This paper studies pyramidal multiresolution design of aperture and W-operators for grayscale images. The multiresolution approach has been used previously to design binary filters with good results, which motivated us to extend the theory for grayscale. The initial results, theoretical and practical are also motivating.
The unconstrained design of optimal digital window-based filters from sample signals is very difficult because of the inability to obtain enough data to make good estimates of the filter parameters. This paper studies the estimation problem by windowing in the range, as well as in the domain. At each point, the signal is viewed through an aperture, which is the product between a domain window and a gray-scale range window. Signal values outside the aperture are project to the limit values inside the aperture. Experiments show that aperture filters can outperform linear filters for deblurring, especially in the restoration of edges. A sampling of the many experiments carried out to study the effects of aperture filters on deblurring is provided.
KEYWORDS: Optical character recognition, Image segmentation, Image processing, Mathematical morphology, Information fusion, Prototyping, Image compression, Mathematics, System integration, Picture Archiving and Communication System
We present the prototype of an OCR that was designed and implemented at the Institute of Mathematics and Statistics of the University of Sao Paulo. The remarkable characteristic of this system is that all the necessary image processing tasks are performed by Mathematical Morphology operators (the so called morphological operators). Thus, we have developed morphological operators to segment scanned images (i.e., identify objects as characters, words and paragraphs), and recognize font styles and character semantics. The morphological operators that perform segmentation were designed by classical heuristic techniques, while the ones that recognize fonts and characters were designed automatically by new computational learning techniques. The fundamental idea under these techniques is the estimation of a morphological operator from observations of input-output image pairs, that describe its ideal performance. The morphological operators designed have been integrated in a system that translate scanned images into RTF text files, with reasonable correction and time performance. This system has been developed in the KHOROS platform, using the MMach (for morphological operators design heuristically) and PAC (for morphological operators designed by learning) toolboxes.
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