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Object Recognition (object + recognition)

Distribution by Scientific Domains

Medical Sciences	35%
Life Sciences	25%
Information Science and Computing	17%
Humanities and Social Sciences	14%
2 Other Domains	9%

Kinds of Object Recognition

visual object recognition

Selected Abstracts

Mechanisms of Visual Object Recognition in Infancy: Five-Month-Olds Generalize Beyond the Interpolation of Familiar Views

INFANCY, Issue 1 2007
Clay Mash
This work examined predictions of the interpolation of familiar views (IFV) account of object recognition performance in 5-month-olds. Infants were familiarized to an object either from a single viewpoint or from multiple viewpoints varying in rotation around a single axis. Object recognition was then tested in both conditions with the same object rotated around a novel axis. Infants in the multiple-views condition recognized the object, whereas infants in the single-view condition provided no evidence for recognition. Under the same 2 familiarization conditions, infants in a 2nd experiment treated as novel an object that differed in only 1 component from the familiar object. Infants' object recognition is enhanced by experience with multiple views, even when that experience is around an orthogonal axis of rotation, and infants are sensitive to even subtle shape differences between components of similar objects. In general, infants' performance does not accord with the predictions of the IFV model of object recognition. These findings motivate the extension of future research and theory beyond the limits of strictly interpolative mechanisms. [source]

Object Recognition in Digital Photogrammetry

THE PHOTOGRAMMETRIC RECORD, Issue 95 2000
T. Schenk
Object recognition and image understanding have increasingly become major subjects of interest for research activity in digital photogrammetry. This paper provides an overview of object recognition in photogrammetry, beginning with a problem statement and brief paradigm description. In order to exemplify the concept, automatic interior orientation is presented as an object recognition problem. Subsequent sections discuss the current status of object recognition by identifying relevant criteria, such as modelling, system strategies and inference components. Such criteria are useful for comparing object recognition systems or proposed approaches. Strengths and weaknesses of current systems are summarized, followed by a more detailed analysis of the modelling problem. Finally, two new approaches (scale-space and fusion of multisensor/multispectral data) are mentioned. These approaches serve as examples of promising new trends which have the potential of advancing object recognition to a new level. [source]

Neural Correlates of Face and Object Recognition in Young Children with Autism Spectrum Disorder, Developmental Delay, and Typical Development

CHILD DEVELOPMENT, Issue 3 2002
Geraldine Dawson
This study utilized electroencephalographic recordings to examine whether young children with autism spectrum disorder (ASD) have impaired face recognition ability. High-density brain event-related potentials (ERPs) were recorded to photos of the child's mother's face versus an unfamiliar female face and photos of a favorite versus an unfamiliar toy from children with ASD, children with typical development, and children with developmental delay, all 3 to 4 years of age (N= 118). Typically developing children showed ERP amplitude differences in two components, P400 and Nc, to a familiar versus an unfamiliar face, and to a familiar versus an unfamiliar object. In contrast, children with ASD failed to show differences in ERPs to a familiar versus an unfamiliar face, but they did show P400 and Nc amplitude differences to a familiar versus an unfamiliar object. Developmentally delayed children showed significant ERP amplitude differences for the positive slow wave for both faces and objects. These data suggest that autism is associated with face recognition impairment that is manifest early in life. [source]

Mechanisms of Visual Object Recognition in Infancy: Five-Month-Olds Generalize Beyond the Interpolation of Familiar Views

Object Recognition in Digital Photogrammetry

CONCEPTUAL CLUSTERING AND CASE GENERALIZATION OF TWO-DIMENSIONAL FORMS

COMPUTATIONAL INTELLIGENCE, Issue 3-4 2006
Silke Jänichen
Case-based object recognition requires a general case of the object that should be detected. Real-world applications such as the recognition of biological objects in images cannot be solved by one general case. A case base is necessary to handle the great natural variations in the appearance of these objects. In this paper, we will present how to learn a hierarchical case base of general cases. We present our conceptual clustering algorithm to learn groups of similar cases from a set of acquired structural cases of fungal spores. Due to its concept description, it explicitly supplies for each cluster a generalized case and a measure for the degree of its generalization. The resulting hierarchical case base is used for applications in the field of case-based object recognition. We present results based on our application for health monitoring of biologically hazardous material. [source]

