Segmentation Using Intensity And Range Data

Perception is the integration of various sensor outputs into a unified vision of the world. A major part of research in machine vision has been limited to the use of individual sensors for building machine vision systems. The use of multisensor data is advocated for object recognition systems, and an algorithm for fusion of intensity and range data for segmentation is proposed. The algorithm consists of two steps. First, the initial seed segmentation is achieved by using the most dominating sensor at a given time. For this purpose the distributions of the intensity and range data are considered, and the image is segmented recursively by using the most significant peak in both histograms. Second, the initial segmentation is refined by using region merging; the regions are merged if the combined strengths of range and intensity boundaries are low. The experimental results for synthetic and real scenes are presented