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Traditional drill and practice (TD) and incremental rehearsal (IR) are two flashcard drill instructional methods previously noted to improve wordrecognition. The current study sought to compare the effectiveness and efficiency of these two methods, as assessed by next day retention assessments, under 2 conditions (i.e., opportunities to respond…
Volpe, Robert J.; Mule, Christina M.; Briesch, Amy M.; Joseph, Laurice M.; Burns, Matthew K.
A balanced, systematic, instructional reading program was designed to increase wordrecognition in beginning readers. The targeted population consisted of first graders in an upper middle class community located in the Chicago suburbs. The lack of wordrecognition was documented through assessments revealing students' phonemic awareness, reading…
A series of progressive demasking and lexical decision experiments investigated how the recognition of targetwords exclusively belonging to one language is affected by the existence of orthographic neighbors from the same or the other language of bilingual participants. Increasing the number of orthographic neighbors in Dutch systematically slowed response times to English targetwords in Dutch\\/English bilinguals, while an
Walter J. B. van Heuven; Ton Dijkstra; Jonathan Grainger
This article provides an overview of bilingualism research on visual wordrecognition in isolation and in sentence context. Many studies investigating the processing of words out-of-context have shown that lexical representations from both languages are activated when reading in one language (language-non-selective lexical access). A newly developed research line asks whether language-non-selective access generalizes to wordrecognition in sentence contexts, providing a language cue and/or semantic constraint information for upcoming words. Recent studies suggest that the language of the preceding words is insufficient to restrict lexical access to words of the target language, even when reading in the native language. Eye tracking studies revealing the time course of word activation further showed that semantic constraint does not restrict language-non-selective access at early reading stages, but there is evidence that it has a relatively late effect. The theoretical implications for theories of bilingual wordrecognition are discussed in light of the Bilingual Interactive Activation+ model (Dijkstra and van Heuven, 2002).
Assche, Eva Van; Duyck, Wouter; Hartsuiker, Robert J.
To examine whether interhemispheric transfer during foveal wordrecognition entails a discontinuity between the information presented to the left and right of fixation, we presented targetwords in such a way that participants fixated immediately left or right of an embedded word (as in "gr*apple", "bull*et") or in the middle of an embedded word…
McCormick, Samantha F.; Davis, Colin J.; Brysbaert, Marc
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.
Rashid, Sheikh Faisal; Shafait, Faisal; Breuel, Thomas M.
Reading is a complex process that draws on a remarkable number of diverse perceptual and cognitive processes. In this review, I provide an overview of computational models of reading, focussing on models of visual wordrecognition–how we recognise individual words. Early computational models had ‘toy’ lexicons, could simulate only a narrow range of phenomena, and frequently had fundamental limitations, such as being able to handle only four-letter words. The most recent models can use realistic lexicons, can simulate data from a range of tasks, and can process words of different lengths. These models are the driving force behind much of the empirical work on reading. I discuss how the data have guided model development and, importantly, I also provide guidelines to help interpret and evaluate the contribution the models make to our understanding of how we read.
Two experiments were conducted investigating the role of visual sequential memory skill in the wordrecognition efficiency of undergraduate university students. Wordrecognition 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…
Models of spoken wordrecognition assume that words are represented as sequences of phonemes. We evaluated this assumption by examining phonemic anadromes, words that share the same phonemes but differ in their order (e.g., sub and bus). Using the visual-world paradigm, we found that listeners show more fixations to anadromes (e.g., sub when bus is the target) than to unrelated words (well) and to words that share the same vowel but not the same set of phonemes (sun). This contrasts with the predictions of existing models and suggests that words are not defined as strict sequences of phonemes. PMID:23456328
Toscano, Joseph C; Anderson, Nathaniel D; McMurray, Bob
We investigate the effects of word characteristics on episodic recognition memory using analyses that avoid Clark's (1973) "language-as-a-fixed-effect" fallacy. Our results demonstrate the importance of modeling word variability and show that episodic memory for words is strongly affected by item noise (Criss & Shiffrin, 2004), as measured by the…
Freeman, Emily; Heathcote, Andrew; Chalmers, Kerry; Hockley, William
Phonology is a lower-level structural aspect of language involving the sounds of a language and their organization in that language. Numerous behavioral studies utilizing priming, which refers to an increased sensitivity to a stimulus following prior experience with that or a related stimulus, have provided evidence for the role of phonology in visual wordrecognition. However, most language studies utilizing priming in conjunction with functional magnetic resonance imaging (fMRI) have focused on lexical-semantic aspects of language processing. The aim of the present study was to investigate the neurobiological substrates of the automatic, implicit stages of phonological processing. While undergoing fMRI, eighteen individuals performed a lexical decision task (LDT) on prime-target pairs including word-word homophone and pseudoword-word pseudohomophone pairs with a prime presentation below perceptual threshold. Whole-brain analyses revealed several cortical regions exhibiting hemodynamic response suppression due to phonological priming including bilateral superior temporal gyri (STG), middle temporal gyri (MTG), and angular gyri (AG) with additional region of interest (ROI) analyses revealing response suppression in the left lateralized supramarginal gyrus (SMG). Homophone and pseudohomophone priming also resulted in different patterns of hemodynamic responses relative to one another. These results suggest that phonological processing plays a key role in visual wordrecognition. Furthermore, enhanced hemodynamic responses for unrelated stimuli relative to primed stimuli were observed in midline cortical regions corresponding to the default-mode network (DMN) suggesting that DMN activity can be modulated by task requirements within the context of an implicit task. PMID:21159322
Wilson, Lisa B; Tregellas, Jason R; Slason, Erin; Pasko, Bryce E; Rojas, Donald C
Abbreviated CID W-22 lists were administered to normal and hearing-impaired adults to test the hypothesis that fewer judiciously chosen items can be used to test wordrecognition without compromising test accuracy. Results show that fewer items can be used if the words are sufficiently difficult and strict passing criteria are employed. (Author/CL)
Previous work has demonstrated that talker-specific representations affect spoken wordrecognition relatively late during processing. However, participants in these studies were listening to unfamiliar talkers. In the present research, we used a long-term repetition-priming paradigm and a speeded-shadowing task and presented listeners with famous talkers. In Experiment 1, half the words were spoken by Barack Obama, and half by Hillary Clinton. Reaction times (RTs) to repeated words were shorter than those to unprimed words only when repeated by the same talker. However, in Experiment 2, using nonfamous talkers, RTs to repeated words were shorter than those to unprimed words both when repeated by the same talker and when repeated by a different talker. Taken together, the results demonstrate that talker-specific details can affect the perception of spoken words relatively early during processing when words are spoken by famous talkers. PMID:24366633
Maibauer, Alisa M; Markis, Teresa A; Newell, Jessica; McLennan, Conor T
Computational modelling has tremendously advanced our understanding of the processes involved in normal and impaired reading. The present Special Issue highlights some new directions in the field of wordrecognition and reading aloud. These new lines of research include the learning of orthographic and phonological representations in both supervised and unsupervised networks, the extension of existing models to multisyllabic word
Johannes C. Ziegler; Jonathan Grainger; Marc Brysbaert
Ehri's developmental model of wordrecognition outlines early reading development that spans from the use of logos to advanced knowledge of oral and written language to read words. Henderson's developmental spelling theory presents stages of word knowledge that progress in a similar manner to Ehri's phases. The purpose of this research study was…
In Experiment I, high-frequency words resulted in poorer recognition performance than did low-frequency words only when the 'new' words on the test were also high-frequency words. When they were low-frequency words, recognition was nearly errorless. These...
The Choquet fuzzy integral provides a useful mechanism for evidence aggregation. It is a flexible method which can represent weighted averages, medians, order statistics, and many other information aggregation mechanisms. In this paper, two applications are described to handwritten wordrecognition: as a match function in a dynamic programming based classifier and as a method for fusing the results from multiple wordrecognition algorithms. In the first case, the results are compared with traditional methods. In the second case, the results are compared with neural network and Borda count approaches.
Reviews the 1886 reading research findings of J. McK. Cattell, and their confirmation by E. B. Huey in 1908; points to considerations that militate against accepting the Cattell/Huey evidence on wordrecognition processes as support for pedagogical practice. (GT)
Phonology is a lower-level structural aspect of language involving the sounds of a language and their organization in that language. Numerous behavioral studies utilizing priming, which refers to an increased sensitivity to a stimulus following prior experience with that or a related stimulus, have provided evidence for the role of phonology in visual wordrecognition. However, most language studies utilizing
Lisa B. Wilson; Jason R. Tregellas; Erin Slason; Bryce E. Pasko; Donald C. Rojas
The holistic paradigm in handwritten wordrecognition treats the word as a single, indivisible entity and attempts to recognize words from their overall shape, as opposed to their character contents. In this survey, we have attempted to take a fresh look at the potential role of the holistic paradigm in handwritten wordrecognition. The survey begins with an overview of
This paper describes a method for the construction of a word graph (or lattice) for large vocabulary, continuous speech recognition. The advantage of a word graph is that a fairly good degree of decoupling between acoustic recognition at the 10-ms level and the final search at the word level using a complicated language model can be achieved. The word graph
In a series of studies, we examined how mothers naturally stress words across multiple mentions in speech to their infants and how this marking influences infants’ recognition of words in fluent speech. We first collected samples of mothers’ infant-directed speech using a technique that induced multiple repetitions of targetwords. Acoustic analyses revealed that mothers systematically alternated between emphatic and nonemphatic stress when talking to their infants. Using the headturn preference procedure, we then tested 7.5-month-old infants on their ability to detect familiarized bisyllabic words in fluent speech. Stress of targetwords (emphatic and nonemphatic) was systematically varied across familiarization and recognition phases of four experiments. Results indicated that, although infants generally prefer listening to words produced with emphatic stress, recognition was enhanced when the degree of emphatic stress at familiarization matched the degree of emphatic stress at recognition.
How do we map the rapid input of spoken language onto phonological and lexical representations over time? Attempts at psychologically-tractable computational models of spoken wordrecognition tend either to ignore time or to transform the temporal input into a spatial representation. TRACE, a connectionist model with broad and deep coverage of speech perception and spoken wordrecognition phenomena, takes the latter approach, using exclusively time-specific units at every level of representation. TRACE reduplicates featural, phonemic, and lexical inputs at every time step in a large memory trace, with rich interconnections (excitatory forward and backward connections between levels and inhibitory links within levels). As the length of the memory trace is increased, or as the phoneme and lexical inventory of the model is increased to a realistic size, this reduplication of time- (temporal position) specific units leads to a dramatic proliferation of units and connections, begging the question of whether a more efficient approach is possible. Our starting point is the observation that models of visual object recognition—including visual wordrecognition—have grappled with the problem of spatial invariance, and arrived at solutions other than a fully-reduplicative strategy like that of TRACE. This inspires a new model of spoken wordrecognition that combines time-specific phoneme representations similar to those in TRACE with higher-level representations based on string kernels: temporally independent (time invariant) diphone and lexical units. This reduces the number of necessary units and connections by several orders of magnitude relative to TRACE. Critically, we compare the new model to TRACE on a set of key phenomena, demonstrating that the new model inherits much of the behavior of TRACE and that the drastic computational savings do not come at the cost of explanatory power.
Hannagan, Thomas; Magnuson, James S.; Grainger, Jonathan
Three experiments examined whether the identification of a visual word is followed by its subvocal articulation during reading. An irrelevant spoken word (ISW) that was identical, phonologically similar, or dissimilar to a visual targetword was presented when the eyes moved to the target in the course of sentence reading. Sentence reading was further accompanied by either a sequential finger tapping task (Experiment 1) or an articulatory suppression task (Experiment 2). Experiment 1 revealed sound-specific interference from a phonologically similar ISW during posttarget viewing. This interference was absent in Experiment 2, where similar and dissimilar ISWs impeded target and posttarget reading equally. Experiment 3 showed that articulatory suppression left the lexical processing of visual words intact and that it did not diminish the influence of visual wordrecognition on eye guidance. The presence of sound-specific interference during posttarget reading in Experiment 1 is attributed to deleterious effects of a phonologically similar ISW on the subvocal articulation of a target. Its absence in Experiment 2 is attributed to the suppression of a target's subvocal articulation. PMID:20192542
This study explores incremental processing in spoken wordrecognition in Russian 5- and 6-year-olds and adults using free-viewing eye-tracking. Participants viewed scenes containing pictures of four familiar objects and clicked on a target embedded in a spoken instruction. In the cohort condition, two object names shared identical three-phoneme onsets. In the noncohort condition, all object names had unique onsets. Coarse-grain
The coordination of word-recognition and oculomotor processes during reading was evaluated in two eye-tracking experiments that examined how word skipping, where a word is not fixated during first-pass reading, is affected by the lexical status of a letter string in the parafovea and ease of recognizing that string. Ease of lexical recognition was manipulated through target-word frequency (Experiment 1) and through repetition priming between prime-target pairs embedded in a sentence (Experiment 2). Using the gaze-contingent boundary technique the targetword appeared in the parafovea either with full preview or with transposed-letter (TL) preview. The TL preview strings were nonwords in Experiment 1 (e.g., bilnk created from the target blink), but were words in Experiment 2 (e.g., sacred created from the target scared). Experiment 1 showed greater skipping for high-frequency than low-frequency targetwords in the full preview condition but not in the TL preview (nonword) condition. Experiment 2 showed greater skipping for targetwords that repeated an earlier prime word than for those that did not, with this repetition priming occurring both with preview of the full target and with preview of the target’s TL neighbor word. However, time to progress from the word after the target was greater following skips of the TL preview word, whose meaning was anomalous in the sentence context, than following skips of the full preview word whose meaning fit sensibly into the sentence context. Together, the results support the idea that coordination between word-recognition and oculomotor processes occurs at the level of implicit lexical decisions.
Research on bilingual wordrecognition suggests that lexical access is non-selective with respect to language, i.e., that word representations of both languages become active during recognition. One piece of evidence is that bilinguals recognise cognates (words that are identical or similar in form and meaning in two languages) faster than…
Lemhofer, Kristin; Dijkstra, Ton; Michel, Marije C.
This paper is aimed at exploring the potential of online words to perform biometric writer recognition. Most of the scientific literature dealing with online writer recognition has focused on signature and somehow disregarded handwritten text. Using a novel recognition system based on stroke categorization and dynamic time warping, it is shown that short sequences of online text (words and combinations
In this paper, we develop a framework for using only the needed data for automatic targetrecognition (ATR) algorithms using the recently developed theory of sparse representations and compressive sensing (CS). We show how sparsity can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm in terms of the recognition rate on the well known Comanche forward-looking infrared (FLIR) data set consisting of ten different military targets at different orientations.
Patel, Vishal M.; Nasrabadi, Nasser M.; Chellappa, Rama
A speaker independent recognition algorithm for connected words is described which uses a word boundary hypothesizer to reduce computational cost, as well as a robust word classifier and an effective scoring strategy. The word boundary hypothesizer predicts possible candidates for word boundaries at a variable rate which is controlled by a difference in adjacent frame spectra, obtained by bandpass filters.
For part I see ibid. vol.8, no. 1 (2000). This paper presents an application of the generalized hidden Markov models to handwritten wordrecognition. The system represents a word image as an ordered list of observation vectors by encoding features computed from each column in the given word image. Word models are formed by concatenating the state chains of the
Clustering coefficient—a measure derived from the new science of networks—refers to the proportion of phonological neighbors of a targetword that are also neighbors of each other. Consider the words bat, hat, and can, all of which are neighbors of the word cat; the words bat and hat are also neighbors of each other. In a perceptual identification task, words with a low clustering coefficient (i.e., few neighbors are neighbors of each other) were more accurately identified than words with a high clustering coefficient (i.e., many neighbors are neighbors of each other). In a lexical decision task, words with a low clustering coefficient were responded to more quickly than words with a high clustering coefficient. These findings suggest that the structure of the lexicon, that is the similarity relationships among neighbors of the targetword measured by clustering coefficient, influences lexical access in spoken wordrecognition. Simulations of the TRACE and Shortlist models of spoken wordrecognition failed to account for the present findings. A framework for a new model of spoken wordrecognition is proposed.
Clustering coefficient-a measure derived from the new science of networks-refers to the proportion of phonological neighbors of a targetword that are also neighbors of each other. Consider the words bat, hat, and can, all of which are neighbors of the word cat; the words bat and hat are also neighbors of each other. In a perceptual identification task, words with a low clustering coefficient (i.e., few neighbors are neighbors of each other) were more accurately identified than words with a high clustering coefficient (i.e., many neighbors are neighbors of each other). In a lexical decision task, words with a low clustering coefficient were responded to more quickly than words with a high clustering coefficient. These findings suggest that the structure of the lexicon (i.e., the similarity relationships among neighbors of the targetword measured by clustering coefficient) influences lexical access in spoken wordrecognition. Simulations of the TRACE and Shortlist models of spoken wordrecognition failed to account for the present findings. A framework for a new model of spoken wordrecognition is proposed. PMID:19968444
We used eyetracking to examine how tonal versus segmental information influence spoken wordrecognition in Mandarin Chinese. Participants heard an auditory word and were required to identify its corresponding picture from an array that included the target item ("chuang2" "bed"), a phonological competitor (segmental: chuang1 "window"; cohort:…
Three cross-modal priming experiments examined the influence of preexposure to pictures and printed words on the speed of spoken wordrecognition. Targets for auditory lexical decision were spoken Dutch words and nonwords, presented in isolation (Experiments 1 and 2) or after a short phrase (Experiment 3). Auditory stimuli were preceded by primes, which were pictures (Experiments 1 and 3) or those pictures' printed names (Experiment 2). Prime-target pairs were phonologically onset related (e.g., pijl-pijn, arrow-pain), were from the same semantic category (e.g., pijl-zwaard, arrow-sword), or were unrelated on both dimensions. Phonological interference and semantic facilitation were observed in all experiments. Priming magnitude was similar for pictures and printed words and did not vary with picture viewing time or number of pictures in the display (either one or four). These effects arose even though participants were not explicitly instructed to name the pictures and where strategic naming would interfere with lexical decision making. This suggests that, by default, processing of related pictures and printed words influences how quickly we recognize spoken words. PMID:24132709
Eye movements of Dutch participants were tracked as they looked at arrays of four words on a computer screen and followed spoken instructions (e.g., "Klik op het woord buffel": Click on the word buffalo). The arrays included the target (e.g., buffel), a phonological competitor (e.g., buffer, buffer), and two unrelated distractors. Targets were monosyllabic or bisyllabic, and competitors mismatched targets only on either their onset or offset phoneme and only by one distinctive feature. Participants looked at competitors more than at distractors, but this effect was much stronger for offset-mismatch than onset-mismatch competitors. Fixations to competitors started to decrease as soon as phonetic evidence disfavouring those competitors could influence behaviour. These results confirm that listeners continuously update their interpretation of words as the evidence in the speech signal unfolds and hence establish the viability of the methodology of using eye movements to arrays of printed words to track spoken-wordrecognition. PMID:17455074
Visual speech perception has become a topic of considerable interest to speech researchers. Previous research has demonstrated that perceivers neurally encode and use speech information from the visual modality, and this information has been found to facilitate spoken wordrecognition in tasks such as lexical decision (Kim, Davis, & Krins, 2004). In this paper, we used a cross-modality repetition priming paradigm with visual speech lexical primes and auditory lexical targets to explore the nature of this priming effect. First, we report that participants identified spoken words mixed with noise more accurately when the words were preceded by a visual speech prime of the same word compared with a control condition. Second, analyses of the responses indicated that both correct and incorrect responses were constrained by the visual speech information in the prime. These complementary results suggest that the visual speech primes have an effect on lexical access by increasing the likelihood that words with certain phonetic properties are selected. Third, we found that the cross-modality repetition priming effect was maintained even when visual and auditory signals came from different speakers, and thus different instances of the same lexical item. We discuss implications of these results for current theories of speech perception.
Buchwald, Adam B.; Winters, Stephen J.; Pisoni, David B.
A subsyllabic phonological unit, the antibody, has received little attention as a potential fundamental processing unit in wordrecognition. The psychological reality of the antibody in Korean recognition was investigated by looking at the performance of subjects presented with nonwords and words in the lexical decision task. In Experiment 1, the…
Orthographic influences in spoken wordrecognition have been previously examined in alphabetic languages. However, it is unknown whether orthographic information affects spoken wordrecognition in Chinese, which has a clean dissociation between orthography (O) and phonology (P). The present study investigated orthographic effects using event…
This paper proposes a probabilistic framework to define and evaluate confidence measures for wordrecognition. We describe a novel method to combine different knowledge sources and estimate the confidence in a word hypothesis, via a neural network. We also propose a measure of the joint performance of the recognition and confidence systems. The definitions and algorithms are illustrated with results
Mitch Weintraub; F. Beaufays; Z. Rivlin; Y. Konig; A. Stolcke
Two experiments explored rapid extraction of gist from a visual text and its influence on wordrecognition. In both, a short text (sentence) containing a targetword was presented for 200 ms and was followed by a targetrecognition task. Results showed that participants recognized contextually anomalous wordtargets less frequently than contextually consistent counterparts (Experiment 1). This context effect was obtained when sentences contained the same semantic content but with disrupted syntactic structure (Experiment 2). Results demonstrate that words in a briefly presented visual sentence are processed in parallel and that rapid extraction of sentence gist relies on a primitive representation of sentence context (termed protocontext) that is semantically activated by the simultaneous presentation of multiple words (i.e., a sentence) before syntactic processing. PMID:21560469
Serial attention models of eye-movement control during reading were evaluated in an eye-tracking experiment that examined how lexical activation combines with visual information in the parafovea to affect word skipping (where a word is not fixated during first-pass reading). Lexical activation was manipulated by repetition priming created through prime-target pairs embedded within a sentence. The boundary technique (Rayner, 1975) was used to determine whether the targetword was fully available during parafoveal preview or whether it was available with transposed letters (e.g., Herman changed to Hreman). With full parafoveal preview, the targetword was skipped more frequently when it matched the earlier prime word (i.e., was repeated) than when it did not match the earlier prime word (i.e., was new). With transposed-letter (TL) preview, repetition had no effect on skipping rates despite the great similarity of the TL preview string to the targetword and substantial evidence that TL strings activate the words from which they are derived (Perea & Lupker, 2003). These results show that lexically based skipping is based on full recognition of the letter string in parafoveal preview and does not involve using the contextual constraint to compensate for the reduced information available from the parafovea. These results are consistent with models of eye-movement control during reading in which successive words in a text are processed 1 at a time (serially) and in which wordrecognition strongly influences eye movements. PMID:22686842
Most words in English are ambiguous between different interpretations; words can mean different things in different contexts. We investigate the implications of different types of semantic ambiguity for connectionist models of wordrecognition. We present a model in which there is competition to activate distributed semantic representations. The…
Rodd, Jennifer M.; Gaskell, M. Gareth; Marslen-Wilson, William D.
Word reading in alphabetic languages involves letter identification, independently of the format in which these letters are written. This process of letter "regularization" is sensitive to word context, leading to the recognition of a word even when numbers that resemble letters are inserted among other real letters (e.g., M4TERI4L). The present…
Molinaro, Nicola; Dunabeitia, Jon Andoni; Marin-Gutierrez, Alejandro; Carreiras, Manuel
In the paper the lexical ambiguity resolution is presented. The paper is specifically focused on the processing of words, models of wordrecognition, context effect, trying to find an answer to how the reader-listener determines the contextually appropriate meaning of a word. Ambiguity resolution is analyzed and explored in two perspectives: the…
? Lincoln Laboratory has developed a new automatic targetrecognition (ATR) system that provides significantly improved target-recognition performance compared with ATR systems that use conventional synthetic-aperture radar (SAR) image-processing techniques. We achieve significant improvement in target-recognition performance by using a new superresolution image- processing technique that enhances SAR image resolution and image quality prior to performing targetrecognition. A computationally
Leslie M. Novak; Gregory J. Owirka; William S. Brower; Alison L. Weaver
Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of wordrecognition. The influence of emotion on wordrecognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted U, or interactive with arousal. In the present study, we used a sample of 12,658 words and included many lexical and semantic control factors to determine the precise nature of the effects of arousal and valence on wordrecognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in wordrecognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of wordrecognition. (PsycINFO Database Record (c) 2014 APA, all rights reserved). PMID:24490848
Children with hyperlexia read words spontaneously before the age of five, have impaired comprehension on both listening and reading tasks, and have wordrecognition skill above expectations based on cognitive and linguistic abilities. One student with hyperlexia and another student with higher wordrecognition than comprehension skills who started to read words at a very early age were followed over several years from the primary grades through high school when both were completing a second-year Spanish course. The purpose of the present study was to examine the foreign language (FL) wordrecognition, spelling, reading comprehension, writing, speaking, and listening skills of the two students and another high school student without hyperlexia. Results showed that the student without hyperlexia achieved higher scores than the hyperlexic student and the student with above average wordrecognition skills on most FL proficiency measures. The student with hyperlexia and the student with above average wordrecognition skills achieved higher scores on the Spanish proficiency tasks that required the exclusive use of phonological (pronunciation) and phonological/orthographic (wordrecognition, spelling) skills than on Spanish proficiency tasks that required the use of listening comprehension and speaking and writing skills. The findings provide support for the notion that wordrecognition and spelling in a FL may be modular processes and exist independently of general cognitive and linguistic skills. Results also suggest that students may have stronger FL learning skills in one language component than in other components of language, and that there may be a weak relationship between FL wordrecognition and oral proficiency in the FL. PMID:20563785
This investigation deals with developmental changes in children's ability to process graphological features of words. The graphological features studied were letter positions and word shape. (Author/DEP)
The relative abilities of word frequency, contextual diversity, and semantic distinctiveness to predict accuracy of spoken wordrecognition in noise were compared using two data sets. Word frequency is the number of times a word appears in a corpus of text. Contextual diversity is the number of different documents in which the word appears in that corpus. Semantic distinctiveness takes into account the number of different semantic contexts in which the word appears. Semantic distinctiveness and contextual diversity were both able to explain variance above and beyond that explained by word frequency, which by itself explained little unique variance.
Johns, Brendan T.; Gruenenfelder, Thomas M.; Pisoni, David B.; Jones, Michael N.
Three experiments examined whether the identification of a visual word is followed by its subvocal articulation during reading. An irrelevant spoken word (ISW) that was identical, phonologically similar, or dissimilar to a visual targetword was presented when the eyes moved to the target in the course of sentence reading. Sentence reading was…
Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20-38 years) performed wordrecognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during wordrecognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct wordrecognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports wordrecognition in difficult listening conditions. PMID:24285902
Vaden, Kenneth I; Kuchinsky, Stefanie E; Cute, Stephanie L; Ahlstrom, Jayne B; Dubno, Judy R; Eckert, Mark A
Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20–38 years) performed wordrecognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during wordrecognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct wordrecognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports wordrecognition in difficult listening conditions.
An approach to handprinted wordrecognition is described. The approach is based on the use of generating multiple possible segmentations of a word image into characters and matching these segmentations to a lexicon of candidate strings. The segmentation process uses a combination of connected component analysis and distance transform-based, connected character splitting. Neural networks are used to assign character confidence
Paul D. Gader; Michael W. Whalen; Margaret Ganzberger; Dan Hepp
Introduction: Constant time delay has been identified as an evidence-based practice to teach print sight words and picture recognition (Browder, Ahlbrim-Delzell, Spooner, Mims, & Baker, 2009). For the study presented here, we tested the effectiveness of constant time delay to teach new braille words. Methods: A single-subject multiple baseline…
This study examined the influence of phonotactic probability on wordrecognition in English-speaking toddlers. Typically developing toddlers completed a preferential looking paradigm using familiar words, which consisted of either high or low phonotactic probability sound sequences. The participants' looking behavior was recorded in response…
MacRoy-Higgins, Michelle; Shafer, Valerie L.; Schwartz, Richard G.; Marton, Klara
The present work aims at demonstrating that visual training associated with the act of reading modifies the way we perceive printed words. As reading does not train all parts of the retina in the same way but favors regions on the side in the direction of scanning, visual wordrecognition should be better at retinal locations that are frequently…
Behavioral and modeling evidence suggests that words compete for recognition during auditory word identification, and that phonological similarity is a driving factor in this competition. The present study used event-related potentials [ERPs] to examine the temporal dynamics of different types of phonological competition (i.e., cohort and rhyme). ERPs were recorded during a novel picture-word matching task, where a target picture was followed by an auditory word that either matched the target (CONE-cone), or mismatched in one of three ways: rhyme (CONE-bone), cohort (CONE-comb), and unrelated (CONE-fox). Rhymes and cohorts differentially modulated two distinct ERP components, the Phonological Mismatch Negativity [PMN] and the N400, revealing the influences of pre-lexical and lexical processing components in speech recognition. Cohort mismatches resulted in late increased negativity in the N400, reflecting disambiguation of the later point of miscue and the combined influences of top-down expectations and misleading bottom-up phonological information on processing. In contrast, we observed a reduction in the N400 for rhyme mismatches, reflecting lexical activation of rhyme competitors. Moreover, the observed rhyme effects suggest that there is interaction between phoneme-level and lexical-level information in the recognition of spoken words. The results support the theory that both levels of information are engaged in parallel during auditory wordrecognition in a way that permits both bottom-up and top-down competition effects.
Desroches, Amy S.; Newman, Randy Lynn; Joanisse, Marc F.
