This paper describes a new algorithm for obtaining rules automatically from training examples. The algorithm is applicable to examples involving both objects: with discrete and continuous-valued attributes. The paper explains a new quantization procedure fur continuous-valued attributes and shows how appropriate ranges of values of various attributes are obtained. The algorithm uses a decision-tree-based approach for obtaining rules, but unlike other tree-based algorithms such as ID3, it allows more than one attribute at a node which greatly improves its performance. The ability of the algorithm to obtain a measure of partial match further enhances its generalization characteristic. The algorithm produces the same rules irrespective of the order of presentation of training examples. The algorithm has been demonstrated on classification problems. The results have compared favorably with those obtained by existing inductive learning algorithms.

In this correspondence, we present a new computationally efficient three-dimensional (3-D) object segmentation technique. The technique is based on the detection of edges in the image. The edges can be classified as belonging to one of the three categories: fold edges, semistep edges (defined here), and secondary edges. The 3-D image is sliced to create equidepth contours (EDCs). Three types of critical points are extracted from the EDCs. A subset of the edge pixels is extracted first using these critical points. The edges are grown from these pixels through the application of some masks proposed in this correspondence. The constraints of the masks can be adjusted depending on the noise present in the image. The total computational effort is small since the masks are applied only over a small neighborhood of critical points (edge regions). Furthermore, the algorithm can be implemented in parallel, as edge growing from different regions can be carried out independently of each other.>