JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Performance Improvement of Stereo Matching by Image Segmentation based on Color and Multi-threshold
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Performance Improvement of Stereo Matching by Image Segmentation based on Color and Multi-threshold
Kim, Eun Kyeong; Cho, Hyunhak; Jang, Eunseok; Kim, Sungshin;
  PDF(new window)
 Abstract
This paper proposed the method to improve performance of a pixel, which has low accuracy, by applying image segmentation methods based on color and multi-threshold of brightness. Stereo matching is the process to find the corresponding point on the right image with the point on the left image. For this process, distance(depth) information in stereo images is calculated. However, in the case of a region which has textureless, stereo matching has low accuracy and bad pixels occur on the disparity map. In the proposed method, the relationship between adjacent pixels is considered for compensating bad pixels. Generally, the object has similar color and brightness. Therefore, by considering the relationship between regions based on segmented regions by means of color and multi-threshold of brightness respectively, the region which is considered as parts of same object is re-segmented. According to relationship information of segmented sets of pixels, bad pixels in the disparity map are compensated efficiently. By applying the proposed method, the results show a decrease of nearly 28% in the number of bad pixels of the image applied the method which is established.
 Keywords
Stereo matching;Image segmentation;Disparity Map;Color;Brightness;
 Language
Korean
 Cited by
 References
1.
G. N. DeSouza, and A. C. Kak, "Vision for mobile robot navigation: A survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 237-267, 2002. crossref(new window)

2.
C. Hane, et al., "Stereo depth map fusion for robot navigation," IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1618-1625, 2011.

3.
H. H. Min, D. S. Yoo, and Y. T. Kim, "Fuzzy Tracking Control Based on Stereo Images for Tracking of Moving Robot," Journal of Korean Institute of Intelligent Systems, vol. 22, no. 2, pp. 198-204, 2012. crossref(new window)

4.
D. Scharstein, and R. Szeliski, "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms," International Journal of Computer Vision, vol. 47, no. 7, pp. 7-42, 2002. crossref(new window)

5.
F. Tombari, et al., "Classification and evaluation of cost aggregation methods for stereo correspondence," IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008

6.
X. Sun, et al., "Stereo matching with reliable disparity propagation," International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, pp. 132-139, 2011.

7.
I. Oh, Computer Vision, Hanbit Academy Inc., 2014.

8.
T. Sag, and M. Cunkas, "Color image segmentation based on multiobjective artificial bee colony optimization," Applied Soft Computing, vol. 34, pp. 389-401, 2015 crossref(new window)

9.
W. Skarbek, et al., "Colour image segmentation-a survey," 1994.

10.
B. I. Choi, and C. H. Rhee, "Fuzzy Kernel K-Nearest Neighbor Algorithm for Image Segmentation," Journal of Korean Institute of Intelligent Systems, vol. 15, no. 7, pp. 828-833, 2005.

11.
P. Corke, Robotics, Vision and Control, Springer, 2011.

12.
H. Li, H. He, and Y. Wen, "Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation," Optik-International Journal for Light and Electron Optics, pp. 4817-4822, 2015.

13.
D. Scharstein, and R. Szeliski, "Middlebury Stereo Vision Page", Available: http://vision.middlebury.edu/stereo/, 2002, [Accessed: November 03, 2015]

14.
Y. H. Cho, "Shape Image Recognition by Using Histogram-based Correlation," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 4, pp. 548-553, 2010. crossref(new window)

15.
N. Duan, et al., "Multi-thresholds Selection Based on Plane Curves," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 2, pp. 279-284, 2010. crossref(new window)

16.
S. B. Roh, et al., "Space Partition using Context Fuzzy c-Means Algorithm for Image Segmentation," Journal of Korean Institute of Intelligent Systems, vol. 20, no. 3, pp. 368-374, 2010. crossref(new window)