Range Image Segmentation through Pattern Analysis of the Multiscale Wavelet Transform

This work presents an image segmentation method for range data that uses multiscale wavelet analysis in combination with statistical pattern recognition. A feature-detection framework based on multiscale analysis and pattern recognition has several potential advantages over other feature detection systems. These advantages are detection of features at different scales (i.e., features of all sizes), robustness, and few or no free parameters. Our system creates a fuzzy edge map and derives a segmentation from this edge detection. A scale-space signature is the vector of measurements at different scales taken at a single point in an image. We analyze these 1-D signatures with traditional pattern-recognition methods. We train a pattern-recognition system with scale-space signatures from the edge points of a training image. Once trained, the system determines the degree ofedgenessof points in a new image. The goal is to create a system that exploits the advantages of a multiscale, pattern-recognition framework.