This paper illustrates a method, called HEAT, for image indexing based on texture information. The texture's partitioning element is first put into 1-D form and then its Hierarchical Entropy-based Representation is obtained. This representation is used to index the texture in the space of features. The same representation is well suited for contour data, and it has invariance and robustness properties that make it attractive for incorporation into larger systems. A comparison with another performing method is carried out, and the experiments show that the two techniques have slightly different strong points, suggesting different fields of application. In the experimental section, a case study involving over 2500 mammographies from different sources is presented and discussed.

The paper describes a technique called ISE for image segmentation using entropy. The relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of gray levels. In order to implement the model, we have introduced a well-known technique of Problem Solving. In our model, the most important roles are played by the Evaluation Function (EF) and the Control Strategy. The EF is related to the ratio between the entropy of one region or zone of the picture and the entropy of the entire picture, while the Control Strategy determines the optimal path in the search tree (quadtree) so that the nodes in the optimal path have minimal entropy. The paper shows some comparisons between ISE and classical edge detection techniques.

This paper describes an algorithm for still image compression called B-tree triangular coding (BTTC). The coding scheme is based on the recursive decomposition of the image domain into right-angled triangles arranged in a binary tree. The method is attractive because of its fast encoding, O(n log n), and decoding, /spl Theta/(n), where n is the number of pixels, and because it is easy to implement and to parallelize. Experimental studies indicate that BTTC produces images of satisfactory quality from a subjective and objective point of view, One advantage of BTTC over JPEG is its shorter execution time.

This paper describes an algorithm for image compression called B-tree triangular coding (BTTC). An image is considered as a discrete 3D surface, which is then represented by a set of polyhedrons. The image domain is partitioned into right-angled triangles constituting the polyhedrons' bases. Each polyhedron is characterized by the vertices of its triangular upper face, near the approximated surface. During the approximation process the polyhedrons are organized into a B-tree that will be represented as a binary string; the B-tree's leaves are the polyhedrons needed to restore the image during decompression. The computing time is O(nlogn) for compression and /spl Theta/(n) for decompression, where n is the number of pixels. Especially in decompression, this is a very fast method if compared to the standard techniques (e.g. JPEG).

The paper describes a possible model of the human perceptive process. In this paper the relation between the entropy of an image domain and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from the analysis of the image histogram associating a Gaussian distribution to the maximum frequency of grey levels. With the aim of implementing the model, we have introduced a well known technique of problem solving. The most important roles of our model are played by the evaluation function (EF) and the control strategy. So the EF is related to the ratio between the entropy of one region or zone of the picture and the entropy of the entire picture. The control strategy determines the optimal path in the quadtree so that the nodes of the optimal path have minimal entropy. The paper shows some comparisons between the method and classical edge detection techniques.