New anti-blur and illumination-robust combined invariant for stereo vision in human belly reconstruction
H. Liu, Kuangrong Hao, Yongsheng Ding · The Imaging Science Journal · 2014
This paper presents a human belly surface image matching technology based on stereo vision system. Our aim is to still recover the human belly surface shape accurately and efficiently in spite of the influences derived from illumination variation, blur affection and image transformation on the 2D images concerning the frontal and rear sides of belly acquired by only four cameras. For this purpose, we devote to develop an innovative stereo matching algorithm which includes two major phases. In the first phase, a novel feature descriptor is constructed by substituting blur invariance into new illumination-robust combined invariants based on Hu-moment, which can avoid the various influences from the actual photographing. The external factors that may cause the images non-ideal such as the illumination variation, the camera photosensitive discrepancy from the different perspective images, the motion of the organism, noise and so on. Meanwhile, the pixel gradient-level intensity is brought into similarity measure, which greatly overcomes the noise influence. In the second phase, strict geometric constraints composed of the distance and the slope between matching pairs are proposed for eliminating the error matching pairs, consequently, more exact matching pairs obtained at this stage, subsequently these exact pairs are taken as the seeds to generate dense cloud data so as to ensure to recover the panorama of subject’s belly realistically. The experimental results reveal that not only the cost of the presented method is more inexpensive, but also the set-up is more simply than those of the 3D scanner, with highly similar measurement accuracy of human belly shape to the 3D scanner method.