Behavioral and modeling evidence suggests that words compete for recognition during auditory word identification, and that phonological similarity is a driving factor in this competition. The present study used event-related potentials (ERPs) to examine the temporal dynamics of different types of phonological competition (i.e., cohort and rhyme). ERPs were recorded during a novel picture-word matching task, where a target picture was followed by an auditory word that either matched the target (CONE-cone), or mismatched in one of three ways: rhyme (CONE-bone), cohort (CONE-comb), and unrelated (CONE-fox). Rhymes and cohorts differentially modulated two distinct ERP components, the phonological mismatch negativity and the N400, revealing the influences of prelexical and lexical processing components in speech recognition. Cohort mismatches resulted in late increased negativity in the N400, reflecting disambiguation of the later point of miscue and the combined influences of top-down expectations and misleading bottom-up phonological information on processing. In contrast, we observed a reduction in the N400 for rhyme mismatches, reflecting lexical activation of rhyme competitors. Moreover, the observed rhyme effects suggest that there is an interaction between phoneme-level and lexical-level information in the recognition of spoken words. The results support the theory that both levels of information are engaged in parallel during auditory wordrecognition in a way that permits both bottom-up and top-down competition effects. PMID:18855555
Desroches, Amy S; Newman, Randy Lynn; Joanisse, Marc F
Although infants begin to encode and track novel words in fluent speech by 7.5 months, their ability to recognize words is somewhat limited at this stage. In particular, when the surface form of a word is altered, by changing the gender or affective prosody of the speaker, infants begin to falter at spoken wordrecognition. Given that natural speech is replete with variability, only some of which is determines the meaning of a word, it remains unclear how infants might ever overcome the effects of surface variability without appealing to meaning. In the current set of experiments, consequences of high and low variability are examined in preverbal infants. The source of variability, vocal affect, is a common property of infant-directed speech with which young learners have to contend. Across a series of four experiments, infants' abilities to recognize repeated encounters of words, as well as to reject similar-sounding words, are investigated in the context of high and low affective variation. Results point to positive consequences of affective variation, both in creating generalizable memory representations for words, but also in establishing phonologically precise memories for words. Conversely, low variability appears to degrade wordrecognition on both fronts, compromising infants' abilities to generalize across different affective forms of a word and to detect similar-sounding items. Findings are discussed in the context of principles of categorization, both of a linguistic and non-linguistic variety, which may potentiate the early growth of a lexicon.
Several researches published about artificial neural networks are connected with military problems. This research put forward ideas connected with the processing of military information to search and identify targets-automatic targetrecognition (ATR). A main-purpose automatic targetrecognition system did not exist. The research put forward here was demonstrated on military data, however it could only be considered as a proof
The paper proposed a novel automatic targetrecognition (ATR) system for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition (MSTAR) public release database. First MSTAR image chips are represented as fine and raw feature vectors, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the
Many applications reported in artificial neural networks are associated with military problems. This paper reviews concepts associated with the processing of military data to find and recognize targets—automatic targetrecognition (ATR). A general-purpose automatic targetrecognition system does not exist. The work presented here is demonstrated on military data, but it can only be consideredproof of principle until systems are
Steven K. Rogers; John M. Colombi; Curtis E. Martin; James C. Gainey; Kenneth H. Fielding; Tom J. Burns; Dennis W. Ruck; Matthew Kabrisky; Mark E. Oxley
Many applications reported in artificial neural networks are associated with military problems. This paper reviews concepts associated with the processing of military data to find and recognize targets -- automatic targetrecognition (ATR). A general-purpose automatic targetrecognition system does not exist. The work presented here is demonstrated on military data, but it can only be considered proof of principle
Steven K. Rogers; John M. Colombi; Curtis E. Martin; James C. Gainey; Kenneth H. Fielding; Thomas J. Burns; Dennis W. Ruck; Matthew Kabrisky; Mark E. Oxley
Describes experiments that examined the effects of orthographic similarity on lexical decisions and compared decision times to words when they are preceded by unrelated words. Results indicate that lexical decisions about spoken words were shown to be influenced by the spelling of an immediately preceding item. (SED)
In the present PET study, we examined brain activity related to processing of pictures and printed words in episodic memory. Our goal was to determine how the perceptual format of objects (verbal versus pictorial) is reflected in the neural organization of episodic memory for common objects. We investigated this issue in relation to encoding and recognition with a particular focus on medial temporal-lobe (MTL) structures. At encoding, participants saw pictures of objects or their written names and were asked to make semantic judgments. At recognition, participants made yes-no recognition judgments in four different conditions. In two conditions, target items were pictures of objects; these objects had originally been encoded either in picture or in word format. In two other conditions, target items were words; they also denoted objects originally encoded either as pictures or as words. Our data show that right MTL structures are differentially involved in picture processing during encoding and recognition. A posterior MTL region showed higher activation in response to the presentation of pictures than of words across all conditions. During encoding, this region may be involved in setting up a representation of the perceptual information that comprises the picture. At recognition, it may play a role in guiding retrieval processes based on the perceptual input, i.e. the retrieval cue. Another more anterior right MTL region was found to be differentially involved in recognition of objects that had been encoded as pictures, irrespective of whether the retrieval cue provided was pictorial or verbal in nature; this region may be involved in accessing stored pictorial representations. Our results suggest that left MTL structures contribute to picture processing only during encoding. Some regions in the left MTL showed an involvement in semantic encoding that was picture specific; others showed a task-specific involvement across pictures and words. Together, our results provide evidence that the involvement of some but not all MTL regions in episodic encoding and recognition is format specific. PMID:11194410
Köhler, S; Moscovitch, M; Winocur, G; McIntosh, A R
Reports two sets of lexical priming experiments in which the morphological, semantic, and orthographic relationships between primes and targets are varied in three SOA conditions. Results showed that morphological structure plays a significant role in early visual recognition of English words that is independent of both semantic and orthographic…
Rastle, Kathleen; Davis, Matt H.; Marslen-Wilson, William D.; Tyler, Lorraine K.
Automatic targetrecognition (ATR)-the use of computer processing to detect and identify targets (such as tanks, howitzers, and armored personnel carriers) automatically-is becoming critically important in several military applications. ATR systems can re...
When phoneme categories of a non-native language do not correspond to those of the native language, non-native categories may be inaccurately perceived. This may impair non-native spoken-wordrecognition. Weber and Cutler investigated the effect of phonetic discrimination difficulties on competitor activation in non-native listening. They tested whether Dutch listeners use English phonetic contrasts to resolve potential competition. Eye movements of Dutch participants were monitored as they followed spoken English instructions to click on pictures of objects. A target picture (e.g., picture of a paddle) was always presented along with distractor pictures. The name of a distractor picture either shared initial segments with the name of the target picture (e.g., target paddle, /paedl/ and competitor pedal, /pEdl/) or not (e.g., strawberry and duck). Half of the target-competitor pairs contained English vowels that are often confused by Dutch listeners (e.g., /ae/ and /E/ as in ``paddle-pedal''), half contained vowels that are unlikely to be confused (e.g., /ae/ and /aI/ as in ``parrot-pirate''). Dutch listeners fixated distractor pictures with confusable English vowels longer than distractor pictures with distinct vowels. The results demonstrate that the sensitivity of non-native listeners to phonetic contrasts can result in spurious competitors that should not be activated for native listeners.
Advanced mammalian target identification derived from the perception of target's manifold and measurement manifolddistance. It does not rely on object's segmented accuracy, not depend on target's variety model, and adapt to a range of changes on targets. In this paper, based on the existed manifold learning algorithm, set up a new bionic automatic targetrecognition model, discussed the targets manifold knowledge acquisition and the knowledge expression architecture, gave a manifold knowledge-based new method for automatic targetrecognition. Experiments show that the new method has a strong adaptability to targets various transform, and has a very high correctly identification probability.
Automatic targetrecognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for
Detection, location, and targetrecognition require scanning and processing of large images and are computationally expensive. Their implementation in real time is thus quite difficult. The Zoom-lens model is suggested in this paper as a possible solution for decreasing the computational burden usually associated with automatic targetrecognition (ATR). The original field of view (FOV) is partitioned into overlapping regions
Infrared imagers used to acquire data for automatic targetrecognition are inherently limited by the physical properties of their components. Fortunately, image super-resolution techniques can be applied to overcome the limits of these imaging systems. This increase in resolution can have potentially dramatic consequences for improved automatic targetrecognition (ATR) on the resultant higher-resolution images. We will discuss superresolution techniques
Raymond S. Wagner; Donald E. Waagen; Mary L. Cassabaum
This paper presents an automatic targetrecognition (ATR) system for laser radar (LADAR) imagery, designed to classify objects at multiple levels of discrimination (target detection, classification, and recognition) from single LADAR images. Segmentation is performed in both the range and non-range LADAR channels and results combined to increase object detection rate or decrease false positive detection rate. Through use of
The purpose of this study was to determine the relation between wordrecognition errors made at a letter-sound pattern level on a word list and on a curriculum-based measurement oral reading fluency measure (CBM-ORF) for typical and struggling elementary readers. The participants were second, third, and fourth grade typical and struggling readers…
Flynn, Lindsay J.; Hosp, John L.; Hosp, Michelle K.; Robbins, Kelly P.
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 wordrecognition. 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 wordrecognition and other domains (e.g., object recognition). PMID:24373885
Traditional automatic targetrecognition (ATR) systems discriminate based upon target size, target shape, or both. In this paper, an ATR algorithm is proposed that exploits aircraft-class specific kinematics to assess the tracked target's likelihood. Prior information on kinematics includes the physical parameters of the aircraft, allowable input forces to a pilot, and pilot behavior in the aircraft. It is shown
A lexical decision experiment with Dutch-English bilinguals compared the effect of word frequency on visual wordrecognition\\u000a in the first language with that in the second language. Bilinguals showed a considerably larger frequency effect in their\\u000a second language, even though corpus frequency was matched across languages. Experiment 2 tested monolingual, native speakers\\u000a of English on the English materials from Experiment
Wouter Duyck; Dieter Vanderelst; Timothy Desmet; Robert J. Hartsuiker
STUDIES of primates1 and of patients with brain lesions2 have shown that the visual system represents the external world in regions and pathways specialized to compute visual features and attributes. For example, object recognition is performed by a ventral pathway located in the inferior portion of the temporal lobe3. We studied visual processing of words and word-like stimuli (letter-strings) by
We propose a ground targetrecognition method based on 3-D laser radar data. The method handles general 3-D scattered data. It is based on the fact that man-made objects of complex shape can be decomposed to a set of rectangles. The ground targetrecognition method consists of four steps; 3-D size and orientation estimation, target segmentation into parts of approximately
Christina Grönwall; Fredrik Gustafsson; Mille Millnert
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-wordrecognition. 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 targetword 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-wordrecognition by providing suprasegmental information about the presence of primary lexical stress. PMID:24134065
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 wordrecognition 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 wordrecognition. PMID:24814725
Fractal image processing technology has been recognized as having great potential in automatic targetrecognition (ATR) and image compression. In this paper, Physical Optics Corporation demonstrates the feasibility of using a fractal image processing technique as a new and efficient approach for signature, pattern, and object recognition. Using optical Fourier transform and a ring-wedge detection technique, we generate and measure
Judy Chen; Andrew A. Kostrzewski; Dai H. Kim; Gajendra D. Savant; Jeongdal Kim; Anatoly A. Vasiliev
Of recent interest in automatic targetrecognition (ATR) is the problem of combining the merits of multiple classifiers. This is commonly done by “fusing” the soft-outputs of several classifiers into making a single decision. We observe that the improvement in recognition rates afforded by these approaches is due to the complementary yet correlated information captured by different features\\/signal representations that
An exploratory investigation was made of cross-modality matching within the context of wordrecognition skills among beginning adult readers. The specific aim of the study was to assess the possibility that a deficit in cross-modality matching might be potentially useful as a diagnostic and predictive indicator of the rate at which adults learn to…
Seven children who had unusually precocious word-recognition skills and otherwise had multiple significant developmental deviations were identified. Past findings are reviewed along with psychometric results and clinical observations of the seven hyperlexic children. Implications concerning the syndrome of hyperlexia and how this behavioral pattern may disrupt the acquisition of appropriate modalities of communication are discussed. PMID:7133911
A study involving a high-school student with hyperlexia and a student with above average wordrecognition skills, found they scored higher on Spanish proficiency tasks that required the exclusive use of phonological and phonological/orthographic skills than on Spanish proficiency tasks requiring listening comprehension and speaking and writing…
Past findings were reviewed with psychometric results and clinical observations of seven children with unusually precocious word-recognition skills and otherwise multiple significant developmental deviations. Implications concerning the syndrome of hyperlexia and how the behavior pattern may disrupt the acquisition of appropriate modalities of…
This paper relates to the confidence measures for isolated wordrecognition sys- tem. Two types of confidence are introduced: online garbage model based like- lihood ratio and semi-syllable based posterior probability. Linear classification is adopted to combine the two confidence scores. Traditional evaluation of con- fidence measure is adopted in the experiments. The experimental result shows, after the combining in
A novel approach for applying hidden Markov models (HMM) to automatic targetrecognition (ATR) is proposed. The HMM-ATR captures target and background appearance variability by exploiting flexible statistical models. The method utilizes an unsupervised training procedure to estimate the statistical model parameters. Experiments upon a synthetic aperture radar (SAR) database were performed to test robustness over range of target pose,
This paper describes two possible ways of hit recognition in a target. First method is based on frame differencing with use of a stabilization algorithm to eliminate movements of a target. Second method uses flood fill with random seed point definition to find hits in the target scene.
This paper shows that the nature of letters--consonant versus vowel--modulates the process of letter position assignment during visual wordrecognition. We recorded Event Related Potentials while participants read words in a masked priming semantic categorization task. Half of the words included a vowel as initial, third, and fifth letters (e.g., acero [steel]). The other half included a consonant as initial, third, and fifth (e.g., farol [lantern]). Targets could be preceded 1) by the initial, third, and fifth letters (relative position; e.g., aeo-acero and frl-farol), 2) by 3 consonants or vowels that did not appear in the targetword (control; e.g., iui-acero and tsb-farol), or 3) by the same words (identity: acero-acero, farol-farol). The results showed modulation in 2 time windows (175-250 and 350-450 ms). Relative position primes composed of consonants produced similar effects to the identity condition. These 2 differed from the unrelated control condition, which showed a larger negativity. In contrast, relative position primes composed of vowels produced similar effects to the unrelated control condition, and these 2 showed larger negativities as compared with the identity condition. This finding has important consequences for cracking the orthographic code and developing computational models of visual wordrecognition. PMID:19273459
Carreiras, Manuel; Duñabeitia, Jon Andoni; Molinaro, Nicola
The main goal of this research effort was to develop an integrated target sensing and recognition strategy. Secondary goals of this work were to construct novel image representation and analysis algorithms to facilitate content based image retrieval. We d...
We have built the NYU ATR Laboratory, also known as the RLAB, a computational laboratory for research and education in Automatic TargetRecognition (ATR). The laboratory contains a cluster of workstations connected by a fast network, significant data stor...
Two masked priming experiments investigated the time course of the activation of sub-phonemic information during visual wordrecognition. EEG was recorded as participants read targets with voiced and unvoiced final consonants (e.g., fad and fat), preceded by nonword primes that were incongruent or congruent in voicing and vowel duration (e.g., fap or faz). Experiment 1 used a long duration mask (100 ms) between prime and target, whereas Experiment 2 used a short mask (22 ms). Phonological feature congruency began modulating the amplitude of brain potentials by 80 ms; the feature incongruent condition evoked greater negativity than the feature congruent condition in both Experiments. The early onset of the congruency effect indicates that skilled readers initially activate sub-phonemic feature information during word identification. Congruency effects also appeared in the middle and late periods of wordrecognition, suggesting that readers use phonological representations in multiple aspects of visual wordrecognition.
Background If all available acoustic phonetic information of words is used during lexical access and consequently stored in the mental lexicon, then all pseudowords that deviate in a single acoustic feature from a word should hamper wordrecognition. By contrast, models assuming underspecification of redundant phonological information in the mental lexicon predict a differential disruption of wordrecognition dependent on the phonological structure of the pseudoword. Using neurophysiological measures, the present study tested the predicted asymmetric disruption by assuming that coronal place of articulation for consonants is redundant. Methods Event-related potentials (ERPs) were recorded during a lexical decision task. The focus of interest was on word medial consonants. The crucial pseudowords were created by replacing the place of articulation of the medial consonant in German disyllabic words. We analyzed the differential temporal characteristics of the N400 pseudoword effect. Results N400 amplitudes for pseudowords were enhanced compared to words. As the uniqueness and deviation points differ for coronal and non-coronal items, the ERPs had to be correspondingly adjusted. The adjusted ERPs revealed that the N400 pseudoword effect starts earlier for coronal than for non-coronal pseudoword variants. Thus, non-coronal variants are accepted as words longer than the coronal variants. Conclusion Our results indicate that lexical representations of words containing medial coronal consonants are initially activated by their corresponding non-coronal pseudowords. The most plausible explanation for the asymmetric neuronal processing of coronal and non-coronal pseudoword variants is an underspecified coronal place of articulation in the mental lexicon.
PURPOSE The purpose of this study was to demonstrate improved precision of wordrecognition scores (WRSs) by increasing list length and analyzing phonemic errors. METHOD Pure-tone thresholds (frequencies between 0.25 and 8.0 kHz) and WRSs were measured in 3 levels of speech-shaped noise (50, 52, and 54 dB HL) for 24 listeners with normal hearing. WRSs were obtained for half-lists and full lists of Northwestern University Test No. 6 (Tillman & Carhart, 1966) words presented at 48 dB HL. A resampling procedure was used to derive dimensionless effect sizes for identifying a change in hearing using the data. This allowed the direct comparison of the magnitude of shifts in WRS (%) and in the average pure-tone threshold (dB), which provided a context for interpreting the WRS. RESULTS WRSs based on a 50-word list analyzed by the percentage of correct phonemes were significantly more sensitive for identifying a change in hearing than the WRSs based on 25-word lists analyzed by percentage of correct words. CONCLUSION Increasing the number of items that contribute to a WRS significantly increased the test's ability to identify a change in hearing. Clinical and research applications could potentially benefit from a more precise wordrecognition test, the only basic audiologic measure that estimates directly the distortion component of hearing loss and its effect on communication. PMID:24686502
Schlauch, Robert S; Anderson, Elizabeth S; Micheyl, Christophe
This paper investigates the recognition of unknown words in Chinese parsing. Two methods are proposed to handle this problem. One is the modification of a character-based model. We model the emission probability of an unknown word using the first and last characters in the word. It aims to reduce the POS tag ambiguities of unknown words to improve the parsing performance. In addition, a novel method, using graph-based semisupervised learning (SSL), is proposed to improve the syntax parsing of unknown words. Its goal is to discover additional lexical knowledge from a large amount of unlabeled data to help the syntax parsing. The method is mainly to propagate lexical emission probabilities to unknown words by building the similarity graphs over the words of labeled and unlabeled data. The derived distributions are incorporated into the parsing process. The proposed methods are effective in dealing with the unknown words to improve the parsing. Empirical results for Penn Chinese Treebank and TCT Treebank revealed its effectiveness.
Huang, Qiuping; He, Liangye; Wong, Derek F.; Chao, Lidia S.
This paper investigates the recognition of unknown words in Chinese parsing. Two methods are proposed to handle this problem. One is the modification of a character-based model. We model the emission probability of an unknown word using the first and last characters in the word. It aims to reduce the POS tag ambiguities of unknown words to improve the parsing performance. In addition, a novel method, using graph-based semisupervised learning (SSL), is proposed to improve the syntax parsing of unknown words. Its goal is to discover additional lexical knowledge from a large amount of unlabeled data to help the syntax parsing. The method is mainly to propagate lexical emission probabilities to unknown words by building the similarity graphs over the words of labeled and unlabeled data. The derived distributions are incorporated into the parsing process. The proposed methods are effective in dealing with the unknown words to improve the parsing. Empirical results for Penn Chinese Treebank and TCT Treebank revealed its effectiveness. PMID:24895681
Huang, Qiuping; He, Liangye; Wong, Derek F; Chao, Lidia S
Targetrecognition can be enhanced by reducing image degradation due to atmospheric turbulence. This is accomplished by an adaptive optic system. We discuss the forms of degradation when a target is viewed through the atmosphere1: scintillation from ground targets on a hot day in visible or infrared light; beam spreading and wavering around in time; atmospheric turbulence caused by motion of the target or by weather. In the case of targets we can use a beacon laser that reflects back from the target into a wavefront detector to measure the effects of turbulence on propagation to and from the target before imaging.1 A deformable mirror then corrects the wavefront shape of the transmitted, reflected or scattered data for enhanced imaging. Further, recognition of targets is enhanced by performing accurate distance measurements to localized parts of the target using lidar. Distance is obtained by sending a short pulse to the target and measuring the time for the pulse to return. There is inadequate time to scan the complete field of view so that the beam must be steered to regions of interest such as extremities of the image during image recognition. Distance is particularly valuable to recognize fine features in range along the target or when segmentation is required to separate a target from background or from other targets. We discuss the issues involved.
Previous tests of toddlers’ phonological knowledge of familiar words using wordrecognition tasks have examined syllable onsets but not word-final consonants (codas). However, there are good reasons to suppose that children’s knowledge of coda consonants might be less complete than their knowledge of onset consonants. To test this hypothesis, the present study examined 14- to 21-month-old children’s knowledge of the phonological forms of familiar words by measuring their comprehension of correctly-pronounced and mispronounced instances of those words using a visual fixation task. Mispronunciations substituted onset or coda consonants. Adults were tested in the same task for comparison with children. Children and adults fixated named targets more upon hearing correct pronunciations than upon hearing mispronunciations, whether those mispronunciations involved the word’s initial or final consonant. In addition, detailed analysis of the timing of adults’ and children’s eye movements provided clear evidence for incremental interpretation of the speech signal. Children’s responses were slower and less accurate overall, but children and adults showed nearly identical temporal effects of the placement of phonological substitutions. The results demonstrate accurate encoding of consonants even in words children cannot yet say.
Quadratic correlation filters (QCFs) have been used successfully to detect and recognize targets embedded in background clutter. Recently, a QCF called the Rayleigh quotient quadratic correlation filter (RQQCF) was formulated for automatic targetrecognition (ATR) in IR imagery. Using training images from target and clutter classes, the RQQCF explicitly maximized a class separation metric. What we believe to be a novel approach is presented for ATR that synthesizes the RQQCF using compressed images. The proposed approach considerably reduces the computational complexity and storage requirements while retaining the high recognition accuracy of the original RQQCF technique. The advantages of the proposed scheme are illustrated using sample results obtained from experiments on IR imagery.
Ragothaman, Pradeep; Mikhael, Wasfy B.; Muise, Robert R.; Mahalanobis, Abhijit
FPGAs can be used to build systems for automatic targetrecognition (ATR) that achieve an order of magnitude increase in performance over systems built using general purpose processors. This improvement is possible because the bit-level operations that comprise much of the ATR computational burden map extremely efficiently into FPGAs, and because the specificity of ATR target templates can be leveraged
John Villasenor; Brian Schoner; Kang-Ngee Chia; Charles Zapata; Hea Joung Kim; Chris Jones; Shane Lansing; Bill Mangione-Smith
The fundamental problems of automatic targetrecognition (ATR) are discussed. A new approach to ATR is suggested that includes: a new method of scoring ATR performance, a new concept of artificial images, a new method called probing for extracting target signature knowledge from image experts, and suggestions for coping with the problem of insufficient test data and algorithm obsolescence
A primary strength of the XCS approach is its ability to create maximally accurate general rules. In automatic targetrecognition (ATR) there is a need for robust performance beyond so-called standard operating conditions (SOCs, those conditions for which training data is available) to extended operating conditions (EOCs, conditions of known targets that cannot be foreseen and trained for). EOCs include
The paper presents a application method of detecting moving ground target based on micro accelerometer. Because vehicles moving over ground generate a succession of impacts, the soil disturbances propagate away from the source as seismic waves. Thus, we can detect moving ground vehicles by means of detecting seismic signals using a seismic tranasducer, and automatically classify and recognize them by data fusion method. The detection system on the basis of MEMS technology is small volume, light weight, low poer, low cost and can work under poor circumstances. In order to recognize vehicle targets, seismic properties of typical vehicle targets are researched in the paper. A data fusion technique of artifical neural networks (NAA) is applied to recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed. The improved BP algorithm had been used recognition of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct. ANN data fusion is effective to solve the recognition problem of moving vehicle target, and the micro accelerometer can be used in targetrecognition.
This paper deals with two experiments with a large vocabulary isolated word recognizer. The first compares word error rates for 1) meaningful sentences belonging to actual documents and 2) random word lists from the same vocabulary. The error rate is considerably lower for random word lists. The second experiment investigates the performance of the recognition system on sentences containing words
L. Bahl; S. Das; P. de Souza; F. Jelinek; S. Katz; R. Mercer; M. Picheny
A speech input/output system is presented that can be used to communicate with a task oriented system. Human speech commands and synthesized voice output extend conventional information exchange capabilities between man and machine by utilizing audio input and output channels. The speech input facility is comprised of a hardware feature extractor and a microprocessor implemented isolated word or phrase recognition system. The recognizer offers a medium sized (100 commands), syntactically constrained vocabulary, and exhibits close to real time performance. The major portion of the recognition processing required is accomplished through software, minimizing the complexity of the hardware feature extractor.
Voice recognition software is hardly new--attempts at capturing spoken words and turning them into written text have been available to consumers for about two decades. But what was once an expensive and highly unreliable tool has made great strides in recent years, perhaps most recognized in programs such as Nuance's Dragon NaturallySpeaking…
The author describes the improvements in a time synchronous beam search strategy for a 10000-word continuous speech recognition task. The improvements are based on two measures: a tree-organization of the pronunciation lexicon and a novel look-ahead technique at the phoneme level, both of which interact directly with the detailed search at the state levels of the phoneme models. Experimental tests
Target detection and recognition using polarimetric SAR data has been studied by using PHARUS and RAMSES data collected during the MIMEX campaign. Additionally very high-resolution ISAR data was used. A basic detection and recognition scheme has been developed, which includes polarimetric speckle- filtering, CFAR detection and the extraction of geometrical, intensity and polarimetric features. From the SAR images we conclude that polarimetric features can be useful to discriminate targets from clutter. At resolutions of 1 meter or better, shape and orientation recognition can be obtained with these features. To classify the targets, other features or other techniques have to be used. Examples are polarimetric decomposition techniques, of which two have been explored using the ISAR data.
In this paper, we present an improved approach to recognize human action based on the BOW model and the pLSA model. We propose an improved feature with optical flow to build our bag of words. This feature is able to reduce the high dimension of the pure optical flow template and also has abundant motion information. Then, we use the topic model of pLSA (probabilistic Latent Semantic Analysis) to classify human actions in a special way. We find that the existing methods lead to some mismatching of words due to the k-means clustering approach. To reduce the probability of mismatching, we add the spatial information to each word and improve the training and testing approach. Our approach of recognition is tested on two datasets, the KTH datasets and WEIZMANN datasets. The result shows its good performance.
The present study investigated the electrophysiological correlates of morphological processing in Chinese compound word reading using a delayed repetition priming paradigm. Participants were asked to passively view lists of two-character compound words containing prime-target pairs separated by a few items. In a Whole Word repetition condition, the prime and target were the same real words (e.g., , manager-manager). In a Constituent repetition condition, the prime and target were swapped in terms of their constituent position (e.g., , the former is a pseudo-word and the later means nurse). Two ERP components including N200 and N400 showed repetition effects. The N200 showed a negative shift upon repetition in the Whole Word condition but this effect was delayed for the Constituent condition. The N400 showed comparable amplitude reduction across the two priming conditions. The results reveal different aspects of morphological processing with an early stage associated with N200 and a late stage with N400. There was also a possibility that the N200 effect reflect general cognitive processing, i.e., the detection of low-probability stimuli.
Du, Yingchun; Hu, Weiping; Fang, Zhuo; Zhang, John X.
In this paper we present a system for the detection and recognition of targets in second generation forward looking infrared (FLIR) images. The system uses new algorithms for target detection and segmentation of the targets. Recognition is based on a methodology far targetrecognition by parts. A diffusion based approach for determining the parts of a target is also presented
Children with severe learning difficulties who fail to begin wordrecognition can learn to recognise pictures and symbols relatively easily. However, finding an effective means of using pictures to teach wordrecognition has proved problematic. This research explores the use of morphing software to support the transition from picture to word…
A recognition system for connected digits, which uses a statistical classifier to identify words in speaker-independent continuous speech, is described. The system uses the multiple similarity method, a statistical pattern recognition technique. For evaluating word strings, the system uses a scoring method that is independent of the number of words in the strings. It is derived from the a posteriori
Object recognition relies heavily on invariant visual features such as the manner in which lines meet at vertices to form viewpoint-invariant junctions (e.g. T, L). We wondered whether these features also underlie readers’ competence for fast recognition of printed words. Since reading is far too recent to have exerted any evolutionary pressure on brain evolution, visual wordrecognition might be
Marcin Szwed; Laurent Cohen; Emilie Qiao; Stanislas Dehaene
Adults of varying reading comprehension skill learned a set of previously unknown rare English words (e.g., gloaming) in three different learning conditions in which the type of word knowledge was manipulated. The words were presented in one of three conditions: (1) orthography-to-meaning (no phonology); (2) orthography-to-phonology (no meaning); and (3) phonology-to-meaning (no orthography). Following learning, participants made meaning judgments on the learned words, familiar known words, and unpresented (unlearned) rare words while their ERPs were recorded. The behavioral results showed no significant effects of comprehension skill on meaning judgment performance. Contrastingly, the ERP results indicated comprehension skill differences in P600 amplitude; high-skilled readers showed stronger familiarity effects for learned words, whereas less-skilled readers did not distinguish between learned words, familiar words, and unlearned words. Evidence from the P600 and N400 illustrated superior learning of meaning when meaning information was coupled with orthography rather than phonology. These results suggest that the availability of word knowledge (orthography, phonology, and meaning) at learning affects subsequent word identification processes when the words are encountered in a new context.
