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Carnegie Mellon Robotics Institute

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Abstract

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 that vary from frontal view to full profile view and the first algorithm that can reliably detect cars over a wide range of viewpoints.

Notes

Associated Project(s): Object Recognition Using Statistical Modeling

Text Reference

Henry Schneiderman and Takeo Kanade, "A histogram-based method for detection of faces and cars," Proceedings of the 2000 International Conference on Image Processing (ICIP '00), September, 2000, pp. 504 - 507.

BibTeX Reference

@inproceedings{Schneiderman_2000_3530,
   author = "Henry Schneiderman and Takeo Kanade",
   title = "A histogram-based method for detection of faces and cars",
   booktitle = "Proceedings of the 2000 International Conference on Image Processing (ICIP '00)",
   pages = "504 - 507",
   month = "September",
   year = "2000",
   volume = "3",
}

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