Contour model based homography estimation of texture-less planar objects in uncalibrated images. (April 2016)

• Record Type: Journal Article
• Authors: Zhang, Yueqiang; Zhou, Langming; Shang, Yang; Zhang, Xiaohu; Yu, Qifeng
• Is Part Of: Pattern recognition. Volume 52(2016:Apr.)
• Page Start: 375
• Page End: 383
• Publication Date: 2016-04
• Subjects: Machine vision -- Homography recognition -- Homography estimation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.10.023
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: This paper proposes a homograhpy estimation algorithm that uses contour model to track and locate the texture-less object in high clutter environments. The proposed method consists of a homography recognition with the correspondences between model and image lines unknown, followed by a nonlinear homography optimization based on the maximum likelihood criterion. In our approach, the initial estimation of the homography is obtained in the framework of RANSAC-like, which contains two stages: hypothesizing and verifying. The first stage generates a number of homograhpy hypotheses from the correspondences of the quadrangle-like structures. Next, the homography hypotheses are quickly ranked according to a redefined distance function. In the refinement procedure, the model sample points are projected into the image plane. After that, 1D search is utilized along the normal direction to obtain the corresponding image point for each model sample point. Finally, the optimized homography is obtained by minimizing the errors between the sample points and their corresponding image points. Experiments show that the proposed method performs robust homography recognition of texture-less planar objects and maintains accurate and stable homography estimation in the cluttered environments as well as the cases of extreme slant angles. Highlights: We proposed an effective homography recognition based on quadrangle-like structures. We derived a new distance function between lines in the probabilistic criterion. We presented a robust homography optimization without image contour extraction. We solved the nonlinear homography refinement by the maximum likelihood approach.