Function regression by using fuzzy Hough transforms

Function regression can be viewed as template matching in an augmented space spanned by independent variables and function values. This formulation of function regression enables us to reject outlier data and to preserve discontinuities in functions. In this paper, such a function regression method based on fuzzy Hough transforms is presented. The implementation of this approach by using neural networks is illustrated, and a supervised learning algorithm based on function interpolation of sparse data is proposed. The present method is used in image smoothing, segmentation by clustering of image pixels, and is also used in random dot stereo vision including transparent patterns.