD patients and 31 age- and gender-matched healthy participants completed a culturally suitable non-verbal emotion recognition task and received clinical assessments for social anxiety and depressive symptoms. A repeated measures analysis of variance was conducted to examine group differences in emotion recognition. Compared to healthy participants, SAD patients were significantly less accurate at recognising facial and prosodic emotions, and spent more time on emotion recognition. The differences were mainly driven by the lower accuracy and longer reaction times for recognising fearful emotions in SAD patients. Within the SAD patients, lower accuracy of sad face recognition was associated with higher severity of depressive and social anxiety symptoms, particularly with avoidance symptoms. These findings may represent a cross-modality pattern of avoidance in the later stage of identifying negative emotions in SAD. This pattern may be linked to clinical symptom severity.