Balass, Michal; Nelson, Jessica R.; Perfetti, Charles A.
Although the word-frequency effect is one of the most established findings in spoken-wordrecognition, the precise processing locus of this effect is still a topic of debate. In this study, we used event-related potentials (ERPs) to track the time course of the word-frequency effect. In addition, the neighborhood density effect, which is known to…
Dufour, Sophie; Brunelliere, Angele; Frauenfelder, Ulrich H.
In this activity about neuron/target muscle recognition (page 44 of the PDF), learners arranged in two rows facing away from each other use string to simulate neural development. The lesson guide, part of NASA's "The Brain in Space: A Teacher's Guide with Activities for Neuroscience" includes background information and evaluation strategies.
We present an approach to a general decision support system. The aim is to cover the complete process for automatic targetrecognition, from sensor data to the user interface. The approach is based on a query-based information system, and include tasks like feature extraction from sensor data, data association, data fusion and situation analysis. Currently, we are working with data
Tobias Horney; Jorgen Ahlberg; Christina Gronwall; Martin Folkesson; Karin Silvervarg; Jorgen Fransson; Lena Klasen; Erland Jungert; Fredrik Lantz; Morgan Ulvklo
Automatic targetrecognition (ATR) is an important capability for defense applications. Many aspects of image understanding (IU) research are traditionally used to solve ATR problems. The authors discuss ATR applications and problems in developing real-world ATR systems and present the status of technology for these systems. They identify several IU problems that need to be resolved in order to enhance
Automatic targetrecognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for a scale of interest. The algorithm seeks to identify y(test sample) as a linear combination of the dictionary elements : y=Ax, where A ? Rnxm(n<target. The algorithm can reject clutter and background, which are part of the input image. The detection and recognition problems are solved by finding the sparse-solution to the undetermined system y=Ax via Orthogonal Matching Pursuit (OMP) and l1 minimization techniques. Visible and MWIR imagery collected by the Army Night Vision and Electronic Sensors Directorate (NVESD) was utilized to test the algorithm. Results show an average detection and recognition rates above 95% for targets at ranges up to 3Km for both image modalities.
The paper presents a hybrid neural network system for automatic targetrecognition, or ATR. The ATR system uses a hybrid of a biological inspired neural net called the Pulse Coupled Neural Net, PCNN, and traditional feedforward neural nets. The PCNN is an iterative neural network in which, for example, a grey scale input image results in a 1D time signal
Joakim Waldemark; V. Becanovic; Th. Lindblad; C. S. Lindsey
The use of a neocognitron in an automatic targetrecognition (ATR) system is described. An image is acquired, edge detected, segmented, and centered on a log-spiral grid using subsystems not discussed in the paper. A conformal transformation is used to map the log-spiral grid to a computation plane in which rotations and scalings are transformed to displacements along the vertical
The temporal locus of morphological decomposition in spoken-wordrecognition was explored in three experiments in which French participants detected the initial CV (LA) or CVC (LAV) in matched monomorphemic pseudosuffixed (lavande) and polymorphemic-suffixed (lavage) carrier words. The proportion of foil trials was increased across experiments (0, 50, or 100%) to delay the moment when participants responded. For the experiment without foils and with the fastest reaction times, a similar pattern of results was obtained for the two types of carrier words. In contrast, an interaction between target type and morphological status of the carrier was obtained when the proportion of foils was higher and the detection latencies were slower. These results point to a late processing locus of morphological decomposition. PMID:10433738
An approach to sea targetrecognition using an incoherent marine surveillance radar is examined in this paper. Reasons for reducing the target of recognition to its tonnage class, approximate size and resulting maneuvering abilities are described. An algorithm for automatic targetrecognition that effectively eliminates the negative impact of noise, sea clutter and distance from the target is described. Possibilities
The word-frequency effect (WFE) in recognition memory refers to the finding that more rare words are better recognized than more common words. We demonstrate that a familiarity-discrimination model operating on data from a semantic word-association space yields a robust WFE in data on both hit rates and false-alarm rates. Our modeling results…
Monaco, Joseph D.; Abbott, L. F.; Kahana, Michael J.
Quadratic correlation filters (QCFs) have been used successfully to detect and recognize targets embedded in background clutter. Recently, a QCF called the Rayleigh quotient quadratic correlation filter (RQQCF) was formulated for automatic targetrecognition (ATR) in IR imagery. Using training images from target and clutter classes, the RQQCF explicitly maximized a class separation metric. What we believe to be a novel approach is presented for ATR that synthesizes the RQQCF using compressed images. The proposed approach considerably reduces the computational complexity and storage requirements while retaining the high recognition accuracy of the original RQQCF technique. The advantages of the proposed scheme are illustrated using sample results obtained from experiments on IR imagery. PMID:17609718
Ragothaman, Pradeep; Mikhael, Wasfy B; Muise, Robert R; Mahalanobis, Abhijit
Memory studies utilizing long-term repetition priming have generally demonstrated that priming is greater for low-frequency words than for high-frequency words and that this effect persists if words intervene between the prime and the target. In contrast, word-recognition studies utilizing masked short-term repetition priming typically show that the magnitude of repetition priming does not differ as a function of word frequency and does not persist across intervening words. We conducted an eye-tracking while reading experiment to determine which of these patterns more closely resembles the relationship between frequency and repetition during the natural reading of a text. Frequency was manipulated using proper names that were high-frequency (e.g., Stephen) or low-frequency (e.g., Dominic). The critical name was later repeated in the sentence, or a new name was introduced. First-pass reading times and skipping rates on the critical name revealed robust repetition-by-frequency interactions such that the magnitude of the repetition-priming effect was greater for low-frequency names than for high-frequency names. In contrast, measures of later processing showed effects of repetition that did not depend on lexical frequency. These results are interpreted within a framework that conceptualizes eye-movement control as being influenced in different ways by lexical- and discourse-level factors.
We propose a novel automatic targetrecognition (ATR) system for classification of three types of ground vehicles in the MSTAR public release database. First, each image chip is pre-processed by extracting fine and raw feature sets, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive boosting (AdaBoost) algorithm with the radial basis function (RBF) net as the base learner. Since the RBF net is a binary classifier, we decompose our multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method, specifying a dictionary of code words for the set of three possible classes. AdaBoost combines the classification results of the RBF net for each binary problem into a code word, which is then "decoded" as one of the code words (i.e., ground-vehicle classes) in the specified dictionary. Along with classification, within the AdaBoost framework, we also conduct efficient fusion of the fine and raw image-feature vectors. The results of large-scale experiments demonstrate that our ATR scheme outperforms the state-of-the-art systems reported in the literature.
A method for improving wordrecognition for people with age-related maculopathies, which cause a loss of central vision, is discussed. It is found that the use of individualized compensation filters based on an person's normalized contrast sensitivity function can improve wordrecognition for people with age-related maculopathies. It is shown that 27-70 pct more magnification is needed for unfiltered words compared to filtered words. The improvement in wordrecognition is positively correlated with the severity of vision loss.
Five hyperlexic boys (4-5 to 10-1 years), who had been diagnosed with infantile autism or pervasive developmental delay in early childhood, were evaluated. Measures of intelligence, single-wordrecognition and comprehension, and picture naming were administered to determine the precocity or deficiency of reading recognition and comprehension, the underlying mechanisms of oral reading, and possible parallels with the acquired dyslexia subtypes. The results indicated that hyperlexia may be operationalized as unexpected reading precocity as compared to IQ; however, reading comprehension was not unexpectedly deficient. The phonological route to reading appeared to be preferred to the lexical route, and the overall pattern of performance most closely paralleled that of the surface dyslexic subtype. PMID:3651809
Children's memories for the link between a newly trained word and its referent have been the focus of extensive past research. However, memory for the word form itself is rarely assessed among preschool-age children. When it is, children are typically asked to verbally recall the forms, and they generally perform at floor on such tests. To better measure children's memory for word forms, we aimed to design a more sensitive test that required recognition rather than recall, provided spatial cues to off-set the phonological memory demands of the test, and allowed pointing rather than verbal responses. We taught 12 novel word-referent pairs via ostensive naming to sixteen 4- to 6-year-olds and measured their memory for the word forms after a week-long retention interval using the new spatially supported form recognition test. We also measured their memory for the word-referent links and the generalization of the links to untrained referents with commonly used recognition tests. Children demonstrated memory for word forms at above chance levels; however, their memory for forms was poorer than their memory for trained or generalized word-referent links. When in error, children were no more likely to select a foil that was a close neighbor to the target form than a maximally different foil. Additionally, they more often selected correct forms that were among the first six than the last six to be trained. Overall, these findings suggest that children are able to remember word forms after a limited number of ostensive exposures and a long-term delay. However, word forms remain more difficult to learn than word-referent links and there is an upper limit on the number of forms that can be learned within a given period of time. PMID:24639660
For years researchers have worked toward finding a way to allow people to talk to machines in the same manner a person communicates to another person. This verbal man to machine interface, called speech recognition, can be grouped into three types: isolated wordrecognition, connected wordrecognition, and continuous speech recognition. Isolated word recognizers recognize single words with distinctive pauses before and after them. Continuous speech recognizers recognize speech spoken as one person speaks to another, continuously without pauses. Connected wordrecognition is an extension of isolated wordrecognition which recognizes groups of words spoken continuously. A group of words must have distinctive pauses before and after it, and the number of words in a group is limited to some small value (typically less than six). If these types of recognition systems are to be successful in the real world, they must be speaker independent and support a large vocabulary. They also must be able to recognize the speech input accurately and in real time. Currently there is no system which can meet all of these criteria because a vast amount of computations are needed. This thesis examines the use of parallel processing to reduce the computation time for speech recognition.
This paper summarizes current research into the applications of neural networks for radar ship targetrecognition. Three very different neural architectures are investigated and compared, namely; the feedforward network with backpropagation, Kohonen's (1990) supervised learning vector quantization network, and Simpson's (see IEEE Trans on Neural Networks, vol.3, no.5, p.776-787, 1992) fuzzy min-max neural network. In all cases, preprocessing in the
Automatic targetrecognition (ATR) is a computationally intensive problem that benefits from the abilities of the Connection Machine (CM), a massively parallel computer used for data-level parallel computing. The large computational resources of the CM can efficiently handle an approach to ATR that uses parallel stereo-matching and neural-network algorithms. Such an approach shows promise as an ATR system of satisfactory performance. 13 refs.
Automatic targetrecognition (ATR) technique has been applied in both civil and military. In this paper, we present a new optical pattern recognition system for targetrecognition. This system includes synthetic discriminate function (SDF) based practical optimized filters for the 3-D targets, the Reference Filter Libs for high correlation SNR, the mapping between the input (object regions) and the output
In this paper we have introduce a novel hybrid method framework for feature extraction using genetic algorithms in Automatic TargetRecognition. In this paper we have given a complete framework and approach for making the system of Automatic TargetRecognition System we suggest the name of IVATRs (Intelligent Video Automatic TargetRecognition System). This framework will be helpful for making
Syed Faisal Ali; Jafreezal Jaffar; Aamir Saeed Malik
Using advanced technology, a new automatic targetrecognition (ATR) system has been developed that provides significantly improved targetrecognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in targetrecognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing
Wordrecognition tests primarily use percent correct to measure performance. Additional information may be gained by analyzing awareness of accurate perceptions (AA), awareness of errant perceptions (AE), composite awareness (AC), and awareness symmetry (AS). Awareness measures were derived from subjects' assignment of confidence ratings to a two-item multiple choice response test by designating ``YES'' or ``NO'' that their chosen response is accurate. Each response/confidence rating was categorized as a hit, miss, false alarm, or correct rejection. Awareness equations were: AA=hits/(hits+misses); AE=correct rejections/(correct rejections+false alarms); AC=SQRT (AA squared+AE squared); AS=0.707(AA-AE). Thus, AC is the vector to Cartesian coordinates AA, AE; AS is the distance of this point from a diagonal representing symmetrical awareness. Wordrecognition and awareness was investigated under two signal-to-noise ratios (3 and 6 dB). The Diagnostic Rhyme Test was presented at 50 dBHL to eight normal-hearing adults. Six replicates were obtained. Awareness measures provided additional performance information. Percent correct increased significantly as signal-to-noise ratio improved, but AE decreased and AS did not change significantly.
The ability to recognize spoken words interrupted by silence was investigated with young normal-hearing listeners and older listeners with and without hearing impairment. Targetwords from the revised SPIN test by Bilger et al. [J. Speech Hear. Res. 27(1), 32–48 (1984)] were presented in isolation and in the original sentence context using a range of interruption patterns in which portions of speech were replaced with silence. The number of auditory “glimpses” of speech and the glimpse proportion (total duration glimpsed/word duration) were varied using a subset of the SPIN targetwords that ranged in duration from 300 to 600?ms. The words were presented in isolation, in the context of low-predictability (LP) sentences, and in high-predictability (HP) sentences. The glimpse proportion was found to have a strong influence on wordrecognition, with relatively little influence of the number of glimpses, glimpse duration, or glimpse rate. Although older listeners tended to recognize fewer interrupted words, there was considerable overlap in recognition scores across listener groups in all conditions, and all groups were affected by interruption parameters and context in much the same way.
The word-frequency effect (WFE) in recognition memory refers to the finding that more rare words are better recognized than more common words. We demonstrate that a familiarity-discrimination model operating on data from a semantic word-association space yields a robust WFE in data on both hit rates and false-alarm rates. Our modeling results suggest that word frequency is encoded in the
The moving and stationary targetrecognition (MSTAR) model- based automatic targetrecognition (ATR) system utilizes a paradigm which matches features extracted form an unknown SAR target signature against predictions of those features generated from models of the sensing process and candidate target geometries. The candidate target geometry yielding the best match between predicted and extracted features defines the identify of
Many applications reported in artificial neural networks are associated with military problems. This paper reviews concepts associated with the processing of military data to find and recognize targets -- automatic targetrecognition (ATR). A general-purpose automatic targetrecognition system does not exist. The work presented here is demonstrated on military data, but it can only be considered proof of principle until systems are fielded and proven `under- fire.' ATR data can be in the form of nonimaging one-dimensional sensor returns, such as ultra-high range-resolution radar returns (UHRR) for air-to-air automatic targetrecognition and vibration signatures from a laser radar for recognition of ground targets. The ATR data can be two-dimensional images. The most common ATR images are infrared, but current systems must also deal with synthetic aperture radar (SAR) images. Finally, the data can be three-dimensional, such as sequences of multiple exposures taken over time from a nonstationary world. Targets move, as do sensors, and that movement can be exploited by the ATR. Hyperspectral data, which are views of the same piece of the world looking at different spectral bands, is another example of multiple image data; the third dimension is now wavelength and not time. ATR system design usually consists of four stages. The first stage is to select the sensor or sensors to produce the target measurements. The next stage is the preprocessing of the data and the location of regions of interest within the data (segmentation). The human retina is a ruthless preprocessor. Physiologically motivated preprocessing and segmentation is demonstrated along with supervised and unsupervised artificial neural segmentation techniques. The third design step is feature extraction and selection: the extraction of a set of numbers which characterize regions of the data. The last step is the processing of the features for decision making (classification). The area of classification is where most ATR related neural network research has been accomplished. The relation of neural classifiers to Bayesian techniques is emphasized along with the more recent use of feature sequences to enhance classification.
Rogers, Steven K.; Colombi, John M.; Martin, Curtis E.; Gainey, James C., Jr.; Fielding, Kenneth H.; Burns, Thomas J.; Ruck, Dennis W.; Kabrisky, Matthew; Oxley, Mark E.
WordRecognition is generally difficult and imprecise if we use just one method. In this article, data fusion is applied to wordrecognition by integration of two features extracted form human speech: speech spectrograph and time domain features (spectral coefficients) . Four different methods are applied to fusion of these features, including weighted averaging, k-means clustering, fuzzy k-means and fuzzy
The nature of wordrecognition difficulties in developmental dyslexia is still a topic of controversy. We investigated the contribution of phonological processing deficits and uncertainty to the wordrecognition difficulties of dyslexic children by mathematical diffusion modeling of visual and auditory lexical decision data. The first study showed…
Zeguers, Maaike H. T.; Snellings, Patrick; Tijms, Jurgen; Weeda, Wouter D.; Tamboer, Peter; Bexkens, Anika; Huizenga, Hilde M.
Despite the growing number of studies highlighting the complex process of acquiring second language (L2) wordrecognition skills, comparatively little research has examined the relationship between wordrecognition and passage-level reading ability in L2 learners; further, the existing results are inconclusive. This study aims to help fill the…
Previous research has found that orthographic information can influence auditory wordrecognition. However, there is still debate about the locus of this effect (lexical versus nonlexical, strategic versus automatic). In the present study, we explored whether orthographic effects in auditory wordrecognition could be structural-residual effects…
Effects of phonemic awareness and naming speed on the speed and accuracy of Dutch children's wordrecognition were investigated in a longitudinal study. Both the speed and accuracy of wordrecognition at the end of Grade 2 were predicted by naming speed from both kindergarten and Grade 1, after control for autoregressive relations, kindergarten…
Verhagen, Wim G. M.; Aarnoutse, Cor A. J.; van Leeuwe, Jan F. J.
Influences of phonological awareness and naming speed on the speed and accuracy of Dutch children's wordrecognition were investigated in a longitudinal study. The speed and accuracy of wordrecognition at the ends of Grades 1 and 2 were predicted by naming speed from both the beginning and end of Grade 1, after control for autoregressive…
Empirical work and models of visual wordrecognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates wordrecognition performance. In the present study, we examined differences among individuals who contributed to the English…
Yap, Melvin J.; Balota, David A.; Sibley, Daragh E.; Ratcliff, Roger
An offline recognition system for Arabic handwritten words is presented. The recognition system is based on a semi-continuous 1-dimensional HMM. From each binary word image normalization parameters were estimated. First height, length, and baseline skew are normalized, then fea- tures are collected using a sliding window approach. This paper presents these methods in more detail. Some parame- ters were modified
Many studies in bilingual visual wordrecognition have demonstrated that lexical access is not language selective. However, research on bilingual wordrecognition in the auditory modality has been scarce, and it has yielded mixed results with regard to the degree of this language nonselectivity. In the present study, we investigated whether…
Lagrou, Evelyne; Hartsuiker, Robert J.; Duyck, Wouter
The study of the effects of typographical factors on lexical access has been rather neglected in the literature on visual-wordrecognition. Indeed, current computational models of visual-wordrecognition employ an unrefined letter feature level in their coding schemes. In a letter recognition experiment, Pelli, Burns, Farell, and Moore-Page (2006), letters in Bookman boldface produced more efficiency (i.e., a higher ratio of thresholds of an ideal observer versus a human observer) than the letters in Bookman regular under visual noise. Here we examined whether the effect of bold emphasis can be generalized to a common visual-wordrecognition task (lexical decision: "is the item a word?") under standard viewing conditions. Each stimulus was presented either with or without bold emphasis (e.g., actor vs. actor). To help determine the locus of the effect of bold emphasis, word-frequency (low vs. high) was also manipulated. Results revealed that responses to words in boldface were faster than the responses to the words without emphasis -this advantage was restricted to low-frequency words. Thus, typographical features play a non-negligible role during visual-wordrecognition and, hence, the letter feature level of current models of visual-wordrecognition should be amended. PMID:25012276
Speech processing in adults is continuous: as acoustic-phonetic information is heard, listeners' interpretation of the speech is updated incrementally. The present studies used a visual fixation technique to examine whether young children also interpret speech continuously. In Experiments 1 and 2, 24-month-old children looked at visual displays while hearing sentences. Sentences each contained a targetword labeling one of the
A major issue in the study of word perception concerns the nature (perceptual or nonperceptual) of sentence context effects. The authors compared effects of legal, word replacement, nonword replacement, and transposed contexts on targetword performance using the Reicher-Wheeler task to suppress nonperceptual influences of contextual and lexical constraint. Experiment 1 showed superior targetword performance for legal (e.g., \\
A major issue in the study of word perception concerns the nature (perceptual or nonperceptual) of sentence context effects. The authors compared effects of legal, word replacement, nonword replacement, and transposed contexts on targetword performance using the Reicher–Wheeler task to suppress nonperceptual influences of contextual and lexical constraint. Experiment 1 showed superior targetword performance for legal (e.g., \\
In this paper, a robust automatic targetrecognition algorithm in FLIR imagery is proposed. Target is first segmented out from the background using wavelet transform. Segmentation process is accomplished by parametric Gabor wavelet transformation. Invariant features that belong to the target, which is segmented out from the background, are then extracted via moments. Higher-order moments, while providing better quality for identifying the image, are more sensitive to noise. A trade-off study is then performed on a few moments that provide effective performance. Bayes method is used for classification, using Mahalanobis distance as the Bayes' classifier. Results are assessed based on false alarm rates. The proposed method is shown to be robust against rotations, translations and scale effects. Moreover, it is shown to effectively perform under low-contrast objects in FLIR images. Performance comparisons are also performed on both GPU and CPU. Results indicate that GPU has superior performance over CPU.
The present study examined cortical oxygenation changes during lexical decision on words and pseudowords using functional Near-Infrared Spectroscopy (fNIRS). Focal hyperoxygenation as an indicator of functional activation was compared over three target areas over the left hemisphere. A 52-channel Hitachi ETG-4000 was used covering the superior frontal gyrus (SFG), the left inferior parietal gyrus (IPG) and the left inferior frontal gyrus (IFG). To allow for anatomical inference a recently developed probabilistic mapping method was used to determine the most likely anatomic locations of the changes in cortical activation [Tsuzuki, D., Jurcak, V., Singh, A.K., Okamoto, M., Watanabe, E., Dan, I., 2007. Virtual spatial registration of stand-alone fNIRS data to MNI space. NeuroImage 43 (4), 1506-1518. Subjects made lexical decisions on 50 low and 50 high frequency words and 100 pseudowords. With respect to the lexicality effect, words elicited a larger focal hyperoxygenation in comparison to pseudowords in two regions identified as the SFG and left IPG. The SFG activation difference was interpreted to reflect decision-related mechanisms according to the Multiple Read-Out Model [Grainger, J., Jacobs, A.M., 1996. Orthographic processing in visual wordrecognition: A multiple read-out model. Psychological Review 103, 518-565]. The greater oxygenation response to words in the left IPG suggests that this region connects orthographic, phonological and semantic representations. A decrease of deoxygenated hemoglobin was observed to low frequency in comparison to high frequency words in a region identified as IFG. This region's sensitivity to word frequency suggests its involvement in grapheme-phoneme conversion, or its role during the selection of pre-activated semantic candidates. PMID:18262438
Hofmann, Markus J; Herrmann, Martin J; Dan, Ippeita; Obrig, Hellmuth; Conrad, Markus; Kuchinke, Lars; Jacobs, Arthur M; Fallgatter, Andreas J
A fast method of handwritten wordrecognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match.
Across 3 different wordrecognition tasks, distributional analyses were used to examine the joint effects of stimulus quality and word frequency on underlying response time distributions. Consistent with the extant literature, stimulus quality and word frequency produced additive effects in lexical decision, not only in the means but also in the…
Previous tests of toddlers' phonological knowledge of familiar words using wordrecognition tasks have examined syllable onsets but not word-final consonants (codas). However, there are good reasons to suppose that children's knowledge of coda consonants might be less complete than their knowledge of onset consonants. To test this hypothesis, the…
Data and models about recognition and recall of words and non words are unified using a real-time network processing theory. Lexical decision and word frequency effect data are analyzed in terms of theoretical concepts that have unified data about development of circular reactions, imitation of novel sounds, the matching of phonetic to articulatory requirements, serial and paired associate verbal learning,
The present study investigated whether the balance of neighborhood distribution (i.e., the way orthographic neighbors are spread across letter positions) influences visual wordrecognition. Three word conditions were compared. Word neighbors were either concentrated on one letter position (e.g.,nasse/basse-lasse-tasse-masse) or were unequally…
Robert, Christelle; Mathey, Stephanie; Zagar, Daniel
A pulsed ladar based object-recognition system with applications to automatic targetrecognition (ATR) is presented. The approach used is to fit the sensed range images to range templates extracted through a laser physics based simulation applied to geometric target models. A projection-based prescreener filters out more than 80% of candidate templates. For recognition, an M of N pixel matching scheme
Qinfen Zheng; Sandor Z. Der; Hesham Ibrahim Mahmoud
Numerous methods have been applied in automatic targetrecognition (ATR) systems now, and a lot of factors can impact the system robustness and the recognition ratio. There is always a need for a system structure to adapt different recognition algorithms and provide rapid performance evaluation and comparisons of these algorithms. In this paper, a hierarchical modular structure for automatic target
Rui Song; Hu Ji; Shengping Xia; Weidong Hu; Wenxian Yu
The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user s tendencies while the user is selecting targets and to increase the user s productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.
The Feature Analyst is a computer program for assisted (partially automated) recognition of targets in images. This program was developed to accelerate the processing of high-resolution satellite image data for incorporation into geographic information systems (GIS). This program creates an advanced user interface that embeds proprietary machine-learning algorithms in commercial image-processing and GIS software. A human analyst provides samples of target features from multiple sets of data, then the software develops a data-fusion model that automatically extracts the remaining features from selected sets of data. The program thus leverages the natural ability of humans to recognize objects in complex scenes, without requiring the user to explain the human visual recognition process by means of lengthy software. Two major subprograms are the reactive agent and the thinking agent. The reactive agent strives to quickly learn the user's tendencies while the user is selecting targets and to increase the user's productivity by immediately suggesting the next set of pixels that the user may wish to select. The thinking agent utilizes all available resources, taking as much time as needed, to produce the most accurate autonomous feature-extraction model possible.
With the development of laser technology, Three-Dimensional (3D) imaging sensors based on Laser Radar (LADAR) gradually possess vast application in complicated battlefield of modern warfare. LADAR can detect much more targets than other sensors, such as infrared imaging and radar imaging. Range image and intensity image can be obtained through using LADAR, and they are suitable for automatic targetrecognition. At present the research of automatic targetrecognition technology for LADAR is a hot problem. Main work of this paper is composed of three parts: Firstly, current research and application of automatic targetrecognition technology for LADAR are demonstrated; Secondly, main problems in automatic targetrecognition for LADAR are thoroughly analyzed, including problems in five stages: preprocessing, target detection, feature extraction, founding pattern database and performance evaluation; Finally, a detailed survey is set forth about technical approach of automatic targetrecognition for LADAR, including six components: preprocessing, target detection, feature extraction, recognition, modeling and simulation, performance evaluation.
This paper describes several different types of constraints that can be placed on multilayered feedforward neural networks which are used for automatic targetrecognition (ATR). We show how unconstrained networks are likely to give poor generalization on the ATR problem. We also show how the ATR problem requires a special type of classifier called a one-class classifier. The network constraints come in two forms: architectural constraints and learning constraints. Some of the constraints are used to improve generalization, while others are incorporated so that the network will be forced to perform one-class classification. 14 refs
Hush, D.R.; Clark, Shang-Ying [New Mexico Univ., Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering; Moya, M.M. [Sandia National Labs., Albuquerque, NM (United States)
This paper describes several different types of constraints that can be placed on multilayered feedforward neural networks which are used for automatic targetrecognition (ATR). We show how unconstrained networks are likely to give poor generalization on the ATR problem. We also show how the ATR problem requires a special type of classifier called a one-class classifier. The network constraints come in two forms: architectural constraints and learning constraints. Some of the constraints are used to improve generalization, while others are incorporated so that the network will be forced to perform one-class classification. 14 refs
Hush, D.R.; Clark, Shang-Ying (New Mexico Univ., Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering); Moya, M.M. (Sandia National Labs., Albuquerque, NM (United States))
Automatic TargetRecognition (ATR) is an extremely important capability for defense applications. Many aspects of Image Understanding (IU) research are traditionally used to solve ATR problems. In this paper, the authors discuss ATR applications and problems in developing real-world ATR systems, and present the status of technology for these systems. They identify several IU problems that need to be resolved in order to enhance the effectiveness of ATR-based weapon systems. Finally, they conclude that technological gains in developing robust ATR systems will also lead to significant advances in many other areas of applications of image understanding.
Bhanu, B. (Univ. of California, Riverside, CA (United States)); Jones, T.L. (Center for Night Vision and Electro-Optics, Fort Belvoir, VA (United States))
The government of Bangladesh introduced national ID cards in 2008 for all peoples of age 18 years and above. This card is now a de-facto identity document and finds diverse applications in vote casting, bank account opening, telephone subscribing as well as in many real life transactions and security checking. To get real fruits of this versatile ID card, automated retrieving and recognition of an independent person from this extra large national database is an ultimate necessity. This work is the first step to fill this gap in making the recognition in automated fashion. Here we have investigated an image analysis technique to extract the words that will be used in subsequent recognition steps. At first scanned ID card image is used as an input into the computer system and then the target text region is separated from the picture region. The text region is used for separation of lines and words on the basis of the vertical and horizontal projections of image intensity, respectively. Experimentation using real national ID cards confirms the effectiveness of our technique.
