Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 213-220

  Su, Hao ^a,d   Sun, Min ^b   Fei Fei, Li ^c   Savarese, Silvio ^b  

^a Princeton University   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c Stanford University   (United States)

^d BEIHANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

3D OBJECT; 3D VIEWPOINT; CELL PHONE; DATA SETS; GENERATIVE MODEL; GEOMETRICAL CONSTRAINTS; INCREMENTAL LEARNING; MODEL LEARNING; MULTI-VIEWS; OBJECT CLASS; OBJECT DETECTION; PARAMETERIZED; POSE ESTIMATION; PROBABILISTIC REPRESENTATION; STATE-OF-THE-ART ALGORITHMS; TRAINING IMAGE; TRIANGULAR MESHES; VIDEO CLIPS;

COMPUTER VISION; EVOLUTIONARY ALGORITHMS; LEARNING ALGORITHMS; OBJECT RECOGNITION; SPHERES;

THREE DIMENSIONAL;

EID: 77953177125     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459168     Document Type: Conference Paper

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