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A Statistical Approach to 3D Object Detection Applied to Faces and Cars
H. Schneiderman
doctoral dissertation, tech. report CMU-RI-TR-00-06, Robotics Institute, Carnegie Mellon University, May, 2000.

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In this thesis, we describe a statistical method for 3D object detection. In this method, we decompose the 3D geometry of each object into a small number of viewpoints. For each viewpoint, we construct a decision rule that determines if the object is present at that specific orientation. Each decision rule uses the statistics of both object appearance and "non-object" visual appearance. We represent each set of statistics 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 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.

Associated center: VASC
Associated labs/groups: Video Surveillance and Monitoring and Face Group
Associated projects: Object Recognition Using Statistical Modeling, Face Detection Databases, Face Detection, and Face Databases

H. Schneiderman, A Statistical Approach to 3D Object Detection Applied to Faces and Cars, doctoral dissertation, tech. report CMU-RI-TR-00-06, Robotics Institute, Carnegie Mellon University, May, 2000.

@phdthesis{Schneiderman_2000_3332,
   author = "Henry Schneiderman",
   title = "A Statistical Approach to 3D Object Detection Applied to Faces and Cars",
   school = "Robotics Institute, Carnegie Mellon University",
   month = "May",
   year = "2000",
   address = "Pittsburgh, PA"
}