Using a Reference Point for Local Configuration of SIFT-like Features for Object Recognition with Serious Background Clutter
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- Klinkigt Martin
- Osaka Prefecture University German Research Center for Artificial Intelligence
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- Kise Koichi
- Osaka Prefecture University
Abstract
Object recognition can be performed on local or global features. While local features are more robust against occlusions, global features are more powerful to distinguish among many objects. In this paper we propose a novel approach in construction of a shape model from local features aimed at achieving high discriminative power as global features have, while keeping the robustness of local features. We utilize a common reference point expressing the relative position of local features like in a star graph representation. This model is dynamically calculated during recognition which makes it flexible. With our approach we achieve an improved recognition performance of 2% compared to other shape models and even 6% compared to approaches that do not utilize shape information.
Journal
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- IPSJ Transactions on Computer Vision and Applications
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IPSJ Transactions on Computer Vision and Applications 3 110-121, 2011
Information Processing Society of Japan
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Details 詳細情報について
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- CRID
- 1390282680269262976
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- NII Article ID
- 130002124228
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- ISSN
- 18826695
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed