Conditional density learning via regression with application to deformable shape segmentation

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Volumn , Issue , 2008, Pages

  Zhang, Jingdan ^a,b   Zhou, Shaohua Kevin ^a   Comaniciu, Dorin ^a   McMillan, Leonard ^b  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CANNING; CHLORINE COMPOUNDS; COMPUTER VISION; DEFORMATION; EDUCATION; FEATURE EXTRACTION; FOOD PROCESSING; GRADIENT METHODS; IMAGE PROCESSING; IMAGE SEGMENTATION; LARGE SCALE SYSTEMS; LEARNING ALGORITHMS; OPTIMIZATION; PARAMETER ESTIMATION; PATTERN RECOGNITION; PROBABILITY;

CONDITIONAL DENSITIES; CONDITIONAL PROBABILITY DENSITY; CORRECT SOLUTION; DATA SAMPLING; DENSITY FUNCTIONS; GRADIENT DESCENT; GRADIENT INFORMATIONS; HIGH DIMENSIONALITY; LEARNING APPROACHES; LOCAL OPTIMIZATIONS; MODEL PARAMETERS; MULTILEVEL REFINEMENT; NON-LINEARITIES; OPTIMAL SOLUTIONS; OPTIMIZATION ALGORITHMS; REGRESSION PROBLEMS; SHAPE SEGMENTATION; TARGET DENSITY; TARGET FUNCTIONS; TRAINING DATA SETS; UNIMODAL; VISION PROBLEMS;

PROBABILITY DENSITY FUNCTION;

EID: 51949099490     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2008.4587480     Document Type: Conference Paper

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