Shape, Contour and Grouping in Computer Vision

• Corporate Author: SpringerLink (Online service)
• Other Authors: Forsyth, David A. (Editor, http://id.loc.gov/vocabulary/relators/edt), Mundy, Joseph L. (Editor, http://id.loc.gov/vocabulary/relators/edt), Gesu, Vito di (Editor, http://id.loc.gov/vocabulary/relators/edt), Cipolla, Roberto (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1999.
• Edition: 1st ed. 1999.
• Series: Lecture Notes in Computer Science, 1681
• Subjects: Optical data processing.; Pattern recognition.; Computer graphics.; Artificial intelligence.; Image Processing and Computer Vision.; Pattern Recognition.; Computer Graphics.; Artificial Intelligence.

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 gestu...

Table of Contents:

• 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.