Simultaneous Object Recognition and Segmentation by Image Exploration

Methods based on local, viewpoint invariant features have proven capable of recognizing objects in spite of viewpoint changes, occlusion and clutter. However, these approaches fail when these factors are too strong, due to the limited repeatability and discriminative power of the features. As additional shortcomings, the objects need to be rigid and only their approximate location is found. We present an object recognition approach which overcomes these limitations. An initial set of feature correspondences is first generated. The method anchors on it and then gradually explores the surrounding area, trying to construct more and more matching features, increasingly farther from the initial ones. The resulting process covers the object with matches, and simultaneously separates the correct matches from the wrong ones. Hence, recognition and segmentation are achieved at the same time. Only very few correct initial matches suffice for reliable recognition. Experimental results on still im...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Vittorio Ferrari, Tinne Tuytelaars, Luc J. Van Gool
• Keywords: Computer Vision, Correct Initial Matches, ECCV 2004, Object Recognition Approach, Viewpoint Changes, Viewpoint Invariant Feature, Viewpoint Invariant Features