The virtual interaction panel: an easy control tool in augmented reality systems

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 3-4 2004
M. L. Yuan
Abstract In this paper, we propose and develop an easy control tool called Virtual Interaction Panel (VirIP) for Augmented Reality (AR) systems, which can be used to control AR systems. This tool is composed of two parts: the design of the VirIPs and the tracking of an interaction pen using a Restricted Coulomb Energy (RCE) neural network. The VirIP is composed of some virtual buttons, which have meaningful information that can be activated by an interaction pen during the augmentation process. The interaction pen is a general pen-like object with a certain color distribution. It is tracked using a RCE network in real-time and used to trigger the VirIPs for AR systems. In our system, only one camera is used for capturing the real world. Therefore, 2D information is used to trigger the virtual buttons to control the AR systems. The proposed method is real-time because the RCE-based image segmentation for a small region is fast. It can be used to control AR systems quite easily without any annoying sensors attached to entangling cables. This proposed method has good potential in many AR applications in manufacturing, such as assembly without the need for object recognition, collaborative product design, system control, etc. Copyright © 2004 John Wiley & Sons, Ltd. [source]

A perspective factorization method for Euclidean reconstruction with uncalibrated cameras

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 4 2002
Mei Han
Abstract Structure from motion (SFM), which is recovering camera motion and scene structure from image sequences, has various applications, such as scene modelling, robot navigation, object recognition and virtual reality. Most of previous research on SFM requires the use of intrinsically calibrated cameras. In this paper we describe a factorization-based method to recover Euclidean structure from multiple perspective views with uncalibrated cameras. The method first performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. The process of updating a projective solution to a full metric one is referred as normalization in most factorization-based SFM methods. We present three normalization algorithms which enforce Euclidean constraints on camera calibration parameters to recover the scene structure and the camera calibration simultaneously, assuming zero skew cameras. The first two algorithms are linear, one for dealing with the case that only the focal lengths are unknown, and another for the case that the focal lengths and the constant principal point are unknown. The third algorithm is bilinear, dealing with the case that the focal lengths, the principal points and the aspect ratios are all unknown. The results of experiments are presented. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Cognitive visual dysfunctions in preterm children with periventricular leukomalacia

DEVELOPMENTAL MEDICINE & CHILD NEUROLOGY, Issue 12 2009
ELISA FAZZI MD PHD
Aim, Cognitive visual dysfunctions (CVDs) reflect an impairment of the capacity to process visual information. The question of whether CVDs might be classifiable according to the nature and distribution of the underlying brain damage is an intriguing one in child neuropsychology. Method, We studied 22 children born preterm (12 males, 10 females; mean age at examination 8y, range 6,15y; mean gestational age 30wks, range 28,36wks) with periventricular leukomalacia, spastic diplegia, normal intelligence (mean Full-scale IQ 84; mean Verbal IQ 97; mean Performance IQ 74), and normal visual acuity, focusing on higher visual functions. Brain magnetic resonance images (MRI) were analysed to establish the presence of lesions along the primary optic pathway, in the occipitoparietal and occipitotemporal regions. Results, Most children displayed an uneven cognitive profile, with deficits in visual object recognition, visual imagery, visual,spatial skills, and visual memory, and sparing of visual associative abilities, non-verbal intelligence, and face and letter recognition. Conventional brain MRI did not document major alterations of parietal and temporal white matter, or cortical alteration of areas involved in visual associative functions. Interpretation, We suggest a widespread involvement of higher visual processing systems, involving both the ventral and dorsal streams, in preterm children with periventricular leukomalacia. The lack of major alterations on conventional MRI does not exclude the possibility of malfunctioning of higher visual processing systems, expressing itself through discrete CVDs. Possible mechanisms underlying these neuropsychological deficits are discussed. [source]

