An Optical Flow Based Segmentation Method for Objects Extraction
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
An Optical Flow Based Segmentation Method for Objects Extraction

Authors: C. Lodato, S. Lopes

Abstract:

This paper describes a segmentation algorithm based on the cooperation of an optical flow estimation method with edge detection and region growing procedures. The proposed method has been developed as a pre-processing stage to be used in methodologies and tools for video/image indexing and retrieval by content. The addressed problem consists in extracting whole objects from background for producing images of single complete objects from videos or photos. The extracted images are used for calculating the object visual features necessary for both indexing and retrieval processes. The first task of the algorithm exploits the cues from motion analysis for moving area detection. Objects and background are then refined using respectively edge detection and region growing procedures. These tasks are iteratively performed until objects and background are completely resolved. The developed method has been applied to a variety of indoor and outdoor scenes where objects of different type and shape are represented on variously textured background.

Keywords: Motion Detection, Object Extraction, Optical Flow, Segmentation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077766

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1850

References:


[1] R. Pohle and K. Toennies, "Segmentation of medical images using adaptive region growing", Proc. SPIE Medical Imaging 2001, San Diego, CA, 2001, 1337-1346.
[2] G. A. Ruza, and P. A. Estéveza, "Image segmentation using fuzzy minmax neural networks for wood defect detection", Proc. IPROMS 2005, online web-based conference, July 2005, to be published by Elsevier.
[3] A. K. Jain and M. P. Dubuisson, "Segmentation of x-ray and c-scan images of fiber reinforced composite materials", Pattern Recognition, 25, pp. 257-269, 1992.
[4] P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient graph based image segmentation", International Journal of Computer Vision 59(2), 167- 181, 2004.
[5] J. Malik, S. Belongie, T. Leung and J. Shi, "Contour and texture analysis for image segmentation", International Journal of Computer Vision 43(1), 7-27, 2001
[6] U. Montanari, "On the optimal detection of curves in noisy pictures", Comm. of the ACM, vol.14:335-345, 1971.
[7] P. Parent and S. Zucker, "Trace inference, curvature, consistency, and curve detection", IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 11 , Issue 8, Pages: 823 - 839, August 1989.
[8] A. Sha-ashua and S. Ullman, "Structural saliency: the detection of globally salient structures using a locally connected network", in Proc. 2nd Int. Conf. Computer Vision, Tampa, FL, USA, 1988, pp 321-327.
[9] L. Williams and D. Jacobs, "Stochastic completion fields: a neural model of illusory contour shape and salience", in Proc. 5th Int. Conf. Computer Vision, Cambridge, MA, 1995, pp. 408-415.
[10] L. A. Vese and T. F. Chan, "A multiphase level set framework for image segmentation using the Mumford and Shah model", International Journal of Computer Vision 50(3), 271-293, 2002.
[11] N. Paragios, "A variational approach for the segmentation of the left ventricle in cardiac image analysis", International Journal of Computer Vision 50(3), 345-362, 2002.
[12] X. Wang, L. He and W. Wee, "Deformable contour method: a constrained optimization approach", International Journal of Computer Vision 59(1), 87-108, 2004.
[13] B. Appleton and H. Talbot, "Globally optimal geodesic active contours", Journal of Mathematical Imaging and Vision 23:67-86, 2005
[14] T. Amiaz and N. Kiryati, "Dense discontinuous optical flow via contourbased segmentation", in Proc. ICIP 2005, Genova, Italy, September 2005, Vol. III, pp. 1264-1267.
[15] C. L. Zitnick, N. Jojic and S. B. Kang, "Consistent segmentation for optical flow estimation", in Proc. ICCV 2005, Beijing, China, October 2005.
[16] F. Ranchin and F. Dibos, "Moving objects segmentation using optical flow estimation", in Proc. Workshop on Mathematics and Image Analysis, Paris, September 2004.
[17] R. Vidal and S. Sastry, "Segmentation of dynamic scenes from image intensities", in Proc. IEEE Workshop on Vision and Motion Computing, Orlando FL, December 2002, pp. 44-49.
[18] D. Cremers, "A variational framework for image segmentation combining motion estimation and shape regularization", in Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Madison, Wisconsin, June 2003.
[19] A. G. Bors and I. Pitas, "Optical flow estimation and moving object segmentation based on median radial basis function network", IEEE Trans. Image Process., vol. 7, no. 5, pp. 693--702, May 1998.
[20] J. L. Barron, D. J. Fleet, and S. S. Beauchemin, "Performance of optical flow techniques", International Journal of Computer Vision 12(1), pp. 43-77, 1994.
[21] E. Francomano, C. Lodato, S. Lopes and A. Tortorici, "An algorithm for optical flow computation based on a quasi-interpolant operator", Journal of Computational Methods in Science and Engineering (JCMSE) to be published.
[22] B. K. Horn and B. G. Schunck, "Determining optical flow", Artificial Intelligence. August 1981.
[23] M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation", Journal of Electronic Imaging 13(1), 146-165, January 2004.
[24] A. Abutaleb, "Automatic thresholding of gray-level pictures using twodimensional entropy", Computer Vision, Graphics, and Image Processing, Volume 47, Issue 1, Pages 22-32, July 1989.
[25] M. Roggero. "Object segmentation with region growing and principal component analysis", in Proc. ISPRS Commission III, Symposium 2002, Graz, Austria, September 2002, pp. A-289-294.
[26] J. Fan, D. K. Y. Yau, A. K. Elmagarmid and W. G. Aref, "Automatic image segmentation by integrating color-edge extraction and seeded region growing", IEEE Trans. Image Process., vol.10, no.10, pp.1454- 1466, Oct. 2001.
[27] R. Adams and L. Bischof, "Seeded region growing", IEEE Trans. Pattern Anal. Mach. Intell., vol.16, no.6, pp.641-647, June 1994.
[28] S. A. Hojjatoleslami and J. Kittler, "Region growing: A new approach", IEEE Trans. Image Process., vol.7, no.7, pp.1079-1084, July 1998.
[29] Y. L. Chang and X. Li, "Adaptive image region-growing", IEEE Trans. Image Process., vol.3, no.6, pp.868-872, Nov. 1994.