Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
권호 표지
DOI QR코드

DOI QR Code

Segmentation of Target Objects Based on Feature Clustering in Stereoscopic Images

입체영상에서 특징의 군집화를 통한 대상객체 분할

  • Jang, Seok-Woo (Department of Digital Media, Anyang University) ;
  • Choi, Hyun-Jun (Department of Electronic Engineering, Mokpo National Maritime University) ;
  • Huh, Moon-Haeng (Department of Digital Media, Anyang University)
  • 장석우 (안양대학교 디지털미디어학과) ;
  • 최현준 (목포해양대학교 전자공학과) ;
  • 허문행 (안양대학교 디지털미디어학과)
  • Received : 2012.07.31
  • Accepted : 2012.10.11
  • Published : 2012.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Since the existing methods of segmenting target objects from various images mainly use 2-dimensional features, they have several constraints due to the shortage of 3-dimensional information. In this paper, we therefore propose a new method of accurately segmenting target objects from three dimensional stereoscopic images using 2D and 3D feature clustering. The suggested method first estimates depth features from stereo images by using a stereo matching technique, which represent the distance between a camera and an object from left and right images. It then eliminates background areas and detects foreground areas, namely, target objects by effectively clustering depth and color features. To verify the performance of the proposed method, we have applied our approach to various stereoscopic images and found that it can accurately detect target objects compared to other existing 2-dimensional methods.

다양한 영상으로부터 사용자가 원하는 대상 물체를 정확하게 분할하는 기존의 기법은 2차원적인 특징을 위주로 사용하므로 3차원적인 정보가 부족하여 여러 제한사항이 존재한다. 따라서 본 논문에서는 연속적으로 입력되는 3차원의 스테레오 입체 영상으로부터 2차원과 3차원의 특징을 결합하여 군집화함으로써 대상 물체를 보다 강건하게 분할하는 기법을 제안한다. 제안된 방법에서는 먼저 촬영된 장면의 좌우 스테레오 영상으로부터 스테레오 정합 알고리즘을 이용해 영상의 각 화소별로 카메라와 물체 사이의 거리를 나타내는 깊이 특징을 추출한다. 그런 다음, 깊이 특징과 색상 특징을 효과적으로 군집화하여 배경에 해당하는 영역을 제외하고, 전경에 해당하는 대상 물체를 감지한다. 실험에서는 본 논문에서 제안된 방법을 여러 가지 영상에 적용하여 테스트를 해 보았으며, 제안된 방법이 기존의 2차원 기반의 물체 분리 방법에 비해 보다 강건하게 대상물체를 분할함을 확인하였다.

Keywords

References

  1. X. Zhao, Y. Satoh, H. Takauji, S. Kaneko, K. Iwata, R. Ozaki, "Object Detection Based on a Robust and Accurate Statistical Multi-point-pair Model," Pattern Recognition, Vol. 44, No. 6, pp. 1296-1311, Jun. 2011. https://doi.org/10.1016/j.patcog.2010.11.022
  2. X. Yang, H. Liu, L. J. Latecki, "Contour-based Object Detection as Dominant Set Computation," Pattern Recognition, Vol. 45, No. 5, pp. 1927- 1936, May 2012. https://doi.org/10.1016/j.patcog.2011.11.010
  3. S.-Y. Kim, "Dynamic Modeling of Eigenbackground for Object Tracking," Journal of the Korea Society of Computer and Information, Vol. 17, No. 4, pp. 67-74, Apr. 2012. https://doi.org/10.9708/jksci.2012.17.4.067
  4. K. Huang, L. Wang, T. Tan, S. Maybank, "A Real-Time Object Detecting and Tracking System for Outdoor Night Surveillance," Pattern Recognition, Vol. 41, No. 1, pp. 432-444, Jan. 2008. https://doi.org/10.1016/j.patcog.2007.05.017
  5. W.-S. Lee, H.-H. Kim, Y.-G. Cho, "Passenger Monitoring Method Using Optical Flow and Difference Image," In Proc. of the Fall Conference of the Korean Society for Railway, pp. 1966-1972, Oct. 2010.
  6. S.P.N. Singh, P.J. Csonka, K.J. Waldron, "Optical Flow Aided Motion Estimation for Legged Locomotion," In Proc. of the IEEE International Conference on Intelligent Robots and Systems, pp. 1738-1743, 2006.
  7. Y.-M. Paik, H.-J. Choi, Y.-H. Seo, D.-U. Kim, "A Study on the Outlier Improvement Method Using Cost Function," In Proc. of the Conference of the Korean Society of Broadcast Engineers, Vol. 2009, No. 11, pp. 269-272, 2009.
  8. A. Jama and S. Rakshit, "Augmenting Graph Cut with TV-L Approach for Robust Stereo Matching," In Proc. of the International Conference on Image Information Processing, pp. 1-6, 2011.
  9. S.-W. Jang, Y.-J. Park, G.-Y. Kim, H.-I. Choi, M.-C. Hong, "An Adult Image Identification System based on Robust Skin Segmentation," Journal of Imaging Science and Technology, Society for Imaging Science and Technology, Vol. 55. No. 2, pp. 020508-1-020508-10, Mar. 2011. https://doi.org/10.2352/J.ImagingSci.Technol.2011.55.2.020508
  10. W.-S. Kim, Digital Image Processing and Pattern Recognition, Life & Power Press, 2007.
  11. G.-J. Liu, X.-L. Tang, H.-D. Cheng, J.-H. Huang, and J.-F. Liu, "A Novel Approach for Tracking High Speed Skaters in Sports Using a Panning Camera," Pattern Recognition, Vol. 42, No. 11, pp. 2922-2935, Nov. 2009. https://doi.org/10.1016/j.patcog.2009.03.022
  12. H.-H. Do, S. Melnik, and E. Rahm, "Comparison of Schema Matching Evaluations," Lecture Notes in Computer Science, Vol. 2593, pp.221-237, 2003.