In this paper a generalized algorithm for fast Harr wavelet transform was developed for the compression of monochrome and color images, and was evaluated through three quantitative measures: namely, mean square error, mean absolute error and signal-to-noise ratio. Excellent reconstructed images were obtained with high rates of compression. The computational advantage of this 2D FHWT over the conventional 2D FFT is 2.506 for an image of 1024/spl times/1024 pixels in size.

A system for interpretation of images of paper-based line drawings is described. Since a typical drawing contains both text strings and graphics, an algorithm has been developed to locate and separate text strings of various font sizes, styles, and orientations. This is accomplished by applying the Hough transform to the centroids of connected components in the image. The graphics in the segmented image are processed to represent thin entities by their core-lines and thick objects by their boundaries. The core-lines and boundaries are segmented into straight line segments and curved lines. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Such a description includes the location and attributes of simple polygonal shapes, circles, and interconnecting lines, and a description of the spatial relationships and occlusions among them. Hatching and filling patterns are also identified. The performance of the system is evaluated using several test images, and the results are presented. The superiority of these algorithms in generating meaningful interpretations of graphics, compared to conventional data compression schemes, is clear from these results.>

A system for automatic recognition and description of graphical primitives and their interconnections in a mixed text/graphics image is described. The input to the system is a high-resolution binary image obtained by scanning paper-based documents. The Hough transform is applied to the connected components of the image to locate and separate text strings of various font size and orientation. The graphics in the segmented image is processed to represent thin entities by their core lines and thick objects by their boundaries. Lines of various types are identified and segmented into straight line and curved line segments during this process. The line segments and their interconnections are analyzed to locate minimum redundancy loops which are adequate to generate a succinct description of the graphics. Various hatching and filling patterns in the image are identified and described. The algorithm has been implemented as software, and its robustness and performance have evaluated tested using a number of test images. The results for a simple test image are reported.>

An algorithm is designed to automatically segment a discrete curve into appropriate number of parts, each of which is to be effectively represented by a circular arc. Thus a concise representation can be obtained. The algorithm suggested is computationally efficient. Extensive experiments were conducted in applying this technique to curves of various shapes. Results show that the representations generated are accurate for even very complex curves.>