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Shape, Contour and Grouping in Computer Vision [electronic resource] /

By: Forsyth, David A [author.].
Contributor(s): Mundy, Joseph L [author.] | Gesú, Vito di [author.] | Cipolla, Roberto [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1681Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1999.Description: VIII, 350 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540468059.Subject(s): Computer science | Artificial intelligence | Computer graphics | Image processing | Pattern recognition | Computer Science | Image Processing and Computer Vision | Pattern Recognition | Computer Graphics | Artificial Intelligence (incl. Robotics)Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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.
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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 { 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.

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