Temporal asymmetries and interactions between dorsal and ventral visual pathways during object recognition
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Abstract Despite their anatomical and functional distinctions, there is growing evidence that the dorsal and ventral visual pathways interact to support object recognition. However, the exact nature of these interactions remains poorly understood. Is the presence of identity-relevant object information in the dorsal pathway simply a byproduct of ventral input? Or, might the dorsal pathway be a source of input to the ventral pathway for object recognition? In the current study, we used high-density EEG—a technique with high temporal precision and spatial resolution sufficient to distinguish parietal and temporal lobes—to characterise the dynamics of dorsal and ventral pathways during object viewing. Using multivariate analyses, we found that category decoding in the dorsal pathway preceded that in the ventral pathway. Importantly, the dorsal pathway predicted the multivariate responses of the ventral pathway in a time-dependent manner, rather than the other way around. Together, these findings suggest that the dorsal pathway is a critical source of input to the ventral pathway for object recognition.An approach to object recognition in an industrial environment is described. With the hypothesis that the universe of objects is limited and that the attitudes in space of any object are constrained by the working environment to a few possibilities, a method is obtained that determines minimal descriptions for each model/object pair. These descriptions, which are discriminant of each model, are used by the recognition strategy planner. It automatically determines which descriptions can be conveniently used to identify the object(s) looked for and in what order. Uncertainties due to overlapping are signaled and tentative classifications listed.< >
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Recognition of objects is accomplished through the use of cues that depend on internal representations of familiar shapes. We used a paradigm of perceptual learning during visual search to explore what features human observers use to identify objects. Human subjects were trained to search for a target object embedded in an array of distractors, until their performance improved from near-chance levels to over 80% of trials in an object-specific manner. We determined the role of specific object components in the recognition of the object as a whole by measuring the transfer of learning from the trained object to other objects sharing components with it. Depending on the geometric relationship of the trained object with untrained objects, transfer to untrained objects was observed. Novel objects that shared a component with the trained object were identified at much higher levels than those that did not, and this could be used as an indicator of which features of the object were important for recognition. Training on an object also transferred to the components of the object when these components were embedded in an array of distractors of similar complexity. These results suggest that objects are not represented in a holistic manner during learning but that their individual components are encoded. Transfer between objects was not complete and occurred for more than one component, regardless of how well they distinguish the object from distractors. This suggests that a joint involvement of multiple components was necessary for full performance.
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3D single-object recognition
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This paper presents the recognition of object categories and object arrangements using You Only Look Once framework. Daily life objects are usually surrounded by other objects. The nearby objects around a target are obstacles to object picking. Therefore, different grasp strategies are needed depending on the object arrangements as well as the object categories. The recognition method of two arrangement categories of books (horizontal stacking and vertical stacking) is discussed in this paper. The recognition results show that the method is useful for recognizing object categories and object arrangements.
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We present an approach to function-based object recognition that reasons about the functionality of an object's initiative parts. We extend the popular "recognition by parts" shape recognition framework to support "recognition, by functional parts", by combining a set of functional primitives and their relations with a set of abstract volumetric shape primitives and their relations. Previous approaches have relied on more global object features, often ignoring the problem of object segmentation, and thereby restricting themselves to range images of unoccluded scenes. We show how these shape primitives and relations can be easily recovered from superquadric ellipsoids which, in turn, can be recovered from either range or intensity images of occluded scenes. Furthermore, the proposed framework supports both unexpected (bottom-up) object recognition and expected (top-down) object recognition. We demonstrate the approach on, a simple domain by recognizing a restricted class of hand-tools from 2-D images.< >
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The idea of a division between a dorsal and a ventral visual stream is one of the most basic principles of visual processing in the brain ([Milner and Goodale, 1995][1]). The ventral stream originates in primary visual cortex and extends along the ventral surface into the temporal cortex; the dorsal
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Identification
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Canny edge detector
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Automatic learning of 3D object descriptions for the recognition of future instances of sensed objects is an important feature of vision systems. In this paper, we describe a clustering-based method for learning object description and a scheme for subsequent classification using this description. The description derived is an evidence rulebase that discriminates between the object classes. During classification, a sensed object is either recognized as one of the objects in the database or rejected as an unknown object. The rules fired by the object are used to eliminate hypotheses for which there is no evidence in the sensed object and to arrive at its identity and pose.
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Proposes a method of object recognition using appearance models accumulated into a RFID (radio frequency identification) tag attached to the environment. Robots recognize the object using appearance models accumulated in the tag on the object. If the robot fails in recognition, it acquires a model of the object and accumulates it to the tag. Since robots in the environment observe the object from different points of view at different time, various appearance models are accumulated as time passes. In order to accumulate many models, eigenspace analysis is applied. The eigenspace is reconstructed every time robots acquire the model. Experimental result of object recognition shows effectiveness of the proposed method.
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