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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wang, O. Dumcke, M. Smolic, A. Gross, M. |
| Copyright Year | 1992 |
| Abstract | One of the most fundamental problems in image processing and computer vision is the inherent ambiguity that exists between texture edges and object boundaries in real-world images and video. Despite this ambiguity, many applications in computer vision and image processing often use image edge strength with the assumption that these edges approximate object depth boundaries. However, this assumption is often invalidated by real world data, and this discrepancy is a significant limitation in many of today's image processing methods. We address this issue by introducing a simple, low-level, and patch-consistency assumption that leverages the extra information present in video data to resolve this ambiguity. Through analyzing how well patches can be modeled by simple transformations over time, we can obtain an indication of which image edges correspond to texture edges versus object boundaries. Our approach is simple to implement and has the potential to improve a wide range of image and video-based applications by suppressing the detrimental effects of strong texture edges on regularization terms. We validate our approach by presenting results on a variety of scene types and directly incorporating our augmented edge map into existing image segmentation and optical flow applications, showing results that better correspond to object boundaries. |
| Sponsorship | IEEE Signal Processing Society |
| Page Count | 8 |
| File Size | 6785400 |
| Starting Page | 5063 |
| Ending Page | 5070 |
| File Format | |
| ISSN | 10577149 |
| Volume Number | 22 |
| Issue Number | 12 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image edge detection Optical imaging Image segmentation Measurement Vectors Adaptive optics Cameras object segmentation Image processing image recognition image sequence analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Graphics and Computer-Aided Design Software |
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