Weighted Census Transform and Guide Filtering based Depth Map Generation Method

가중치를 이용한 센서스 변환과 가이드 필터링 기반깊이지도 생성 방법

  • 문지훈 (광주과학기술원 전기전자컴퓨터공학부) ;
  • 호요성 (광주과학기술원 전기전자컴퓨터공학부)
  • Received : 2016.07.26
  • Accepted : 2017.01.19
  • Published : 2017.02.25


Generally, image contains geometrical and radiometric errors. Census transform can solve the stereo mismatching problem caused by the radiometric distortion. Since the general census transform compares center of window pixel value with neighbor pixel value, it is hard to obtain an accurate matching result when the difference of pixel value is not large. To solve that problem, we propose a census transform method that applies different 4-step weight for each pixel value difference by applying an assistance window inside the window kernel. If the current pixel value is larger than the average of assistance window pixel value, a high weight value is given. Otherwise, a low weight value is assigned to perform a differential census transform. After generating an initial disparity map using a weighted census transform and input images, the gradient information is additionally used to model a cost function for generating a final disparity map. In order to find an optimal cost value, we use guided filtering. Since the filtering is performed using the input image and the disparity image, the object boundary region can be preserved. From the experimental results, we confirm that the performance of the proposed stereo matching method is improved compare to the conventional method.


  1. "3dv systems, z-cam,"
  2. D. Possuart and D. Laurendeau, "3D sensing for industrial computer vision, in advances in machine vision," chapter 3, pp. 122-159, 1989.
  3. H. Hirschmuller and D. Scharstein, "Evaluation of Stereo Matching Costs on Image with Radiometric Differences," IEEE Transactions on PAMI, vol. 31, no. 9, pp. 1582-1599, Sept. 2009.
  4. R. Zabih and J. Woodfill, "Non-parametric Local Transform for Computing Visual Correspondece," ECCV, pp. 151-158, 1994.
  5. W. Fife and J. Archibald, "Improved Census Transforms for Resource-Optimized Stereo Vision," IEEE Transaction CSVT, pp. 60-73, June, 2012.
  6. K. He, J. Sun, and X. Tang, "Guided Image Filtering," IEEE Transaction on PAMI, vol. 35, no. 6, pp. 1397-1409, Oct. 2012.
  7. N. Draper and H. Smith, "Applied regression analysis. 2nd," John Wiley, pp. 341-391, 1981.
  8. C. Rhemann, A. Hosni, M. Bleyer, C. Rother and M. Gelautz, "Fast Cost-Volume Filtering for Visual Correspondence and Beyond," In Proceedings of Conference on CVPR, 2011.
  9. V. Borisagar, M. Zaveri, "Census and segmentationbased disparity estimation algorithm using region merging," JSIP, vol. 6, pp. 191-202, Aug. 2015.
  10. M. Weber, M. Humenberger, W. kubinger, "A very fast census-based stereo matching implementation on a graphic processing unit," ICCV, pp. 786-793, Oct. 2009.