Coarse-to-fine object recognition using shock graphs.
Published: 2004
Language: eng
Number of pages: 88
Description: Shock graphs have emerged as a powerful generic 2-D shape representation. However, most approaches typically assume that the silhouette has been correctly segmented. In this thesis, we present a framework for shock graph-based object recognition in less contrived scenes. The approach consists of two steps, beginning with a construction of a region scale-space. For a given region, we traverse this scale-space, using a model hypothesis to guide a region grouping process that improves the hypothesis. The result represents the best subset of regions that matches a given object model. In the second step, the region-model correspondence is used to initialize an active skeleton that includes a shock graph-based energy term, allowing the contour to fine tune the region shape while deforming under a stricter set of model constraints. Together, the two components provide a coarse-to-fine, model-based segmentation/recognition framework.
Notes: Adviser:  Sven Dickinson.

Thesis (M.Sc.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Masters Abstracts International, Volume: 43-03, page: 0870.

MICR copy on microfiche (1 microfiche).