Akhter, Md. Rezwan; Bhuiyan, Md. Hasanuzzaman; Uddin, Mohammad Shorif
Manipulations of either discrete emotions (e.g. happiness) or affective dimensions (e.g. positivity) have a long tradition in emotion research, but interactive effects have never been studied, based on the assumption that the two underlying theories are incompatible. Recent theorizing suggests, however, that the human brain relies on two affective processing systems, one working on the basis of discrete emotion categories, and the other working along affective dimensions. Presenting participants with an orthogonal manipulation of happiness and positivity in a lexical decision task, the present study meant to test the appropriateness of this assumption in emotion wordrecognition. Behavioral and electroencephalographic data revealed independent effects for both variables, with happiness affecting the early visual N1 component, while positivity affected an N400-like component and the late positive complex. These results are interpreted as evidence for a sequential processing of affective information, with discrete emotions being the basis for later dimensional appraisal processes. PMID:24713350
Briesemeister, Benny B; Kuchinke, Lars; Jacobs, Arthur M
In this paper we examine whether the recognition of a spoken noun is affected by the gender marking--masculine or feminine--that is carried by a preceding word. In the first of two experiments, the gating paradigm was used to study the access of French nouns that were preceded by an appropriate gender marking, carried by an article, or preceded by no gender marking. In the second experiment, subjects were asked to make a lexical decision on the same material. A very strong facilitatory effect was found in both cases. The origin of the gender-marking effect is discussed, as well as the level of processing involved--lexical or syntactic. PMID:7991355
Speech perception flexibly adapts to short-term regularities of ambient speech input. Recent research demonstrates that the function of an acoustic dimension for speech categorization at a given time is relative to its relationship to the evolving distribution of dimensional regularity across time, and not simply to a fixed value along the dimension. Two experiments examine the nature of this dimension-based statistical learning in online wordrecognition, testing generalization of learning across phonetic categories. While engaged in a wordrecognition task guided by perceptually unambiguous voice-onset time (VOT) acoustics signaling stop voicing in either bilabial rhymes, beer and pier, or alveolar rhymes, deer and tear, listeners were exposed incidentally to an artificial "accent" deviating from English norms in its correlation of the pitch onset of the following vowel (F0) with VOT (Experiment 1). Exposure to the change in the correlation of F0 with VOT led listeners to down-weight reliance on F0 in voicing categorization, indicating dimension-based statistical learning. This learning was observed only for the "accented" contrast varying in its F0/VOT relationship during exposure; learning did not generalize to the other place of articulation. Another group of listeners experienced competing F0/VOT correlations across place of articulation such that the global correlation for voicing was stable, but locally correlations across voicing pairs were opposing (e.g., "accented" beer and pier, "canonical" deer and tear, Experiment 2). Listeners showed dimension-based learning only for the accented pair, not the canonical pair, indicating that they are able to track separate acoustic statistics across place of articulation, that is, for /b-p/ and /d-t/. This suggests that dimension-based learning does not operate obligatorily at the phonological level of stop voicing. PMID:24364708
The orthographic uniqueness point (OUP) refers to the first letter of a word that, reading from left to right, makes the word unique. It has recently been proposed that OUPs might be relevant in wordrecognition and their influence could inform the long-lasting debate of whether – and to what extent – printed words are recognized serially or in parallel. The present study represents the first investigation of the neural and behavioral effects of OUP on visual wordrecognition. Behaviourally, late OUP words were identified faster and more accurately in a lexical decision task. Analysis of event-related potentials demonstrated a hemispheric asymmetry on the N170 component, with the left hemisphere appearing to be more sensitive to the position of the OUP within a word than the right hemisphere. These results suggest that processing of centrally presented words is likely to occur in a partially parallel manner, as an ends-in scanning process.
Izura, Cristina; Wright, Victoria C.; Fouquet, Nathalie
Biomedical Named Entity Recognition (BNER), which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR) features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements in F-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.
The Choquet fuzzy integral is applied to handwritten wordrecognition. A handwritten wordrecognition system is described. The wordrecognition system assigns a recognition confidence value to each string in a lexicon of candidate strings. The system uses a lexicon-driven approach that integrates segmentation and recognition via dynamic programming matching. The dynamic programming matcher finds a segmentation of the word image for each string in the lexicon. The traditional match score between a segmentation and a string is an average. In this paper, fuzzy integrals are used instead of an average. Experimental results demonstrate the utility of this approach. A surprising result is obtained that indicates a simple choice of fuzzy integral works better than a more complex choice.
Gader, Paul D.; Mohamed, Magdi A.; Keller, James M.
Automatic targetrecognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Targetrecognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.
We describe a general approach for the representation and recognition of 3D objects, as it applies to Automatic TargetRecognition (ATR) tasks. The method is based on locally adaptive target segmentation, biologically motivated image processing and a novel view selection mechanism that develops 'visual filters' responsive to specific target classes to encode the complete viewing sphere with a small number
For many decades attempts to accomplish Automatic TargetRecognition have been made using both visual and FLIR camera systems. A recurring problem in these approaches is the segmentation problem, which is the separation between the target and its background. This paper describes an approach to Automatic TargetRecognition using a laser gated viewing system. Here laser-flash illumination is used in
This paper deals with the processing adopted for shape extraction from the 2D-presentation (image) in radar auto- matic targetrecognition field. The goal is to provide help- ful information to human operator for targetrecognition. However, extracting the target characteristics from a radar echoes is the rather difficult task. Hence, several kinds of radar signatures can be employed to acquire
Abdelmalek Toumi; Brigitte Hoeltzener; Ali Khenchaf
We introduce a novel joint sparse representation based multi-view automatic targetrecognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision. Extensive experiments have been carried out on moving and stationary target
Haichao Zhang; Nasser M. Nasrabadi; Yanning Zhang; Thomas S. Huang
A novel synthetic aperture radar (SAR) automatic targetrecognition (ATR) approach based on Curvelet Transform is proposed. However, the existing approaches can not extract the more effective feature. In this paper, our method is concentrated on a new effective representation of the moving and stationary target acquisition and recognition (MSTAR) database to obtain a more accurate target region and reduce
The authors present a general structure for dynamic information processing systems. Some principle ideas for dynamic processing techniques are emphasized by two practical applications in the radar signal processing field. The first is radar weak target detection under strong sea clutter, and the second is radar ship targetrecognition. The corresponding practical detection system and the ship targetrecognition system
Imagery analysis systems utilize Automatic TargetRecognition (ATR) methods in order to improve the accuracy of human-based analysis and save time. Often, ATR methods perform poorly in obtaining these objectives, due to reliance on outdated prior information, while human operators possess updated information that remains unused. This paper presents an interactive targetrecognition (or ITR) application. The operator marks sample target pixels by an intuitive user-interface. Then machine-learning techniques generate algorithms tailored for their recognition in imagery. The resulting detection map is dynamically controlled by the operator, suiting his needs. The application enables targetrecognition in zero prior information environments.
ATR in two dimensional images is valuable for precision guidance, battlefield awareness and surveillance applications. Current ATR methods are largely data-driven and as a result, their recognition accuracy relies on the quality of training dataset. These methods fail to reliably recognize new target types and targets in new backgrounds and/or atmospheric conditions. Thus, there is a need for an ATR solution that can constantly update itself with information from new data samples (samples may belong to existing classes, background clutter or new target classes). In the paper, this problem is addressed in two steps: 1) Incremental learning with Fully Adaptive Approximate Nearest Neighbor Classifier (FAAN) - A novel data structure is designed to allow incremental learning in approximate nearest neighbor classifier. New data samples are assimilated at reduced complexity and memory without retraining on existing data samples, 2) Data Categorization using Data Effectiveness Measure (DEM) - DEM of a data sample is a degree to which each sample belongs to a local cluster of samples. During incremental learning, DEM is used to filter out redundant samples and outliers, thereby reducing computational complexity and avoiding data imbalance issues. The performance of FAAN is compared with proprietary Bagging-based Incremental Decision Tree (ABAFOR) implementation. Tests performed on Army ATR database with over 37,000 samples shows that while classification accuracy of FAAN is comparable to ABAFOR (both close to 95%), the process of incremental learning is significantly quicker.
Automatic targetrecognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Targetrecognition was implemented based on the proposed pattern
People can discriminate real words from nonwords even when the latter are orthographically and phonologically word-like, presumably because words activate specific lexical and\\/or semantic information. We investigated the neural correlates of this identification process using event-related functional magnetic resonance imaging (fMRI). Participants performed a visual lexical decision task under conditions that encouraged specific word identification: Nonwords were matched to words
J. R. Binder; K. A. McKiernan; M. E. Parsons; C. F. Westbury; E. T. Possing; J. N. Kaufman; L. Buchanan
DARPA\\/Air Force Research Laboratory Moving and Stationary Target Acquisition and Recognition (MSTAR) program is developing state-of-the-art model based vision approach to Synthetic Aperture Radar (SAR) Automatic TargetRecognition (ATR). The model-based approach requires using off-line developed target models in an on-line hypothesize-and-test manner to compare predicted target signatures with image data and output target reports. Central to this model-based ATR
'Application of Wavelets to Automatic TargetRecognition,' is the second phase of multiphase project to insert compactly supported wavelets into an existing or near-term Department of Defense system such as the Longbow fire control radar for the Apache Attack Helicopter. In this contract, we have concentrated mainly on the classifier function. During the first phase of the program ('Application of Wavelets to Radar Data Processing'), the feasibility of using wavelets to process high range resolution profile (HRRP) amplitude returns from a wide bandwidth radar system was demonstrated. This phase obtained fully polarized wide bandwidth radar HRRP amplitude returns and processed, them with wavelet and wavelet packet or (best basis) transforms. Then, by mathematically defined nonlinear feature selection, we showed that significant improvements in the probability of correct classification are possible, up to 14 percentage points maximum (4 percentage points average) improvement when compared to the current classifier performance. In addition, we addressed the feasibility of using wavelet packets' best basis to address target registration, man made object rejection, clutter discriminations, and synthetic aperture radar scene speckle removal and object registration.
We present an automatic targetrecognition system (ATR) for hyperspectral imagery. The system has been designed to use the output from ORASIS (the Optical Real-time Adaptive Spectral Identification System), a hyperspectral analysis package designed at the Naval Research Laboratory. The ATR system is capable of performing both targetrecognition (including subpixel identification) and anomaly detection, in near real-time and with
David Gillis; Peter J. Palmadesso; Jeffrey H. Bowles
JPL is developing a comprehensive Automatic TargetRecognition (ATR) system that consists of an innovative anomaly detection preprocessing module and an automatic training targetrecognition module. The anomaly detection module is trained with an imaging data feature retrieved from an imaging sensor suite that represents the states of the normalcy model. The normalcy model is then trained from a self-organizing
ABSTRACT Infrared imagers used to acquire data for automatic targetrecognition are inherently limited by the physical properties of their components. Fortunately, image super-resolution techniques can be applied to overcome the limits of these imaging systems. This increase in resolution can have potentially dramatic consequences for improved automatic targetrecognition (ATR) on the resultant higher-resolution images. We will discuss superresolution
The present study examined how contextual learning and in particular emotionality conditioning impacts the neural processing of words, as possible key factors for the acquisition of words' emotional connotation. 21 participants learned on five consecutive days associations between meaningless pseudowords and unpleasant or neutral pictures using an evaluative conditioning paradigm. Subsequently, event-related potentials were recorded while participants implicitly processed the learned emotional relevance in a lexical decision paradigm. Emotional and neutral words were presented together with the conditioned pseudowords and a set of new pseudowords. Conditioned and new pseudowords differed in the late positive complex. Emotionally and neutrally conditioned stimuli differed in an early time window (80-120 ms) and in the P300. These results replicate ERP effects known from emotion wordrecognition and indicate that contextual learning and in particular evaluative conditioning is suitable to establish emotional associations in words, and to explain early ERP effects in emotion wordrecognition. PMID:23291494
Transformation invariant automatic targetrecognition (ATR) has been an active research area due to its widespread applications in defense, robotics, medical imaging and geographic scene analysis. The primary goal for this paper is to obtain an on-line ATR system for targets in presence of image transformations, such as rotation, translation, scale and occlusion as well as resolution changes. We investigate biologically inspired adaptive critic design (ACD) neural network (NN) models for on-line learning of such transformations. We further exploit reinforcement learning (RL) in ACD framework to obtain transformation invariant ATR. We exploit two ACD designs, such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) to obtain transformation invariant ATR. We obtain extensive statistical evaluations of proposed on-line ATR networks using both simulated image transformations and real benchmark facial image database, UMIST, with pose variations. Our simulations show promising results for learning transformations in simulated images and authenticating out-of plane rotated face images. Comparing the two on-line ATR designs, HDP outperforms DHP in learning capability and robustness and is more tolerant to noise. The computational time involved in HDP is also less than that of DHP. On the other hand, DHP achieves a 100% success rate more frequently than HDP for individual targets, and the residual critic error in DHP is generally smaller than that of HDP. Mathematical analyses of both our RL-based on-line ATR designs are also obtained to provide a sufficient condition for asymptotic convergence in a statistical average sense. PMID:21571610
Spoken wordrecognition, during gating, appears intact in specific language impairment (SLI). This study used gating to investigate the process in adolescents with autism spectrum disorders plus language impairment (ALI). Adolescents with ALI, SLI, and typical language development (TLD), matched on nonverbal IQ listened to gated words that varied…
Tasks of word reading in Chinese and English; nonverbal IQ; speeded naming; and units of syllable onset (a phoneme measure), syllable, and tone detection awareness were administered to 211 Hong Kong Chinese children ages 4 and 5. In separate regression equations, syllable awareness was equally associated with Chinese and English wordrecognition.…
In this article, we summarize a good portion of the CASL research program on reading in the early grades. We first describe investigations conducted in kindergarten, where our focus was on the development of decoding and wordrecognition. Then we discuss studies conducted in first grade, where we continued to emphasize decoding and word…
Acute alcohol intoxication effects on memory were examined using a recollection-based wordrecognition memory task and a repetition priming task of memory for the same information without explicit reference to the study context. Memory cues were equivalent across tasks; encoding was manipulated by varying the frequency of occurrence (FOC) of words…
Two experiments examined the dynamics of lexical activation in spoken-wordrecognition. In both, the key materials were pairs of onset-matched picturable nouns varying in frequency. Pictures associated with these words, plus two distractor pictures were displayed. A gating task, in which participants identified the picture associated with…
The investigation of visual wordrecognition has been a major accomplishment of cognitive science. Two on-line methodologies, eye movements and event-related potentials, stand out in the search for the holy grail – an absolute time measure of when, how and why we recognize visual words while reading. Although each technique has its own experimental limitations, we suggest, by means of
In recent years, some researchers have proposed that a fundamental component of the wordrecognition process is that each fovea is divided precisely at its vertical midline and that information either side of this midline projects to different, contralateral hemispheres. Thus, when a word is fixated, all letters to the left of the point of…
This paper compares segmentation-based and non-segmentation based techniques for cursive wordrecognition. In our segmentation based technique, every word is segmented into characters, the chain code features are extracted from segmented characters, the features are fed to neural network classifier and finally the words are constructed using a string compare algorithm. In our non-segmentation based technique, the chain code features
Character recognition results are typically post-processed by dictionary look-up methods. Still, the quality of resulting word hypotheses remains lousy. This paper describes and compares three known methods for word-level post-processing of OCRed documents which all are based on purely statistical means of syntactic language modelling. The three methods compared and tested are described and especially their application to word syntax
This paper summarizes techniques proposed for off-line Arabic wordrecognition. This point of view concerns the human reading\\u000a favoring an interactive mechanism between global memorization and local verification sim- plifying the recognition of complex\\u000a scripts such as Arabic. According to this consideration, specific papers are analyzed with comments on strategies.
Present descriptors for Automatic TargetRecognition (ATR) performance are inadequate for use in comparing algorithms that are purported to be a solution to the problem. The use of receiver operator characteristic curves (ROCs) is a defacto standard, but they do not communicate several key performance measures, including (i) intrinsic separation between classes in the input space, (ii) the efficacy of the mapping induced by the algorithm, (iii) the complexity of the algorithmic mapping, and (iv) a measure of the generalization of the proposed solution. Previous work by Sims et. al.2,5 has addressed the distortion of the evaluation sets to indicate an algorithm's capability (or lack thereof) for generalization and handling of unspecified cases. This paper addresses the rethinking of the summary statistics used for understanding the performance of a solution. We propose new approaches for solution characterization, allowing algorithm performance comparison in an equitable and insightful manner. This paper proffers some examples and suggests directions for new work from the community in this field.
Waagen, Donald; Hester, Charles; Schmid, Ben; Phillips, Margaret; Thompson, M. Shane; Vanstone, Steven; Risko, Kelly
An automatic targetrecognition apparatus (10) is provided, having a video camera/digitizer (12) for producing a digitized image signal (20) representing an image containing therein objects which objects are to be recognized if they meet predefined criteria. The digitized image signal (20) is processed within a video analysis subroutine (22) residing in a computer (14) in a plurality of parallel analysis chains such that the objects are presumed to be lighter in shading than the background in the image in three of the chains and further such that the objects are presumed to be darker than the background in the other three chains. In two of the chains the objects are defined by surface texture analysis using texture filter operations. In another two of the chains the objects are defined by background subtraction operations. In yet another two of the chains the objects are defined by edge enhancement processes. In each of the analysis chains a calculation operation independently determines an error factor relating to the probability that the objects are of the type which should be recognized, and a probability calculation operation combines the results of the analysis chains.
Baumgart, Chris W. (Santa Fe, NM); Ciarcia, Christopher A. (Los Alamos, NM)
In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The wordrecognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.
Kumar, Deepak; Anil Prasad, M. N.; Ramakrishnan, A. G.
What makes some words easy for infants to recognize, and other words difficult? We addressed this issue in the context of prior results suggesting that infants have difficulty recognizing verbs relative to nouns. In this work, we highlight the role played by the distributional contexts in which nouns and verbs occur. Distributional statistics predict that English nouns should generally be easier to recognize than verbs in fluent speech. However, there are situations in which distributional statistics provide similar support for verbs. The statistics for verbs that occur with the English morpheme -ing, for example, should facilitate verb recognition. In two experiments with 7.5- and 9.5-month-old infants, we tested the importance of distributional statistics for wordrecognition by varying the frequency of the contextual frames in which verbs occur. The results support the conclusion that distributional statistics are utilized by infant language learners and contribute to noun-verb differences in wordrecognition. PMID:24908342
Willits, Jon A; Seidenberg, Mark S; Saffran, Jenny R
Handwritten wordrecognition is a difficult problem. In the standard segmentation-based approach to handwritten wordrecognition, individual character class confidence scores are combined to estimate confidences concerning the various hypothesized identities for a word. The standard combination method is the mean. Previously, we demonstrated that the Choquet integral provided higher recognition rates than the mean. Our previous work with the Choquet integral relied on a restricted class of measures. For this class of measures, operators based on the Choquet integral are equivalent to a subset of a class of operators known as linear combinations of order statistics. In this paper, we extend our previous work to find the optimal LOS operator for combining character class confidence scores. Experimental results are provided on about 1300 word images.
Robust real-time recognition of multiple targets with varying pose requires heavy computational loads, which are often too demanding to be performed online at the sensor location. Thus an important problem is the performance of ATR algorithms on highly-compressed video sequences transmitted to a remote facility. We investigate the effects of H.264 video compression on correlation-based recognition algorithms. Our primary test bed is a collection of fifty video sequences consisting of long-wave infrared (LWIR) and mid-wave infrared (MWIR) imagery of ground targets. The targets are viewed from an aerial vehicle approaching the target, which introduces large amounts of scale distortion across a single sequence. Each sequence is stored at seven different levels of compression, including the uncompressed version. We employ two different types of correlation filters to perform frame-by-frame targetrecognition: optimal tradeoff synthetic discriminant function (OTSDF) filters and a new scale-tolerant filter called fractional power Mellin radial harmonic (FPMRH) filters. In addition, we apply the Fisher metric to compressed target images to evaluate target class separability and to estimate recognition performance as a function of video compression rate. Targets are centered and cropped according to ground truth data prior to separability analysis. We compare our separability estimates with the actual recognition rates achieved by the best correlation filter for each sequence. Numerical results are provided for several targetrecognition examples.
Kerekes, Ryan A.; Vijaya Kumar, B. V. K.; Sims, S. Richard F.
This study examined effects of lexical factors on children's spoken wordrecognition across a 1-year time span, and contributions to phonological awareness and nonword repetition. Across the year, children identified words based on less input on a speech-gating task. For word repetition, older children improved for the most familiar words. There…
Metsala, Jamie L.; Stavrinos, Despina; Walley, Amanda C.
We examined the effects of sensorimotor experience in two visual wordrecognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., "mask") and a set of low BOI…
Siakaluk, Paul D.; Pexman, Penny M.; Aguilera, Laura; Owen, William J.; Sears, Christopher R.
In a series of experiments, the authors investigated the effects of talker variability on children’s wordrecognition. In Experiment 1, when stimuli were presented in the clear, 3- and 5-year-olds were less accurate at identifying words spoken by multiple talkers than those spoken by a single talker when the multiple-talker list was presented first. In Experiment 2, when words were presented in noise, 3-, 4-, and 5-year-olds again performed worse in the multiple-talker condition than in the single-talker condition, this time regardless of order; processing multiple talkers became easier with age. Experiment 3 showed that both children and adults were slower to repeat words from multiple-talker than those from single-talker lists. More important, children (but not adults) matched acoustic properties of the stimuli (specifically, duration). These results provide important new information about the development of talker normalization in speech perception and spoken wordrecognition.
Komaki and Akahane-Yamada (Proc. ICA2004) used 2AFC translation task in vocabulary training, in which the targetword is presented visually in orthographic form of one language, and the appropriate meaning in another language has to be chosen between two choices. Present paper examined the effect of audio-visual presentation of targetword when native speakers of Japanese learn to translate English words into Japanese. Pairs of English words contrasted in several phonemic distinctions (e.g., /r/-/l/, /b/-/v/, etc.) were used as word materials, and presented in three conditions; visual-only (V), audio-only (A), and audio-visual (AV) presentations. Identification accuracy of those words produced by two talkers was also assessed. During pretest, the accuracy for A stimuli was lowest, implying that insufficient translation ability and listening ability interact with each other when aurally presented word has to be translated. However, there was no difference in accuracy between V and AV stimuli, suggesting that participants translate the words depending on visual information only. The effect of translation training using AV stimuli did not transfer to identification ability, showing that additional audio information during translation does not help improve speech perception. Further examination is necessary to determine the effective L2 training method. [Work supported by TAO, Japan.
The k-nearest neighbour (KNN) rule using Euclidean distance is actually the same as template matching method under the maximum correlation coefficient criterion (MCC-TIMM), which has been widely used in high resolution rang profiles (HRRPs) based radar automatic targetrecognition (RATR). The nearest neighbor rule treats each training sample equally without consideration of different recognition performances due to its congregation around
In this paper, we introduce a novel joint sparse representation based automatic targetrecognition (ATR) method using multiple views, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views for a single joint recognition decision. We cast the problem as a multi-variate regression model and recover
Haichao Zhang; Nasser M. Nasrabadi; Thomas S. Huang; Yanning Zhang
Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. A real application of SVMs for synthetic aperture radar automatic targetrecognition (SAR\\/ATR) is presented and the result is compared with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition).
We present a new method to recognize activity patterns from video acquired by a camera mounted on the target (i.e., activity performer). Because of this unconventional camera setting, algorithms for activity recognition must be redesigned because the activity performer never appears in the video. We approach this recognition problem indirectly by observing background changes in the acquired image sequences. A
Lu Li; Hong Zhang; Wenyan Jia; Zhi-Hong Mao; Yuhu You; Mingui Sun
Empirical work and models of visual wordrecognition have traditionally focused on group-level performance. Despite the emphasis on the prototypical reader, there is clear evidence that variation in reading skill modulates wordrecognition performance. In the present study, we examined differences among individuals who contributed to the English Lexicon Project (http://elexicon.wustl.edu), an online behavioral database containing nearly 4 million wordrecognition (speeded pronunciation and lexical decision) trials from over 1,200 participants. We observed considerable within- and between-session reliability across distinct sets of items, in terms of overall mean response time (RT), RT distributional characteristics, diffusion model parameters (Ratcliff, Gomez, & McKoon, 2004), and sensitivity to underlying lexical dimensions. This indicates reliably detectable individual differences in wordrecognition performance. In addition, higher vocabulary knowledge was associated with faster, more accurate wordrecognition performance, attenuated sensitivity to stimuli characteristics, and more efficient accumulation of information. Finally, in contrast to suggestions in the literature, we did not find evidence that individuals were trading-off their utilization of lexical and nonlexical information. PMID:21728459
Yap, Melvin J; Balota, David A; Sibley, Daragh E; Ratcliff, Roger
Specificity effects in spoken wordrecognition were previously examined by examining the circumstances under which variability in speaking rate affects participants perception of spoken words. The wordrecognition and memory literatures are now replete with demonstrations that variability has representational and processing consequences. The research focuses on one of the conditions expected to influence the extent to which variability plays a role in spoken wordrecognition, namely time course of processing. Based on previous work, it was hypothesized that speaking rate variability would only affect later stages of spoken wordrecognition. The results confirmed this hypothesis: Specificity effects were only obtained when processing was relatively slow. However, previous stimuli not only differed in speaking rate, but also in articulation style (i.e., casual and careful). Therefore, in the current set of experiments, it was sought to determine whether the same pattern of results would be obtained with stimuli that only differed in speaking rate (i.e., in the absence of articulation style differences). Moreover, to further generalize time course findings, the stimuli were produced by a different speaker than the speaker in the earlier study. The results add to the knowledge of the circumstances under which variability affects the perception of spoken words.
This paper proposes a radar targetrecognition algorithm based on a feature set extracted from the target characteristic polarization states (CPS) and evaluated at a set of target resonant frequencies in the frequency domain. The algorithm involves measuring the proximity between training (stored) prototypes and a test prototype. For this task, the algorithm implements the nearest neighbour (NN) algorithm at
Automated targetrecognition software has been designed to perform image segmentation and scene analysis. Specifically, this software was developed as a package for the Army's Minefield and Reconnaissance and Detector (MIRADOR) program. MIRADOR is an on/o...
With the advantages of stealthiness, all weather effectiveness, visible targetrecognition and long affect distance, infrared thermal imaging system play important role in scouting, aiming and tracking. In order to eliminate influences of thermal camouflage to traditional intensity infrared thermal imaging system, we proposed design method of ARM based infrared camouflage targetrecognition system. Considering the measurement of Stokes parameters, we analyzed design method of polarized image acquisition module, designed ARM core board and its data connection with other devices, adopted LCD to display polarization image computed out by ARM. We also studied embedded Linux platform and polarized image processing program based on this platform, finally actualized the design method of ARM based infrared camouflage targetrecognition system. Results of our experiment show that data stream can be successfully transmitted between modules of the system and the platform we used is fast enough to run polarized image processing program. It's an effective method of using ARM to actualize infrared camouflage targetrecognition system.
JPL is developing a comprehensive Automatic TargetRecognition (ATR) system that consists of an innovative anomaly detection preprocessing module and an automatic training targetrecognition module. The anomaly detection module is trained with an imaging data feature retrieved from an imaging sensor suite that represents the states of the normalcy model. The normalcy model is then trained from a self-organizing learning system over a period of time and fed into the anomaly detection module for scene anomaly monitoring and detection. The "abnormal" event detection will be sent to a human operator for further investigation responses. The targetrecognition will be continuously updated with the "normal' input sensor data. The combination of the anomaly detection preprocessing module to the re-trainable targetrecognition processor will result in a dynamic ATR system that is capable of automatic detection of anomaly event and provide an early warning to a human operator for in-time warning and response.
There is increasing evidence that orthographic information has an impact on spoken word processing. However, much of this evidence comes from tasks that are subject to strategic effects. In the three experiments reported here, we examined activation of orthographic information during spoken word processing within a paradigm that is unlikely to…
There is considerable interest in how individuals process single words when they are heard. An examination of the literature reveals an interesting, yet little-explored contradiction between the assumptions underlying the neighborhood activation model (NAM) and contemporary models of word retrieval. In this study, participants listened to CVC…
The utility of target shadows for automatic targetrecognition (ATR) in synthetic aperture radar (SAR) imagery is investigated. Although target shadow, when available, is not a powerful target discriminating feature, it can effectively increase the overall accuracy of the target classification when it is combined with other target discriminating features such as peaks, edges, and corners. A second and more
Synthetic Aperture Radar (SAR) imaging and Automatic TargetRecognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has recently received increased interest. High Range Resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise)
Tulving and Thomson recently contradicted the generation-recognition theory of recall with the phrase 'recognition failure of recallable words.' This phrase, however, is not an admissable summary of their experiments. The word 'light' in the cue-target pa...
This paper presents visual detection and recognition of flying targets (e.g. planes, missiles) based on automatically extracted shape and object texture information, for application areas like alerting, recognition and tracking. Targets are extracted based on robust background modeling and a novel contour extraction approach, and object recognition is done by comparisons to shape and texture based query results on a previously gathered real life object dataset. Application areas involve passive defense scenarios, including automatic object detection and tracking with cheap commodity hardware components (CPU, camera and GPS).
Kovács, Levente; Utasi, Ákos; Kovács, Andrea; Szirányi, Tamás
Recognition memory studies often find that emotional items are more likely than neutral items to be labelled as studied. Previous work suggests this bias is driven by increased memory strength/familiarity for emotional items. We explored strength and bias interpretations of this effect with the conjecture that emotional stimuli might seem more familiar because they share features with studied items from the same category. Categorical effects were manipulated in a recognition task by presenting lists with a small, medium or large proportion of emotional words. The liberal memory bias for emotional words was only observed when a medium or large proportion of categorised words were presented in the lists. Similar, though weaker, effects were observed with categorised words that were not emotional (animal names). These results suggest that liberal memory bias for emotional items may be largely driven by effects of category membership. PMID:24303902
White, Corey N; Kapucu, Aycan; Bruno, Davide; Rotello, Caren M; Ratcliff, Roger
In this paper, we introduce a novel joint sparse representation based automatic targetrecognition (ATR) method using multiple views, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views for a single joint recognition decision. We cast the problem as a multi-variate regression model and recover the sparse representations for the multiple views simultaneously. The recognition is accomplished via classifying the target to the class which gives the minimum total reconstruction error accumulated across all the views. Extensive experiments have been carried out on Moving and Stationary Target Acquisition and Recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear Support Vector Machine (SVM), kernel SVM as well as a sparse representation based classifier. Experimental results demonstrate that the effectiveness as well as robustness of the proposed joint sparse representation ATR method.