Environmental impoverishment and aging alter object recognition, spatial learning, and dentate gyrus astrocytes

EUROPEAN JOURNAL OF NEUROSCIENCE, Issue 3 2010
Daniel G. Diniz
Abstract Environmental and age-related effects on learning and memory were analysed and compared with changes observed in astrocyte laminar distribution in the dentate gyrus. Aged (20 months) and young (6 months) adult female albino Swiss mice were housed from weaning either in impoverished conditions or in enriched conditions, and tested for episodic-like and water maze spatial memories. After these behavioral tests, brain hippocampal sections were immunolabeled for glial fibrillary acid protein to identify astrocytes. The effects of environmental enrichment on episodic-like memory were not dependent on age, and may protect water maze spatial learning and memory from declines induced by aging or impoverished environment. In the dentate gyrus, the number of astrocytes increased with both aging and enriched environment in the molecular layer, increased only with aging in the polymorphic layer, and was unchanged in the granular layer. We suggest that long-term experience-induced glial plasticity by enriched environment may represent at least part of the circuitry groundwork for improvements in behavioral performance in the aged mice brain. [source]

Rapid categorization of achromatic natural scenes: how robust at very low contrasts?

EUROPEAN JOURNAL OF NEUROSCIENCE, Issue 7 2005
Marc J.-M.
Abstract The human visual system is remarkably good at categorizing objects even in challenging visual conditions. Here we specifically assessed the robustness of the visual system in the face of large contrast variations in a high-level categorization task using natural images. Human subjects performed a go/no-go animal/nonanimal categorization task with briefly flashed grey level images. Performance was analysed for a large range of contrast conditions randomly presented to the subjects and varying from normal to 3% of initial contrast. Accuracy was very robust and subjects were performing well above chance level (, 70% correct) with only 10,12% of initial contrast. Accuracy decreased with contrast reduction but reached chance level only in the most extreme condition (3% of initial contrast). Conversely, the maximal increase in mean reaction time was ,,60 ms (at 8% of initial contrast); it then remained stable with further contrast reductions. Associated ERPs recorded on correct target and distractor trials showed a clear differential effect whose amplitude and peak latency were correlated respectively with task accuracy and mean reaction times. These data show the strong robustness of the visual system in object categorization at very low contrast. They suggest that magnocellular information could play a role in ventral stream visual functions such as object recognition. Performance may rely on early object representations which lack the details provided subsequently by the parvocellular system but contain enough information to reach decision in the categorization task. [source]

Detection of animals in natural images using far peripheral vision

EUROPEAN JOURNAL OF NEUROSCIENCE, Issue 5 2001
Simon J. Thorpe
Abstract It is generally believed that the acuity of the peripheral visual field is too poor to allow accurate object recognition and, that to be identified, most objects need to be brought into foveal vision by using saccadic eye movements. However, most measures of form vision in the periphery have been done at eccentricities below 10° and have used relatively artificial stimuli such as letters, digits and compound Gabor patterns. Little is known about how such data would apply in the case of more naturalistic stimuli. Here humans were required to categorize briefly flashed (28 ms) unmasked photographs of natural scenes (39° high, and 26° across) on the basis of whether or not they contained an animal. The photographs appeared randomly in nine locations across virtually the entire extent of the horizontal visual field. Accuracy was 93.3% for central vision and decreased almost linearly with increasing eccentricity (89.8% at 13°, 76.1% at 44.5° and 71.2% at 57.5°). Even at the most extreme eccentricity, where the images were centred at 70.5°, subjects scored 60.5% correct. No evidence was found for hemispheric specialization. This level of performance was achieved despite the fact that the position of the image was unpredictable, ruling out the use of precued attention to target locations. The results demonstrate that even high-level visual tasks involving object vision can be performed using the relatively coarse information provided by the peripheral retina. [source]

