| Abstract: | Lightness algorithms, which have been proposed as a model for human vision, are aimed at recovering surface reflectance in a close approximation. They attempt to separate reflectance data from illumination data by thresholding a spatial derivative of the image intensity. This, however, works only reliable in a world of plane Mondrians. An extension of the classical lightness approach of Land and McCann (1971) to curved surfaces is presented. Assuming smooth surfaces with Lambertian reflection properties and leaving aside occlusions and cast shadows, the separation of those components of the intensity gradient due to reflectance from those due to irradiance is posed as a constraint minimization problem. To do so, two classification operators were introduced which identify potential reflectance and irradiance data using a scale-space filtering approach. Two exemplary applications of the proposed extended lightness algorithm in the field of visual telecommunications are presented: (i) the simulation of more uniformly illuminated videophone portrait scenes to give dynamic range compressed images with a most realistic appearance and (ii) the synthesis of videophone portrait images from model-based coded data with a correct illumination effect. In both applications, the extended lightness algorithm is employed for estimating the reflectance functions at facial surfaces. Results obtained by applying the extended lightness algorithm are compared with results obtained by conventional methods known from literature. |
