Expert vision systems integrating image segmentation and recognition processes

In this paper, we propose a prototype rule-based system which integrates segmentation and recognition processes to analyze and classify objects in an image. This is quite different from the traditional image analysis paradigm which treats segmentation as a prerequisite for recognition and interpretation. There are four basic components in the system, i.e., low-level image processing, feature computation, domain-independent, and domain-dependent subsystems. In the low-level image processing subsystem, various “nonpurposive” operators are employed to divide the image into uniform and homogeneous regions based on the information of intensities. The feature computation subsystem extracts features of each individual region. The domain-independent subsystem employs weak knowledge to filter out “obviously impossible” regions while the domain-dependent subsystem uses domain-specific knowledge to improve the results and finally recognize the objects of interest in the image. Two sets of images are used to demonstrate the capability and flexibility of this system. One set consists of distributor caps (auto parts) of different shapes. The other set is composed of tomographical image pairs acquired by MRI and PET.