Visual object recognition

Synthesis Lectures on Artificial Intelligence and Machine Learning

Volumn 11, Issue , 2011, Pages 1-180

  Grauman, Kristen ^a   Leibe, Bastian ^b  

^a University of Texas at Austin   (United States)

^b RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

dealing with outliers in correspondences; detection and description of local invariant features; detection via sliding windows; efficient algorithms for matching local features; Generalized distance transform; global representations versus local descriptors; histograms of oriented gradients and rectangular features; Hough voting; methods to verify geometric consistency according to parameterized geometric transformations; part based models; pyramid match kernels; RANSAC and the Generalized Hough transform; star graph models and fully connected constellations; the Deformable Part based Model; the Implicit Shape Model; tree based and hashing based search algorithms; visual vocabularies and bags of words; window based descriptors

Indexed keywords

DEALING WITH OUTLIERS IN CORRESPONDENCES; DETECTION AND DESCRIPTION OF LOCAL INVARIANT FEATURES; DETECTION VIA SLIDING WINDOWS; EFFICIENT ALGORITHMS FOR MATCHING LOCAL FEATURES; GENERALIZED DISTANCE TRANSFORM; GLOBAL REPRESENTATIONS VERSUS LOCAL DESCRIPTORS; HISTOGRAMS OF ORIENTED GRADIENTS AND RECTANGULAR FEATURES; HOUGH VOTING; METHODS TO VERIFY GEOMETRIC CONSISTENCY ACCORDING TO PARAMETERIZED GEOMETRIC TRANSFORMATIONS; PART-BASED MODELS; PYRAMID MATCH KERNELS; RANSAC AND THE GENERALIZED HOUGH TRANSFORM; STAR GRAPH MODELS AND FULLY CONNECTED CONSTELLATIONS; THE DEFORMABLE PART-BASED MODEL; THE IMPLICIT SHAPE MODEL; TREE-BASED AND HASHING-BASED SEARCH ALGORITHMS; VISUAL VOCABULARIES AND BAGS-OF-WORDS; WINDOW-BASED DESCRIPTORS;

COMPUTER VISION; CONTENT BASED RETRIEVAL; DEFORMATION; GEOMETRY; GRAPHIC METHODS; HOUGH TRANSFORMS; IMAGE MATCHING; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; OBJECT RECOGNITION; ROBOTICS; STARS; TREES (MATHEMATICS);

MATHEMATICAL TRANSFORMATIONS;

EID: 79955445232     PISSN: 19394608     EISSN: 19394616     Source Type: Book Series    
DOI: 10.2200/S00332ED1V01Y201103AIM011     Document Type: Article

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