Understanding the shape and structure of objects is undoubtedly extremely important for object recognition, but the most common pattern recognition method currently used is machine learning, which often requires a lar...
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Detecting object in unseen images is an challenging task because of the strong clutter background, various scale of object and the deformation of class. In this paper, we present a shape-based object detection model u...
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Detecting object in unseen images is an challenging task because of the strong clutter background, various scale of object and the deformation of class. In this paper, we present a shape-based object detection model u...
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ISBN:
(纸本)9781479957521
Detecting object in unseen images is an challenging task because of the strong clutter background, various scale of object and the deformation of class. In this paper, we present a shape-based object detection model using scale-invariant fragment feature which is approximated by conjunctive short straight segments. This is a novel shape descriptor for object detection by bypassing estimation of scale of object in natural scene. Utilizing those local and consistent segments, we improve the robustness of model to natural background and deformation of object. We experiment our model on two texture-less image datasets, INRIA horses dataset and Weizmann horses dataset. The results demonstrate our model outperform those state-of-the-art methods.
Area V4 lies in the middle of the ventral visual pathway in primate brains. It is an intermediate stage in the visual processing for object discrimination. V4 neurons exhibit selectivity to complex boundary conformati...
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Visual area V4 lies in the middle of the ventral visual pathway in the primate brain. It is an intermediate stage in the visual processing for object discrimination. It plays an important role in the neural mechanism ...
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ISBN:
(纸本)9781479914821
Visual area V4 lies in the middle of the ventral visual pathway in the primate brain. It is an intermediate stage in the visual processing for object discrimination. It plays an important role in the neural mechanism of visual attention and shape recognition. V4 neurons exhibit selectivity for salient features of contour conformation. In this paper, we propose a novel model of V4 neurons based on a multilayer neural network inspired by recent studies on V4. Its low-level layers consist of computational units simulating simple cells and complex cells in the primary visual cortex. These layers extract preliminary visual features including edges and orientations. The V4 computational units calculate the entropy of the extracted features as a measure of visual saliency. The salient features are then selected and encoded with a layer of Restricted Boltzmann Machine to generate an intermediate representation of object shapes. The model was evaluated in shape distinction, handwritten digits classification, feature detection, and feature matching experiments. The results demonstrate that this model generates discriminative local representation of object shapes. It provides clues to understand the high level representation of visual stimuli in the brain.
In areas of artificial intelligence and computer vision, object representation and recognition is an important topic, and lots of methods have been developed for it. However, analysis and obtain the knowledge of objec...
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In this paper, we proposed a method which presented a new definition of different multi-step interval ISI-distance distribution of single neuronal spike trains and formed a new feature vector to represent the original...
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In this paper, we explore a new local image descriptor based on the modeling of ganglion cells array at the retina. We first introduce the mathematical model of a single ganglion cell and detailed distribution charact...
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Hubel and Wiesel's hypothesis on the emergence of orientation selectivity of simple cells meets some difficulties. It requires the receptive fields of GC and LGN to be highly similar in size and sub-structure whil...
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The non-classical receptive field (nCRF) is a large area outside the classical receptive field (CRF). Stimulating such area alone fails to elicit neural responses but can modulate the neural response to CRF stimulatio...
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