Shape analysis with multiscale HTS

Abstract

Currently, given the spread of computers through society, the task of recognizing visual patterns is being more and more automated, in particular to treat the large and growing amount of digital images available. Applications in many areas, such as biometrics, content-based image retrieval, and medical diagnostic, make use of image processing, as well as techniques for the extraction and analysis of their characteristics, in order to identify persons, objects, gestures, texts, etc. The basic features that are used for image analysis are: color, texture and shape. Recently, we proposed a new shape descriptor called HTS (Hough Transform Statistics), which is based on the Hough space to represent and recognize objects in images by their shapes. The results obtained by HTS on public image databases have shown that this new shape descriptor, besides showing high accuracy levels, better than many traditional shape descriptors proposed in the literature, is fast, since it has an algorithm of linear complexity. In this research project we intend to explore the possibilities of scale-space representation of this new shape descriptor, besides proposing improvements to the HTS. Scale is a key parameter in computer vision and the theory of scale-space refers to the space formed when observing, simultaneously, especial aspects of an image at several scales, being the scale the third dimension. The multiscale HTS method and possible extensions of the original HTS method will be evaluated on same databases and their performance will be compared with the main shape descriptors found in the literature. (AU)