The Collection of
Computer Science Bibliographies

Bibliography of "Integrating Qualitative and Quantitative Shape Recovery"

[   About   |  Browse   |   Statistics   ]

Number of references:171Last update:August 26, 1997
Number of online publications:31Supported:no
Most recent reference:June 1994

in  ;
Publication year: in:, since:, before: (four digit years)
Options: , ,

You may use Lucene syntax, available fields are: ti (title), au (author), yr (publications year).

Information on the Bibliography

Authors:
Sven J. Dickinson
Department of Computer Science
University of Toronto
Toronto, Ontario, Canada M5S 1A4

Dimitri Metaxas
Department of Computer and Information Science
University of Pennsylvania
Philadelphia, PA 19104-6389

Abstract:
Recent work in qualitative shape recovery and object recognition has focused on solving the ``what is it'' problem, while avoiding the ``where is it'' problem. In contrast, typical CAD-based recognition systems have focused on the ``where is it'' problem, while assuming they know what the object is. Although each approach addresses an important aspect of the 3-D object recognition problem, each falls short in addressing the complete problem of recognizing and localizing 3-D objects from a large database. In this paper, we first synthesize a new approach to shape recovery for 3-D object recognition that decouples recognition from localization by combining basic elements from these two approaches. Specifically, we use qualitative shape recovery and recognition techniques to provide strong fitting constraints on physics-based deformable model recovery techniques. Secondly, we extend our previously developed technique of fitting deformable models to occluding image contours to the case of image data captured under general orthographic, perspective, and stereo projections. On one hand, integrating qualitative knowledge of the object being fitted to the data, along with knowledge of occlusion supports a much more robust and accurate quantitative fitting. On the other hand, recovering object pose and quantitative surface shape not only provides a richer description for indexing, but supports interaction with the world when object manipulation is required. This paper presents the approach in detail and applies it to real imagery.
Keywords:
qualitative and quantitative shape recovery, physics-based modeling, deformable model fitting, object representation, object recognition
Comment:
The paper is also available

Browsing the bibliography

Bibliographic Statistics

Types:
article(77), inproceedings(60), techreport(15), incollection(12), book(6), inbook(1)
Fields:
author(171), title(171), year(171), pages(122), journal(77), volume(75), booktitle(72), address(61), number(61), month(38), publisher(24), editor(17), institution(15), series(5), key(3), type(3), chapter(2), adress(1), page(1)
Distribution of publication dates:
Distribution of publication dates

Valid XHTML 1.1!  Valid CSS!