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We present a nonparametric and efficient method for shape localization that improves on the traditional sub-window search in capturing the fine geometry of an object from a small number of feature points. Our method implies that the discrete set of features capture more appearance and shape information than is commonly exploited. We use the a-complex by Edelsbrunner et al. to build a filtration of...
In this paper an appearance based, compositional approach to the recognition of deformable objects is presented. First, a hierarchical object model is proposed. On different levels of abstraction the model represents object categories, different views of an object, the parts of an object and basic feature vectors. Then, a training method based on multiple clustering steps is described. This paper...
In this paper, we propose a novel contextual descriptor which combines the contextual information and local appearance. Based on Gibbs distribution, a local descriptor is designed. By assembling the contextual information and local descriptors, a new partial contextual descriptor (PCD) is finally presented. Combining pyramid match kernel (PMK) and SVM, we test our new descriptor and obtain higher...
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