Fast Color Image Segmentation Using Commodity Hardware

AAAI Conferences 

James Bruce Tucker Balch Manuela Veloso School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract Vision systems employing region segmentation by color are crucial in applications such as object tracking, automated manufacturing and mobile robotics. Traditionally, systems employing realtime color-based segmentation are either implemented in hardware, or as very specific software systems that take advantage of domain knowledge to attain the necessary efficiency. However, we have found that with careful attention to algorithm efficiency fast color image segmentation can be accomplished using commodity image capture and CPU hardware. This paper describes a system capable of tracking several hundred regions of up to 32 colors at 30 Hertz on general purpose commodity hardware. The software system is composed of three main parts; a color threshold classifier, a region merger to calculate connected components, and a separation and sorting system to gather various region features and sort them by size. The algorithms and representations will be described, as well as descriptions of three applications in which it has been used.