Abstract Segmentation of objects serves as a key of image an alysis and pattern recognition. The main focus of the paper is related to the development of efficient algorithms for automatic segmentation of objects in image sequ ences. Fast, automatic and robust segmentation are necessary in many aspec ts of multimedia applications due to its capability that it can automatically detect objects appearance and in addi tion it can be used for object tracking system. The extraction of semantically meaningful video object for tracking a nd surveillance application can be obtained in the process of implementing the proposed algorithms. Further in this paper a co mparative study between video object extraction bas ed on change detection and model matching techniques is given. Experimenta l results prove the effectiveness of the proposed algorithms.

References

[1] Bouthemy P. and Francois E., Motion Segmentation and Qualitative Dynamic Scene Analysis from an Image Sequence, International Journal of Computer Vision , vol. 10, no. 2, pp. 157)182, 1993.

[2] Camillo G., Octavia C., and Mario S., Segmentation for Robust Tracking in the Presence of Severe Occlusion, Computer Journal of IEEE Transaction on Image Processing , vol. 13, no. 2, pp. 299)307, 2004.

[3] Canny J., A Computational Approach to Edge Detection, Computer Journal of IEEE Transactions Pattern Analysis and Machine Intelligence , vol. 8 no. 6, pp. 679)698, 1986.

[4] Changick K. and Jenq H., Fast and Automatic Video Object Segmentation and Tracking for Content Based Applications, Computer Journal of IEEE Transactions on Circuits and System for Video Technology , vol. 12, no. 2, pp. 122, 2002.

[5] Changick K. and Jenq H., Fast and Robust Video Object Segmentation in Video Sequence, Computer Journal of IEEE Transactions on Circuits and System for Video Technology , vol. 11, no. 11, pp. 1160)1170, 2002.

[6] Demin W., Unsupervised Video Segmentation Based on Watersheds and Temporal Tracking, Computer Journal of IEEE Transaction on Circuits and Systems for Video Technology , vol. 8, no. 5, pp. 525)538, 1998.

[7] Haifeng X. and Akmal A., Automatic Moving Object Extraction for Content)Based Applications, Computer Journal of IEEE Transactions on Circuits and Systems for Video Technology , vol. 14, no. 6, pp. 914)920, 2004.

[8] Jinhui P., Shipeng L., and Ya Q., Automatic Extraction of Moving Objects Using Multiple Features and Multiple Frames, Computer Journal of IEEE International Symposium on Circuits and Systems , vol. 1, no. 1, pp. 413)416, 2000.

[9] Luc V. and Piene S., Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations, Computer Journal of IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583)598, 1991.

[10] Meyer F. and Beucher S., Morphological Segmentation , Communications Image Representation, vol. 1, no. 1, pp. 21)46, 1990.

[11] Moscheni F., Bhattacharjee S., and Kunt M., Spatiotemporal Segmentation Based on Region Merging, Computer Journal of IEEE Transactions Pattern Anal Machine Intelligence , vol. 20, no. 9, pp. 897)915, 1998.

[12] Yao N. and Kai M., Adaptive Rood Pattern Search for Fast Block Matching Motion Estimation, Computer Journal of IEEE Transactions Image Processing , vol. 11, no. 12, pp. 1442)1448, 2002. Kavitha Ganesan graduated in electronics and communication engineering in 2001 from Joseph s College of Engineering, University of Madras, India. She earned her Master s degree in applied electronics in January 2003 from Anna University, Chennai, India. Shanmugam Jalla obtained his ME and PhD degrees from MIT, Anna University, India. in instrumentation engineering. He served in the Indian Air Force for about 15 years and then joined the Department of Instrumentation Technology, MIT, Anna University, as lecturer since March 1984. The International Arab Journal of Information Technology, Vol. 6, No. 2, April 2009 116