Department of Pattern Recognition

A range image segmentation method based on a recursive adaptive regression model prediction for detecting range image step discontinuities which are present at object face borders. Detected face borders guides subsequent region growing step where neighbouring face curves are grouped together. Region growing based on curve segments instead of pixels like in classical approaches significantly speed up the algorithm. Curves to be grown are represented using the cubic spline model. Curves from the same region are required to have similar curvature and slope but they do not need to be of maximal length through the corresponding object face.

An algorithm for planar face segmentation in range images. The segmentation is based on a combination of recursive adaptive regression model prediction for detecting range image step discontinuities which are present at object face borders and of a region growing on surface lines. Border pixels are detected in two perpendicular directions and detection results are combined together. Two predictors in each direction use identical contextual information from the pixel's neighbourhood and they mutually compete for the most optimal discontinuity detection.

An efficient and robust type of unsupervised colour texture segmentation method.