Neural substrates of tactile object recognition: An fMRI study

HUMAN BRAIN MAPPING, Issue 4 2004
Catherine L. Reed
Abstract A functional magnetic resonance imaging (fMRI) study was conducted during which seven subjects carried out naturalistic tactile object recognition (TOR) of real objects. Activation maps, conjunctions across subjects, were compared between tasks involving TOR of common real objects, palpation of "nonsense" objects, and rest. The tactile tasks involved similar motor and sensory stimulation, allowing higher tactile recognition processes to be isolated. Compared to nonsense object palpation, the most prominent activation evoked by TOR was in secondary somatosensory areas in the parietal operculum (SII) and insula, confirming a modality-specific path for TOR. Prominent activation was also present in medial and lateral secondary motor cortices, but not in primary motor areas, supporting the high level of sensory and motor integration characteristic of object recognition in the tactile modality. Activation in a lateral occipitotemporal area associated previously with visual object recognition may support cross-modal collateral activation. Finally, activation in medial temporal and prefrontal areas may reflect a common final pathway of modality-independent object recognition. This study suggests that TOR involves a complex network including parietal and insular somatosensory association cortices, as well as occipitotemporal visual areas, prefrontal, and medial temporal supramodal areas, and medial and lateral secondary motor cortices. It confirms the involvement of somatosensory association areas in the recognition component of TOR, and the existence of a ventrolateral somatosensory pathway for TOR in intact subjects. It challenges the results of previous studies that emphasize the role of visual cortex rather than somatosensory association cortices in higher-level somatosensory cognition. Hum. Brain Mapping 21:236,246, 2004. © 2004 Wiley-Liss, Inc. [source]

The Effect of Vocabulary Size on Toddlers' Receptiveness to Unexpected Testimony About Category Membership

INFANCY, Issue 2 2007
Vikram K. Jaswal
Children must be willing to accept some of what they hear "on faith," even when that testimony conflicts with their own expectations. The study reported here investigated the relation among vocabulary size, object recognition, and 24-month-olds' (N = 40) willingness to accept potentially surprising testimony about the category to which an object belongs. Results showed that children with larger vocabularies were better able to recognize atypical exemplars of familiar categories than children with smaller vocabularies. However, they were also most likely to accept unexpected testimony that an object that looked like a member of one familiar category was actually a member of another. These results indicate that 24-month-olds trust classifications provided by adult labeling patterns even when they conflict with the classifications children generate on their own. [source]

Mechanisms of Visual Object Recognition in Infancy: Five-Month-Olds Generalize Beyond the Interpolation of Familiar Views

Guiding a mobile robot with cellular neural networks

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 6 2002
Xavier Vilasís-Cardona
Abstract We show how cellular neural networks (CNNs) are capable of providing the necessary signal processing needed for visual navigation of an autonomous mobile robot. In this way, even complex feature detection and object recognition can be obtained in real time by analogue hardware, making fully autonomous real-time operation feasible. An autonomous robot was first simulated and then implemented by simulating the CNN with a DSP. The robot is capable of navigating in a maze following lines painted on the floor. Images are processed entirely by a CNN-based algorithm, and navigation is controlled by a fuzzy-rule-based algorithm. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Color invariant object recognition using entropic graphs

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 5 2006
Jan C. van Gemert
Abstract We present an object recognition approach using higher-order color invariant features with an entropy-based similarity measure. Entropic graphs offer an unparameterized alternative to common entropy estimation techniques, such as a histogram or assuming a probability distribution. An entropic graph estimates entropy from a spanning graph structure of sample data. We extract color invariant features from object images invariant to illumination changes in intensity, viewpoint, and shading. The Henze,Penrose similarity measure is used to estimate the similarity of two images. Our method is evaluated on the ALOI collection, a large collection of object images. This object image collection consists of 1000 objects recorded under various imaging circumstances. The proposed method is shown to be effective under a wide variety of imaging conditions. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 146,153, 2006 [source]

