Matching pursuit-based shape representation and recognition using scale-space

In this paper, we propose an analytical low-level representation of images, obtained by a decomposition process, namely the matching pursuit (MP) algorithm, as a new way of describing objects through a general continuous description using an affine invariant dictionary of basis function (BFs). This description is used to recognize multiple objects in images. In the learning phase, a template object is decomposed, and the extracted subset of BFs, called meta-atom, gives the description of the object. This description is then naturally extended into the linear scale-space using the definition of our BFs, and thus providing a more general representation of the object. We use this enhanced description as a predefined dictionary of the object to conduct an MP-based shape recognition task into the linear scale-space. The introduction of the scale-space approach improves the robustness of our method: we avoid local minima issues encountered when minimizing a nonconvex energy function. We show results for the detection of complex synthetic shapes, as well as real world (aerial and medical) images. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 162-180, 2006

Published in:
International Journal of Imaging Systems and Technology, 6, 15, 162-180
Other identifiers:

 Record created 2007-03-27, last modified 2019-03-16

Download fulltext

Rate this document:

Rate this document:
(Not yet reviewed)