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Putting Objects in Perspective

Derek Hoiem, Alexei A. Efros and Martial Hebert
Carnegie Mellon University, Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. 2137 - 2144, June, 2006

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Abstract

Image understanding requires not only individually esti- mating elements of the visual world but also capturing the interplay among them. In this paper, we provide a frame- work for placing local object detection in the context of the overall 3D scene by modeling the interdependence of ob- jects, surface orientations, and camera viewpoint. Most object detection methods consider all scales and locations in the image as equally likely. We show that with probabilistic estimates of 3D geometry, both in terms of surfaces and world coordinates, we can put objects into perspective and model the scale and location variance in the image. Our approach reflects the cyclical nature of the problem by allowing probabilistic object hypotheses to re- fine geometry and vice-versa. Our framework allows pain- less substitution of almost any object detector and is easily extended to include other aspects of image understanding. Our results confirm the benefits of our integrated approach.

BibTeX Reference
@conference{Hoiem-2006-9503,
title = {Putting Objects in Perspective},
author = {Derek Hoiem and Alexei A. Efros and Martial Hebert},
booktitle = {Proc. IEEE Computer Vision and Pattern Recognition (CVPR)},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2006},
volume = {2},
pages = {2137 - 2144},
address = {Pittsburgh, PA},
}
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