Recognizing Objects by Their Appearance Using Eigenimages
SOFSEM '00 Proceedings of the 27th Conference on Current Trends in Theory and Practice of Informatics
Contour-based partial object recognition using symmetry in image databases
Proceedings of the 2005 ACM symposium on Applied computing
Detection and localization of the top object in the stack of objects
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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In this paper, we discuss an appearance matching technique for the interpretation of color scenes containing occluded objects. Dealing with occlusions is very difficult, and we have explored the use of an iterative, coarse-to-fine correlation-based method that uses hypothesized occlusion events to modify the scene-to-template similarity measure at run-time. Specifically, a binary mask is used to adaptively exclude regions of the template image from the correlation computation. At each iteration, these masks are adjusted based on higher resolution scene data and the occluding interactions between multiple object hypotheses. We present results which demonstrate the technique is reasonably robust over a large database of color test scenes containing objects at a variety of scales, and tolerates minor object rotations and global illumination variations.