Hierarchical Implicit Shape Modeling |
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| Institution: | 1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran;2. Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada;1. Department of Electrical and Computer Engineering, Concordia University, Montréal, QC H3G 2W1, Canada;2. Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC H3G 2W1, Canada;1. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin N.T., Hong Kong;2. Hong Kong Applied Science and Technology Research Institute (ASTRI), Shatin N.T., Hong Kong |
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| Abstract: | In this paper, a new hierarchical approach for object detection is proposed. Object detection methods based on Implicit Shape Model (ISM) efficiently handle deformable objects, occlusions and clutters. The structure of each object in ISM is defined by a spring like graph. We introduce hierarchical ISM in which structure of each object is defined by a hierarchical star graph. Hierarchical ISM has two layers. In the first layer, a set of local ISMs are used to model object parts. In the second layer, structure of parts with respect to the object center is modeled by global ISM. In the proposed approach, the obtained parts for each object category have high discriminative ability. Therefore, our approach does not require a verification stage. We applied the proposed approach to some datasets and compared the performance of our algorithm to comparable methods. The results show that our method has a superior performance. |
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| Keywords: | Object recognition Statistical part-based object recognition Implicit shape model Hierarchical Implicit Shape Model Hierarchical star graph Discriminative parts Parts filter Histogram of gradients |
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