Search results for '*Object Recognition' (try it on Scholar)

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  1. Glyn W. Humphreys & Emer M. E. Forde (2001). Hierarchies, Similarity, and Interactivity in Object Recognition: “Category-Specific” Neuropsychological Deficits. Behavioral and Brain Sciences 24 (3):453-476.score: 90.0
    Category-specific impairments of object recognition and naming are among the most intriguing disorders in neuropsychology, affecting the retrieval of knowledge about either living or nonliving things. They can give us insight into the nature of our representations of objects: Have we evolved different neural systems for recognizing different categories of object? What kinds of knowledge are important for recognizing particular objects? How does visual similarity within a category influence object recognition and representation? What is the nature of (...)
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  2. Terence V. Sewards & Mark A. Sewards (2002). On the Neural Correlates of Object Recognition Awareness: Relationship to Computational Activities and Activities Mediating Perceptual Awareness. Consciousness and Cognition 11 (1):51-77.score: 90.0
    Based on theoretical considerations of Aurell (1979) and Block (1995), we argue that object recognition awareness is distinct from purely sensory awareness and that the former is mediated by neuronal activities in areas that are separate and distinct from cortical sensory areas. We propose that two of the principal functions of neuronal activities in sensory cortex, which are to provide sensory awareness and to effect the computations that are necessary for object recognition, are dissociated. We provide examples of (...)
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  3. Irina M. Harris & Paul E. Dux (2005). Orientation-Invariant Object Recognition: Evidence From Repetition Blindness. Cognition 95 (1):73-93.score: 90.0
    The question of whether object recognition is orientation-invariant or orientation-dependent was investigated using a repetition blindness (RB) paradigm. In RB, the second occurrence of a repeated stimulus is less likely to be reported, compared to the occurrence of a different stimulus, if it occurs within a short time of the first presentation. This failure is usually interpreted as a difficulty in assigning two separate episodic tokens to the same visual type. Thus, RB can provide useful information about which representations (...)
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  4. Martin Wiesmann & Alumit Ishai (2010). Training Facilitates Object Recognition in Cubist Paintings. Frontiers in Human Neuroscience 4.score: 90.0
    To the naïve observer, cubist paintings contain geometrical forms in which familiar objects are hardly recognizable, even in the presence of a meaningful title. We used fMRI to test whether a short training session about Cubism would facilitate object recognition in paintings by Picasso, Braque and Gris. Subjects, who had no formal art education, were presented with titled or untitled cubist paintings and scrambled images, and performed object recognition tasks. Relative to the control group, trained subjects recognized more (...)
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  5. Randall O'Reilly Dean Wyatte, Seth Herd, Brian Mingus (2012). The Role of Competitive Inhibition and Top-Down Feedback in Binding During Object Recognition. Frontiers in Psychology 3.score: 90.0
    How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain's binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. (...)
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  6. Assaf Harel, Dwight Kravitz & Chris I. Baker (2013). Beyond Perceptual Expertise: Revisiting the Neural Substrates of Expert Object Recognition. Frontiers in Human Neuroscience 7:885.score: 90.0
    Real-world expertise provides a valuable opportunity to understand how experience shapes human behavior and neural function. In the visual domain, the study of expert object recognition, such as in car enthusiasts or bird watchers, has produced a large, growing, and often-controversial literature. Here, we synthesize this literature, focusing primarily on results from functional brain imaging, and propose an interactive framework that incorporates the impact of high-level factors, such as attention and conceptual knowledge, in supporting expertise. This framework contrasts with (...)
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  7. Marius V. Peelen Jaap Munneke, Valentina Brentari (2013). The Influence of Scene Context on Object Recognition is Independent of Attentional Focus. Frontiers in Psychology 4.score: 90.0
    Humans can quickly and accurately recognize objects within briefly presented natural scenes. Previous work has provided evidence that scene context contributes to this process, demonstrating improved naming of objects that were presented in semantically consistent scenes (e.g., a sandcastle on a beach) relative to semantically inconsistent scenes (e.g., a sandcastle on a football field). The current study was aimed at investigating which processes underlie the scene consistency effect. Specifically, we tested: 1) whether the effect is due to increased visual feature (...)
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  8. Linda B. Smith Meagan Yee, Susan S. Jones (2012). Changes in Visual Object Recognition Precede the Shape Bias in Early Noun Learning. Frontiers in Psychology 3.score: 90.0
    Two of the most formidable skills that characterize human beings are language and our prowess in visual object recognition. They may also be developmentally intertwined. Two experiments, a large sample cross-sectional study and a smaller sample 6-month longitudinal study of 18- 24 month olds tested a hypothesized developmental link between changes in the visual object representation and noun learning. Previous findings in visual object recognition indicate that children’s ability to recognize common basic level categories from sparse structural shape (...)
