Two Stages of Curve Detection Suggest Two Styles of Visual Computation

The problem of detecting curves in visual images arises in both computer vision and biological visual systems. Our approach integrates constraints from these two sources and suggests that there are two different stages to curve detection, the first resulting in a local description, and the second in a global one. Each stage involves a different style of computation: in the first stage, hypotheses are represented explicitly and coarsely in a fixed, preconfigured architecture; in the second stage, hypotheses are represented implicitly and more finely in a dynamically constructed architecture. We also show how these stages could be related to physiology, specifying the earlier parts in a relatively fine-grained fashion and the later ones more coarsely.

Black light: how sensors filter spectral variation of the illuminant

Visual sensor responses may be used to classify objects on the basis of their surface reflectance functions. In a color image, the image data are represented as a vector of sensor responses at each point in the image. This vector depends both on the surface reflectance function and on the spectral power distribution of the ambient illumination. Algorithms designed to classify objects on the basis of their surface reflectance functions typically attempt to overcome the dependence of the sensor responses on the illuminant by integrating sensor data collected from multiple surfaces. In machine vision applications, we show that it is often possible to design the sensor spectral responsivities so that the vector direction of the sensor responses does not depend upon the illuminant. We state the conditions under which this is possible and perform an illustrative calculation. In biological systems, where the sensor responsivities are fixed, we show that some changes in the illumination cause no change in the sensor responses. We call such changes in illuminant black illuminants. It is possible to express any illuminant as the sum of two unique components. One component is a black illuminant. We call the second component the visible component. The visible component of an illuminant completely characterizes the effect of the illuminant on the vector of sensor responses.