Read e-book online Computer Vision – ECCV 2008: 10th European Conference on PDF

By Jan J. Koenderink (auth.), David Forsyth, Philip Torr, Andrew Zisserman (eds.)

ISBN-10: 3540886818

ISBN-13: 9783540886815

The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed complaints of the tenth ecu convention on machine imaginative and prescient, ECCV 2008, held in Marseille, France, in October 2008.

The 243 revised papers provided have been conscientiously reviewed and chosen from a complete of 871 papers submitted. The 4 books conceal the full variety of present matters in laptop imaginative and prescient. The papers are equipped in topical sections on acceptance, stereo, humans and face reputation, item monitoring, matching, studying and contours, MRFs, segmentation, computational images and lively reconstruction.

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Additional resources for Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I

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Feng Liu and Michael Gleicher 437 Scene Discovery by Matrix Factorization . . . . . . . . . . . . . . Nicolas Loeff and Ali Farhadi 451 Simultaneous Detection and Registration for Ileo-Cecal Valve Detection in 3D CT Colonography . . . . . . . . . . . . . . . . . . . . . Le Lu, Adrian Barbu, Matthias Wolf, Jianming Liang, Luca Bogoni, Marcos Salganicoff, and Dorin Comaniciu 465 Constructing Category Hierarchies for Visual Recognition .

Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi 830 845 Passive Reflectometry . . . . . . . . . . . . . . . . . . . . . . Fabiano Romeiro, Yuriy Vasilyev, and Todd Zickler 859 Fusion of Feature- and Area-Based Information for Urban Buildings Modeling from Aerial Imagery . . . . . . . . . . . . . . . . . . . Lukas Zebedin, Joachim Bauer, Konrad Karner, and Horst Bischof 873 Author Index . . .

The formulation (7) is attractive in that it scales smoothly with the degree of overlap between the solutions, which is important to allow the learning process to utilize partial detections for training. In the case that yi or y indicate that the object is not present in the image, we have a loss of 0 if the labels agree, and 1 if they disagree, which yields the usual notion of margin for an SVM. This setup automatically enforces by a maximum margin approach two conditions that are important for localization.

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Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I by Jan J. Koenderink (auth.), David Forsyth, Philip Torr, Andrew Zisserman (eds.)


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