Shape, Contour and Grouping in Computer Vision
Shape, Contour and Grouping in Computer Vision [electronic resource] /
edited by David A. Forsyth, Joseph L. Mundy, Vito di Gesu, Roberto Cipolla.
- 1st ed. 1999.
- VIII, 350 p. online resource.
- Lecture Notes in Computer Science, 1681 1611-3349 ; .
- Lecture Notes in Computer Science, 1681 .
An Empirical-Statistical Agenda for Recognition -- A Formal-Physical Agenda for Recognition -- Shape -- Shape Models and Object Recognition -- Order Structure, Correspondence, and Shape Based Categories -- Quasi-Invariant Parameterisations and Their Applications in Computer Vision -- Shading -- Representations for Recognition Under Variable Illumination -- Shadows, Shading, and Projective Ambiguity -- Grouping -- Grouping in the Normalized Cut Framework -- Geometric Grouping of Repeated Elements within Images -- Constrained Symmetry for Change Detection -- Grouping Based on Coupled Diffusion Maps -- Representation and Recognition -- Integrating Geometric and Photometric Information for Image Retrieval -- Towards the Integration of Geometric and Appearance-Based Object Recognition -- Recognizing Objects Using Color-Annotated Adjacency Graphs -- A Cooperating Strategy for Objects Recognition -- Statistics, Learning and Recognition -- Model Selection for Two View Geometry:A Review -- Finding Objects by Grouping Primitives -- Object Recognition with Gradient-Based Learning.
Computer vision has been successful in several important applications recently. Vision techniques can now be used to build very good models of buildings from pictures quickly and easily, to overlay operation planning data on a neuros- geon’s view of a patient, and to recognise some of the gestures a user makes to a computer. Object recognition remains a very di cult problem, however. The key questions to understand in recognition seem to be: (1) how objects should be represented and (2) how to manage the line of reasoning that stretches from image data to object identity. An important part of the process of recognition
9783540468059
10.1007/3-540-46805-6 doi
Computer vision.
Pattern recognition systems.
Computer graphics.
Artificial intelligence.
Computer Vision.
Automated Pattern Recognition.
Computer Graphics.
Artificial Intelligence.
TA1634
006.37
An Empirical-Statistical Agenda for Recognition -- A Formal-Physical Agenda for Recognition -- Shape -- Shape Models and Object Recognition -- Order Structure, Correspondence, and Shape Based Categories -- Quasi-Invariant Parameterisations and Their Applications in Computer Vision -- Shading -- Representations for Recognition Under Variable Illumination -- Shadows, Shading, and Projective Ambiguity -- Grouping -- Grouping in the Normalized Cut Framework -- Geometric Grouping of Repeated Elements within Images -- Constrained Symmetry for Change Detection -- Grouping Based on Coupled Diffusion Maps -- Representation and Recognition -- Integrating Geometric and Photometric Information for Image Retrieval -- Towards the Integration of Geometric and Appearance-Based Object Recognition -- Recognizing Objects Using Color-Annotated Adjacency Graphs -- A Cooperating Strategy for Objects Recognition -- Statistics, Learning and Recognition -- Model Selection for Two View Geometry:A Review -- Finding Objects by Grouping Primitives -- Object Recognition with Gradient-Based Learning.
Computer vision has been successful in several important applications recently. Vision techniques can now be used to build very good models of buildings from pictures quickly and easily, to overlay operation planning data on a neuros- geon’s view of a patient, and to recognise some of the gestures a user makes to a computer. Object recognition remains a very di cult problem, however. The key questions to understand in recognition seem to be: (1) how objects should be represented and (2) how to manage the line of reasoning that stretches from image data to object identity. An important part of the process of recognition
9783540468059
10.1007/3-540-46805-6 doi
Computer vision.
Pattern recognition systems.
Computer graphics.
Artificial intelligence.
Computer Vision.
Automated Pattern Recognition.
Computer Graphics.
Artificial Intelligence.
TA1634
006.37