A Statistical Model for 3D Object Detection Applied to Faces and Cars
Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, Vol. 1, pp. 746 - 751, June, 2000
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
BibTeX
@conference{Schneiderman-2000-8042,author = {Henry Schneiderman and Takeo Kanade},
title = {A Statistical Model for 3D Object Detection Applied to Faces and Cars},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {2000},
month = {June},
volume = {1},
pages = {746 - 751},
publisher = {IEEE},
keywords = {Pattern Recognition, Computer Vision, Face Detection, Car Detection, Automobile Detection, Statistical Classification, Histogram, Wavelet, AdaBoost, View-Based Modeling},
}
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