Automatic Generation of Image-Segmentation Processes

Solutions to real-world image-segmentation problems typically require many image-processing steps. Unfortunately, the user must decide how to construct this sequence of steps for a given problem. So far, no system has been proposed to make the construction of these processes \easy" for the user. As a result, segmentation processes are often laboriously developed by an image-processing expert. In this paper, we describe a method for automatically generating image-segmentation processes for arbitrary images. Our method uses cue-based image analysis. The user provides problem-speciic information via easily deened cues. Two types of cues can be deened: (1) iconic cues, which are image-based and constructed by drawing directly onto the image data; and (2) symbolic cues, which are verbally speciied facts. The cues are interpreted to help select image-processing functions. The user need not be an image-processing expert|he must only understand the signii-cance of the speciied cues for a particular problem.