Abstract
This paper presents a computational model for obtaining relative depth information from image contours. Local occlusion properties such as T-junctions and concavity are used to arrive at a global percept of distinct surfaces at various relative depths. A multi-layer representation is used to classify each image pixel into the appropriate depth plane based on the local information from the occluding contours. A Bayesian framework is used to incorporate the constraints defined by the contours and the prior constraints. A solution corresponding to the maximum posteriori probability is then determined, resulting in a depth assignment and surface assignment for each image site or pixel. The algorithm was tested on various contour images, including two classes of illusory surfaces: the Kanizsa and the line termination illusory contours.
| Original language | English (US) |
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| Pages | 4343-4348 |
| Number of pages | 6 |
| State | Published - Dec 1 1994 |
| Event | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994 |
Other
| Other | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
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| City | Orlando, FL, USA |
| Period | 6/27/94 → 6/29/94 |