Zhang, Haichao; Nasrabadi, Nasser M.; Huang, Thomas S.; Zhang, Yanning
Speech remains intelligible despite the elimination of canonical acoustic correlates of phonemes from the spectrum. A portion of this perceptual flexibility can be attributed to modulation sensitivity in the auditory-to-phonetic projection, though signal-independent properties of lexical neighborhoods also affect intelligibility in utterances composed of words. Three tests were conducted to estimate the effects of exposure to natural and sine-wave samples of speech in this kind of perceptual versatility. First, sine-wave versions of the easy/hard word sets were created, modeled on the speech samples of a single talker. The performance difference in recognition of easy and hard words was used to index the perceptual reliance on signal-independent properties of lexical contrasts. Second, several kinds of exposure produced familiarity with an aspect of sine-wave speech: 1) sine-wave sentences modeled on the same talker; 2) sine-wave sentences modeled on a different talker, to create familiarity with a sine-wave carrier; and 3) natural sentences spoken by the same talker, to create familiarity with the idiolect expressed in the sine-wave words. Recognition performance with both easy and hard sine-wave words improved after exposure only to sine-wave sentences modeled on the same talker. Third, a control test showed that signal-independent uncertainty is a plausible cause of differences in recognition of easy and hard sine-wave words. The conditions of beneficial exposure reveal the specificity of attention underlying versatility in speech perception.
Remez, Robert E.; Dubowski, Kathryn R.; Broder, Robin S.; Davids, Morgana L.; Grossman, Yael S.; Moskalenko, Marina; Pardo, Jennifer S.; Hasbun, Sara Maria
Model-based Automatic TargetRecognition (ATR) algorithms are adept at recognizing targets in high fidelity 3D LADAR imagery. Most current approaches involve a matching component where a hypothesized target and target pose are iteratively aligned to pre-segmented range data. Once the model-to-data alignment has converged, a match score is generated indicating the quality of match. This score is then used to
Magnús S. Snorrason; Thom R. Goodsell; Camille R. Monnier; Mark R. Stevens
MIT Lincoln Laboratory is responsible for developing the ATR (automatic targetrecognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper presents ATR performance results for 10- and 20-target MSE classifiers using high-resolution SAR (synthetic
Recent evidence demonstrates that American Sign Language (ASL) signs are active during print wordrecognition in deaf bilinguals who are highly proficient in both ASL and English. In the present study, we investigate whether signs are active during print wordrecognition in two groups of unbalanced bilinguals: deaf ASL-dominant and hearing…
The relationship between word and stress pattern recognition ability and hearing level was explored by administering the Children's Auditory Test to hearing-impaired young adults (N=27). For wordrecognition, subjects with average hearing loss between 85 and 100 decibels demonstrated a wide range of performance not predictable from their…
The patterns of wordrecognition errors among native and nonnative speakers of English in adult basic education classes were compared in a study that focused on the 212 of the 676 learners in the Adult Reading Components Study who scored between grade equivalent (GE) 4 and 6 in wordrecognition. Key findings were as follows: (1) highly similar…
Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in targetrecognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of targetrecognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.
We describe a general approach for the representation and recognition of 3D objects, as it applies to Automatic TargetRecognition (ATR) tasks. The method is based on locally adaptive target segmentation, biologically motivated image processing and a novel view selection mechanism that develops 'visual filters' responsive to specific target classes to encode the complete viewing sphere with a small number of prototypical examples. The optimal set of visual filters is found via a cross-validation-like data reduction algorithm used to train banks of back propagation (BP) neural networks. Experimental results on synthetic and real-world imagery demonstrate the feasibility of our approach.
We present a method for deriving an automatic targetrecognition (ATR) system using geospatial features and a Data Model populated decision architecture in the form of a self-organizing knowledge base. The goal is to derive an ATR that recognizes targets it has seen before while minimizing false alarms (zero false alarms). We present an investigation of the performance of analytical Data Models as a sensor and data fusion process for automatic targetrecognition (ATR), and summarize results including on a 2 km background run where no false alarms were encountered.
With the development of laser technology, Three-Dimensional (3D) imaging sensors based on Laser Radar (LADAR) gradually possess vast application in complicated battlefield of modern warfare. LADAR can detect much more targets than other sensors, such as infrared imaging and radar imaging. Range image and intensity image can be obtained through using LADAR, and they are suitable for automatic targetrecognition.
Xiao-Qing Chen; Jun-Guo Ma; Hong-Zhong Zhao; Qiang Fu
The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic targetrecognition systems using traditional imagery in a tactical battlefield environment. Linear
James Geurts; Dennis W. Ruck; Steven K. Rogers; Mark E. Oxley; Dallas Barr
In modern day warfare, reconnaissance operations such as automatic targetrecognition(ATR) using unmanned aerial vehicles(UAVs) constitute a strategic war tactic. Traditionally, ATR is performed by UAVs that fly within the reconnaissance area to collect image data through sensors and upload the data to a central base station for analyzing and identifying potential targets. The centralized approach to ATR introduces several
We propose a novel automatic targetrecognition (ATR) system for classification of three types of ground vehicles in the MSTAR public release database. First, each image chip is pre-processed by extracting fine and raw feature sets, where raw features compensate for the target pose estimation error that corrupts fine image features. Then, the chips are classified by using the adaptive
The past decade has witnessed rapid development in accurate modeling of 3Dtargets and multiple sensor fusion in automatic targetrecognition (ATR), however,the scientific study for quantifying non-target objects in a cluttered scene has madevery limited progress, due to its enormous difficulties. In this paper, we study twoimportant themes in ATR: I) clutter modeling -- how can we build generic and
We present a method for deriving an automatic targetrecognition (ATR) system using geospatial features and a Data Model populated decision architecture in the form of a self-organizing knowledge base. The goal is to derive an ATR that recognizes targets it has seen before while minimizing false alarms (zero false alarms). We present an investigation of the performance of analytical
Holger Jaenisch; James Handley; Nathaniel Albritton; John Koegler; Steven Murray; Willie Maddox; Stephen Moren; Tom Alexander; William Fieselman; Robert Caspers
A new algorithm is presented for automatic targetrecognition (ATR) where the templates are obtained via singular value decomposition (SVD) of high range resolution (HRR) profiles. SVD analysis of a large class of HRR data reveals that the range-space eigenvectors corresponding to the largest singular value accounts for more than 90% of the target energy. Hence, it is proposed that
This paper develops a Bayesian gamma mixture model approach to automatic targetrecognition (ATR). The specific problem considered is the classification of radar range profiles (RRPs) of military ships. However, the approach developed is relevant to the generic discrimination problem. We model the radar returns (data measurements) from each target as a gamma mixture distribution. Several different motivations for the
Human target identification performance based on target silhouettes is measured and compared to that of complete targets. The target silhouette identification performance of automated region based and contour based shape identification algorithms are also compared. The region based algorithms of interest are Zernike Moment Descriptor (ZMD), Geometric Moment Descriptor (GMD), and Grid Descriptor (GD) while the contour based algorithms considered are Fourier Descriptor (FD), Multiscale Fourier Descriptor (MFD), and Curvature Scale Space Descriptor (CS). The results from the human perception experiments indicate that at high levels of degradation, human identification of target based on silhouettes is better than that of complete targets. The shape recognition algorithm comparison shows that GD performs best, very closely followed by ZMD. In general region based shape algorithms perform better that contour based shape algorithms.
Chari, Srikant K.; Halford, Carl E.; Jacobs, Eddie
The boundary paradigm, in combination with parafoveal masks, is the main technique for studying parafoveal preprocessing during reading. The rationale is that the masks (e.g., strings of X's) prevent parafoveal preprocessing, but do not interfere with foveal processing. A recent study, however, raised doubts about the neutrality of parafoveal masks. In the present study, we explored this issue by means of fixation-related brain potentials (FRPs). Two FRP conditions presented rows of five words. The task of the participant was to judge whether the final word of a list was a "new" word, or whether it was a repeated (i.e., "old") word. The critical manipulation was that the final word was X-masked during parafoveal preview in one condition, whereas another condition presented a valid preview of the word. In two additional event-related brain potential (ERP) conditions, the words were presented serially with no parafoveal preview available; in one of the conditions with a fixed timing, in the other word presentation was self-paced by the participants. Expectedly, the valid-preview FRP condition elicited the shortest processing times. Processing times did not differ between the two ERP conditions indicating that "cognitive readiness" during self-paced processing can be ruled out as an alternative explanation for differences in processing times between the ERP and the FRP conditions. The longest processing times were found in the X-mask FRP condition indicating that parafoveal X-masks interfere with foveal wordrecognition. PMID:23888130
The boundary paradigm, in combination with parafoveal masks, is the main technique for studying parafoveal preprocessing during reading. The rationale is that the masks (e.g., strings of X's) prevent parafoveal preprocessing, but do not interfere with foveal processing. A recent study, however, raised doubts about the neutrality of parafoveal masks. In the present study, we explored this issue by means of fixation-related brain potentials (FRPs). Two FRP conditions presented rows of five words. The task of the participant was to judge whether the final word of a list was a “new” word, or whether it was a repeated (i.e., “old”) word. The critical manipulation was that the final word was X-masked during parafoveal preview in one condition, whereas another condition presented a valid preview of the word. In two additional event-related brain potential (ERP) conditions, the words were presented serially with no parafoveal preview available; in one of the conditions with a fixed timing, in the other word presentation was self-paced by the participants. Expectedly, the valid-preview FRP condition elicited the shortest processing times. Processing times did not differ between the two ERP conditions indicating that “cognitive readiness” during self-paced processing can be ruled out as an alternative explanation for differences in processing times between the ERP and the FRP conditions. The longest processing times were found in the X-mask FRP condition indicating that parafoveal X-masks interfere with foveal wordrecognition.
The present study addressed the issue of syllable activation during visual recognition of French words. In addition, it was investigated whether word orthographic information underlies syllable effects. To do so, words were selected according to the frequency of their first syllable (high versus low) and the frequency of the orthographic…
In this paper, we propose a new supervised feature extraction algorithm in synthetic aperture radar automatic targetrecognition (SAR ATR), called generalized neighbor discriminant embedding (GNDE). Based on manifold learning, GNDE integrates class and neighborhood information to enhance discriminative power of extracted feature. Besides, the kernelized counterpart of this algorithm is also proposed, called kernel-GNDE (KGNDE). The experiment in this paper shows that the proposed algorithms have better recognition performance than PCA and KPCA.
Recent research on bilingualism has shown that lexical access in visual wordrecognition by bilinguals is not selective with respect to language. In the present study, the authors investigated language-independent lexical access in bilinguals reading sentences, which constitutes a strong unilingual linguistic context. In the first experiment,…
Duyck, Wouter; Van Assche, Eva; Drieghe, Denis; Hartsuiker, Robert J.
The paper opens with an evaluation of the BIA model of bilingual wordrecognition in the light of recent empirical evidence. After pointing out problems and omissions, a new model, called the BIA+, is proposed. Structurally, this new model extends the old one by adding phonological and semantic lexical representations to the available orthographic ones, and assigns a different role
The purpose of this article is to describe a strategy called the Poetry Academy used to boost reading skills in elementary school students. The Poetry Academy paired struggling readers with a community volunteer to read poetry on a weekly schedule to practice fluency, work on wordrecognition abilities, and build confidence. A research study took…
This study investigated knowledge of letter names and letter sounds, their learning, and their contributions to wordrecognition. Of 123 preschoolers examined on letter knowledge, 65 underwent training on both letter names and letter sounds in a counterbalanced order. Prior to training, children were more advanced in associating letters with their…
Investigates whether readers of Turkish (which has a simple relation between spelling and sound) depend more on decoding for wordrecognition than readers of English (which has an "opaque" orthography). Suggests that readers become less dependent on phonological mediation with experience and that this reduction is more rapid for readers of opaque…
This article describes the Dual Route Cascaded (DRC) model, a computational model of visual wordrecognition and reading aloud. The DRC is a computational realization of the dual-route theory of reading, and is the only computational model of reading that can perform the 2 tasks most commonly used to study reading: lexical decision and reading aloud. For both tasks, the
Max Coltheart; Kathleen Rastle; Conrad Perry; Robyn Langdon; Johannes Ziegler
A normative study was conducted using the Deese/Roediger-McDermott paradigm (DRM) to obtain false recognition for 60 six-word lists in Spanish, designed with a completely new methodology. For the first time, lists included words (e.g., bridal, newlyweds, bond, commitment, couple, to marry) simultaneously associated with three critical words (e.g., love, wedding, marriage). Backward associative strength between lists and critical words was taken into account when creating the lists. The results showed that all lists produced false recognition. Moreover, some lists had a high false recognition rate (e.g., 65%; jail, inmate, prison: bars, prisoner, cell, offender, penitentiary, imprisonment). This is an aspect of special interest for those DRM experiments that, for example, record brain electrical activity. This type of list will enable researchers to raise the signal-to-noise ratio in false recognition event-related potential studies as they increase the number of critical trials per list, and it will be especially useful for the design of future research. PMID:21298572
Two experiments explore the activation of semantic information during spoken wordrecognition. Experiment 1 shows that as the name of an object unfolds (e.g., lock), eye movements are drawn to pictorial representations of both the named object and semantically related objects (e.g., key). Experiment 2 shows that objects semantically related to an…
Purpose: Phonological activation during visual wordrecognition was studied in deaf and hearing children under two circumstances: (a) when the use of phonology was not required for task performance and might even hinder it and (b) when the use of phonology was critical for task performance. Method: Deaf children mastering written Dutch and Sign…
This study compared visual wordrecognition (speechreading) in video sequences showing either full face or lips plus mandible to 26 normal hearing college students and 4 adults with bilateral sensorineural hearing loss. Percent phoneme correct scores were similar in the two conditions and scores significantly improved for the repeated measure in…
Malay is a consistent alphabetic orthography with complex syllable structures. The focus of this research was to investigate wordrecognition performance in order to inform reading interventions for low-progress early readers. Forty-six Grade 1 students were sampled and 11 were identified as low-progress readers. The results indicated that both…
The role of morphological, semantic, and form-based factors in the early stages of visual wordrecognition was investigated across different SOAs in a masked priming paradigm, focusing on English derivational morphology. In a first set of experiments, stimulus pairs co-varying in morphological decomposability and in semantic and orthographic…
Marslen-Wilson, William D.; Bozic, Mirjana; Randall, Billi
While studies of college-level readers have yielded evidence both for and against the use of phonological or speech recoding in the recognition of written words, no consistent picture of when recoding occurs has yet emerged. However, one model, the adjunct access model, can account for the previous research findings. According to this model,…
Although previous research has established that multiple top-down factors guide the identification of words during speech processing, the ultimate range of information sources that listeners integrate from different levels of linguistic structure is still unknown. In a set of experiments, we investigate whether comprehenders can integrate…
Automatic TargetRecognition (ATR) of moving targets has recently received increased interests. High Range Resolution (HRR) radar mode provides a promising approach which relies on processing high-resolution 'range profiles' over multiple look angles. To achieve a robust, reliable and cost effective approach for HRR-ATR, a model-based approach is investigated in this paper. A subset of the Moving and Stationary Target
A visual semantic categorization task in English was performed by native English speakers (Experiment 1) and late bilinguals whose first language was Japanese (Experiment 2) or Spanish (Experiment 3). In the critical conditions, the targetword was a homophone of a correct category exemplar (e.g., A BODY OF WATER-SEE; cf. SEA) or a word that…
Ota, Mitsuhiko; Hartsuiker, Robert J.; Haywood, Sarah L.
Emotional tone of voice (ETV) is essential for optimal verbal communication. Research has found that the impact of variation in nonlinguistic features of speech on spoken wordrecognition differs according to a time course. In the current study, we investigated whether intratalker variation in ETV follows the same time course in two long-term repetition priming experiments. We found that intratalker variability in ETVs affected reaction times to spoken words only when processing was relatively slow and difficult, not when processing was relatively fast and easy. These results provide evidence for the use of both abstract and episodic lexical representations for processing within-talker variability in ETV, depending on the time course of spoken wordrecognition. PMID:23405913
The importance of vocabulary in reading comprehension emphasizes the need to accurately assess an individual's familiarity with words. The present article highlights problems with using occurrence counts in corpora as an index of word familiarity, especially when studying individuals varying in reading experience. We demonstrate via computational…
The goal of this dissertation is to examine how brain regions respond to different types of competition during word comprehension and word production. I will present three studies that attempt to enhance the current understanding of which brain regions are sensitive to different aspects of competition and how the nature of the stimuli and the…
In contrast with machine vision, human can recognize an object from complex background with great flexibility. For example, given the task of finding and circling all cars (no further information) in a picture, you may build a virtual image in mind from the task (or target) description before looking at the picture. Specifically, the virtual car image may be composed of the key components such as driver cabin and wheels. In this paper, we propose a component-based targetrecognition method by simulating the human recognition process. The component templates (equivalent to the virtual image in mind) of the target (car) are manually decomposed from the target feature image. Meanwhile, the edges of the testing image can be extracted by using a difference of Gaussian (DOG) model that simulates the spatiotemporal response in visual process. A phase correlation matching algorithm is then applied to match the templates with the testing edge image. If all key component templates are matched with the examining object, then this object is recognized as the target. Besides the recognition accuracy, we will also investigate if this method works with part targets (half cars). In our experiments, several natural pictures taken on streets were used to test the proposed method. The preliminary results show that the component-based recognition method is very promising.
We examined the effects of sensorimotor experience in two visual wordrecognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., mask) and a set of low BOI words (e.g., ship) were created, matched on imageability and concreteness. Facilitatory BOI effects were observed in lexical decision and phonological lexical decision tasks: responses were faster for high BOI words than for low BOI words. We discuss how our findings may be accounted for by (a) semantic feedback within the visual wordrecognition system, and (b) an embodied view of cognition (e.g., Barsalou's perceptual symbol systems theory), which proposes that semantic knowledge is grounded in sensorimotor interactions with the environment. PMID:17258186
Siakaluk, Paul D; Pexman, Penny M; Aguilera, Laura; Owen, William J; Sears, Christopher R
Recently, we have investigated the use of Arabic linguistic knowledge to improve the recognition of wide Arabic word lexicon. A neural-linguistic approach was proposed to mainly deal with canonical vocabulary of decomposable words derived from tri-consonant healthy roots. The basic idea is to factorize words by their roots and schemes. In this direction, we conceived two neural networks TNN_R and TNN_S to respectively recognize roots and schemes from structural primitives of words. The proposal approach achieved promising results. In this paper, we will focus on how to reach better results in terms of accuracy and recognition rate. Current improvements concern especially the training stage. It is about 1) to benefit from word letters order 2) to consider "sisters letters" (letters having same features), 3) to supervise networks behaviors, 4) to split up neurons to save letter occurrences and 5) to solve observed ambiguities. Considering theses improvements, experiments carried on 1500 sized vocabulary show a significant enhancement: TNN_R (resp. TNN_S) top4 has gone up from 77% to 85.8% (resp. from 65% to 97.9%). Enlarging the vocabulary from 1000 to 1700, adding 100 words each time, again confirmed the results without altering the networks stability.
The global war on terror has plunged US and coalition forces into a battle space requiring the continuous adaptation of tactics and technologies to cope with an elusive enemy. As a result, technologies that enhance the intelligence, surveillance, and reconnaissance (ISR) mission making the warfighter more effective are experiencing increased interest. In this paper we show how a new generation of smart cameras built around foveated sensing makes possible a powerful ISR technique termed Cascaded ATR. Foveated sensing is an innovative optical concept in which a single aperture captures two distinct fields of view. In Cascaded ATR, foveated sensing is used to provide a coarse resolution, persistent surveillance, wide field of view (WFOV) detector to accomplish detection level perception. At the same time, within the foveated sensor, these detection locations are passed as a cue to a steerable, high fidelity, narrow field of view (NFOV) detector to perform recognition level perception. Two new ISR mission scenarios, utilizing Cascaded ATR, are proposed.
Stem homographs are pairs of words with the same orthographic description of their stem but which are semantically and morphologically unrelated (e.g. in Spanish: rata/rato (rat/moment)). In priming tasks, stem homographs produce inhibition, unlike morphologically related words (loca/loco (madwoman/madman)) which produce facilitation. An event-related potentials study was conducted to compare morphological and stem homographic priming effects. The results show a similar attenuation of the N400 component at the 350-500 ms temporal window for the two conditions. In contrast, a broad negativity occurs only for stem homographs at the 500-600 ms window. This late negativity is interpreted as the consequence of an inhibitory effect for stem homographs that delays the stage of meaning integration. PMID:11803121
Barber, Horacio; Domínguez, Alberto; de Vega, Manuel
In the context of command-and-control applications, we exploit confidence measures in order to classify single-word utterances into two categories: utterances within the vocabulary which are recognized correctly, and other utterances, namely out-of- vocabulary (OOV) or misrecognized utterances. To this end, we investigate the classification error rate (CER) of several classes of confidence measures and transformations. In particular, we employed data-independent
Synthetic aperture radar (SAR) imaging and automatic targetrecognition (ATR) of moving targets pose a significant challenge due to the inherent difficulty of focusing moving targets. As a result, ATR of moving targets has received increased interest. High range resolution (HRR) radar mode offers an approach for recognizing moving targets by forming focused HRR profiles with significantly enhanced target-to-(clutter+noise) (T\\/(C+N))
R. Williams; J. Westerkamp; D. Gross; A. Palomion; T. Fister
This paper systematically reviews 10 years of research that several Army Laboratories conducted in object recognition algorithms, processors, and evaluation techniques. In the military, object recognition is applied to the discrimination of military targets, ranging from human-aided to autonomous operations, and is called automatic targetrecognition (ATR). The research described here has been concentrated in human-aided targetrecognition applications, but
James A. Ratches; C. P. Walters; Rudolf G. Buser; B. D. Guenther
We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.
Boukharouba, Abdelhak [Departement de Genie electrique, Universite 08 Mai 45 de Guelma (Algeria); Bennia, Abdelhak [Departement d'Electronique, Universite Mentouri de Constantine (Algeria)
A new method of recognizing the aircraft number of a radar target from a narrowband IF signal of non-coherent radar is presented. According to the received narrowband IF echo signal, its autocorrelation matrix is computed. The feature vector is the eigenvalue of the autocorrelation matrix, and the orthogonal transformation is accomplished to remove the unnecessary information in the feature. The
Jiang Jing; Wang Shouyong; Yu Lan; Zuo Delin; Yang Zhaoming; Tang Changwen
In this paper, we describe efforts to improve the performance of FEATURE, the Carnegie-Mellon University speaker-independent speech recognition system that classifies isolated letters of the English alphabet by enabling the system to learn the acoustical characteristics of individual speakers. Even when features are designed to be speaker-independent, it is frequently observed that feature values may vary more from speaker to
Slow neural oscillations (~1-15Hz) are thought to orchestrate the neural processes of spoken language comprehension. However, functional subdivisions within this broad range of frequencies are disputed, with most studies hypothesizing only about single frequency bands. The present study utilizes an established paradigm of spoken wordrecognition (lexical decision) to test the hypothesis that within the slow neural oscillatory frequency range, distinct functional signatures and cortical networks can be identified at least for theta- (~3-7Hz) and alpha-frequencies (~8-12Hz). Listeners performed an auditory lexical decision task on a set of items that formed a word-pseudoword continuum: ranging from (1) real words over (2) ambiguous pseudowords (deviating from real words only in one vowel; comparable to natural mispronunciations in speech) to (3) pseudowords (clearly deviating from real words by randomized syllables). By means of time-frequency analysis and spatial filtering, we observed a dissociation into distinct but simultaneous patterns of alpha power suppression and theta power enhancement. Alpha exhibited a parametric suppression as items increasingly matched real words, in line with lowered functional inhibition in a left-dominant lexical processing network for more word-like input. Simultaneously, theta power in a bilateral fronto-temporal network was selectively enhanced for ambiguous pseudowords only. Thus, enhanced alpha power can neurally 'gate' lexical integration, while enhanced theta power might index functionally more specific ambiguity-resolution processes. To this end, a joint analysis of both frequency bands provides neural evidence for parallel processes in achieving spoken wordrecognition. PMID:24747736
Strauß, Antje; Kotz, Sonja A; Scharinger, Mathias; Obleser, Jonas
Laser active imaging is fit to conditions such as no difference in temperature between target and background, pitch-black night, bad visibility. Also it can be used to detect a faint target in long range or small target in deep space, which has advantage of high definition and good contrast. In one word, it is immune to environment. However, due to the affect of long distance, limited laser energy and atmospheric backscatter, it is impossible to illuminate the whole scene at the same time. It means that the target in every single frame is unevenly or partly illuminated, which make the recognition more difficult. At the same time the speckle noise which is common in laser active imaging blurs the images . In this paper we do some research on laser active imaging and propose a new targetrecognition method based on multi-frame images . Firstly, multi pulses of laser is used to obtain sub-images for different parts of scene. A denoising method combined homomorphic filter with wavelet domain SURE is used to suppress speckle noise. And blind deconvolution is introduced to obtain low-noise and clear sub-images. Then these sub-images are registered and stitched to combine a completely and uniformly illuminated scene image. After that, a new targetrecognition method based on contour moments is proposed. Firstly, canny operator is used to obtain contours. For each contour, seven invariant Hu moments are calculated to generate the feature vectors. At last the feature vectors are input into double hidden layers BP neural network for classification . Experiments results indicate that the proposed algorithm could achieve a high recognition rate and satisfactory real-time performance for laser active imaging.
Wang, Can-jin; Sun, Tao; Wang, Tin-feng; Chen, Juan
A pulsed ladar based object-recognition system with applications to automatic targetrecognition (ATR) is presented. The approach used is to fit the sensed range images to range templates extracted through a laser physics based simulation applied to geometric target models. A projection-based prescreener filters out more than 80% of candidate templates. For recognition, an M of N pixel matching scheme for internal shape matching is combined with a silhouette matching scheme. The system was trained on synthetic data obtained from the simulation, and has been blind tested on a data set containing real ladar images of military vehicles at various orientations and ranges. Successful blind testing on real imagery demonstrates the utility of synthetic imagery for training of recognizers operating on ladar imagery. PMID:18249646
Acoustic sensors can be used to detect, track and identify non-line-of-sight targets passively. Attempts to alter acoustic emissions often result in an undesirable performance degradation. This research project investigates the use of neural networks for differentiating between features extracted from the acoustic signatures of sources. Acoustic data were filtered and digitized using a commercially available analog-digital convertor. The digital data
A novel synthetic aperture radar (SAR) automatic targetrecognition (ATR) approach based on Curvelet Transform is proposed. However, the existing approaches can not extract the more effective feature. In this paper, our method is concentrated on a new effective representation of the moving and stationary target acquisition and recognition (MSTAR) database to obtain a more accurate target region and reduce feature dimension. Firstly, MSTAR database can be extracted feature through the optimal sparse representation by curvelets to obtain a clear target region. However, considering the loss of part of edges of image. We extract coarse feature, which is to compensate fine feature error brought by segmentation. The final features consisting of fine and coarse feature are classified by SVM with Gaussian radial basis function (RBF) kernel. The experiments show that our proposed algorithm can achieve a better correct classification rate.
Recent studies show that emotional stimuli impair the identification of subsequently presented, briefly flashed stimuli. In the present study, we investigated whether emotional distractors (primes) impaired target processing when presentation of the target stimulus was not impoverished. In lexical decision, animacy decision, rhyme decision, and nonword naming, targets were presented in such a manner that they were clearly visible (i.e., targets were not masked and presented until participants responded). In all tasks taboo-sexual distractors caused a slowdown in responding to the subsequent neutral target. Our results indicate that the detrimental effects of emotional distractors are not confined to paradigms in which visibility of the target is limited. Moreover, impairments were obtained even when semantic processing of stimuli was not required. PMID:21592945
Zeelenberg, René; Bocanegra, Bruno R; Pecher, Diane
In this paper we discuss the problem about the targetrecognition by the high resolution radar range profiles. Several feature extraction methods for computing shift invariants are simply reviewed: such as bispectrum, differential cepstrum, then the differential power spectrum (DPS) based features are introduced to this study. A multi-layered feed-forward neural network with simulated annealing resilient propagation (SARPROP) algorithm is
The goal of this paper is to show an approach to targetrecognition (ATR) that allows for efficient updating of the recognition algorithm of a fusion agent when new symbolic information becomes available. This information may, for instance, provide additional characterization of a known type of target, or supply a description of a new type of target. The new symbolic information can be either posted on a web page or provided by another agent. The sensory information can be obtained from two imaging sensors. In our scenario the fusion agent, after noticing such an event, processes the new symbolic information and incorporates it into its recognition rules. To achieve this goal the fusion agent needs to understand the symbolic information. This capability is achieved through the use of an ontology. Both the fusion agent and the knowledge provider (it may be another software agent or a human annotator) know the ontology, and the web based information is annotated using that ontology. In this paper we describe the approach, provide examples of symbolic target descriptions, describe an ATR scenario, and show some initial results of simulations for the selected scenario. The discussion in this paper shows the advantages of the proposed approach over that in which the recognition algorithm is fixed.
