Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Granular Computing: An Emerging Paradigm
Granular Computing: An Emerging Paradigm
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Background-subtraction using contour-based fusion of thermal and visible imagery
Computer Vision and Image Understanding
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Generalized rough sets, entropy, and image ambiguity measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Video Tracking: Theory and Practice
Video Tracking: Theory and Practice
Spatiotemporal approach for tracking using rough entropy and frame subtraction
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Design of interval type-2 fuzzy models through optimal granularity allocation
Applied Soft Computing
Rough-wavelet granular space and classification of multispectral remote sensing image
Applied Soft Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
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A new spatio-temporal segmentation approach for moving object(s) detection and tracking from a video sequence is described. Spatial segmentation is carried out using rough entropy maximization, where we use the quad-tree decomposition, resulting in unequal image granulation which is closer to natural granulation. A three point estimation based on Beta Distribution is formulated for background estimation during temporal segmentation. Reconstruction and tracking of the object in the target frame is performed after combining the two segmentation outputs using its color and shift information. The algorithm is more robust to noise and gradual illumination change, because their presence is less likely to affect both its spatial and temporal segments inside the search window. The proposed methods for spatial and temporal segmentation are seen to be superior to several related methods. The accuracy of reconstruction has been significantly high.