Perceptual completion of occluded surfaces
Computer Vision and Image Understanding
Perceptual organization of occluding contours of opaque surfaces
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
A computational approach to illusory contour perception based on the tensor voting technique
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
| Hi-index | 0.00 |
When a three dimensional scene is projected to the two dimensional receptive field of a camera or a biological vision system, all depth information is lost. Even without a knowledgebase, i. e. without knowing what object can be seen, it is possible to reconstruct the depth information. Beside stereoscopic depth cues, also a number of moncular depth cues can be used. One of the most important monocular depth cues ist the occlusion of object boundaries. Therefore one of the elaborated tasks for the low level image processing stage of a vision system is the completion of cluttered or occluded object boundaries and the depth assignment of overlapped boundaries. We describe a method for depth ordering and figure-ground segregation from monocular depth cues, namely the arrangement of so-called illusory contours at junctions in the edge map of an image. Therefore, a computational approach to the perception of illusory contours, based on the tensor voting technique, is introduced and compared with an alternative contour completion realized by spline interpolation. While most approaches assume, that the position of junctions and the orientations of associated contours are already known, we also consider the preprocessing steps that are necessary for a robust perception task. This implies the anisotropic diffusion of the input image in order to simplify the image contents while preserving the edge information.