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JACIII Vol.14 No.6 pp. 645-653
doi: 10.20965/jaciii.2010.p0645
(2010)

Paper:

Spatial Object Segmentation Using Stereo Images

Yong Hao*, Lifeng He**, Tsuyoshi Nakamura*,
Yuyan Chao***, and Hidenori Itoh*

*Department of Computer Science and Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi 466-8555, Japan

**Graduate School of Information Science and Technology, Aichi Prefectural University, Nagakute-cho, Aichi 480-1198, Japan

***Graduate School of Environment Management, Nagoya Sangyo University, Owariasahi-city, Aichi 488-8711, Japan

Received:
January 23, 2010
Accepted:
July 15, 2010
Published:
September 20, 2010
Keywords:
object segmentation, image segmentation, stereo image, minimum spanning tree clustering
Abstract

The framework of our proposed for segmenting objects using spatial location information from stereo images. An efficient graph-based image segmentation algorithm within this framework for combining changes in optical features and physical location to segment reality scenes into perceptually and semantically uniform regions. Optical and physical location are extracted using k-means clustering, and we propose a rules table for combining optical and spatial features together. The performance of our proposed framework is demonstrated in a series of reality-scene images using experimental data from the Middlebury stereo image data
(http://vision.middlebury.edu/stereo/data/).

Cite this article as:
Yong Hao, Lifeng He, Tsuyoshi Nakamura,
Yuyan Chao, and Hidenori Itoh, “Spatial Object Segmentation Using Stereo Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.6, pp. 645-653, 2010.
Data files:
References
  1. [1] N. Otsu, “A threshold selection method from grey-level histograms,” IEEE Trans. Syst., Man, Cybern., Vol.SMC-8, pp. 62-66, 1978.
  2. [2] S. Belongie, C. Carson, H. Greenspan, and J. Malik, “Color and texturebased image segmentation using EM and its application to content based image retrieval,” ICCV, pp. 675-682, 1998.
  3. [3] D. K. Panjwani and G. Healey, “Markov random-field models for unsupervised segmentation of textured color images,” PAMI, Vol.17, pp. 939-954, Oct. 1995.
  4. [4] J. Wang, “Stochastic relaxation on partitions with connected components and its application to image segmentation,” IEEE Trans. Pattern Anal. Machine Intell., Vol.20, No.6, pp. 619-636, 1998.
  5. [5] L. Shafarenko, M. Petrou, and J. Kittler, “Automatic watershed segmentationof randomly textured color images,” IEEE Trans. Pattern Anal. Machine Intell., Vol.6, No.11, pp. 1530-1544, 1997.
  6. [6] W. Ma and B. S. Manjunath, “Edge flow: a technique for boundary detection and image segmentation,” IEEE Trans. Image Processing, Vol.9, pp. 1375-1388, Aug. 2000.
  7. [7] J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. Pattern Anal. Machine Intell., Vol.22, pp. 888-905, Aug. 2000.
  8. [8] J. Sun, N.-N. Zheng, and H.-Y. Shum, “Stereo Matching Using Belief Propagation,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.25, pp. 787-800, 2003.
  9. [9] P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient Belief Propagation for Early Vision,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, Vol.1, pp. 261-268, 2004.
  10. [10] D. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,” Int. J. Computer Vision, Vol.47, pp. 7-42, 2003.
  11. [11] P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph-based image segmentation,” Int. J. of Computer Vision, Vol.59, No.2, pp. 167-181, 2004.

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Last updated on Aug. 03, 2021