Training-free, generic object detection using locally adaptive regression kernels

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 32, Issue 9, 2010, Pages 1688-1704

  Seo, Hae Jong ^a   Milanfar, Peyman ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

correlation and regression analysis; image representation; Object detection

Indexed keywords

COSINE SIMILARITY; DATA SETS; DESCRIPTORS; IMAGE REPRESENTATIONS; IMAGING CONDITIONS; LOCAL REGRESSION; MATRIX; NON-PARAMETRIC; NONMAXIMA SUPPRESSION; OBJECT DETECTION; OBJECT OF INTERESTS; OPTIMALITY; PRE-PROCESSING STEP; PRIOR KNOWLEDGE; SALIENT FEATURES; SCALE AND ROTATION; SIGNIFICANCE TEST; TARGET IMAGES; VISUAL OBJECTS;

ALGORITHMS; IMAGE SEGMENTATION; OBJECT RECOGNITION; REGRESSION ANALYSIS; TARGETS;

IMAGE MATCHING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; METHODOLOGY; REGRESSION ANALYSIS; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77955432445     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2009.153     Document Type: Article

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