| Resumen |
We present an approach to color image segmentation by applying it to recognition and vectorization of geo-images (satellite, cartographic) using knowledge-based learning and self-learning system. This approach exploits the user's experience providing the knowledge domain in the form of the prescribed feature-attribute set. That is a simultaneous segmentation-recognition system when segmented geographical objects of interest (alphanumeric, punctual, linear, and area) are labeled by the system in same, but are different for each type of objects, gray-level values. We exchange the source image by a number of simplified images (composites). Every composite is associated with certain image feature. Some of the composites that contain the objects of interest are used in the following object detection-recognition by means of association to the segmented objects corresponding "names" from the user-defined subject domain. The specification of features and object names associated with perspective composite representations is regarded as a type of knowledge domain, which allows automatic or interactive system's learning. Additionally, we describe the fine-to-coarse scale method of the raster-to-vector conversion in which the "knowledge" of cartographic patterns into small-scale map aids in recognizing the corresponding patterns into large-scale map of the same territory. The results of gray-level and color image segmentation- recognitionvectorization are shown. |
