<title>Range image segmentation through pattern analysis of multiscale difference information</title>

Abstract

This work presents an image segmentation method for range data that uses multi-scale wavelet analysis in combination with statistical pattern recognition. We train a pattern- recognition system with scale-space data from the edge points of a training image. Once trained the system can determine the degree of edgeness of points in a new image. Before designing the segmentation system we set forth several goals. We desire that the system detect boundaries of small as well as large objects, be robust, and have few or no free parameters. Edges in an image respond to edge detectors at different scales; therefore combining edge detection information at multiple scales can create a more complete and robust edge detection. Scale-space refers to a family of derived signals where the fine-scale information is successively suppressed as scale increases. Edge points in images have a specific signature over scale space. We use a pattern recognition method to analyze these signatures as 1-D signals and therefore label edges in an image based on its multi-scale response to a wavelet transform. A fuzzy pattern classifier with one class determines the degree of membership in the edge class for each pixel in the image. Assigning this degree of membership to each pixel creates a fuzzy edge map. a watershed algorithm then creates a segmentation from this edge map. We use the wavelet transform to generate the scale space of a range image. We choose a spline wavelet used by Mallat. A simple, synthetic image with added noise and known edges provides a training set for the pattern recognition system. Known edge points from the image create a probability density function indicating membership in an edge class. The results from analyzing a complex real image are shown.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.