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Segmenting Simply Connected Moving Objects in a Static Scene
November 1994 (vol. 16 no. 11)
pp. 1138-1142

A new segmentation algorithm is derived, based on an object-background probability estimate exploiting the experimental fact that the statistics of local image derivatives show a Laplacian distribution. The objects' simple connectedness is included directly into the probability estimate and leads to an iterative optimization approach that can be implemented efficiently. This new approach avoids early thresholding, explicit edge detection, motion analysis, and grouping.

[1] P.J. Burt, R. Hingorani, and R.J. Kolczynski, "Mechanisms for isolating component patterns in the sequential analysis of multiple motion,"Proc. IEEE Workshop on Visual Motion, Nassau Inn, Princeton, NJ, 1991, pp. 187-193.
[2] J. Bergen, P. Burt, R. Hingorani, and S. Peleg, "Computing two motions from three frames," inProc. 3rd Int. Conf. Comput. Vision, Osaka, Dec. 1990, pp. 27-32.
[3] R. C. Jain, "Segmentation of frame sequences obtained by a moving observer,"IEEE Trans. Pattern Anal. Machine Intell., vol. 6, no. 5, pp. 624-629, 1984.
[4] M. Leung and Y. Yang, "Human body motion segmentation in a complex scene,"Patt. Recogn., vol. 20, no. 1, pp. 55-64, 1987.
[5] A. Shio and J. Sklansky, "Segmentation of people in motion,"IEEE Proc., pp. 325-332, 1991.
[6] S. D. Blostein and T. S. Huang, "Detecting small, moving objects in image sequences using sequential hypothesis testing,"IEEE Trans. Signal Processing, vol. 39, no. 7, pp. 1611-1929, 1991.
[7] G. W. Donohoeet al., "Change detection for target detection and classification in video sequences,"Proc. Int. Conf. Acoustics, Speech Signal Processing, New York, 1988, pp. 1084-1087.
[8] Y. Z. Hsuet al., "New likelihood test methods for change detection in image sequences,"CVGIP, vol. 26, 1984, pp. 73-106.
[9] B. Pratt,Digital Image Processing, 2nd edition, John Wiley and Sons, New York, 1992.
[10] A. Papoulis,Probability, Random Variables and Stochastic Processes, 2nd ed. New York: McGraw-Hill, Inc., 1987.
[11] M. Bichsel, "Segmenting simply connected moving objects in a static scene," Tech. Rep. 93.20, University of Zurich, Computer Science Department, 1993.
[12] M. Bichsel and A. P. Pentland, "A simple algorithm for shape from shading,"Proc. IEEE CVPR Conf., Champaign, Illinois, 1992, pp. 459-465.
[13] P. J. Burt, "Fast filter transforms for image processing,"Computer Vision, Graphics and Image Processing., vol. 16, pp. 20-51, 1981.

Index Terms:
probability; statistical analysis; optimisation; iterative methods; image segmentation; simply connected moving objects; static scene; segmentation algorithm; object-background probability estimate; statistic; local image derivatives; Laplacian distribution; iterative optimization approach
Citation:
M. Bichsel, "Segmenting Simply Connected Moving Objects in a Static Scene," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 11, pp. 1138-1142, Nov. 1994, doi:10.1109/34.334396
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