In this paper, an automatic targetrecognition algorithm is presented based on a framework for learning dictionaries for simultaneous sparse signal representation and feature extraction. The dictionary learning algorithm is based on class supervised simultaneous orthogonal matching pursuit while a matching pursuit-based similarity measure is used for classification. We show how the proposed framework can be helpful for efficient utilization
Vishal M. Patel; Nasser M. Nasrabadi; Rama Chellappa
Composite classifiers consisting of a number of component classifiers have been designed and evaluated on the problem of automatic targetrecognition (ATR) using a large set of real forward-looking infrared (FLIR) imagery. Two existing classifiers are used as the building blocks for our composite classifiers. The performance of the proposed composite classifiers are compared based on their classification ability and
Lin-cheng Wang; Lipchen Alex Chan; Nasser M. Nasrabadi; Sandor Z. Der
The signal recognition particle (SRP) is a conserved ribonucleoprotein complex that binds to targeting sequences in nascent secretory and membrane proteins. The SRP guides these proteins to the cytoplasmic membrane in prokaryotes and the endoplasmic reticulum membrane in eukaryotes via an interaction with its cognate receptor. The E. coli SRP is relatively small and is currently used as a model
In many model-based automatic targetrecognition (ATR) systems the size of the model catalog can be a critical factor in determining the viability of the system. We examine an ATR system which uses synthetic high range resolution (HRR) radar data to determine how the classification performance is affected by the compression of the HRR model catalog. For this purpose the
The key point of marine search and rescue is to find out and recognize the distress objects. At present, the visual search method is usually adopted to detect the ships in distress, and this method can only be used at good sea condition and visibility. In this paper, a new target detection and recognition system is proposed. The parameters of
In this paper, we perform a number of theoretical studies on constant frequency (CF) pulse waveform design and diversity in radar sensor networks (RSN): (1) the conditions for waveform co-existence, (2) interferences among waveforms in RSN, (3) waveform diversity combining in RSN. As an application example, we apply the waveform design and diversity to automatic targetrecognition (ATR) in RSN
The problem of reliable automatic targetrecognition (ATR) from incoherent radar returns is discussed. In the problems under consideration, feature extraction methods are divided into two basic types: (1) Feature extraction directly based on time-domain description; (2) Feature extraction based on multiple transformation technique. In this paper, we shall demonstrate the problem of feature extraction by considering two examples of
Automatic targetrecognition (ATR) is a very difficult problem due to the variety of conditions under which an ATR system may be required to operate. Because the number of operations required to execute a particular ATR algorithm can vary greatly from one scenario to another; a fixed hardware and software architecture will usually not be able to execute a given
Reliable automatic targetrecognition (ATR) systems based on inverse synthetic aperture radar (ISAR) images require a robust feature selection. An ATR system based on polarimetric ISAR images has been recently proposed that extracts bright scat- terers and uses their polarimetric signatures to define classification features. Since bright scatterers could be the results of multiple scattering, the concept of polarimetrically persistent
An important issue in the choice of which comput- ing platform to deploy for an embedded application is relating the following three quantities: quality of the results, throughput of the system, and computing resources. Here, we investigate the relationship be- tween these three quantities for an Automatic TargetRecognition (ATR) application using Synthetic Aper- ture Radar (SAR) images. In this
Joseph A. O'Sullivan; Mark A. Franklin; Michael D. DeVore; Roger D. Chamberlain
A review is presented of ATR (automatic targetrecognition), and some of the highlights of neural network technology developments that have the potential for making a significant impact on ATR are presented. In particular, neural network technology developments in the areas of collective computation, learning algorithms, expert systems, and neurocomputer hardware could provide crucial tools for developing improved algorithms and
A cooperation between the European Aeronautic Defence and Space Company AG (EADS) and the Bavarian Ministry of the Interior was started at the beginning of 2001 to develop an application for automatic targetrecognition technology for police helicopter missions. The Bavarian Police Air Support Unit is the main support partner and first user. Bavarian polic helicopters are equipped with a
An infrared automatic targetrecognition (IR ATR) application is accelerated with an FPGA co-processor board. The board features and the application are first stated. The FPGA design is then described. The achieved performance is reported and analyzed at the end
Jack S. N. Jean; Xuejun Liang; Brian Drozd; Karen A. Tomko
This report presents our work, supported under the research grant ARO DAAL03-92-G-0141, on the development of an algorithm for generating the conditional mean estimates of functions of target positions, orientation and type in recognition and tracking of ...
\\u000a Since synthetic aperture radar (SAR) images are very sensitive to the pose variation of targets, SAR automatic targetrecognition\\u000a (ATR) is a well-known very challenging problem. This paper introduces an effective method for SAR ATR by using a combination\\u000a of kernel singular value decomposition (KSVD) and principal component analysis (PCA) for feature extraction and the nearest\\u000a neighbor classifier (NNC) for
\\u000a We address the problem of automatic targetrecognition (ATR) using a multi-agent swarm of unmanned aerial vehicles(UAVs) deployed\\u000a within a reconnaissance area. Traditionally, ATR is performed by UAVs that fly within the reconnaissance area to collect image\\u000a data through sensors and upload the data to a central base station for analyzing and identifying potential targets. The centralized\\u000a approach to ATR
Prithviraj Dasgupta; Stephen O’Hara; Plamen V. Petrov
Correlation filters (CFs) can detect multiple targets in one scene making them well-suited for automatic targetrecognition (ATR) applications. We present a method to efficiently compute the Quadratic CF (QCF) capable of detecting multiple targets from two classes. We use a Kalman filter framework to combine information from successive correlation outputs in a probabilistic way integrating the ATR tasks of
Reduction of false alarm with acceptable accuracy of classification rate is a challenge in Automatic TargetRecognition (ATR) using Synthetic Aperture Radar (SAR) images. This report addresses the evaluation of polarimetric techniques features, benefits o...
Visual search performance can be negatively affected when both targets and distracters share a dimension relevant to the task. This study examined if visual search performance would be influenced by distracters that affect a dimension irrelevant from the task. In Experiment 1 within the letter string of a letter search task, target letters were embedded within a word. Experiment 2 compared targets embedded in words to targets embedded in nonwords. Experiment 3 compared targets embedded in words to a condition in which a word was present in a letter string, but the target letter, although in the letter string, was not embedded within the word. The results showed that visual search performance was negatively affected when a target appeared within a high frequency word. These results suggest that the interaction and effectiveness of distracters is not merely dependent upon common features of the target and distracters, but can be affected by word frequency (a dimension not related to the task demands).
Speech recognition can be difficult and effortful for older adults, even for those with normal hearing. Declining frontal lobe cognitive control has been hypothesized to cause age-related speech recognition problems. This study examined age-related changes in frontal lobe function for 15 clinically normal hearing adults (21–75 years) when they performed a wordrecognition task that was made challenging by decreasing word intelligibility. Although there were no age-related changes in wordrecognition, there were age-related changes in the degree of activity within left middle frontal gyrus (MFG) and anterior cingulate (ACC) regions during wordrecognition. Older adults engaged left MFG and ACC regions when words were most intelligible compared to younger adults who engaged these regions when words were least intelligible. Declining gray matter volume within temporal lobe regions responsive to word intelligibility significantly predicted left MFG activity, even after controlling for total gray matter volume, suggesting that declining structural integrity of brain regions responsive to speech leads to the recruitment of frontal regions when words are easily understood. Electronic supplementary material The online version of this article (doi:10.1007/s10162-008-0113-3) contains supplementary material, which is available to authorized users.
The on-going development of an automatic targetrecognition 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 targetrecognition 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.
Protein translocation begins with the efficient targeting of secreted and membrane proteins to complexes embedded within the membrane. In Eukarya and Bacteria, this is achieved through the interaction of the signal recognition particle (SRP) with the nascent polypeptide chain. In Archaea, homologs of eukaryal and bacterial SRP-mediated translocation pathway components have been identified. Biochemical analysis has revealed that although the archaeal system incorporates various facets of the eukaryal and bacterial targeting systems, numerous aspects of the archaeal system are unique to this domain of life. Moreover, it is becoming increasingly clear that elucidation of the archaeal SRP pathway will provide answers to basic questions about protein targeting that cannot be obtained from examination of eukaryal or bacterial models. In this review, recent data regarding the molecular composition, functional behavior and evolutionary significance of the archaeal signal recognition particle pathway are discussed.
Recognition of a spoken word phonological variant--schwa vowel deletion (e.g., corporate --> corp'rate)--was investigated in vowel detection (absent/present) and syllable number judgment (two or three syllables) tasks. Variant frequency corpus analyses (Patterson, LoCasto, & Connine, 2003) were used to select words with either high or low schwa vowel deletion rates. Speech continua were created for each word in which schwa vowel length was manipulated (unambiguous schwa-present and schwa-absent endpoints, along with intermediate ambiguous tokens). Matched control nonwords were created with identical schwa vowel continua and surrounding segments. The low-deletion-rate words showed more three-syllable judgments than did the high-deletion-rate words. Matched control nonwords did not differ as a function of deletion rate. Experiments 2 and 3 showed a lexical decision reaction time advantage for more frequent surface forms, as compared with infrequent ones, for schwa-deleted (Experiment 2) and schwa-present (Experiment 3) stimuli. The results are discussed in terms of representations of variant forms of words based on variant frequency. PMID:18459250
Connine, Cynthia M; Ranbom, Larissa J; Patterson, David J
In gender-marking languages, the gender of the noun determines the form of the preceding article. In this study, we examined whether French-learning toddlers use gender-marking information on determiners to recognize words. In a split-screen preferential looking experiment, 25-month-olds were presented with picture pairs that referred to nouns with either the same or different genders. The targetword in the auditory instruction was preceded either by the correct or incorrect gender-marked definite article. Toddlers' looking times to target shortly after article onset demonstrated that targetwords were processed most efficiently in different-gender grammatical trials. While target processing in same-gender grammatical trials recovered in the subsequent time window, ungrammatical articles continued to affect processing efficiency until much later in the trial. These results indicate that by 25 months of age, French-learning toddlers use gender information on determiners when comprehending subsequent nouns. PMID:19371366
The paper considers the following problem: given a 3D model of a reference target and a sequence of images of a 3D scene, identify the object in the scene most likely to be the reference target and determine its current pose. Finding the best match in each frame independently of previous decisions is not optimal, since past information is ignored. Our solution concept uses a novel Bayesian framework for multi target tracking and object recognition to define and sequentially update the probability that the reference target is any one of the tracked objects. The approach is applied to problems of automatic lock-on and missile guidance using a laser radar seeker. Field trials have resulted in high target hit probabilities despite low resolution imagery and temporarily highly occluded targets.
A holistic system for the recognition of handwritten Farsi\\/Arabic words using right}left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map (SOFM), is used for
Mehdi Dehghan; Karim Faez; Majid Ahmadi; Malayappan Shridhar
Although it is of lifelong importance, reading ability is studied primarily in children and adolescents. We examined variation in wordrecognition in 347 middle-aged male twin pairs. Overall heritability (a2) was 0.45, and shared environmental influences (c2) were 0.28. However, parental education moderated heritability such that a2 was 0.21 at the lowest parental education level and 0.69 at the highest
William S. Kremen; Kristen C. Jacobson; Hong Xian; Seth A. Eisen; Brian Waterman; Rosemary Toomey; Michael C. Neale; Ming T. Tsuang; Michael J. Lyons
Automatic targetrecognition (ATR) usually become difficult when target is blocked by clouds, or low image contrast, or target repeat mode in a complex optical imaging environment. The ground targetrecognition method based on landmarks is a good way for aircraft navigation, which can solve unobvious targetrecognition problems in a complex wide-field scene. In combination with the characteristics of selective attention in human visual system, this paper systematic study the construction rules for the cluster of landmarks, present a landmarks dynamic allocation method in ground targetrecognition, which can effectively improve the stability and accuracy of targetrecognition.
Three experiments used a visual fixation technique to examine whether toddlers interpret speech continuously. Found that 24-month-olds had delayed responses when a competing distractor picture's label overlapped phonetically with the target at onset, but not when the pictures' labels rhymed, showing that children monitored speech stream…
The results of a case study about the application of an advanced method for automatic targetrecognition to infrared imagery taken from police helicopter missions are presented. The method consists of the following steps: preprocessing, classification, fusion, postprocessing and tracking, and combines the three paradigms image pyramids, neural networks and bayesian nets. The technology has been developed using a variety of different scenes typical for military aircraft missions. Infrared cameras have been in use for several years at the Bavarian police helicopter forces and are highly valuable for night missions. Several object classes like 'persons' or 'vehicles' are tested and the possible discrimination between persons and animals is shown. The analysis of complex scenes with hidden objects and clutter shows the potentials and limitations of automatic targetrecognition for real-world tasks. Several display concepts illustrate the achievable improvement of the situation awareness. The similarities and differences between various mission types concerning object variability, time constraints, consequences of false alarms, etc. are discussed. Typical police actions like searching for missing persons or runaway criminals illustrate the advantages of automatic targetrecognition. The results demonstrate the possible operational benefits for the helicopter crew. Future work will include performance evaluation issues and a system integration concept for the target platform.
Hyperspectral sensors allow a considerable improvement in the performance of a targetrecognition process to be achieved. This characteristic is particular interesting in a lot of military and civilian remote sensing applications, such as automatic targetrecognition (ATR) and surveillance of wide areas. In this framework, real time processing of the observed scenario is becoming a key issue, because it permits the operator to provide immediate assessment of the surveyed area. In the literature is presented a line-by-line real time implementation of the widely used Constrained Energy Minimization (CEM) target detector. However, experimental results show that sometimes the CEM filter produces False Alarms (FAs) corresponding to rare objects, whose spectra are angularly very different from the target signature and from the natural background classes in the image. A solution to such a problem is presented in this work: the proposed strategy is based on the decision fusion of the CEM and the SAM algorithms. Only those pixels that pass the CEM-stage are processed by the SAM algorithm. The second stage allows false alarms to be reduced by preserving most of target pixels. The fusion strategy is applied to an experimental hyperspectral data set to recognize a known green target. Detection performance is numerically evaluated and compared to the one of the classical CEM detector.
The primary objective of phase 1 of this research project is to conduct multidisciplinary research that will contribute to fundamental scientific knowledge in several of the USAF critical technology areas. Specifically, neural networks, signal processing techniques, and electro-optic capabilities are utilized to solve problems associated with automated target acquisition, recognition, and tracking. To accomplish the stated objective, several tasks have been identified and were executed.
A radar high resolution range profile (HRRP) contains rather detailed structural information of a target and provides us a more reliable tool for targetrecognition. Various feature extraction methods have been developed and applied to radar targetrecognition successfully. Usually, different features contain different discriminative information. So the recognition performance will be improved by utilizing different features extracted from HRRP
Model-based Automatic TargetRecognition (ATR) algorithms are adept at recognizing targets in high fidelity 3D LADAR imagery. Most current approaches involve a matching component where a hypothesized target and target pose are iteratively aligned to pre-segmented range data. Once the model-to-data alignment has converged, a match score is generated indicating the quality of match. This score is then used to rank one model hypothesis over another. The main drawback of this approach is twofold. First, to ensure the correct target is recognized, a large number of model hypotheses must be considered. Even with a highly accurate indexing algorithm, the number of target types and variants that need to be explored is prohibitive for real-time operation. Second, the iterative matching step must consider a variety of target poses to ensure that the correct alignment is recovered. Inaccurate alignments produce erroneous match scores and thus errors when ranking one target hypothesis over another. To compensate for such drawbacks, we explore the use of situational awareness information already available to an image analyst. Examples of such information include knowledge of the surrounding terrain (to assess potential occlusion levels) and targets of interest (to account for target variants).
Snorrason, Magnús S.; Goodsell, Thom R.; Monnier, Camille R.; Stevens, Mark R.
We used event-related potentials (ERPs) to compare auditory wordrecognition in children with specific language impairment (SLI group; N=14) to a group of typically developing children (TD group; N=14). Subjects were presented with pictures of items and heard auditory words that either matched or mismatched the pictures. Mismatches overlapped expected words in word-onset (cohort mismatches; see: DOLL, hear: dog), rhyme (CONE -bone), or were unrelated (SHELL -mug). In match trials, the SLI group showed a different pattern of N100 responses to auditory stimuli compared to the TD group, indicative of early auditory processing differences in SLI. However, the phonological mapping negativity (PMN) response to mismatching items was comparable across groups, suggesting that just like TD children, children with SLI are capable of establishing phonological expectations and detecting violations of these expectations in an online fashion. Perhaps most importantly, we observed a lack of attenuation of the N400 for rhyming words in the SLI group, which suggests that either these children were not as sensitive to rhyme similarity as their typically developing peers, or did not suppress lexical alternatives to the same extent. These findings help shed light on the underlying deficits responsible for SLI. PMID:23523986
Malins, Jeffrey G; Desroches, Amy S; Robertson, Erin K; Newman, Randy Lynn; Archibald, Lisa M D; Joanisse, Marc F
Automatic targetrecognition (ATR) is a challenging problem that involves extraction of critical information about a target vehicle from a sequence of complex images. Previous ATR systems have been only partially successful and have produced high false-alarm rates. This paper reviews ATR and presents some of the highlights of neural network technology developments that have the potential for making a significant impact on ATR. In particular, neural network technology developments in the areas of collective computation, learning algorithms, expert systems, and neurocomputer hardware could provide crucial tools for developing improved algorithms and computational hardware for ATR.
Though much is known about how words are recognized, little research has focused on how a degraded signal affects the fine-grained temporal aspects of real-time wordrecognition. The perception of degraded speech was examined in two populations with the goal of describing the time course of wordrecognition and lexical competition. Thirty-three postlingually deafened cochlear implant (CI) users and 57 normal hearing (NH) adults (16 in a CI-simulation condition) participated in a visual world paradigm eye-tracking task in which their fixations to a set of phonologically related items were monitored as they heard one item being named. Each degraded-speech group was compared with a set of age-matched NH participants listening to unfiltered speech. CI users and the simulation group showed a delay in activation relative to the NH listeners, and there is weak evidence that the CI users showed differences in the degree of peak and late competitor activation. In general, though, the degraded-speech groups behaved statistically similarly with respect to activation levels. PMID:24041330
Farris-Trimble, Ashley; McMurray, Bob; Cigrand, Nicole; Tomblin, J Bruce
In this paper, an automatic targetrecognition (ATR) system based on synthetic aperture radar (SAR) is proposed. This ATR system can play an important role in the simulation of up-to-data battlefield environment and be used in ATR research. To establish an integral and available system, the processing of SAR image was divided into four main stages which are de-noise, detection, cluster-discrimination and segment-recognition, respectively. The first three stages are used for searching region of interest (ROI). Once the ROIs are extracted, the recognition stage will be taken to compute the similarity between the ROIs and the templates in the electromagnetic simulation software National Electromagnetic Scattering Code (NESC). Due to the lack of the SAR raw data, the electromagnetic simulated images are added to the measured SAR background to simulate the battlefield environment8. The purpose of the system is to find the ROIs which can be the artificial military targets such as tanks, armored cars and so on and to categorize the ROIs into the right classes according to the existing templates. From the results we can see that the proposed system achieves a satisfactory result.
A targetrecognition system is described using 3-D mathematical models which simulate radar images. The simulated radar images are created from radar cross section (RCS) responses of the 3-D models and compared with measured target radar images. The 3-D models consist of several thousands facets, and one facet size is less than the radar resolution. An RCS response of each facet in the models is calculated by the modified geometrical theory of diffraction (GTD) method using the information of the radar frequency and the target aspect angle. The RCS response of each facet is projected onto the 2-D plane based on target aspect angle to create the final simulation radar images. The system is verified to be able to simulate even a ship radar imagery, in spite of the difficulty in the simulation due to its structural complexity. Evaluations were made for this recognition system by comparing the simulated ship images created from the 3-D models with the real ship images obtained by an airborne MITSUBISHI-SAR which has the capability of obtaining the X-band 1m resolution SAR and ISAR images, and the system has been proved to have the classification accuracy of better than 90%.
In this paper we overview the nonlinear matched filtering for photon counting recognition with 3D passive sensing. The first and second order statistical properties of the nonlinear matched filtering can improve the recognition performance compared to the linear matched filtering. Automatic target reconstruction and recognition are addressed for partially occluded objects. The recognition performance is shown to be improved significantly
Evidence indicates that adequate phonological abilities are necessary to develop proficient reading skills and that later in life phonology also has a role in the covert visual wordrecognition of expert readers. Impairments of acoustic perception, such as deafness, can lead to atypical phonological representations of written words and letters, which in turn can affect reading proficiency. Here, we report an experiment in which young adults with different levels of acoustic perception (i.e., hearing and deaf individuals) and different modes of communication (i.e., hearing individuals using spoken language, deaf individuals with a preference for sign language, and deaf individuals using the oral modality with less or no competence in sign language) performed a visual lexical decision task, which consisted of categorizing real words and consonant strings. The lexicality effect was restricted to deaf signers who responded faster to real words than consonant strings, showing over-reliance on whole word lexical processing of stimuli. No effect of stimulus type was found in deaf individuals using the oral modality or in hearing individuals. Thus, mode of communication modulates the lexicality effect. This suggests that learning a sign language during development shapes visuo-motor representations of words, which are tuned to the actions used to express them (phono-articulatory movements vs. hand movements) and to associated perceptions. As these visuo-motor representations are elicited during on-line linguistic processing and can overlap with the perceptual-motor processes required to execute the task, they can potentially produce interference or facilitation effects.
Background In the current era of scientific research, efficient communication of information is paramount. As such, the nature of scholarly and scientific communication is changing; cyberinfrastructure is now absolutely necessary and new media are allowing information and knowledge to be more interactive and immediate. One approach to making knowledge more accessible is the addition of machine-readable semantic data to scholarly articles. Results The Word add-in presented here will assist authors in this effort by automatically recognizing and highlighting words or phrases that are likely information-rich, allowing authors to associate semantic data with those words or phrases, and to embed that data in the document as XML. The add-in and source code are publicly available at http://www.codeplex.com/UCSDBioLit. Conclusions The Word add-in for ontology term recognition makes it possible for an author to add semantic data to a document as it is being written and it encodes these data using XML tags that are effectively a standard in life sciences literature. Allowing authors to mark-up their own work will help increase the amount and quality of machine-readable literature metadata.
We offer a computational model of gaze planning during reading that consists of two main components: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the wordrecognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting.
Paper presents an comparative evaluation of\\u000d\\u000a features extraction algorithm for a real-time\\u000d\\u000a isolated wordrecognition system based on FPGA. The\\u000d\\u000a Mel-frequency cepstral, linear frequency cepstral,\\u000d\\u000a linear predictive and their cepstral coefficients\\u000d\\u000a were implemented in hardware\\/software design. The\\u000d\\u000a proposed system was investigated in speaker\\u000d\\u000a dependent mode for 100~different Lithuanian\\u000d\\u000a words. The robustness of features extraction\\u000d\\u000a algorithms was tested recognizing the speech
Tomyslav Sledevi; Artras Serackis; Gintautas Tamuleviius; Dalius Navakauskas
Participants performed a 2-choice categorization task on visible wordtargets that were preceded by novel (unpracticed) prime words. The prime words were presented for 33 ms and followed either immediately (Experiments 1-3) or after a variable delay (Experiments 1 and 4) by a pattern mask. Both subjective and objective measures of prime visibility…
Ortells, Juan J.; Mari-Beffa, Paloma; Plaza-Ayllon, Vanesa
It has been known for some time that the recognition of a noun is affected by the gender marking, such as masculine or feminine, that is carried by a preceding word. In this study, we used auditory naming to examine how early and late English-French bilinguals react to gender marking when processing French. The early bilinguals showed clear facilitation and inhibition effects, but the late bilinguals were totally insensitive to gender marking, whether congruent or incongruent. The results are discussed in terms of current accounts of gender processing as well as age of acquisition and regular use of the gender-marking language. PMID:11407427
This paper describes the application of a visual pattern recognition neural network in a hybrid model based automatic targetrecognition (ATR) system. This neural network forms the feature extraction front end of the ATR and is derived from the Neocognitron network first proposed by K. Fukushima. For complex targetrecognition, modifications to the basic Neocognitron network paradigm were required to
For many decades attempts to accomplish Automatic TargetRecognition have been made using both visual and FLIR camera systems. A recurring problem in these approaches is the segmentation problem, which is the separation between the target and its background. This paper describes an approach to Automatic TargetRecognition using a laser gated viewing system. Here laser-flash illumination is used in combination with a gating viewer such that only a small part in the distance domain is seen in a single image. In our approach, using an Intevac LIVAR 4000 imaging system, we combined several images with different gate settings to construct a 3D data cube. This paper describes the preprocessing and filtering steps taken to obtain a range image for which pixel values represent the distance between camera and objects in the illuminated scene. Depth segmentation is performed using the global histogram of this range image. After this depth segmentation very good 2D object segmentations can be obtained which can be used to classify persons and vehicles. An outlook will be given towards operational application of this procedure.
Much evidence indicates that emotion enhances memory, but the precise effects of the two primary factors of arousal and valence remain at issue. Moreover, the current knowledge of emotional memory enhancement is based mostly on small samples of extremely emotive stimuli presented in unnaturally high proportions without adequate affective, lexical, and semantic controls. To investigate how emotion affects memory under conditions of natural variation, we tested whether arousal and valence predicted recognition memory for over 2500 words that were not sampled for their emotionality, and we controlled a large variety of lexical and semantic factors. Both negative and positive stimuli were remembered better than neutral stimuli, whether arousing or calming. Arousal failed to predict recognition memory, either independently or interactively with valence. Results support models that posit a facilitative role of valence in memory. This study also highlights the importance of stimulus controls and experimental designs in research on emotional memory. PMID:24041838
Radar target automatic recognition is a comprehension problem. We have researched the practical radar data and concluded some valuable viewpoint. With it we put forward a whole scheme to achieve radar automatic targetrecognition. First the OS CAFR and binary accumulation algorithm is taken to accurately detect target. Here we lower self-adapt threshold to get more targets which maybe be
Haitao Jia; Wei Zhang; Ke Zhang; Chi Zhang; Chuanyang Dai
Automatic targetrecognition (ATR) is an important capability for defense application. ATR removes the human operator from the process of target acquisition and classification, reducing the reaction time to possible threats and can be used to gun target engagement. This paper presents one technique used to solve the automatic targetrecognition problem in synthetic aperture radars (SAR) images, that is
João Paulo Pordeus Gomes; José Fernando Basso Brancalion; David Fernandes
A review is presented of ATR (automatic targetrecognition), and some of the highlights of neural network technology developments that have the potential for making a significant impact on ATR are presented. In particular, neural network technology developments in the areas of collective computation, learning algorithms, expert systems, and neurocomputer hardware could provide crucial tools for developing improved algorithms and computational hardware for ATR. The discussion covers previous ATR system efforts. ATR issues and needs, early vision and collective computation, learning and adaptation for ATR, feature extraction, higher vision and expert systems, and neurocomputer hardware. PMID:18282821
The generation of complete databases of IR data is extremely useful for training human observers and testing automatic pattern recognition algorithms. Field data may be used for realism, but require expensive and time-consuming procedures. IR scene simulation methods have emerged as a more economical and efficient alternative for the generation of IR databases. A novel approach to IR target simulation is presented in this paper. Model vehicles at 1:24 scale are used for the simulation of real targets. The temperature profile of the model vehicles is controlled using resistive circuits which are embedded inside the models. The IR target is recorded using an Inframetrics dual channel IR camera system. Using computer processing we place the recorded IR target in a prerecorded background. The advantages of this approach are: (1) the range and 3D target aspect can be controlled by the relative position between the camera and model vehicle; (2) the temperature profile can be controlled by adjusting the power delivered to the resistive circuit; (3) the IR sensor effects are directly incorporated in the recording process, because the real sensor is used; (4) the recorded target can embedded in various types of backgrounds recorded under different weather conditions, times of day etc. The effectiveness of this approach is demonstrated by generating an IR database of three vehicles which is used to train a back propagation neural network. The neural network is capable of classifying vehicle type, vehicle aspect, and relative temperature with a high degree of accuracy.
A probe-based approach combined with image modeling is used to recognize targets in spatially resolved, single frame, forward looking infrared (FLIR) imagery. A probe is a simple mathematical function that operates locally on pixel values and produces an output that is directly usable by an algorithm. An empirical probability density function of the probe values is obtained from a local region of the image and used to estimate the probability that a target of known shape is present. Target shape information is obtained from three-dimensional (3-D) computer-aided design (CAD) models. Knowledge of the probe values along with probe probability density functions and target shape information allows the likelihood ratio between a target hypothesis and background hypothesis to be written. The generalized likelihood ratio test is then used to accept one of the target poses or to choose the background hypothesis. We present an image model for infrared images, the resulting recognition algorithm, and experimental results on three sets of real and synthetic FLIR imagery. PMID:18282881
The vast majority of neural and computational models of visual-wordrecognition 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-wordrecognition. 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. PMID:23948388
This paper presents a novel feature vector to be used with a robust automatic targetrecognition (ATR) classifier designed for a ground surveillance radar. A three element feature vector has been used where features are based on radar audio signal of 100 milliseconds duration. The short feature length allows fast real-time implementation of the classifier. Classification is done using a
S. Liaqat; S. A. Khan; M. B. Ihsan; S. Z. Asghar; A. Ejaz; A. I. Bhatti
We present an approach to automatic targetrecognition (ATR) from synthetic aperture radar (SAR) imagery which combines advantages of both model-based and template-based approaches. Prior observations are used to estimate the statistical properties of reflectance over regions in the training scene. These target-centered statistical models can then be used to estimate the statistical properties of sensor output for arbitrary pose.
University students participated in five experiments concerning the effects of unmasked, orthographically similar, primes on visual wordrecognition in the lexical decision task (LDT) and naming tasks. The modal prime-target stimulus onset asynchrony (SOA) was 350 ms. When primes were words that were orthographic neighbors of the targets, and…
Several feature extraction and selection methods for an existing automatic targetrecognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.
Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin
The signal recognition particle (SRP) and its receptor comprise a universally conserved and essential cellular machinery that couples the synthesis of nascent proteins to their proper membrane localization. The past decade has witnessed an explosion in in-depth mechanistic investigations of this targeting machine at increasingly higher resolution. In this review, we summarize recent work that elucidates how the SRP and SRP receptor interact with the cargo protein and the target membrane, respectively, and how these interactions are coupled to a novel GTPase cycle in the SRP•SRP receptor complex to provide the driving force and enhance the fidelity of this fundamental cellular pathway. We also discuss emerging frontiers where important questions remain to be addressed.
