Blick ins Buch

Shape, Contour and Grouping in Computer Vision

David A. Forsyth, Joseph L. Mundy, Vito di Gesu, Roberto Cipolla (Herausgeber)

Buch | Softcover

VIII, 350 Seiten

1999
Springer Berlin (Verlag)
978-3-540-66722-3 (ISBN)

Lese- und Medienproben

Inhaltsverzeichnis (PDF)

Artikel merken

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 { perhaps, almost all of it { involves assembling bits of image information into helpful groups. There is a wide variety of possible criteria by which these groups could be established { a set of edge points that has a symmetry could be one useful group; others might be a collection of pixels shaded in a particular way, or a set of pixels with coherent colour or texture. Discussing this process of grouping requires a detailed understanding of the relationship between what is seen in the image and what is actually out there in the world.

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.

Erscheint lt. Verlag	3.11.1999
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	VIII, 350 p.
Verlagsort	Berlin
Sprache	englisch
Maße	156 x 234 mm
Gewicht	515 g
Themenwelt	Informatik ► Grafik / Design ► Digitale Bildverarbeitung
	Mathematik / Informatik ► Informatik ► Software Entwicklung
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Bildverarbeitung • Cognition • computer vision • Hardcover, Softcover / Informatik, EDV/Informatik • HC/Informatik, EDV/Informatik • Image Processing • learning • machine learning • Mustererkennung • Object recognition • Object Representation • pattern recognition • Shading
ISBN-10	3-540-66722-9 / 3540667229
ISBN-13	978-3-540-66722-3 / 9783540667223
Zustand	Neuware