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  9. David J. Jilk Randall C. O'Reilly, Dean Wyatte, Seth Herd, Brian Mingus (2013). Recurrent Processing During Object Recognition. Frontiers in Psychology 4.score: 90.0
    How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of of naturally-occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness (...)
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  10. Thomas Serre Sébastien M. Crouzet (2011). What Are the Visual Features Underlying Rapid Object Recognition? Frontiers in Psychology 2.score: 90.0
    Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without (...)
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  11. Guy Wallis (2013). Toward a Unified Model of Face and Object Recognition in the Human Visual System. Frontiers in Psychology 4.score: 90.0
    Our understanding of the mechanisms and neural substrates underlying visual recognition in humans has made considerable progress over the past thirty years. During this period a divide has developed between the fields of object and face recognition. In the psychological literature, in particular, there has been a palpable disconnect between the two fields. This paper follows a trend in part of the face-recognition literature to try to reconcile what we know about these two forms of recognition (...)
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  12. Dean Wyatte, Seth Herd, Brian Mingus & Randall O'Reilly (2012). The Role of Competitive Inhibition and Top-Down Feedback in Binding During Object Recognition. Frontiers in Psychology 3.score: 90.0
    How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain's binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. (...)
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  13. Meagan Yee, Susan S. Jones & Linda B. Smith (2012). Changes in Visual Object Recognition Precede the Shape Bias in Early Noun Learning. Frontiers in Psychology 3.score: 90.0
    Two of the most formidable skills that characterize human beings are language and our prowess in visual object recognition. They may also be developmentally intertwined. Two experiments, a large sample cross-sectional study and a smaller sample 6-month longitudinal study of 18- 24 month olds tested a hypothesized developmental link between changes in the visual object representation and noun learning. Previous findings in visual object recognition indicate that children’s ability to recognize common basic level categories from sparse structural shape (...)
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  14. Robert C. Bolles & Daniel E. Bailey (1956). Importance of Object Recognition in Size Constancy. Journal of Experimental Psychology 51 (3):222.score: 75.0
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  15. Michael H. Herzog (2006). The Relationship of Visual Masking and Basic Object Recognition in Healthy Observers and Patients with Schizophrenia. In Gmen, Haluk; Breitmeyer, Bruno G. (2006). The First Half Second: The Microgenesis and Temporal Dynamics of Unconscious and Conscious Visual Processes. (Pp. 259-274). Cambridge, Ma, Us: Mit Press. Xi, 410 Pp.score: 75.0
     
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  16. Sanjay Kumar, M. J. Riddoch & Glyn Humphreys (2013). Mu Rhythm Desynchronization Reveals Motoric Influences of Hand Action on Object Recognition. Frontiers in Human Neuroscience 7.score: 75.0
  17. Javid Sadr & Pawan Sinha (2004). Object Recognition and Random Image Structure Evolution. Cognitive Science 28 (2):259-287.score: 75.0
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  18. Jean-Louis Dessalles & Laleh Ghadakpour (2003). Object Recognition is Not Predication. Behavioral and Brain Sciences 26 (3):290-291.score: 60.0
    Predicates involved in language and reasoning are claimed to radically differ from categories applied to objects. Human predicates are the cognitive result of a contrast between perceived objects. Object recognition alone cannot generate such operations as modification and explicit negation. The mechanism studied by Hurford constitutes at best an evolutionary prerequisite of human predication ability.
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  19. Shimon Edelman, Complex Cells and Object Recognition.score: 60.0
    Nearest-neighbor correlation-based similarity computation in the space of outputs of complex-type receptive elds can support robust recognition of 3D objects. Our experiments with four collections of objects resulted in mean recognition rates between 84% (for subordinate-level discrimination among 15 quadruped animal shapes) and 94% (for basic-level recognition of 20 everyday objects), over a 40 40 range of viewpoints, centered on a stored canonical view and related to it by rotations in depth. This result has interesting implications for (...)
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  20. Jay Hegdé, Serena K. Thompson, Mark Brady & Daniel Kersten (2012). Object Recognition in Clutter: Cortical Responses Depend on the Type of Learning. Frontiers in Human Neuroscience 6.score: 60.0
    Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI) of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects’ recognition performance for both (...)
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  21. Daniel Kersten Jay Hegdé, Serena K. Thompson, Mark Brady (2012). Object Recognition in Clutter: Cortical Responses Depend on the Type of Learning. Frontiers in Human Neuroscience 6.score: 60.0
    Theoretical studies suggest that the visual system uses prior knowledge of visual objects to recognize them in visual clutter, and posit that the strategies for recognizing objects in clutter may differ depending on whether or not the object was learned in clutter to begin with. We tested this hypothesis using functional magnetic resonance imaging (fMRI) of human subjects. We trained subjects to recognize naturalistic, yet novel objects in strong or weak clutter. We then tested subjects’ recognition performance for both (...)
