A technique for color quantization is described, which consists of two processes. The first process is based on the analysis of the histograms of the three color components of the RGB input image. The second process performs clustering of the colors quantized by the first process, based on their Euclidean distance. At the end of the second process, the output image is obtained by replacing the color of each pixel of the input image with the closest representative color. The obtained results are satisfactory from both the qualitative and the quantitative point of view.

Summary form only given. The aim of this paper is to accomplish a prototype system for automatic data management able to support project activities in the field of architecture and engineering, in addition to improving the performance of existing software. This automatic data management consists of both consulting and sharing operations that favour the communication with the customer and the collaboration among experts, and the automatic comparison of AutoCAD drawings that increase the efficiency of the planning process.

The paper presents a novel procedure to hierarchically decompose a multiscale discrete skeleton. The skeleton is a linear pattern representation that is generally recognized as a good shape descriptor. For discrete images, the discrete skeleton is often preferable. Multiresolution representations are convenient for many image analysis tasks. Our resulting skeleton decomposition shows two different types of hierarchy. The first type of hierarchy is one of different scales, as the original pattern is converted into an AND-pyramid and the skeleton is computed for each resolution level. The second type of hierarchy is established at each level of the pyramid by identifying and ranking skeleton subsets according to their permanence, where permanence is a property intrinsically related to local pattern thickness. To achieve the decomposition, both bottom-up and top-down analysis in the sense of moving from higher to lower resolution and vice versa are used. The bottom-up analysis is used to ensure that a part of the skeleton that is connected at a higher resolution level is also connected (if at all present) in the next, lower resolution level. The top-down analysis is used to build the permanence hierarchy ranking the skeleton components. Our procedure is based on the use of (3/spl times/3) local operations in digital images, so it is fast and easy to implement. This skeleton decomposition procedure is most effective on patterns having different thickness in different regions. A number of examples of decompositions of multiscale skeletons (with and without loops) are shown. The skeletons are, in most cases, nicely decomposed into meaningful parts. The procedure is general and not limited to any specific application.

Coarse-to-fine skeletons of digital patterns in binary images are computed on a grey-level pyramid. This is built in such a way that pattern shape and topology are preserved at all resolution levels, as faithfully as possible. A key problem is spurious holes, created at lower resolutions by region merging, that would originate spurious loops in coarse skeletons. This is solved by a novel technique exploiting the grey-level pyramid.