IntroductionIn computer vision, the goal of which is to identi& objects and their positions by examining images, one of the key steps is computing the surface normal of the visible surface at each point ('3ixel") in the image. Many sources of information are studied, such as outlines of surfaces, intensity gradients, object motion, and color. This article presents a method for analyzing a standard color image to determine the amount of interface ("specular") and body ("diffuse") rejection at each pixel. The interface rejection represents the highlights from the original image, and the body rejection represents the original image with highlights removed. Such intrinsic images are of interest because the geometric properties of each type of rejection are simpler than the geometric properties of intensity in a black-andwhite image. The method is based upon a physical model of rejection which states that two distinct types of rejection-interface and body rejection-occur, and that each type can be decomposed into a relative spectral distribution and a geometric scale factor. This model is far more general than typical models used in computer vision and computer graphics, and includes most such models as special cases. In addition, the model does not assume a point light source or uniform illumination distribution over the scene. The properties of tristimulus integration are used to derive a new model of pixel-value color distribution, and this model is exploited in an algorithm to derive the desired quantities. Suggestions are provided for extending the model to deal with diffuse illumination and for analyzing the two components of rejection.

Objective. The literature on gender and technology use finds that women and men differ significantly in their attitudes toward their technological abilities. Concurrently, existing work on science and math abilities of students suggests that such perceived differences do not always translate into actual disparities. We examine the yet-neglected area concerning gender differences with respect to Internet-use ability.In particular, we test how self-perceived abilities are related to actual abilities and how these may differ by gender. Methods. We use new data on web-use skill to test empirically whether there are differences in men's and women's abilities to navigate online content. We draw on a diverse sample of adult Internet users to investigate the questions raised. Results. Findings suggest that men and women do not differ greatly in their online abilities. However, we find that women's self-assessed skill is significantly lower than that of men. Conclusions. Women's lower self-assessment regarding their web-use skills may affect significantly the extent of their online behavior and the types of uses to which they put the medium. We discuss the implications of these findings for social inequality.

No abstract

No abstract