An Iterative Local Color Correction Method for Binocular Stereo Vision

被引:0
|
作者
Yuan X. [1 ]
Ran Q. [1 ]
Zhao W. [1 ]
Feng J. [1 ]
机构
[1] State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou
关键词
Color correction; Consistent segmentation; Disparity map; Iterative processing; Stereo matching;
D O I
10.3724/SP.J.1089.2019.17355
中图分类号
学科分类号
摘要
Due to difference of camera parameters, variance of environmental illumination, non-diffuse reflection of object surface, there is always the color discrepancy between stereoscopic image pair in stereo matching, which will decrease the accuracy of disparity computation. To address this problem, an iterative local color correction method is proposed in this paper. First, the Mean shift algorithm is adopted to segment the stereoscopic images with different granularities respectively. Meanwhile, the SIFT features are extracted and used for local region correspondence initially based on object distributions in the images. Then the target image is corrected by using a weighted local color correction function. Because of different view angles of objects in the images, occlusions will occur and cause the initial region correspondence inaccurate. Thus, a stereo matching algorithm is adopted to generate the disparity maps. The region correspondence is then refined based on the dense feature correspondence between the disparity maps, and the weighted local color correction is performed again. The above refinement will be iteratively performed till the disparity result is convergent. Comparing with several color transfer methods on the benchmark image set, the proposed method can improve the color similarity between stereoscopic image pair and improve the accuracy of stereo matching effectively. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码:65 / 75
页数:10
相关论文
共 23 条
  • [1] Scharstein D., Szeliski R., Zabih R., A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision, pp. 131-140, (2001)
  • [2] Zabih R., Woodfill J., Non-parametric local transforms for computing visual correspondence, Proceedings of the European Conference on Computer Vision, pp. 151-158, (1994)
  • [3] Hirschmuller H., Accurate and efficient stereo processing by semi-global matching and mutual information, Proceedings of the IEEE Computer Society Conference on Computer Vision & Pattern Recognition, pp. 807-814, (2005)
  • [4] Hirschmuller H., Scharstein D., Evaluation of cost functions for stereo matching, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2007)
  • [5] Xu W., Mulligan J., Performance evaluation of color correction approaches for automatic multi-view image and video stitching, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 263-270, (2010)
  • [6] Yamamoto K., Oi R., Color correction for multi-view video using energy minimization of view networks, International Journal of Automation and Computing, 5, 3, pp. 234-245, (2008)
  • [7] Jia J.Y., Tang C.K., Image registration with global and local luminance alignment, Proceedings of the IEEE International Conference on Computer Vision, pp. 156-163, (2003)
  • [8] Mouffranc C., Nozick V., Colorimetric correction for stereoscopic camera arrays, Proceedings of the Asian Conference on Computer Vision, 7728, pp. 206-217, (2012)
  • [9] Gui Y.T., Gledhill D., Taylor D., Et al., Colour correction for panoramic imaging, Proceedings of the 6th International Conference on Information Visualization, pp. 483-488, (2002)
  • [10] Zhang M.J., Georganas N.D., Fast color correction using principal regions mapping in different color spaces, Real-Time Imaging, 10, 1, pp. 23-30, (2004)