Object detection with DoG scale-space: A multiple kernel learning approach

IEEE Transactions on Image Processing

Volumn 21, Issue 8, 2012, Pages 3744-3756

  Nilufar, Sharmin ^a   Ray, Nilanjan ^a   Zhang, Hong ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

1 norm support vector machine (SVM); Circular convolution; Difference of Gaussian (DoG) scale space; Multiple kernel learning (MKL)

Indexed keywords

CIRCULAR CONVOLUTIONS; DATA SETS; DETECTION PROBLEMS; DIFFERENCE OF GAUSSIANS; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; KERNEL FUNCTION; LARGE MARGIN PRINCIPLE; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OBJECT DETECTION; OPTIMIZATION PROBLEMS; SAND MINING; SCALE SELECTION; SCALE-SPACE; SHIFT INVARIANT;

OBJECT RECOGNITION; OIL SANDS; OPTIMIZATION;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; NORMAL DISTRIBUTION; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; ARTICLE; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84864144291     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2192130     Document Type: Article

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