Regularization of inverse visual problems involving discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Regularized Solution to Edge Detection
A Regularized Solution to Edge Detection
Ill-Posed Problems and Regularization Analysis in Early Vision
Ill-Posed Problems and Regularization Analysis in Early Vision
Low Bit-Rate Motion Block Detection for Uncompressed Indoor Surveillance
ICCSA '10 Proceedings of the 2010 International Conference on Computational Science and Its Applications
Probabilistic posture classification for Human-behavior analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A large number of surveillance applications require fast action, and since many surveillance applications, motive objects contain most critical information. Fast detection algorithm system becomes a necessity. A problem in computer vision is the determination of weights for multiple objective function optimizations. In this paper we propose techniques for automatically determining the weights, and discuss their properties. The Min-Max Principle, which avoids the problems of extremely low or high weights, is introduced. Expressions are derived relating the optimal weights, objective function values, and total cost. Simulation results show, compared to the conventional work, it can achieve around 40% time saving and higher detection accuracy for both outdoor and indoor surveillance videos.