Obstacle categorization based on hybridizing global and local features

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5864 LNCS, Issue PART 2, 2009, Pages 1-10

  Woo, Jeong Woo ^a   Lim, Young Chul ^b   Lee, Minho ^a  

^a Kyungpook National University   (South Korea)

^b Daegu Gyeongbuk Institute of Science and Technology   (South Korea)

Author keywords

Bottom up saliency map model; C1 feature; Modified GIST; Obstacle categorization; Support Vector Machine

Indexed keywords

BOTTOM-UP SALIENCY MAP MODEL; GLOBAL ANALYSIS; GLOBAL FEATURE; IDENTIFICATION METHOD; IMAGE TRANSLATION; LOCAL FEATURE; ORIENTATION INFORMATION; SALIENCY MAP MODEL;

DATA PROCESSING; IMAGE RETRIEVAL;

SUPPORT VECTOR MACHINES;

EID: 76249133198     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10684-2_1     Document Type: Conference Paper

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