NDLI: Feature-Based Deformable Surface Detection with Self-Occlusion Reasoning

Content Provider	Springer Nature Link
Author	Pizarro, Daniel Bartoli, Adrien
Copyright Year	2011
Abstract	This paper presents a method for detecting a textured deformed surface in an image. It uses (wide-baseline) point matches between a template and the input image. The main contribution of the paper is twofold. First, we propose a robust method based on local surface smoothness capable of discarding outliers from the set of point matches. Our method handles large proportions of outliers (beyond 70% with less than 15% of false positives) even when the surface self-occludes. Second, we propose a method to estimate a self-occlusion resistant warp from point matches. Our method allows us to realistically retexture the input image. A pixel-based (direct) registration approach is also proposed. Bootstrapped by our robust point-based method, it finely tunes the warp parameters using the value (intensity or color) of all the visible surface pixels. The proposed framework was tested with simulated and real data. Convincing results are shown for the detection and retexturing of deformed surfaces in challenging images.
Starting Page	54
Ending Page	70
Page Count	17
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	97
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2011-04-27
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Deformable surfaces Image registration Feature-based registration Pixel-based refinement Robust feature correspondence Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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