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  22. Dave G. Mumby (1999). How Do Animals Solve Object-Recognition Tasks? Behavioral and Brain Sciences 22 (3):461-462.score: 60.0
    This commentary reviews recent evidence that some hippo- campal functions do not depend on perirhinal inputs and discusses how the multiple-process model of recognition may shed interpretive light on previous reports of DNMS reacquisition deficits in pretrained subjects with hippocampal damage. Suggestions are made for determining whether nonhuman subjects solve object-recognition tasks using recollective memory or familiarity judgments.
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  23. Shimon Edelman, (Object Recognition/Multidimensional Scaling/Computational Model).score: 57.0
    differentiaily rated pairwise similarity when confronted with two pairs of objects, each revolving in a separate window on a computer screen. Subject data were pooled using individually weighted MDS (ref. 11; in all the experiments, the solutions were consistent among subjects). In each trial, the subject had to select among two pairs of shapes the one consisting of the most similar shapes. The subjects were allowed to respond at will; most responded within 10 sec. Proximity (that is, perceived similarity) tables (...)
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  24. Matt Craddock Jasna Martinovic, Rebecca Lawson (2012). Time Course of Information Processing in Visual and Haptic Object Classification. Frontiers in Human Neuroscience 6.score: 54.0
    Vision identifies objects rapidly and efficiently. In contrast, object recognition by touch is much slower. Furthermore, haptics usually serially accumulates information from different parts of objects, whereas vision typically processes object information in parallel. Is haptic object identification slower simply due to sequential information acquisition and the resulting memory load or due to more fundamental processing differences between the senses? To compare the time course of visual and haptic object recognition, we slowed visual processing using a novel, restricted (...)
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  25. Thomas J. Palmeri Michael L. Mack (2011). The Timing of Visual Object Categorization. Frontiers in Psychology 2.score: 54.0
    An object can be categorized at different levels of abstraction: as natural or man-made, animal or plant, bird or dog, or as a Northern Cardinal or Pyrrhuloxia. There has been growing interest in understanding how quickly categorizations at different levels are made and how the timing of those perceptual decisions changes with experience. We specifically contrast two perspectives on the timing of object categorization at different levels of abstraction. By one account, the relative timing implies a relative timing of stages (...)
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  26. Ulrike Pompe (2011). Perception and Cognition: The Analysis of Object Recognition. Mentis.score: 51.0
  27. Vincent Hope (2009). Object Perception, Perceptual Recognition, and That-Perception Introduction. Philosophy 84 (4):515-528.score: 48.0
    The philosophy of perception currently considers how perception relates to action. Some distinctions may help, distinguishing object perception from perceptual recognition, and both from that-perception. Examples are seeing a man, recognising a man, and seeing that there is a man. Perceiving an object controls self-location by its recognising an object, which depends on memory of how it looks, controls looking for it and interacting with it, or not, and that-perceiving controls saying that an object exists. Perception controls action. Milner (...)
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  28. Dana Hansen, Paul D. Siakaluk & Penny M. Pexman (2012). The Influence of Print Exposure on the Body-Object Interaction Effect in Visual Word Recognition. Frontiers in Human Neuroscience 6:113-113.score: 48.0
    We examined the influence of print exposure on the body-object interaction (BOI) effect in visual word recognition. High print exposure readers and low print exposure readers either made semantic categorizations (“Is the word easily imageable?”; Experiment 1) or phonological lexical decisions (“Does the item sound like a real English word?”; Experiment 2). The results from Experiment 1 showed that there was a larger facilitatory BOI effect for the low print exposure readers than for the high print exposure readers in (...)
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  29. Jean Lorenceau Stéphane Buffat, Justin Plantier, Corinne Roumes (2012). Repetition Blindness for Natural Images of Objects with Viewpoint Changes. Frontiers in Psychology 3.score: 48.0
    When stimuli are repeated in a rapid serial visual presentation (RSVP), observers sometimes fail to report the second occurrence of a target. This phenomenon is referred to as “repetition blindness” (RB). We report an RSVP experiment with photographs in which we manipulated object viewpoints between the first and second occurrences of a target (0-, 45-, or 90-degree changes), and spatial frequency content. Natural images were spatially filtered to produce low, medium, or high spatial-frequency stimuli. RB was observed for all filtering (...)
