Abstract
An adaptive neural network architecture is proposed in this paper, for efficient video object segmentation and tracking in stereoscopic sequences. The scheme includes: (A) A retraining algorithm that optimally adapts the network weights to the current conditions and simultaneously minimally degrades the previous network knowledge. (B) A semantically meaningful object extraction module for constructing the retraining set of the current conditions and (C) a decision mechanism, which detects the time instances when network retraining is required. The retraining algorithm results in the minimization of a convex function subject to linear constraints. Furthermore description of the current conditions is achieved by appropriate combination of color and depth information. Experimental results on real life video sequences indicate the promising performance of the proposed adaptive neural network-based video object segmentation scheme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
N. Ngan, S. Panchanathan, T. Sikora and M.-T. Sun, “Guest Editorial: Special Issue on Representation and Coding of Images and Video,” IEEE Trans. CSVT, Vol. 8, No. 7, pp. 797–801, November 1998.
JTC1/SC29/WG11 N3156, “MPEG-4 Overview,” Doc. N3156, Maui, Hawaii, December 1999.
S. Y. Kung, “Neural Networks for Intelligent Multimedia Processing,” IEEE Signal Processing Magazine, vol. 14, no. 4, pp. 44–45, July 1997.
Meyer and S. Beucher, “Morphological Segmentation,” Journal of Visual Communication on Image Representation, Vol. 1, No. 1, pp. 21–46, Sept. 1990.
O. J. Morris, M. J. Lee and A. G. Constantinides, “Graph Theory for Image Analysis: an Approach based on the Shortest Spanning Tree,” IEE Proceedings, Vol. 133, pp. 146–152, April 1986.
W. B. Thompson and T. G. Pong, “Detecting Moving Objects,” Int. Journal Computer Vision, Vol. 4, pp. 39–57, 1990.
J. Wang and E. Adelson, “Representing Moving Images with Layers,” IEEE Trans. Image Processing, Vol. 3, pp. 625–638, Sept. 1994.
D. Wang, “Unsupervised Video Segmentation Based on Watersheds and Temporal Tracking,” IEEE Trans. CSVT, Vol. 8, No. 5, pp. 539–546, 1998.
T. Meier, and K. Ngan, “Video Segmentation for Content-Based Coding,” IEEE Trans. CSVT, Vol. 9, No. 8, pp. 1190–1203, 1999.
A. Doulamis, N. Doulamis, S. Kollias, “On Line Retrainable Neural Networks: Improving the Performance of Neural Networks in Image Analysis Problems,” IEEE Trans. on Neural Networks, Vol. 11, No. 1, January 2000.
A. Doulamis, N. Doulamis, K. Ntalianis, and S. Kollias, “Efficient Unsupervised Content-Based Segmentation in Stereoscopic Video Sequences,” Journ. Artificial Intellig. Tools, World Scientific Press, vol. 9, no. 2, pp. 277–303, June 2000.
B. L. Yeo and B. Liu, “Rapid Scene Analysis on Compressed Videos,” IEEE Trans. CSVT, Vol. 5, pp. 533–544, Dec. 1995.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Doulamis, A.D., Ntalianis, K.S., Doulamis, N.D., Kollias, S.D. (2001). Adaptable Neural Networks for Unsupervised Video Object Segmentation of Stereoscopic Sequences. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_147
Download citation
DOI: https://doi.org/10.1007/3-540-44668-0_147
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42486-4
Online ISBN: 978-3-540-44668-2
eBook Packages: Springer Book Archive