24 resultados para 3D object recognition


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Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition. © 2011 Springer-Verlag.

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The perception of global form requires integration of local visual cues across space and is the foundation for object recognition. Here we used magnetoencephalography (MEG) to study the location and time course of neuronal activity associated with the perception of global structure from local image features. To minimize neuronal activity to low-level stimulus properties, such as luminance and contrast, the local image features were held constant during all phases of the MEG recording. This allowed us to assess the relative importance of striate (V1) versus extrastriate cortex in global form perception.

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The ability to recognize individual faces is of crucial social importance for humans and evolutionarily necessary for survival. Consequently, faces may be “special” stimuli, for which we have developed unique modular perceptual and recognition processes. Some of the strongest evidence for face processing being modular comes from cases of prosopagnosia, where patients are unable to recognize faces whilst retaining the ability to recognize other objects. Here we present the case of an acquired prosopagnosic whose poor recognition was linked to a perceptual impairment in face processing. Despite this, she had intact object recognition, even at a subordinate level. She also showed a normal ability to learn and to generalize learning of nonfacial exemplars differing in the nature and arrangement of their parts, along with impaired learning and generalization of facial exemplars. The case provides evidence for modular perceptual processes for faces.

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The dramatic effects of brain damage can provide some of the most interesting insights into the nature of normal cognitive performance. In recent years a number of neuropsychological studies have reported a particular form of cognitive impairment where patients have problems recognising objects from one category but remain able to recognise those from others. The most frequent ‘category-specific’ pattern is an impairment identifying living things, compared to nonliving things. The reverse pattern of dissociation, i.e., an impairment recognising and naming nonliving things relative to living things, has been reported albeit much less frequently. The objective of the work carried out in this thesis was to investigate the organising principles and anatomical correlates of stored knowledge for categories of living and nonliving things. Three complementary cognitive neuropsychological research techniques were employed to assess how, and where, this knowledge is represented in the brain: (i) studies of normal (neurologically intact) subjects, (ii) case-studies of neurologically impaired patients with selective deficits in object recognition, and (iii) studies of the anatomical correlates of stored knowledge for living and nonliving things on the brain using magnetoencephalography (MEG). The main empirical findings showed that semantic knowledge about living and nonliving things is principally encoded in terms of sensory and functional features, respectively. In two case-study chapters evidence was found supporting the view that category-specific impairments can arise from damage to a pre-semantic system, rather than the assumption often made that the system involved must be semantic. In the MEG study, rather than finding evidence for the involvement of specific brain areas for different object categories, it appeared that, when subjects named and categorised living and nonliving things, a non-differentiated neural system was involved.

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In this paper we describe a novel, extensible visualization system currently under development at Aston University. We introduce modern programming methods, such as the use of data driven programming, design patterns, and the careful definition of interfaces to allow easy extension using plug-ins, to 3D landscape visualization software. We combine this with modern developments in computer graphics, such as vertex and fragment shaders, to create an extremely flexible, extensible real-time near photorealistic visualization system. In this paper we show the design of the system and the main sub-components. We stress the role of modern programming practices and illustrate the benefits these bring to 3D visualization. © 2006 Springer-Verlag Berlin Heidelberg.

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A recently proposed colour based tracking algorithm has been established to track objects in real circumstances [Zivkovic, Z., Krose, B. 2004. An EM-like algorithm for color-histogram-based object tracking. In: Proc, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 798-803]. To improve the performance of this technique in complex scenes, in this paper we propose a new algorithm for optimally adapting the ellipse outlining the objects of interest. This paper presents a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability distribution and the expected one. We argue that, by doing this, the shape of the ellipse can be properly adapted in the tracking stage. Experimental results show that the proposed method has favourable performance in shape adaption and object localisation.

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The present study examines the effect of the goodness of view on the minimal exposure time required to recognize depth-rotated objects. In a previous study, Verfaillie and Boutsen (1995) derived scales of goodness of view, using a new corpus of images of depth-rotated objects. In the present experiment, a subset of this corpus (five views of 56 objects) is used to determine the recognition exposure time for each view, by increasing exposure time across successive presentations until the object is recognized. The results indicate that, for two thirds of the objects, good views are recognized more frequently and have lower recognition exposure times than bad views.

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An array of FBG curvature sensors are wavelength-interrogated and the recovered data combined with a three-dimensional algorithm to reconstruct in real time the enveloped object with a 1% to 9% volumetric error. © 2012 OSA.

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Inhibition of return (IOR) effects, in which participants detect a target in a cued box more slowly than one in an uncued box, suggest that behavior is aided by inhibition of recently attended irrelevant locations. To investigate the controversial question of whether inhibition can be applied to object identity in these tasks, in the present research we presented faces upright or inverted during cue and/or target sequences. IOR was greater when both cue and target faces were upright than when cue and/or target faces were inverted. Because the only difference between the conditions was the ease of facial recognition, this result indicates that inhibition was applied to object identity. Interestingly, inhibition of object identity affected IOR both whenencoding a cue face andretrieving information about a target face. Accordingly, we propose that episodic retrieval of inhibition associated with object identity may mediate behavior in cuing tasks.