to propositional logic. In the low-and mid-level areas, Haralick has worked in image texture analysis using spatial gray tone co-occurrence texture features. These features have been used with success on biological cell images, x-ray images, satellite images, aerial images and many other kinds of images taken at small and large scales. In the feature detection area, Haralick has developed the facet model for image processing. The facet model states that many low-level image processing operations can be interpreted relative to what the processing does to the estimated underlying gray tone intensity surface of which the given image is a sampled noisy version. The facet papers develop techniques for edge detection, line detection, noise removal, peak and pit detection, as well as a variety of other topographic gray tone surface features. Haralick's work in shape analysis and extraction uses the techniques of mathematical morphology. He has developed the morphological sampling theorem which establishes a sound shape/size basis for the focus of attention mechanisms which can process image data in a multiresolution mode, thereby making some of the image feature extraction processes execute more efficiently. He has also developed recursive morphological algorithms for the computation of opening and closing transforms. The recursive algorithms permit all possible sized openings or closings for a given structuring element to be computed in constant time per pixel. He also developed statistical morphological methodologies for image analysis and noise removal. and noise removal In the area of document image understanding, Haralick is responsible for the development of comprehensive ground-truthed databases consisting of over 1500 document images, most in English and some in Japanese. The databases are issued on CD-ROMs and are used all around the world by people developing character recognition methodologies and techniques for document image structural decomposition. He has developed algorithms for document image skew angle estimation, zone delineation, and word and text line bounding box delineation. In a series of papers, Haralick has helped influence the computer vision community to be more sensitive to the needs of computer vision performance characterization and covariance propagation for without this kind of analysis Computer Vision has no robust theory. Haralick has contributed to the medical image analysis area particularly working with X-ray ventriculargrams . and echocardiography, These papers developed techniques to identify and delineate anatomically accurate boundaries for the left ventricle of the heart. His most recent work is in the pattern recognition area, particularly in the