Solving the depth of the repeated texture areas based on the clustering algorithm

Zhang Xiong; Jun Zhang; Jinwen Tian

doi:10.1117/12.2205824

14 December 2015 Solving the depth of the repeated texture areas based on the clustering algorithm

Zhang Xiong, Jun Zhang, Jinwen Tian

Proceedings Volume 9812, MIPPR 2015: Automatic Target Recognition and Navigation; 98120O (2015) https://doi.org/10.1117/12.2205824
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

The reconstruction of the 3D scene in the monocular stereo vision needs to get the depth of the field scenic points in the picture scene. But there will inevitably be error matching in the process of image matching, especially when there are a large number of repeat texture areas in the images, there will be lots of error matches. At present, multiple baseline stereo imaging algorithm is commonly used to eliminate matching error for repeated texture areas. This algorithm can eliminate the ambiguity correspond to common repetition texture. But this algorithm has restrictions on the baseline, and has low speed. In this paper, we put forward an algorithm of calculating the depth of the matching points in the repeat texture areas based on the clustering algorithm. Firstly, we adopt Gauss Filter to preprocess the images. Secondly, we segment the repeated texture regions in the images into image blocks by using spectral clustering segmentation algorithm based on super pixel and tag the image blocks. Then, match the two images and solve the depth of the image. Finally, the depth of the image blocks takes the median in all depth values of calculating point in the bock. So the depth of repeated texture areas is got. The results of a lot of image experiments show that the effect of our algorithm for calculating the depth of repeated texture areas is very good.

Citation Download Citation

Zhang Xiong, Jun Zhang, and Jinwen Tian "Solving the depth of the repeated texture areas based on the clustering algorithm", Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 98120O (14 December 2015); https://doi.org/10.1117/12.2205824

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KEYWORDS

Image segmentation

Image processing

Image processing algorithms and systems

Image filtering

3D vision

Reconstruction algorithms

Gaussian filters

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