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Accurately identifying corresponded landmarks from a population of shape instances is the major challenge in constructing statistical shape models. In general, shape-correspondence methods can be grouped into one of two categories: global methods and pair-wise methods. In this paper, we develop a new method that attempts to address the limitations of both the global and pair-wise methods. In particular,...
This paper proposes a novel and effective invariant shape representation by Radon and stationary wavelet transforms for images with complex inner shapes. The proposed method is invariant to general geometrical transformations. Instead of analyzing shapes directly in the spatial domain, the proposed method retrieves features in Radon transform domain by statistical and spectral analysis to make shapes...
In order to detect, track and recognize objects in images, one has to develop statistical models for features representing those objects. Shapes and textures are two main features that are used for this purpose. While past research has developed separate techniques for analyzing these for these two features, our goal is to study them together. We represent the 2D coordinates of the boundary and the...
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