DocumentCode
3404598
Title
Minimum length in the tangent bundle as a model for curve completion
Author
Ben-Yosef, Guy ; Ben-Shahar, Ohad
Author_Institution
Comput. Sci. Dept., Ben-Gurion Univ., Beer-Sheva, Israel
fYear
2010
fDate
13-18 June 2010
Firstpage
2384
Lastpage
2391
Abstract
The phenomenon of visual curve completion, where the visual system completes the missing part (e.g., due to occlusion) between two contour fragments, is a major problem in perceptual organization research. Previous computational approaches for the shape of the completed curve typically follow formal descriptions of desired, image-based perceptual properties (e.g, minimum total curvature, roundedness, etc.). Unfortunately, however, it is difficult to determine such desired properties psychophysically and indeed there is no consensus in the literature for what they should be. Instead, in this paper we suggest to exploit the fact that curve completion occurs in early vision in order to formalize the problem in a space that explicitly abstracts the primary visual cortex. We first argue that a suitable abstraction is the unit tangent bundle R2 × S1 and then we show that a basic principle of “minimum energy consumption” in this space, namely a minimum length completion, entails desired perceptual properties for the completion in the image plane. We present formal theoretical analysis and numerical solution methods, we show results on natural images and their advantage over existing popular approaches, and we discuss how our theory explains recent findings from the perceptual literature using basic principles only.
Keywords
computer vision; curve fitting; natural scenes; visual perception; computational approaches; contour fragments; formal descriptions; formal theoretical analysis; image plane; image-based perceptual property; minimum energy consumption; minimum length completion; natural images; numerical solution methods; perceptual organization research; primary visual cortex; suitable abstraction; tangent bundle; visual curve completion; visual system; Abstracts; Computational modeling; Computer science; Computer vision; Energy consumption; Image analysis; Image segmentation; Psychology; Shape; Visual system;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on
Conference_Location
San Francisco, CA
ISSN
1063-6919
Print_ISBN
978-1-4244-6984-0
Type
conf
DOI
10.1109/CVPR.2010.5539930
Filename
5539930
Link To Document