The automatic targetrecognition (ATR) performance of SAR with subsampled raw data is investigated in this paper. Two schemes are investigated. In scheme A, SAR images are reconstructed from subsampled data by applying compressed sensing (CS) techniques and then targets are classified using either the mean-squared error (MSE) classifier or the point-feature-based classifier. Both classifiers recognize a target by using the magnitude information of dominant scatterers in the image. They fit nicely with the CS framework considering that CS approaches can efficiently recover the bright pixels in SAR images. In scheme B, the smashed-filter classifier is employed without image formation. Instead it makes the classification decision by directly comparing the observed subsampled data with data simulated from reference images. The impact of various subsampling patterns on ATR is investigated since CS theory suggests that some patterns lead to better performance than others. Simulation results show that compared with images formed by the conventional SAR imaging algorithm, CS reconstructed images always lead to much higher recognition rates for both the classifiers in scheme A. The MSE classifier works better than the point-feature-based classifier because the former takes into account both the magnitudes and locations of bright pixels while the latter uses the locations only. The smashed-filter classifier is computationally efficient and can accurately recognize a target even with strong subsampling if appropriate reference images are available. Its application in practice is difficult because it is sensitive to the phases of complex-valued SAR images, which vary too much for different observation angles.
Du, Chaoran; Rilling, Gabriel; Davies, Mike; Mulgrew, Bernard
In the picture-word interference task the naming of a picture is hampered by the presence of a distractor word that is to be ignored. Two main components of this interference effect can be distinguished: an interference effect induced by an unrelated distractor word in comparison with a nonword control, and an additional interference effect that is due to a semantic
To explore the effects of commonly encountered pathology on auditory recognition strategies in elderly participants, magnetoencephalographic (MEG) brain activation patterns and performance were examined in 30 elderly [18 controls and 12 elderly with mild cognitive impairment (MCI) or probable Alzheimer's disease (AD)]. It was predicted that participants with known pathology would reveal different networks of brain activation, compared to healthy elderly, which should correlate with poorer performance. Participants heard a list of words representing common objects, twice. After 20 minutes a list of new and old words was presented and participants judged whether each word was heard earlier. MEG responses were analyzed using a semiautomated source modeling procedure. A cluster analysis using all subjects' MEG sources revealed three dominant patterns of activity which correlated with IQ and task performance. The highest performing group revealed activity in premotor, anterior temporal, and superior parietal lobes with little contribution from prefrontal cortex. Performance and brain activation patterns were also compared for individuals with or without abnormalities such as white matter hyperintensities and/or volume reduction evidenced on their MRIs. Memory performance and activation patterns for individuals with white matter hyperintensities resembled the group of MCI/AD patients. These results emphasize the following: (1) general pathology correlates with cognitive decline and (2) full characterization of the health of elderly participants is important in studies of normal aging since random samples from the elderly population are apt to include individuals with subclinical pathology that can affect cognitive performance. PMID:19962439
Aine, C J; Bryant, J E; Knoefel, J E; Adair, J C; Hart, B; Donahue, C H; Montaño, R; Hayek, R; Qualls, C; Ranken, D; Stephen, J M
Sentence comprehension (SC) studies in typical and impaired readers suggest that reading for meaning involves more extensive brain activation than reading isolated words. Thus far, no reading disability/dyslexia (RD) studies have directly controlled for the wordrecognition (WR) components of SC tasks, which is central for understanding comprehension processes beyond WR. This experiment compared SC to WR in 29, 9–14 year olds (15 typical and 14 impaired readers). The SC-WR contrast for each group showed activation in left inferior frontal and extrastriate regions, but the RD group showed significantly more activation than Controls in areas associated with linguistic processing (left middle/superior temporal gyri), and attention and response selection (bilateral insula, right cingulate gyrus, right superior frontal gyrus, and right parietal lobe). Further analyses revealed this overactivation was driven by the RD group's response to incongruous sentences. Correlations with out-of-scanner measures showed that better word- and text-level reading fluency was associated with greater left occipitotemporal activation, whereas worse performance on WR, fluency, and comprehension (reading and oral) were associated with greater right hemisphere activation in a variety of areas, including supramarginal and superior temporal gyri. Results provide initial foundations for understanding the neurobiological correlates of higher-level processes associated with reading comprehension.
Effects of background talkers (0, 1, 2, 3, 4, 5, 7, 10, and 14) on wordrecognition and awareness of errant/accurate responses were examined. Diagnostic Rhyme Test (DRT) words and background talkers were presented at 70 dB SPL (sound field) to ten normal-hearing subjects. DRT words and background talkers were digitally processed to produce equal VU meter levels. Three replicates were obtained for each condition. Performance measures were: (1) percent correct, corrected for guessing, transformed to rational arcsine units (PCGRAU); (2) subject's awareness of accurate responses (AA); (3) subject's awareness of errant responses (AE); and (4) a geometric-based symmetric awareness (SA). Awareness measures were derived from subject's confidence ratings to DRT stimuli. Both informational and direct masking effects were present. PCGRAU varied nonlinearly as number of talkers increased. One talker provided significantly more masking than two talkers and was equally effective as three, four, five, seven, and ten talkers as well as speech-spectrum noise. Fourteen talkers provided the most masking and was equally effective as white noise. In general, AA tended to diminish while AE tended to improve as additional background talkers were added. SA was best for 14-talker background noise but poorest for one-talker and speech-spectrum noise.
A targetrecognition capability is described that performs: color target detection, target type and pose hypothesis generation, and target type verification by 3-D alignment of target models to range and electro-optical imagery. The term 'coregistration' ...
J. R. Beveridge B. A. Draper M. R. Stevens A. Hanson K. Siejko
The bacterial signal recognition particle (SRP) binds to ribosomes synthesizing inner membrane proteins and, by interaction with the SRP receptor, FtsY, targets them to the translocon at the membrane. Here we probe the conformation of SRP and SRP protein, Ffh, at different stages of targeting by measuring fluorescence resonance energy transfer (FRET) between fluorophores placed at various positions within SRP. Distances derived from FRET indicate that SRP binding to nontranslating ribosomes triggers a global conformational change of SRP that facilitates binding of the SRP receptor, FtsY. Binding of SRP to a signal-anchor sequence exposed on a ribosome-nascent chain complex (RNC) causes a further change of the SRP conformation, involving the flexible part of the Ffh(M) domain, which increases the affinity for FtsY of ribosome-bound SRP up to the affinity exhibited by the isolated NG domain of Ffh. This indicates that in the RNC–SRP complex the Ffh(NG) domain is fully exposed for binding FtsY to form the targeting complex. Binding of FtsY to the RNC–SRP complex results in a limited conformational change of SRP, which may initiate subsequent targeting steps.
Buskiewicz, Iwona A.; Jockel, Johannes; Rodnina, Marina V.; Wintermeyer, Wolfgang
It has recently been shown that interhemispheric communication is needed for the processing of foveally presented words. In this study, we examine whether the integration of information happens at an early stage, before wordrecognition proper starts, or whether the integration is part of the recognition process itself. Two lexical decision…
Van der Haegen, Lise; Brysbaert, Marc; Davis, Colin J.
Abstract We present a system covering the complete process for automatic ground targetrecognition, from sensor data to the user interface, i.e., from low level image processing to high level situation analysis. The system is based on a query lan- guage and a query processor, and includes target detection, targetrecognition, data fusion, presentation and situation analysis. This paper focuses
Jorgen Ahlberg; Martin Folkesson; Christina Gronwall; Tobias Horney; Erland Jungert; Lena Klasen; Morgan Ulvklo
It has been proven that 3D ladar imagery has a strong potential for automatic target detection (ATD) and automatic targetrecognition (ATR); ladars enhance target information, which may then be exploited to yield higher recognition rates and lower false alarms. Although numerous techniques have been proposed for both 3D ATD and 3D ATR, no single approach has proven capable of
Diverse parameters decomposed from quad-polarimetric SAR could become the important basis in targetrecognition, classification and other applications.During the targetrecognition, due to the sidedness of single parameter or two parameters decomposed by same algorithm, obvious differences exist among the results extracted by different parameters. In this paper, a parametric statistics and multidimensional analysis algorithm will be applied in target
The problem of target classification with high-resolution, fully polarimetric, SAR imagery is considered. We propose a framework of using a Bayesian network for feature fusion to deal with the difficult problem of SAR target classification. One difficult problem in SAR feature identification and fusion for target classification is that the features identified may not be independent and that it is not easy to find the 'right' fusion rule to combine them. The Bayesian network model when constructed properly can explicitly represent the conditional independence and dependence between various features and therefore provide a sound and natural framework for feature fusion. This paper summarizes our recent work in SAR targetrecognition using the a feature-based Bayesian inference approach. The approach works on the selected features, which are chosen so that the separability of the original data is well maintained for later classification. Once the original data are mapped into the feature space, the probabilistic model between features and the target is estimated and represented by a Bayesian network, which is then used to calculate the probabilities that a target belongs to one of the given classes based on the observed features. A comparison between the above technique and the traditional statistical approaches such as nearest men and Fisher pairwise is illustrated based on performance on a fully polarimetric ISAR (inverse SAR) image data set. Note that although the feature set used in the paper is obtained from the same sensor, the concepts of feature selection and Bayesian network formulation discussed in the paper are note restricted to this case only. They can be applied for multisensor feature-level fusion as well.
The set of orthogonal eigen-vectors built via principal component analysis (PCA), while very effective for com- pression, can often lead to loss of crucial discriminative information in signals. In this work, we build a new basis set using synthetic aperture radar (SAR) target images via non-negative matrix approximations (NNMAs). Owing to the underlying physics, we expect a non-negative basis and an accompanying non-negative coecient set to be a more accurate generative model for SAR proles than the PCA basis which lacks direct physical interpretation. The NNMA basis vectors while not orthogonal capture discriminative local components of SAR target images. We test the merits of the NNMA basis representation for the problem of automatic targetrecognition using SAR images with a support vector machine (SVM) classier. Experiments on the benchmark MSTAR database reveal the merits of basis selection techniques that can model imaging physics more closely and can capture inter-class variability, in addition to identifying a trade-off between classication performance and availability of training.
Abstract This paper proposes,an automatic,targetrecognition,(ATR) system,based,on the three-dimensional (3D) recon- struction of the target from an image sequence. The main,contribution of this work is twofold: (1) we present a modified voxel coloring reconstruction,algorithm and (2) we employ,the 3D reconstructed,target model,to generate the front and side target templates,at zero depression angle to be used in the targetrecognition,process. Targetrecognition
Mostafa G.-H. Mostafa; Elsayed E. Hemayed; Aly A. Farag
Limiting capabilities of practical recognition systems are de- termined by a variety of factors that include source encod- ing techniques, quality of images, complexity of underlying objects and their projections. Given a source encoding tech- nique, the remaining factors are characteristics of a recogni- tion channel. In this work, we evaluate recognition capacity of a PCA-based Automatic TargetRecognition system.
Research using online comprehension measures with monolingual children shows that speed and accuracy of spoken wordrecognition are correlated with lexical development. Here we examined speech processing efficiency in relation to vocabulary development in bilingual children learning both Spanish and English (n=26 ; 2 ; 6). Between-language…
Marchman, Virginia A.; Fernald, Anne; Hurtado, Nereyda
This article describes the usage of linguistic units and instructional strategies that facilitate wordrecognition for Latino kindergarten students who are beginning to read in Spanish. This case study was based on coding videotaped reading and language arts instruction of two bilingual kindergarten teachers at the beginning, middle, and end of…
Pollard-Durodola, Sharolyn D.; Cedillo, Gabriela Delagarza; Denton, Carolyn A.
We present an MEG study of heteronym recognition, aiming to distinguish between two theories of lexical access: the "early access" theory, which entails that lexical access occurs at early (pre 200 ms) stages of processing, and the "late access" theory, which interprets this early activity as orthographic word-form identification rather than…
A study was conducted to examine the relationship of perceptual motor skills as measured by the Bender Visual Motor Gestalt Test to wordrecognition, oral reading, and silent reading. In addition, perceptual motor skill and auditory comprehension were compared as correlates of the three reading variables. Subjects were 60 primary grade students in…
Recent research has demonstrated that slight increases of inter-letter spacing have a positive impact on skilled readers' recognition of visually presented words. In the present study, we examined whether this effect generalises to young normal readers and readers with developmental dyslexia, and whether increased inter-letter spacing affects the…
Perea, Manuel; Panadero, Victoria; Moret-Tatay, Carmen; Gomez, Pablo
Recent studies of adults have found evidence for consolidation effects in the acquisition of novel words, but little is known about whether such effects are found developmentally. In two experiments, we familiarized children with novel nonwords (e.g., "biscal") and tested their recognition and recall of these items. In Experiment 1, 7-year-olds…
Brown, Helen; Weighall, Anna; Henderson, Lisa M.; Gaskell, M. Gareth
The present study sought to clarify the relations amongst serial decoding, irregular wordrecognition, listening comprehension, facets of oral vocabulary and reading comprehension in two cohorts of children differing in reading level. In the process, the components of the simple view of reading were evaluated. Students in grades 1 (n = 67) and 6…
Purpose: This study examined differences in voiced consonant-vowel (CV) perception in older listeners with normal hearing and in 2 groups of older listeners with matched hearing losses: those with good and those with poor wordrecognition scores. Method: Thirty-six participants identified CVs from an 8-item display from the natural voiced initial…
Phillips, Susan L.; Richter, Scott J.; McPherson, David
Until now, research on bilingual auditory wordrecognition has been scarce, and although most studies agree that lexical access is language-nonselective, there is less consensus with respect to the influence of potentially constraining factors. The present study investigated the influence of three possible constraints. We tested whether language…
Lagrou, Evelyne; Harsuiker, Robert J.; Duyck, Wouter
Functional magnetic resonance imaging was used to investigate the role of phonology in visual wordrecognition (VWR). A group of children between the ages of 7 and 13 participated in a lexical decision task in which lexical frequency and homophony were manipulated. A significant homophone effect was observed for the high-frequency condition, indicating that phonology does indeed play a significant role in VWR. The brain activation patterns also support this idea in that regions that have been linked to phonological processing, the inferior frontal gyrus and the inferior parietal lobe, also revealed a homophone effect. Additionally, the posterior superior temporal cortex showed a homophone effect; however, this activation is argued to be related to lexical competition generated by the high-frequency homophone via the activation of multiple semantic representations. PMID:21660483
In this paper we overview the nonlinear matched filtering for photon counting recognition with 3D passive sensing. The first and second order statistical properties of the nonlinear matched filtering can improve the recognition performance compared to the linear matched filtering. Automatic target reconstruction and recognition are addressed for partially occluded objects. The recognition performance is shown to be improved significantly in the reconstruction space. The discrimination capability is analyzed in terms of Fisher ratio (FR) and receiver operating characteristic (ROC) curves.
A multi-stage automated targetrecognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.
Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver
Performance of Automatic TargetRecognition (ATR) algorithms for Synthetic Aperture Radar (SAR) systems relies heavily on the system performance and specifications of the SAR sensor. A representative multi-stage SAR ATR algorithm [1, 2] is analyzed across imagery containing phase errors in the down-range direction induced during the transmission of the radar's waveform. The degradation induced on the SAR imagery by the phase errors is measured in terms of peak phase error, Root-Mean-Square (RMS) phase error, and multiplicative noise. The ATR algorithm consists of three stages: a two-parameter CFAR, a discrimination stage to reduce false alarms, and a classification stage to identify targets in the scene. The end-to-end performance of the ATR algorithm is quantified as a function of the multiplicative noise present in the SAR imagery through Receiver Operating Characteristic (ROC) curves. Results indicate that the performance of the ATR algorithm presented is robust over a 3dB change in multiplicative noise.
Montagnino, Lee J.; Cassabaum, Mary L.; Halversen, Shawn D.; Rupp, Chad T.; Wagner, Gregory M.; Young, Matthew T.
Our objective has been to find a preferred method for the identification of static targets in single IR images, concentrating on appearance-based methods. This has included thermal modelling of IR signatures and the identification of images of different objects with variation in pose and thermal state. Using principal component analysis, the variances among the images are extracted and represented in a low-dimensional feature eigenspace. Any new image can be projected into the eigenspace by taking an inner product with the basis. The object of interest can be recognized by a nearest-neighbour classification rule, made more accurate by application of over-sampling to the surface manifold by B-spline surface fitting, and made more efficient by a k-d tree search algorithm. To address the problems of recognizing targets in noisy and cluttered images, we have employed a random sampling approach that is based on the principle of high-breakdown point estimation. We have generated a database of images using visible and thermal cameras, in addition to scene simulation software, for use in the learning and recognition/evaluation phases. Our experiments indicate that application of the robust algorithm can reduce the recovery error of the true model image data, for example by a factor of five when the images contain 40% randomly changed image pixels.
Wang, Xun; Kitchin, Matthew R.; Trucco, Emanuele; Wallace, Andrew M.
We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.
In order to gain insight into the interplay between the talker-, listener-, and item-related factors that influence speech perception, a large multi-talker database of digitally recorded spoken words was developed, and was then submitted to intelligibility tests with multiple listeners. Ten talkers produced two lists of words at three speaking rates. One list contained lexically “easy” words (words with few phonetically similar sounding “neighbors” with which they could be confused), and the other list contained lexically “hard” (wordswords with many phonetically similar sounding “neighbors”). An analysis of the intelligibility data obtained with native speakers of English (experiment 1) showed a strong effect of lexical similarity. Easy words had higher intelligibility scores than hard words. A strong effect of speaking rate was also found whereby slow and medium rate words had higher intelligibility scores than fast rate words. Finally, a relationship was also observed between the various stimulus factors whereby the perceptual difficulties imposed by one factor, such as a hard word spoken at a fast rate, could be overcome by the advantage gained through the listener's experience and familiarity with the speech of a particular talker. In experiment 2, the investigation was extended to another listener population, namely, non-native listeners. Results showed that the ability to take advantage of surface phonetic information, such as a consistent talker across items, is a perceptual skill that transfers easily from first to second language perception. However, non-native listeners had particular difficulty with lexically hard words even when familiarity with the items was controlled, suggesting that non-native wordrecognition may be compromised when fine phonetic discrimination at the segmental level is required. Taken together, the results of this study provide insight into the signal-dependent and signal-independent factors that influence spoken language processing in native and non-native listeners.
Many studies have repeatedly shown an orthographic consistency effect in the auditory lexical decision task. Words with phonological rimes that could be spelled in multiple ways (i.e., inconsistent words) typically produce longer auditory lexical decision latencies and more errors than do words with rimes that could be spelled in only one way (i.e., consistent words). These results have been extended to different languages and tasks, suggesting that the effect is quite general and robust. Despite this growing body of evidence, some psycholinguists believe that orthographic effects on spoken language are exclusively strategic, post-lexical, or restricted to peculiar (low-frequency) words. In the present study, we manipulated consistency and word-frequency orthogonally in order to explore whether the orthographic consistency effect extends to high-frequency words. Two different tasks were used: lexical decision and rime detection. Both tasks produced reliable consistency effects for both low- and high-frequency words. Furthermore, in Experiment 1 (lexical decision), an interaction revealed a stronger consistency effect for low-frequency words than for high-frequency words, as initially predicted by Ziegler and Ferrand (1998), whereas no interaction was found in Experiment 2 (rime detection). Our results extend previous findings by showing that the orthographic consistency effect is obtained not only for low-frequency words but also for high-frequency words. Furthermore, these effects were also obtained in a rime detection task, which does not require the explicit processing of orthographic structure. Globally, our results suggest that literacy changes the way people process spoken words, even for frequent words.
Petrova, Ana; Gaskell, M. Gareth; Ferrand, Ludovic
This paper discusses the application of Hidden Markov Models (HMMs) to the Automatic TargetRecognition (TRI-ATR) problem in Synthetic Aperture Radar (SAR) imagery. Related research with applications of the HMMs to solve SAR Automatic TargetRecognition (ATR) problems can also be found in Kottke et al. Our approach is based on a cascade of three stages: preprocessing, feature extraction and
Chanin Nilubol; Quoc H. Pham; Russell M. Mersereau; Mark J. Smith; Mark A. Clements
A hybrid approach to automatic targetrecognition (ATR) combining the complementary strengths of conventional image processing algorithms, artificial neural networks, and knowledge based expert systems is presented. The architecture employs parallel feature and pixel processing channels, the former using a self-organizing neural network and the latter using a supervised learning neural network. The feasibility of the hybrid automatic targetrecognition
Speech Recognition systems, historically, have proven to be cumbersome and insufficiently accurate for a range of applications. The ultimate goal of our proposed technology is to fundamentally change the way current Speech Recognition (SR) systems interact with humans and develop an application that is extremely hardware efficient. Accurate SR and reasonable hardware requirements will afford the average first responder officer, e.g., police officer, a true break-through technology that will change the way an officer performs his duties. The presented technology provides a cutting-edge solution for human-machine interaction through the utilization of a properly solved Wake-Up-Word (WUW) SR problem. This paradigm-shift provides the basis for development of SR systems with truly "Voice Activated" capabilities, impacting all SR based technologies and the way in which humans interact with computers. This shift is a radical departure from the current "push-to-talk" paradigm currently applied to all speech-to-text or speech-recognition applications. To be able to achieve this goal, a significantly more accurate pattern classification and scoring technique is required, which in turn provides SR systems enhanced performance for correct recognition (i.e., minimization of false rejection) as well as correct rejection (i.e., minimization of false acceptance). A revolutionary and innovative classification and scoring technique is used that is a significant enhancement over an earlier method presented in reference . The solution in reference  has been demonstrated to meet the stringent requirements of the WUW-SR task. Advanced solution of  is a novel technique that is model and algorithm independent. Therefore, it could be used to significantly improve performance of existing recognition algorithms and systems. Reduction of error rates of over 40% are commonly observed for both false rejections and false acceptance. In this paper the architecture of the WUW-SR based system as interface to current SR applications is presented. In this system WUW-SR is used as a gateway for truly Voice Activated applications utilizing the current solution without "push-to-talk" paradigm. The technique has been developed with hardware optimization in mind and therefore has the ability to run as a "background" application on a standard Windows-based PC platform.
Dividing attention across multiple words occasionally results in misidentifications whereby letters apparently migrate between words. Previous studies have found that letter migrations preserve within-word letter position, which has been interpreted as support for position-specific letter coding. To investigate this issue, the authors used word pairs like STEP and SOAP, in which a letter in 1 word could migrate to an
How does the meaning of a word affect how quickly we can recognize it? Accounts of visual wordrecognition allow semantic information to facilitate performance but have neglected the role of modality-specific perceptual attention in activating meaning. We predicted that modality-specific semantic information would differentially facilitate lexical decision and reading aloud, depending on how perceptual attention is implicitly directed by each task. Large-scale regression analyses showed the perceptual modalities involved in representing a word's referent concept influence how easily that word is recognized. Both lexical decision and reading-aloud tasks direct attention toward vision, and are faster and more accurate for strongly visual words. Reading aloud additionally directs attention toward audition and is faster and more accurate for strongly auditory words. Furthermore, the overall semantic effects are as large for reading aloud as lexical decision and are separable from age-of-acquisition effects. These findings suggest that implicitly directing perceptual attention toward a particular modality facilitates representing modality-specific perceptual information in the meaning of a word, which in turn contributes to the lexical decision or reading-aloud response. PMID:24099578
Previous research has suggested that faces and words are processed and remembered differently as reflected by different ERP patterns for the two types of stimuli. Specifically, face stimuli produced greater late positive deflections for old items in anterior compared to posterior regions, while word stimuli produced greater late positive deflections in posterior compared to anterior regions. Given that words have existing representations in subjects? long-term memories (LTM) and that face stimuli used in prior experiments were of unknown individuals, we conducted an ERP study that crossed face and letter stimuli with the presence or absence of a prior (stable or existing) memory representation. During encoding, subjects judged whether stimuli were known (famous face or real word) or not known (unknown person or pseudo-word). A surprise recognition memory test required subjects to distinguish between stimuli that appeared during the encoding phase and stimuli that did not. ERP results were consistent with previous research when comparing unknown faces and words; however, the late ERP pattern for famous faces was more similar to that for words than for unknown faces. This suggests that the critical ERP difference is mediated by whether there is a prior representation in LTM, and not whether the stimulus involves letters or faces. PMID:24530268
Online comprehension of naturally spoken and perceptually degraded words was assessed in 95 children ages 12 to 31 months. The time course of wordrecognition was measured by monitoring eye movements as children looked at pictures while listening to familiar targetwords presented in unaltered, time-compressed, and low-pass-filtered forms. Success in wordrecognition varied with age and level of vocabulary development, and with the perceptual integrity of the word. Recognition was best overall for unaltered words, lower for time-compressed words, and significantly lower in low-pass-filtered words. Reaction times were fastest in compressed, followed by unaltered and filtered words. Results showed that children were able to recognize familiar words in challenging conditions and that productive vocabulary size was more sensitive than chronological age as a predictor of children’s accuracy and speed in wordrecognition.
Zangl, Renate; Klarman, Lindsay; Thal, Donna; Fernald, Anne; Bates, Elizabeth
SUMOylation contributes to the regulation of many essential cellular factors. Diverse techniques have been used to explore the functional consequences of protein SUMOylation. Most approaches consider the identification of sequences on substrates, adaptors, or receptors regulating the SUMO conjugation, recognition, or deconjugation. The large majority of the studied SUMOylated proteins contain the sequence [IVL]KxE. SUMOylated proteins are recognized by at least 3 types of hydrophobic SUMO-interacting motifs (SIMs) that contribute to coordinate SUMO-dependent functions. Typically, SIMs are constituted by a hydrophobic core flanked by one or two clusters of negatively charged amino acid residues. Multiple SIMs can integrate SUMO binding domains (SBDs), optimizing binding, and control over SUMO-dependent processes. Here, we present a survey of the methodologies used to study SUMO-regulated functions and provide guidelines for the identification of cis and trans sequences controlling SUMOylation. Furthermore, an integrative analysis of known and putative SUMO substrates illustrates an updated landscape of several SUMO-regulated events. The strategies and analysis presented here should contribute to the understanding of SUMO-controlled functions and provide rational approach to identify biomarkers or choose possible targets for intervention in processes where SUMOylation plays a critical role.
Da Silva-Ferrada, Elisa; Lopitz-Otsoa, Fernando; Lang, Valerie; Rodriguez, Manuel S.; Matthiesen, Rune
Laser detection and ranging (ladar) range images have attracted considerable attention in the field of automatic targetrecognition. Generally, it is difficult to collect a mass of range images for ladar in real applications. However, with small samples, the Hughes effect may occur when the number of features is larger than the size of the training samples. A random subspace ensemble of support vector machine (RSE-SVM) is applied to solve the problem. Three experiments were performed: (1) the performance comparison among affine moment invariants (AMIs), Zernike moment invariants (ZMIs) and their combined moment invariants (CMIs) based on different size training sets using single SVM; (2) the impact analysis of the different number of features about the RSE-SVM and semi-random subspace ensemble of support vector machine; (3) the performance comparison between the RSE-SVM and the CMIs with SVM ensembles. The experiment's results demonstrate that the RSE-SVM is able to relieve the Hughes effect and perform better than ZMIs with single SVM and CMIs with SVM ensembles.
In this paper, we introduce a neural network recognition method, MENN (minimum error neural network) method, in targetrecognition. From the target gray sequences, we can extract some useful characteristics. Then we use these features as the input data of the MENN classifier. By these characteristics, using the MENN classifier we can easily pick out the true targets from the candidate target sequences. MENN recognition method can not only pick out the true target and reject the false targets, but it also gets rid of the baits. Therefore, it has high reliability. Moreover, it has many advantages, for example, its training is a one pass process, its test process is not only simple but also straightforward, and its calculation is simple, etc. On account of those advantages, MENN recognition method is adaptive to the need of realtime processing.
A new algorithm is presented for generating the conditional mean estimates of functions of target positions, orientations and type in recognition, and tracking of an unknown number of targets and target types. Taking a Bayesian approach, a posterior measure is defined on the tracking\\/target parameter space by combining a narrowband sensor array manifold model with a high resolution imaging model,
Three ERP experiments examined the effect of word presentation rate (i.e., stimulus onset asynchrony, SOA) on the time course of word frequency and predictability effects in sentence reading. In Experiments 1 and 2, sentences were presented word-by-word in the screen center at an SOA of 700 and 490ms, respectively. While these rates are typical…
Dambacher, Michael; Dimigen, Olaf; Braun, Mario; Wille, Kristin; Jacobs, Arthur M.; Kliegl, Reinhold
Studies of word production in patients with Alzheimer's disease have identified the age of acquisition of words as an important predictor of retention or loss, with early acquired words remaining accessible for longer than later acquired words. If, as proposed by current theories, effects of age of acquisition reflect the involvement of semantic…
Cuetos, Fernando; Herrera, Elena; Ellis, Andrew W.
In this paper, a new feature extraction algorithm named 2D-DLPP (Two-dimensional Discriminant Locality Preserving Projections) is used for SAR ATR (Synthetic Aperture Radar Automatic TargetRecognition). First, SAR target images are preprocessed by log-transformation and 2D FFT, then 2D-DLPP is applied to extract target feature which can not only preserve local information by capturing the local geometry of manifold but also implement sample dimension reduction effectively. Finally, classification with SVM (Support Vector Machine) is performed to get the good recognition rate. Experimental results based on MSTAR (Moving and Stationary Target Acquisition and Recognition) SAR data demonstrate that 2D-DLPP can obtain more effective target feature and improve the recognition rate obviously compared with 2D-LDA (Two-dimensional Linear Discriminant Analysis).
