Optimal partial shape similarity
| Autor: | Longin Jan Latecki, Diedrich Wolter, Rolf Lakaemper |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Cognitive neuroscience of visual object recognition Pattern recognition Similarity measure Normal case Digital image Active shape model Signal Processing Segmentation Robot vision Computer vision Computer Vision and Pattern Recognition Artificial intelligence business ComputingMethodologies_COMPUTERGRAPHICS Mathematics Shape analysis (digital geometry) |
| Zdroj: | Image and Vision Computing. 23:227-236 |
| ISSN: | 0262-8856 |
| DOI: | 10.1016/j.imavis.2004.06.015 |
| Popis: | Humans are able to recognize objects in the presence of significant amounts of occlusion and changes in the view angle. In human and robot vision, these conditions are normal situations and not exceptions. In digital images one more problem occurs due to unstable outcomes of the segmentation algorithms. Thus, a normal case is that a given shape is only partially visible, and the visible part is distorted. To our knowledge there does not exist a shape representation and similarity approach that could work under these conditions. However, such an approach is necessary to solve the object recognition problem. The main contribution of this paper is the definition of an optimal partial shape similarity measure that works under these conditions. In particular, the presented novel approach to shape-based object recognition works even if only a small part of a given object is visible and the visible part is significantly distorted, assuming the visible part is distinctive. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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