Collective-reward based approach for detection of semi-transparent objects in single images
Computer Vision and Image Understanding
Learning class-specific edges for object detection and segmentation
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
| Hi-index | 0.00 |
This paper addresses the problem of finding objects made of glass (or other transparent materials) in images. Since the appearance of glass objects depends for the most part on what lies behind them, we propose to use binary criteria ("are these two regions made of the same material?") rather than unary ones ("is this glass?") to guide the segmentation process. Concretely, we combine two complementary measures of affinity between regions made of the same material and discrepancy between regions made of different ones into a single objective function, and use the geodesic active contour framework to minimize this function over pixel labels. The proposed approach has been implemented, and qualitative and quantitative experimental results are presented.