Adaptive learning of multi-subspace for foreground detection under illumination changes

Computer Vision and Image Understanding

Volumn 115, Issue 1, 2011, Pages 31-49

  Dong, Y ^a   Desouza, G N ^a  

^a University of Missouri   (United States)

Author keywords

Illumination invariance; Incremental learning; Local PCA; Multiple eigensubspace

Indexed keywords

ADAPTIVE LEARNING; ADAPTIVE LEARNING ALGORITHM; APPEARANCE BASED; CURRENT IMAGE; FEATURE SPACE; FOREGROUND DETECTION; FOREGROUND OBJECTS; FORGETTING FACTORS; FRAME RATE; GAUSSIAN MIXTURE MODEL; GRADUAL CHANGES; ILLUMINATION CHANGES; ILLUMINATION INVARIANCE; ILLUMINATION VARIATION; INCREMENTAL LEARNING; INCREMENTAL UPDATES; INPUT IMAGE; LIGHTING CONDITIONS; LOCAL PCA; LOCAL PRINCIPAL COMPONENT ANALYSIS; MULTIPLE EIGENSUBSPACE; REAL TIME;

IMAGE SEGMENTATION; LEARNING ALGORITHMS; LIGHTING; PRINCIPAL COMPONENT ANALYSIS;

ADAPTIVE ALGORITHMS;

EID: 78751569728     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2010.08.003     Document Type: Article

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