A generalized region labeling algorithm for image coding, restoration, and segmentation

Region extraction and labeling is a fundamental process in building computer systems that automatically analyze and interpret images and image sequences of a scene. It involves extracting “meaningful” regions and their attributes from an image that are of interest in a given domain, e.g., a human face in face recognition and surveillance applications, a vehicle in intelligent highway monitoring, or an industrial tool in automated manufacturing and inspection applications. In this paper, we present a generalized and fast region labeling algorithm that can be applied to numerous image processing tasks: region extraction in binary images, region segmentation from gray scale or color images, region boundary extraction, chain code extraction, run-length coding of images, and image restoration from chain codes. The general applicability of our algorithm stems from its strengths in identifying the fundamental and common computational steps and providing robust and efficient procedures to accomplish these steps without requiring multiple scans of images. Our algorithm is shown to be more effective in terms of space and computational efficiency, in comparison with many existing approaches to region labeling and extraction. Results from our experiments with over 5,000 frames obtained from MPEG-1 video bitstreams demonstrate the good performance of our algorithm in terms of video text segmentation accuracy and computational efficiency