For encoding and recognizing human faces in monochrome images, we propose a new method based on a combination of the discrete cosine transform (DCT), principal component analysis (PCA), and the characteristics of the Human Visual System. The novel aspect of the proposed non-Bayesian, approach is tha
An estimation model of figure segregation based on human visual perception
โ Scribed by Akira Shimaya; Isamu Yoroizawa; Makoto Kosugi
- Publisher
- John Wiley and Sons
- Year
- 1992
- Tongue
- English
- Weight
- 716 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0882-1666
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โฆ Synopsis
Abstract
When observing an overlapped figure, a human tends to interpret the figure by decomposing it into simpler subfigures. In psychology, this attitude is called figure segregation. In general, an overlapped figure can be segregated in many ways, depending on the observer. In the engineering realization of the figure segregation, the frequency of selection for each segregation candidate must be estimated.
Up to now, such factors as symmetry and continuity have been considered qualitatively as the factors affecting the decision about the segregation of the figure. This paper presents first a method to describe quantitatively those factors. Then the significance of the decision factor is determined by comparing the characteristic values of the decision factor in the segregation candidate and the selection frequency of that candidate in the psychological experiment. Furthermore, it is shown that the figure segregation characteristics can be estimated accurately, based on the prediction expression derived from the linear multiple regression analysis.
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