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  30. Martha J. Farah (1990). Visual Agnosia: Disorders of Object Recognition and What They Tell Us About Normal Vision. MIT Press.score: 45.0
  31. William A. Phillips & Wolf Singer (1997). In Search of Common Foundations for Cortical Computation. Behavioral and Brain Sciences 20 (4):657-683.score: 45.0
    It is worthwhile to search for forms of coding, processing, and learning common to various cortical regions and cognitive functions. Local cortical processors may coordinate their activity by maximizing the transmission of information coherently related to the context in which it occurs, thus forming synchronized population codes. This coordination involves contextual field (CF) connections that link processors within and between cortical regions. The effects of CF connections are distinguished from those mediating receptive field (RF) input; it is shown how CFs (...)
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  32. Shimon Edelman (1997). Computational Theories of Object Recognition. Trends in Cognitive Sciences 1 (8):296-304.score: 45.0
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  33. Stéphane Thibierge & Catherine Morin (2013). Identification, Recognition and Misidentification Syndromes: A Psychoanalytical Perspective. Frontiers in Psychology 4.score: 45.0
    Misidentification syndromes are currently often understood as cognitive disorders of either the “sense of uniqueness” (Margariti & Kontaxakis, 2006) or the recognition of people (Ellis, Lewis, 2001). It is however necessary to consider how a normal “sense of uniqueness” or a normal people recognition are acquired by normal or neurotic subjects. It will be shown here that the normal conditions of cognition can be considered as one of the possible forms of a complex structure and not as just (...)
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  34. Lydia Sánchez & Manuel Campos (2011). Object Recognition and Content. Empedocles 2 (2):207-226.score: 45.0
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  35. John Bart Wilburn (1998). A Possible Worlds Model of Object Recognition. Synthese 116 (3):403-438.score: 45.0
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  36. A. Archambault, P. Schyns & A. Oliva (1996). Coarse Structure Affects Object Recognition. In Enrique Villanueva (ed.), Perception. Ridgeview. 97-97.score: 45.0
     
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  37. William A. Barnard, Marshall Breeding & Henry A. Cross (1984). Object Recognition as a Function of Stimulus Characteristics. Bulletin of the Psychonomic Society 22 (1):15-18.score: 45.0
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  38. Moshe Bar (2005). Top-Down Facilitation of Visual Object Recognition. In Laurent Itti, Geraint Rees & John K. Tsotsos (eds.), Neurobiology of Attention. Academic Press. 140--145.score: 45.0
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  39. Irving Biederman (1989). The Uncertain Case for Cultural Effects in Pictorial Object Recognition. Behavioral and Brain Sciences 12 (1):74.score: 45.0
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  40. Olivia S. Cheung, William G. Hayward & Isabel Gauthier (2009). Dissociating the Effects of Angular Disparity and Image Similarity in Mental Rotation and Object Recognition. Cognition 113 (1):128-133.score: 45.0
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  41. Sébastien M. Crouzet & Thomas Serre (2011). What Are the Visual Features Underlying Rapid Object Recognition? Frontiers in Psychology 2.score: 45.0
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  42. Dennis M. Levi David Whitney (2011). Visual Crowding: A Fundamental Limit on Conscious Perception and Object Recognition. Trends in Cognitive Sciences 15 (4):160.score: 45.0
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  43. James J. DiCarlo & David D. Cox (2007). Untangling Invariant Object Recognition. Trends in Cognitive Sciences 11 (8):333-341.score: 45.0
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  44. L. A. A. Doumas & J. E. Hummel (2010). A Computational Account of the Development of the Representations Underlying Object Recognition. Cognitive Science 34:698-712.score: 45.0
     
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  45. Hadyn D. Ellis (1989). The Distinction Between Object Recognition and Picture Recognition. Behavioral and Brain Sciences 12 (1):81.score: 45.0
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  46. Martha J. Farah & Katherine M. Hammond (1988). Mental Rotation and Orientation-Invariant Object Recognition: Dissociable Processes. Cognition 29 (1):29-46.score: 45.0
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  47. Mj Farah & R. Rochlin (1990). Reference Frames and Geometric Primitives in Object Recognition. Bulletin of the Psychonomic Society 28 (6):483-483.score: 45.0
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  48. Martha J. Farah (1994). Specialization Within Visual Object Recognition: Clues From Prosopagnosia and Alexia. In Martha J. Farah & G. Ratcliff (eds.), The Neuropsychology of High-Level Vision. Lawrence Erlbaum. 133--146.score: 45.0
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  49. Mark J. Fenske, Elissa Aminoff, Nurit Gronau & Moshe Bar (2006). Top-Down Facilitation of Visual Object Recognition: Object-Based and Context-Based Contributions. In Susana Martinez-Conde, S. L. Macknik, L. M. Martinez, J.-M. Alonso & P. U. Tse (eds.), Progress in Brain Research. Elsevier Science. 3-21.score: 45.0
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  50. Christian Gerlach (2009). Category-Specificity in Visual Object Recognition. Cognition 111 (3):281-301.score: 45.0
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