This paper describes a new parametric training algorithm for image understanding and computer vision systems. It is developed within the context of human color perception and a color order system. Eventual goal of the research is to obtain a mathematical evaluation criterion to guide the operation of an unsupervised pattern recognition technique for detecting the image clusters or modes in the measurement or color space. For this purpose, the peak modality is selected as the mathematical evaluation criterion. Area, mode dispersion, approximated curvature, and steepness are some of the measured quantities for modality test. Although this systematic procedure is developed primarily for the gray level and color images of natural scenes, it can also be applied to the multispectral images effectively. The proposed method is a computational one and is more suitable for array or parallel processors.
Mehmet Celenk, Mehmet Celenk,
"A Parametric Training Algorithm For Image Understanding Systems", Proc. SPIE 0786, Applications of Artificial Intelligence V, (11 May 1987); doi: 10.1117/12.940616; https://doi.org/10.1117/12.940616