Using dendronal signatures for feature extraction and retrieval

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 4 2000
Luojian Chen
A dendrone is a hierarchical thresholding structure that can be automatically generated from a complex image. The dendrone structure captures the connectedness of objects and subobjects during successive brightness thresholding. Based on connectedness and changes in intensity contours, dendronic representations of objects in images capture the coarse-to-fine unfolding of finer and finer detail, creating a unique signature for target objects that is invariant to lighting, scale, and placement of the object within the image. Subdendrones within the hierarchy are recognizable as objects within the picture. Complex composite images can be autonomously analyzed to determine if they contain the unique dendronic signatures of particular target objects of interest. In this paper, we describe the initial design of the dendronic image characterization environment (DICE) for the generation of dendronic signatures from complex multiband remote imagery. By comparing subdendrones within an image to dendronic signatures of target objects of interest, DICE can be used to match/retrieve target features from a library of composite images. The DICE framework can organize and support a number of alternative object recognition and comparison techniques, depending on the application domain. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 243,253, 2000 [source]

Dempster,Shafer models for object recognition and classification

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 3 2006
A.P. Dempster
We consider situations in which each individual member of a defined object set is characterized uniquely by a set of variables, and we propose models and associated methods that recognize or classify a newly observed individual. Inputs consist of uncertain observations on the new individual and on a memory bank of previously identified individuals. Outputs consist of uncertain inferences concerning degrees of agreement between the new object and previously identified objects or object classes, with inferences represented by Dempster,Shafer belief functions. We illustrate the approach using models constructed from independent simple support belief functions defined on binary variables. In the case of object recognition, our models lead to marginal belief functions concerning how well the new object matches objects in memory. In the classification model, we compute beliefs and plausibilities that the new object lies in defined subsets of an object set. When regarded as similarity measures, our belief and plausibility functions can be interpreted as candidate membership functions in the terminology of fuzzy logic. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 283,297, 2006. [source]

Vision, Action, and Make-Perceive

MIND & LANGUAGE, Issue 4 2008
ROBERT EAMON BRISCOE
I argue inter alia that the enactive account falsely identifies an object's apparent shape with its 2D perspectival shape; that it mistakenly assimilates visual shape perception and volumetric object recognition; and that it seriously misrepresents the constitutive role of bodily action in visual awareness. I argue further that noticing an object's perspectival shape involves a hybrid experience combining both perceptual and imaginative elements,an act of what I call ,make-perceive'. [source]

Object Recognition in Digital Photogrammetry

Animal colour vision , behavioural tests and physiological concepts

BIOLOGICAL REVIEWS, Issue 1 2003
ALMUT KELBER
ABSTRACT Over a century ago workers such as J. Lubbock and K. von Frisch developed behavioural criteria for establishing that non-human animals see colour. Many animals in most phyla have since then been shown to have colour vision. Colour is used for specific behaviours, such as phototaxis and object recognition, while other behaviours such as motion detection are colour blind. Having established the existence of colour vision, research focussed on the question of how many spectral types of photoreceptors are involved. Recently, data on photoreceptor spectral sensitivities have been combined with behavioural experiments and physiological models to study systematically the next logical question: ,what neural interactions underlie colour vision ?,This review gives an overview of the methods used to study animal colour vision, and discusses how quantitative modelling can suggest how photoreceptor signals are combined and compared to allow for the discrimination of biologically relevant stimuli. [source]

Visual object recognition in early Alzheimer's disease: deficits in semantic processing

ACTA NEUROLOGICA SCANDINAVICA, Issue 2 2003
S. Laatu
Objectives , The purpose of the present study was to divide visual object recognition into different stages and to reveal which of these stages are impaired in early Alzheimer's disease (AD). Methods , Performance in object detection, familiarity detection, semantic name and word categorization, and identification with naming were studied by using two-choice reaction-time tasks. Ten patients with newly diagnosed AD and 14 healthy subjects were studied. Results , Patients with early AD had impairments in several stages of the object recognition process. After controlling for the basic visuomotor slowness, they were as fast and as accurate as the controls in object detection, but had difficulties in all stages that required semantic processing. Conclusions , Semantic memory impairments contribute to the deficits in visual object recognition in early AD. Thus, the semantic memory deficit may be manifested in several ways in the difficulties that AD patients experience in everyday life. [source]