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Content Provider | IEEE Xplore Digital Library |
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Author | Gonfaus, J.M. Gevers, T. Gijsenij, A. Roca, F.X. Gonzalez, J. |
Copyright Year | 2012 |
Description | Author affiliation: Faculty of Science, Universiteit van Amsterdam (Gijsenij, A.) || Centre de Visió per Computador Universitat Autònoma de Barcelona (Gonfaus, J.M.; Gevers, T.; Roca, F.X.; Gonzalez, J.) |
Abstract | Edges are caused by several imaging cues such as shadow, material and illumination transitions. Classification methods have been proposed which are solely based on photometric information, ignoring geometry to classify the physical nature of edges in images. In this paper, the aim is to present a novel strategy to handle both photometric and geometric information for edge classification. Photometric information is obtained through the use of quasi-invariants while geometric information is derived from the orientation and contrast of edges. Different combination frameworks are compared with a new principled approach that captures both information into the same descriptor. From large scale experiments on different datasets, it is shown that, in addition to photometric information, the geometry of edges is an important visual cue to distinguish between different edge types. It is concluded that by combining both cues the performance improves by more than 7% for shadows and highlights. |
Starting Page | 1497 |
Ending Page | 1500 |
File Size | 859153 |
Page Count | 4 |
File Format | |
ISBN | 9781467322164 |
ISSN | 10514651 |
e-ISBN | 9784990644109 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2012-11-11 |
Publisher Place | Japan |
Access Restriction | Subscribed |
Rights Holder | ICPR Org Committee |
Subject Keyword | Image edge detection Image color analysis Materials Measurement Support vector machines Geometry Lighting |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition |
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