A robust and real-time texture analysis system using a distributed workstation cluster

This paper presents a parallel approach to the development of a real-time system for recognition of textured objects. In order to find an efficient and effective approach to identify and localize objects in textured images invariant of translation, rotation and scale changes and occlusion, we propose a new method which involves dynamic texture feature extraction and hierarchical image matching. Based on our previous work, we extend the concept of interesting points and develop a dynamic detection procedure on texture energy image which is in conjunction with Laws' texture energy concept and our mask tuning scheme. The search for the best fit between two objects in terms of Hausdorff distance is guided through an interesting point pyramid from coarse level to fine level. In addition, unlike current approaches which mostly rely on specialized multiprocessor architectures for fast processing, we use a distributed workstation cluster to support parallelism, which provides a different approach to real-time computing and is applicable to many classes of tasks.