Image Segmentation For Object Detection on a Deeply Embedded Miniature Robot

In this paper, an image segmentation approach for object detection on the miniature robot BeBot - a deeply embedded system - is presented. In order to enable the robot to detect and identify objects in its environment by means of its camera, an efficient image segmentation approach was developed. The fundamental algorithm bases on the region growing and region merging concept and identifies homogeneous regions consisting of adjacent pixels with similar color. By internally representing a contiguous block of pixels in terms of run-lengths, the computational effort of both the region growing and the region merging operation is minimized. Finally, for subsequent object detection processes, a region is efficiently translated into a statistically feature representation based on discretized moments.