@INPROCEEDINGS { Rsds3455,
author = {Venkatesh Prasad, K.}, 
title = {Computer Vision: Rough Sets of Inputs and Soft Rules of Computing}, 
booktitle = {Rough Sets and Soft Computing. Conference Proceedings}, 
conference = {International Workshop on Rough Sets and Soft Computing}, 
pages = {86--93}, 
publisher = {San Jose State University}, 
address = {San Jose, California, U.S.A.}, 
year = 1994, 
editor = {Lin, Tsau Young}, 
abstract = {In this paper we present a description of computer vision as a process in which rough-set representations of picture-elements (pixels) and intermediate-symbols (such as edges) are transformed using soft-computing rules into a symbolic description of the input data. Since the physical nature of visual data is such that they are often imprecise and incomplete, their description using the principles of rough sets in intuitively appealing. For purposes of pedagogical clarity, a specific computer vision application, namely "Automatic Lipreading", is used to elucidate the key ideas. In this application, the input data is a video recording of human speakers uttering the sounds /ba/, /da/, /fa/, /la/, and /ma/. This data, when digitized, takes the form of a time-indexed sequence of image-frames or a spatio-temporal volume of pixels. The desired output of the computer vision process is a symbolic description of the input data which, in plain English, would amount to saying, for example: "Image frame number N contains a face with a lowered chin and a slightly open mouth in which some teeth are seen and the tongue is not seen". The important steps in the process that realizes this outcome might include: sampling and quantization, spatial and temporal filtering, region-of-interest (ROI) detection, gray-level profile extraction, grey-level peak-valley estimation, and finally symbolic labeling. Traditional implementations of these steps have assumed the data sets to be "precise", operated on them using hard computing rules, but have often yielded "imprecise" results. We will describe how precise results might be obtained by implementations that employ soft-rules of computation and take visual data to be what they often are - imprecise}, 
keywords = {computer vision, rough sets, soft computing, uncertainty, }, 
}