In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic targetrecognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination
Memory disturbances found in obsessive–compulsive disorder (OCD) may partially be related to dysfunction of cortico–subcortical circuits. However, it is still unknown how OCD symptomatology is related to memory processing. To explore this question, event-related potentials (ERPs) were recorded in a continuous word-recognition paradigm in OCD patients with either severe or moderate scores on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) (group S
Yuanyuan Zhang; Sebastian Feutl; Ute Hauser; Claudia Richter-Witte; Philip Schmorl; Hinderk M. Emrich; Detlef E. Dietrich
We used eye tracking to investigate lexical processing in aphasic participants by examining the fixation time course for rhyme (e.g., carrot – parrot) and cohort (e.g., beaker – beetle) competitors. Broca’s aphasic participants exhibited larger rhyme competition effects than age-matched controls. A reanalysis of previously reported data (Yee, Blumstein, & Sedivy, 2008) confirmed that Wernicke’s aphasic participants exhibited larger cohort competition effects. Individual-level analyses revealed a negative correlation between rhyme and cohort competition effect size across both groups of aphasic participants. Computational model simulations were performed to examine which of several accounts of lexical processing deficits in aphasia might account for the observed effects. Simulation results revealed that slower deactivation of lexical competitors could account for increased cohort competition in Wernicke’s aphasic participants; auditory perceptual impairment could account for increased rhyme competition in Broca's aphasic participants; and a perturbation of a parameter controlling selection among competing alternatives could account for both patterns, as well as the correlation between the effects. In light of these simulation results, we discuss theoretical accounts that have the potential to explain the dynamics of spoken wordrecognition in aphasia and the possible roles of anterior and posterior brain regions in lexical processing and cognitive control.
Mirman, Daniel; Yee, Eiling; Blumstein, Sheila E.; Magnuson, James S.
Research on the development of efficiency in spoken language understanding has focused largely on middle-class children learning English. Here we extend this research to Spanish-learning children (n=49; M=2;0; range=1;3–3;1) living in the USA in Latino families from primarily low socioeconomic backgrounds. Children looked at pictures of familiar objects while listening to speech naming one of the objects. Analyses of eye movements revealed developmental increases in the efficiency of speech processing. Older children and children with larger vocabularies were more efficient at processing spoken language as it unfolds in real time, as previously documented with English learners. Children whose mothers had less education tended to be slower and less accurate than children of comparable age and vocabulary size whose mothers had more schooling, consistent with previous findings of slower rates of language learning in children from disadvantaged backgrounds. These results add to the cross-linguistic literature on the development of spoken wordrecognition and to the study of the impact of socioeconomic status (SES) factors on early language development.
HURTADO, NEREYDA; MARCHMAN, VIRGINIA A.; FERNALD, ANNE
We used eye-tracking to investigate lexical processing in aphasic participants by examining the fixation time course for rhyme (e.g., carrot-parrot) and cohort (e.g., beaker-beetle) competitors. Broca's aphasic participants exhibited larger rhyme competition effects than age-matched controls. A re-analysis of previously reported data (Yee, Blumstein, & Sedivy, 2008) confirmed that Wernicke's aphasic participants exhibited larger cohort competition effects. Individual-level analyses revealed a negative correlation between rhyme and cohort competition effect size across both groups of aphasic participants. Computational model simulations were performed to examine which of several accounts of lexical processing deficits in aphasia might account for the observed effects. Simulation results revealed that slower deactivation of lexical competitors could account for increased cohort competition in Wernicke's aphasic participants; auditory perceptual impairment could account for increased rhyme competition in Broca's aphasic participants; and a perturbation of a parameter controlling selection among competing alternatives could account for both patterns, as well as the correlation between the effects. In light of these simulation results, we discuss theoretical accounts that have the potential to explain the dynamics of spoken wordrecognition in aphasia and the possible roles of anterior and posterior brain regions in lexical processing and cognitive control. PMID:21371743
Mirman, Daniel; Yee, Eiling; Blumstein, Sheila E; Magnuson, James S
Correlation filters (CFs) can detect multiple targets in one scene making them well-suited for automatic targetrecognition (ATR) applications. Quadratic CFs (QCFs) can improve performance over linear CFs. QCFs are able to detect one class of targets and reject clutter. We present a method to increase the QCF capabilities to detect two classes of targets and reject clutter. We integrate the ATR tasks of detection, recognition, and tracking algorithms using the Multi-Frame Correlation Filter (MFCF) framework. Our simulation results demonstrate the algorithm's ability to detect multiple targets from two classes while rejecting clutter.
Neptec Design Group has developed a 3D automatic targetrecognition and pose estimation algorithm technology demonstrator in partnership with Canadian DND. This paper discusses the development of the algorithm to work with real sensor data. The recognition approach uses a combination of two algorithms in a multi-step process. The two algorithms provide uncorrelated metrics and are therefore using different characteristics
Chad E. English; Stephane Ruel; Len Melo; Philip M. Church; Jean Maheux
For the Automatic TargetRecognition (ATR) algorithm, the quality of the input image sequence can be a majordetermining factor as to the ATR algorithm`s ability to recognize an object. Based on quality, an image can beeasy to recognize, barely recognizable or even mangled beyond recognition. If a determination of the image qualitycan be made prior to entering the ATR algorithm,
Adults with sensory impairment, such as reduced hearing acuity, have impaired ability to recall identifiable words, even when their memory is otherwise normal. We hypothesize that poorer stimulus quality causes weaker activity in neurons responsive to the stimulus and more time to elapse between stimulus onset and identification. The weaker activity and increased delay to stimulus identification reduce the necessary strengthening of connections between neurons active before stimulus presentation and neurons active at the time of stimulus identification. We test our hypothesis through a biologically motivated computational model, which performs item recognition, memory formation and memory retrieval. In our simulations, spiking neurons are distributed into pools representing either items or context, in two separate, but connected winner-takes-all (WTA) networks. We include associative, Hebbian learning, by comparing multiple forms of spike-timing-dependent plasticity (STDP), which strengthen synapses between coactive neurons during stimulus identification. Synaptic strengthening by STDP can be sufficient to reactivate neurons during recall if their activity during a prior stimulus rose strongly and rapidly. We find that a single poor quality stimulus impairs recall of neighboring stimuli as well as the weak stimulus itself. We demonstrate that within the WTA paradigm of wordrecognition, reactivation of separate, connected sets of non-word, context cells permits reverse recall. Also, only with such coactive context cells, does slowing the rate of stimulus presentation increase recall probability. We conclude that significant temporal overlap of neural activity patterns, absent from individual WTA networks, is necessary to match behavioral data for word recall.
This study examined to what extent young second language (L2) learners showed morphological family size effects in L2 wordrecognition and whether the effects were grade-level related. Turkish-Dutch bilingual children (L2) and Dutch (first language, L1) children from second, fourth, and sixth grade performed a Dutch lexical decision task on words…
de Zeeuw, Marlies; Verhoeven, Ludo; Schreuder, Robert
Comparison of the reading of rhymes by 20 children with cognitive disabilities (Down syndrome or autism) and 20 typically developing children (all matched for wordrecognition skills) found both groups were more similar than dissimilar in their rhyme-recognition accuracy, miscues, and grapheme-phoneme knowledge. (Contains references.) (Author/DB)
Automatic targetrecognition (ATR) in target search phase is very challenging because the target range and mobility are not yet perfectly known, which results in delay-Doppler uncertainty. In this paper, we firstly perform some theoretical studies on radar sensor network (RSN) design based linear frequency modulation (LFM) waveform: (1) the conditions for waveform co-existence, (2) interferences among waveforms in RSN,
This paper introduces techniques for context-aided false alarm reduction for automatic targetrecognition (ATR) in airborne synthetic aperture radar (SAR) images. Candidate target pixels are identified using constant false alarm rate (CFAR) detection. If only a single radar image is available, a 2-D site model is constructed and used to determine regions inhospitable to targets. In multipass imagery, false alarm
The operation and evaluation of a laboratory automatic targetrecognition (ATR) system is described. The ATR system was evaluated against its ability to acquire and track a specific target in the presence of false targets and background clutter. A ten sec...
Fundamental to the model-based paradigm of an Automatic TargetRecognition (ATR) system is an accurate representation (a model) of the physical objects to be recognized. Detailed CAD models of targets of interest can be created using photographs, blueprints, and other intelligence sources. When created this way, the target CAD models are necessarily specific to a particular realization of the vehicle
Wayne D. Williams; Justin Wojdacki; Eric R. Keydel; J. Richard Freeling; Andrew Morgan; Russell Sieron; Stephen A. Stanhope; Vasik G. Rajlich
Since there is no generally applicable definition of the term aided targetrecognition (ATR), the author takes as a working definition the task of mapping scenes to representations and extracting information concerning specific elements from these represe...
The overall objective of this project is to develop transformative theory and algorithms for robust Automated TargetRecognition (ATR). This project addressed the following challenging problems in ATR: modeling uncertainty, small sample size, high dimensi...
As the focus of our present paper, we have used the cascade error projection (CEP) learning algorithm (shown to be hardware-implementable) with on-chip learning (OCL) scheme to obtain three orders of magnitude speed-up in targetrecognition compared to software-based learning schemes. Thus, it is shown, real time learning as well as data processing for targetrecognition can be achieved.
We present a pose-independent Automatic Target Detection and Recognition (ATD/R) System using data from an airborne 3D imaging ladar sensor. The ATD/R system uses geometric shape and size signatures from target models to detect and recognize targets under heavy canopy and camouflage cover in extended terrain scenes. A method for data integration was developed to register multiple scene views to obtain a more complete 3-D surface signature of a target. Automatic target detection was performed using the general approach of "3-D cueing," which determines and ranks regions of interest within a large-scale scene based on the likelihood that they contain the respective target. Each region of interest is further analyzed to accurately identify the target from among a library of 10 candidate target objects. The system performance was demonstrated on five extended terrain scenes with targets both out in the open and under heavy canopy cover, where the target occupied 1 to 5% of the scene by volume. Automatic targetrecognition was successfully demonstrated for 20 measured data scenes including ground vehicle targets both out in the open and under heavy canopy and/or camouflage cover, where the target occupied between 5 to 10% of the scene by volume. Correct target identification was also demonstrated for targets with multiple movable parts that are in arbitrary orientations. We achieved a high recognition rate (over 99%) along with a low false alarm rate (less than 0.01%)
Effects of blocking words by frequency class (high vs. low) and neighborhood density (high vs. low) were examined in two experiments using progressive demasking and lexical decision tasks. The aim was to examine the predictions of a task-specific response criteria account of list-blocking effects. Distinct patterns of blocking effects were obtained in the two tasks. In the progressive demasking task, a pure-list disadvantage was obtained to low frequency-high density words, whereas high frequency-low density produced a trend toward a pure-list advantage. In lexical decision, high-frequency words showed a pure-list advantage that was strongest in high-density words, whereas low frequency-low density words produced a trend toward a pure-list disadvantage. A simulation study implementing task-specific response criteria within the framework of the multiple read-out model provided an accurate description of the blocking effects obtained in the experiments. It is argued that adjustments of task-specific response criteria determine changes in list-blocking effects across different tasks. PMID:15813492
Perea, Manuel; Carreiras, Manuel; Grainger, Jonathan
We present an evaluation of the impact of a recently developed point-enhanced high range-resolution (HRR) radar profile reconstruction method on automatic targetrecognition (ATR) performance. We use several pattern recognition techniques to compare the performance of point-enhanced HRR profiles with conventional Fourier transform-based profiles. We use measured radar data of civilian ships and produce range profiles from such data. We use two types of classifiers to quantify recognition performance. The first type of classifier is based on the nearest neighbor technique. We demonstrate the performance of this classifier using a variety of extracted features, and a number of different distance metrics. The second classifier we use for targetrecognition involves position specific matrices, which have previously been used in gene sequencing. We compare the classification performance of point-enhanced HRR profiles with conventional profiles, and observe that point enhancement results in higher recognition rates in general.
In this paper, a Synthetic Aperture Radar Automatic TargetRecognition approach based on Gaussian process (GP) classification is proposed. It adopts kernel principal component analysis to extract sample features and implements targetrecognition by using GP classification with automatic relevance determination (ARD) function. Compared with k-Nearest Neighbor, NaÃ¯ve Bayes classifier and Support Vector Machine, GP with ARD has the advantage of automatic model selection and hyper-parameter optimization. The experiments on UCI datasets and MSTAR database show that our algorithm is self-tuning and has better recognition accuracy as well.
This research examined phonological awareness (PA) and single word reading in 14 school-age children with autism and 10 age-matched, typically developing (TD) children between 5-7 years. Two measures of PA, an elision task (ELI) and a sound blending task (BLW), were given along with two measures of single word reading, word identification for real…
BACKGROUND: Occipito-temporal N170 component represents the first step where face, object and word processing are discriminated along the ventral stream of the brain. N170 leftward asymmetry observed during reading has been often associated to prelexical orthographic visual word form activation. However, some studies reported a lexical frequency effect for this component particularly during word repetition that appears in contradiction with
Gregory Simon; Laurent Petit; Christian Bernard; Mohamed Rebaï
This paper presents the application of Mutual Information criterion to validate feature sets extracted from handwritten words in Brazilian legal amounts. The lexicon includes a subset of short words without ascenders \\/descenders and subsets of words with the same prefix or suffix. These particularities of the Brazilian lexicon show that is necessary to improve the perpetual feature set with complementary
Cinthia Obladen De Almendra Freitas; Flávio Bortolozzi; Robert Sabourin
We investigated the influences of phonological similarity on the time course of spoken word processing in Mandarin Chinese. Event related potentials were recorded while adult native speakers of Mandarin (N=19) judged whether auditory words matched or mismatched visually presented pictures. Mismatching words were of the following nature: segmental (e.g., picture: hua1 'flower'; sound: hua4 'painting'); cohort (e.g., picture: hua1 'flower'; sound: hui1 'gray'); rhyme (e.g., picture: hua1 'flower'; sound: gua1 'melon'); tonal (e.g., picture: hua1 'flower'; sound: jing1 'whale'); unrelated (e.g., picture: hua1 'flower'; sound: lang2 'wolf'). Expectancy violations in the segmental condition showed an early-going modulation of components (starting at 250 ms post-stimulus onset), suggesting that listeners used tonal information to constrain wordrecognition as soon as it became available, just like they did with phonemic information in the cohort condition. However, effects were less persistent and more left-lateralized in the segmental than cohort condition, suggesting dissociable cognitive processes underlie access to tonal versus phonemic information. Cohort versus rhyme mismatches showed distinct patterns of modulation which were very similar to what has been observed in English, suggesting onsets and rimes are weighted similarly across the two languages. Last, we did not observe effects for whole-syllable mismatches above and beyond those for mismatches in individual components, suggesting the syllable does not merit a special status in Mandarin spoken wordrecognition. These results are discussed with respect to modifications needed for existing models to accommodate the tonal languages spoken by a large proportion of the world's speakers. PMID:22595659
This paper primarily investigates the use of shape-based features by an Automatic TargetRecognition (ATR) system to classify various types of targets in Synthetic Aperture Radar (SAR) images. In specific, shapes of target outlines are represented via Elliptical Fourier Descriptors (EFDs), which, in turn, are utilized as recognition features. According to the proposed ATR approach, a segmentation stage first isolates the target region from shadow and ground clutter via a sequence of fast thresholding and morphological operations. Next, a number of EFDs are computed that can sufficiently describe the salient characteristics of the target outline. Finally, a classification stage based on an ensemble of Support Vector Machines identifies the target with the appropriate class label. In order to experimentally illustrate the merit of the proposed approach, SAR intensity images from the well-known Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset were used as 10-class and 3-class recognition problems. Furthermore, comparisons were drawn in terms of classification performance and computational complexity to other successful methods discussed in the literature, such as template matching methods. The obtained results portray that only a very limited amount of EFDs are required to achieve recognition rates that are competitive to well-established approaches.
This article discusses using sign language to help students with learning disabilities remember sight words. It describes the rationale for using sign language, gives directions for playing a game called Sign-o (similar to the game Bingo), provides extension activities, and includes a game board ready for duplication. (Contains references.)…
The effectiveness of a reading intervention using the whole-word multimedia software "Oxford Reading Tree (ORT) for Clicker" was compared to a reading intervention using traditional ORT Big Books. Developing literacy skills and attitudes towards learning to read were assessed in a group of 17 struggling beginner readers aged 5-6 years. Each child…
Aim at the problem of Automatic TargetRecognition (ATR) for the two color IR imaging system, presented a method for the IR dual band image targetrecognition based on multi- classifiers decision level fusion. This method firstly inputted all kinds of feature vectors of the target image into these relevant classifiers respectively to get the likelihood ratio of the target
Performance in tests of associative memory is generally thought to require recollection while familiarity cannot support memory for associations. However, recent research suggested that familiarity contributes to associative memory when the to-be-associated stimuli are unitized during encoding. Here, we investigated the electrophysiological correlates of retrieval of word pairs after two different encoding conditions. Semantically unrelated word pairs were presented as separate lexical units in a sentence frame (non-unitized word pairs) or together with a definition that allows to combine word pairs to a new concept (unitized word pairs). At test, participants discriminated between word pairs that appeared in the same pairing during study, recombined, or new pairs. Memory processes were examined by means of event-related potentials (ERPs). An early old/new effect with a parietal maximum was found for unitized word pairs while a qualitatively different late old/new effect was elicited by non-unitized word pairs, only. These findings suggest that one-trial-unitized word pairs are recognized differently from non-unitized word pairs. We will discuss the possibility that unitization leads to the engagement of specific forms of familiarity-conceptual fluency and absolute familiarity. PMID:20045471
The process of word form encoding was investigated in primed word naming and word typing with Chinese monosyllabic words. The targetwords shared or did not share the onset consonants with the prime words. The stimulus onset asynchrony (SOA) was 100 ms or 300 ms. Typing required the participants to enter the phonetic letters of the targetword,…
(Abstract)In order to solve larger workload problems when Kernel Nearest Feature Line(KNFL) and Kernel Nearest Feature Plane(KNFP) classifiers compute large data size and high dimensionality, an improved strategy for these two classifiers is proposed according to locally nearest neighbor rule, which reduces the disabled possibility, and promotes the algorithms real-time performances under condition of similar recognition rate. It is tested
Traditionally, Inverse Synthetic Aperture Radar (ISAR) image frames are classified individually in an automatic targetrecognition system. When information from dierent image frames is combined, it is usually in the context of time- averaging to remove statistically independent noise fluctuations between images. The sea state induced variability of the ship target projections between frames, however, also provides additional information about
Tristrom Cooke; Marco Martorella; Brett Haywood; Danny Gibbins
Automatic targetrecognition (ATR) of aircrafts using translation invariant features derived from high range resolution (HRR) profiles and multilayered neural network is presented in this paper. The HRR profile sequences are translation variant in the range resolution cell because of the non-cooperative target maneuvering. The differential power spectrum (DPS) is introduced to extract the translation invariant features. Several learning algorithms
Correlation filters are attractive for automatic targetrecognition (ATR) applications due to such attributes as shift invariance, distortion tolerance and graceful degradation. Composite correlation filters are designed to handle target distortions by training on a set of images that represent the expected distortions during testing. However, if the distortion can be described algebraically, as in the case of in-plane rotation
Ryan A. Kerekes; Marios Savvides; B. V. K. Vijaya Kumar; S. Richard F. Sims
Automatic TargetRecognition (ATR) is an approach by which we identify one or a group of target-objects in a scene. It plays a pivotal role in the challenging fields of defense and civil. Most of the methods in this context are based on fix window-size technique. In this paper we propose a novel approach which gives scale, rotation and translation
A modular neural network classifier has been applied to the problem of automatic targetrecognition using forward-looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks. Each neural network makes a decision based on local features extracted from a specific portion of a target image. The classification decisions of the individual networks are combined to determine the
Lin-cheng Wang; Sandor Z. Der; Nasser M. Nasrabadi
We present a model for classification performance esti- mation for synthetic aperture radar (SAR) automatic targetrecognition. We adopt a model-based approach, in which classification is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are compared using a Bayes likelihood
Hung-chih Chiang; Randolph L. Moses; William W. Irving
An automatic targetrecognition (ATR) system based on rough set-support vector machine (RS-SVM) for SAR targets is proposed in this paper. The system combines the strong feature selection ability of rough set (RS) with the excellent classification ability of SVM together. The wavelet invariant moments firstly are extracted, then selected by using forward greedy numeral attribute reduction algorithm (FGNARA) as
Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic targetrecognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on
Hsuan Ren; Qian Du; Jing Wang; Chein-I Chang; JAMES O. JENSEN; JANET L. JENSEN
In this paper, an automatic aircraft targetrecognition (ATR) framework is presented, which is based on the high resolution range profiles (HRRP) of aircraft targets. This work is divided into two major parts. First, we consider the generation of the HRRP, which includes the modeling and simulation of radar cross section (RCS), the design of step frequency waveform (SFW), and
Jeng-Kuang Hwang; Kun-Yo Lin; Yu-Lun Chiu; Juinn-Horng Deng
The specialization of visual brain areas for fast processing of printed words plays an important role in the acquisition of reading skills. Dysregulation of these areas may be among the deficits underlying developmental dyslexia. The present study examines the specificity of word activation in dyslexic children in 3rd grade by comparing early components of brain potentials elicited by visually presented words vs. strings of meaningless letter-like symbols. Results showed a more pronounced N1 component for words compared to symbols for both groups. The dyslexic group revealed larger left-lateralized, word-specific N1 responses than the typically reading group. Furthermore, positive correlations between N1 amplitudes and reading fluency were found in the dyslexic group. Our results support the notion of N1 as a sensitive index of visual word processing involved in reading fluency.
Fraga Gonzalez, Gorka; Zaric, Gojko; Tijms, Jurgen; Bonte, Milene; Blomert, Leo; van der Molen, Maurits W.
Ladar range images have attracted considerable attention in automatic targetrecognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of targetrecognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples. PMID:23128699
Board of Scientific Advisors Ad Hoc Subcommittee for the Childhood Cancer Therapeutically Applicable Research to Generate Effective Treatment (TARGET) Initiative CHAIR Joe W. Gray, Ph.D. Director Division of Life Sciences Associate Director,
Watching a speaker's facial movements can dramatically enhance our ability to comprehend words, especially in noisy environments. From a general doctrine of combining information from different sensory modalities (the principle of inverse effectiveness), one would expect that the visual signals would be most effective at the highest levels of auditory noise. In contrast, we find, in accord with a recent paper, that visual information improves performance more at intermediate levels of auditory noise than at the highest levels, and we show that a novel visual stimulus containing only temporal information does the same. We present a Bayesian model of optimal cue integration that can explain these conflicts. In this model, words are regarded as points in a multidimensional space and wordrecognition is a probabilistic inference process. When the dimensionality of the feature space is low, the Bayesian model predicts inverse effectiveness; when the dimensionality is high, the enhancement is maximal at intermediate auditory noise levels. When the auditory and visual stimuli differ slightly in high noise, the model makes a counterintuitive prediction: as sound quality increases, the proportion of reported words corresponding to the visual stimulus should first increase and then decrease. We confirm this prediction in a behavioral experiment. We conclude that auditory-visual speech perception obeys the same notion of optimality previously observed only for simple multisensory stimuli.
The study investigates the relative degree and timing of cortical activation across parietal, temporal, and frontal regions during performance of a continuous visual-wordrecognition task in children who experience reading difficulties (N?= 44, RD) and typical readers (N?=?40, NI). Minimum norm estimates of regional neurophysiological activity were obtained from magnetoencephalographic recordings. Children with RD showed bilaterally reduced neurophysiological activity in the superior and middle temporal gyri, and increased activity in rostral middle frontal and ventral occipitotemporal cortices, bilaterally. The temporal profile of activity in the RD group, featured near-simultaneous activity peaks in temporal, inferior parietal, and prefrontal regions, in contrast to a clear temporal progression of activity among these areas in the NI group. These results replicate and extend previous MEG and fMRI results demonstrating atypical, latency-dependent attributes of the brain circuit involved in word reading in children with reading difficulties.
Rezaie, Roozbeh; Simos, Panagiotis G.; Fletcher, Jack M.; Juranek, Jenifer; Cirino, Paul T.; Li, Zhimin; Passaro, Antony D.; Papanicolaou, Andrew C.
The importance of networked automatic targetrecognition systems for surveillance applications is continuously increasing. Because of the requirement of a low cost and limited payload, these networks are traditionally equipped with lightweight, low-cost sensors such as electro-optical (EO) or infrared sensors. The quality of imagery acquired by these sensors critically depends on the environmental conditions, type and characteristics of sensors, and absence of occluding or concealing objects. In the past, a large number of efficient detection, tracking, and recognition algorithms have been designed to operate on imagery of good quality. However, detection and recognition limits under nonideal environmental and/or sensor-based distortions have not been carefully evaluated. We introduce a fully automatic targetrecognition system that involves a Haar-based detector to select potential regions of interest within images, performs adjustment of detected regions, segments potential targets using a region-based approach, identifies targets using Bessel K form-based encoding, and performs clutter rejection. We investigate the effects of environmental and camera conditions on target detection and recognition performance. Two databases are involved. One is a simulated database generated using a 3-D tool. The other database is formed by imaging 10 die-cast models of military vehicles from different elevation and orientation angles. The database contains imagery acquired both indoors and outdoors. The indoors data set is composed of clear and distorted images. The distortions include defocus blur, sided illumination, low contrast, shadows, and occlusions. All images in this database, however, have a uniform (blue) background. The indoors database is applied to evaluate the degradations of recognition performance due to camera and illumination effects. The database collected outdoors includes a real background and is much more complex to process. The numerical results demonstrate that the complexity of the background and the presence of occlusions present a serious challenge for automatic target detection and recognition.
Ground Moving Target Indication (GMTI) radar provides a day\\/night, all-weather, wide-area surveillance capability to detect moving vehicles and personnel. Current GMTI radar sensors are limited to only detecting and tracking targets. The exploitation of GMTI data would be greatly enhanced by a capability to recognize accurately the detections as significant classes of target. Doppler classification exploits the differential internal motion
FASCICLIN III, a cell adhesion molecule of the immunoglobulin superfamily1-3, is expressed by motor neuron RP3 and its synaptic targets (muscle cells 6 and 7) during embryonic neuromuscular development of Drosophila4. We report here that RP3 often incorrectly innervates neighbouring non-target muscle cells when these cells misexpress fasciclin III, but still innervates normal targets in the fasciclin III null mutant.
Akira Chiba; Peter Snow; Haig Keshishian; Yoshik Hotta
People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people's dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve targetrecognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of targetrecognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of targetrecognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of targetrecognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged targetrecognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.
Soori, Umair; Yuen, Peter; Han, Ji Wen; Ibrahim, Izzati; Chen, Wentao; Hong, Kan; Merfort, Christian; James, David; Richardson, Mark
In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic targetrecognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging.
In this paper, we propose photon counting three-dimensional (3D) passive sensing and object recognition using integral imaging. The application of this approach to 3D automatic targetrecognition (ATR) is investigated using both linear and nonlinear matched filters. We find there is significant potential of the proposed system for 3D sensing and recognition with a low number of photons. The discrimination capability of the proposed system is quantified in terms of discrimination ratio, Fisher ratio, and receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first report on photon counting 3D passive sensing and ATR with integral imaging. PMID:19503132
Objective: Attention-deficit/hyperactivity disorder (ADHD) is increasingly diagnosed in adults. In this study we address the question whether there are impairments in recognition memory. Methods: In the present study 13 adults diagnosed with ADHD according to DSM-IV and 13 healthy controls were examined with respect to event-related potentials (ERPs) in a visual continuous wordrecognition paradigm to gain information about recognition memory effects in these patients. Results: The amplitude of one attention-related ERP component, the N1, was significantly increased for the ADHD adults compared with the healthy controls in the occipital electrodes. The ERPs for the second presentation were significantly more positive than the ERPs for the first presentation. This effect did not significantly differ between groups. Conclusion: Neuronal activity related to an early attentional mechanism appears to be enhanced in ADHD patients. Concerning the early or the late part of the old/new effect ADHD patients show no difference which suggests that there are no differences with respect to recollection and familiarity-based recognition processes.
Prox-Vagedes, Vanessa; Steinert, Stefanie; Zhang, Yuanyuan; Roy, Mandy; Dillo, Wolfgang; Emrich, Hinderk M.; Dietrich, Detlef E.; Ohlmeier, Martin D.
The FUSE satellite employs innovative techniques for autonomous target acquisitions and fine pointing control. One of two Fine Error Sensors, incorporated in the optical path of the science instrument, provide the Instrument Data System computer with images, for target identification, and field star centroids, for fine pointing information to the spacecraft attitude control system. A suite of 'toolbox' functions has
Thomas B. Ake; H. Landis Fisher; Jeffrey W. Kruk; Patricia K. Murphy; William R. Oegerle
A method for characterizing radar signatures using the wavelet transform is developed based on the principle of scattering centers. Scattering features represented as multiscale edges can be identified based on their Lipshitz regularity coefficients. The extracted features are directly related to target geometry and can be used for target identification. The denoising algorithm developed by S. Mallet el al. (IEE
Argues that E. Tulving and D. M. Thomson's contradiction of the generation-recognition theory of recall with the phrase 'recognition failure of recallable words' is not an admissable summary of their experiments. The word LIGHT in the cue-target pair head-LIGHT is not the same as the word LIGHT in the free-association pair dark-LIGHT. Subjects who recall LIGHT to head but refuse
The numerals 2-9 were randomly imbedded at one of three locations within words, pronounceable word fragments, and random sequences of letters which were presented tachistoscopically in random order to 24 Ss in a 3 x 3 repeated measures design. (Editor)