<title>Reliable location and regression estimates with application to range image segmentation</title>

摘要

Since they provide direct depth measurements from a scene, range images are important sources of information in many 3D robot vision problems such as navigation and object recognition. Many physical factors, however, contribute noise to the discrete measurements in range images, which leads us to reassess the error distribution in samples taken from real range images. This paper suggests the utility of the L<SUB>p</SUB> norms in yielding reliable estimates of location and regression coefficients. This approach is compared against two commonly used approaches: Equally Weighted Least Squares, which minimizes the L<SUB>2</SUB> norm; and the Chebychev approximation, which minimizes the L<SUB>1</SUB> norm. The problem is of a weighted least squares where the weights are derived from the chosen parameter, p. Of particular interest is this parameter's ability to yield a variety of location estimates spanning from the sample mean to the sample median. These two estimates have a wide application in image processing, especially in noise removal tasks. This paper will show the problems associated with these two techniques, and suggest solutions to minimize these problems. The regression module is used in a region-growing segmentation algorithm to provide a reliable partition of range images.