(Enter summary)
Abstract: We present a new technique for tracking 3D objects
from 2D image sequences through the integration
of qualitative and quantitative techniques.
The deformable models are initialized
based on a previously developed part-based qualitative
shape segmentation system. Using a
physics-based quantitative approach, objects are
subsequently tracked without feature correspondence
based on generalized forces computed from
the stereo images. The automatic prediction of
possible edge occlusion and... (Update)
Context of citations to this paper: More
...would more effectively prevent network contours from converging. 4. 2 Quantitative Object Tracking Our approach to quantitative tracking [4, 5] makes use of our frameworks for qualitative and quantitative shape recovery described in previous sections, as well as a physics based...
.... ambiguous or are heavily occluded, the system must move to a better viewpoint ( 20] and, while moving, the system must track the objects [22, 16]. We have successfully addressed each of these problems within the framework of our hybrid object centered viewer centered...
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BibTeX entry: (Update)
M. Chan, D. Metaxas, and S. Dickinson. A new approach to tracking 3-D objects in 2-D image sequences. In Proceedings, AAAI '94, Seattle, WA, August 1994. http://citeseer.ist.psu.edu/chan94new.html More
@inproceedings{ chan94new,
author = "Michael Chan and Dimitris N. Metaxas and Sven J. Dickinson",
title = "A New Approach to Tracking 3D Objects in 2D Image Sequences",
booktitle = "National Conference on Artificial Intelligence",
pages = "960-965",
year = "1994",
url = "citeseer.ist.psu.edu/chan94new.html" }
Citations (may not include all citations):
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Fitting Parameterized Three-Dimensional Models to Images
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