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RI | Publications | A Statistical Model for 3D Object Detection Applied to Faces and Cars
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Text only version of this site
A Statistical Model for 3D Object Detection Applied to Faces and Cars
H. Schneiderman and T. Kanade
IEEE Conference on Computer Vision and Pattern Recognition, IEEE, June, 2000.
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| Abstract |
In this paper, we describe a statistical method for 3D object detection. We represent the statistics of both object appearance and "non-object" appearance using a product of histograms. Each histogram represents the joint statistics of a subset of wavelet coefficients and their position on the object. Our approach is to use many such histograms representing a wide variety of visual attributes. Using this method, we have developed the first algorithm that can reliably detect human faces with out-of-plane rotation and the first algorithm that can reliably detect passenger cars over a wide range of viewpoints
| Notes |
Associated center: VASC
Associated labs/groups: Video Surveillance and Monitoring, Face Group, and People Image Analysis Consortium
Associated projects: Video Surveillance and Monitoring, Object Recognition Using Statistical Modeling, Face Detection Databases, Face Detection, and Face Databases
| Text Reference |
H. Schneiderman and T. Kanade, "A Statistical Model for 3D Object Detection Applied to Faces and Cars," IEEE Conference on Computer Vision and Pattern Recognition, IEEE, June, 2000.
| BibTeX Reference |
@inproceedings{Schneiderman_2000_3294,
author = "Henry Schneiderman and Takeo Kanade",
title = "A Statistical Model for 3D Object Detection Applied to Faces and Cars",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
month = "June",
year = "2000",
publisher = "IEEE"
}