Real-time range image segmentation using adaptive kernels and Kalman filtering

Segmentation is a fundamental process affecting the overall quality and utility of a machine vision system. Range profile tracking (RPT) is a systematic approach for stable, accurate and high speed segmentation of range images that is based on Kalman filtering. Tests of RPT have produced stable decompositions of second order surfaces bounded by jump and crease discontinuities, having a volumetric error of a few percent, in under 6 sec. for a wide variety of conditions. Results from over 900 tests on synthetic scenes and 150 real range images are presented.