Spectral Gradient: A Surface Reflectance Measurement Invariant to Geometry and Incident Illumination

Although photometric data is a readily available dense source of information in intensity images, it is not widely used in computer vision. A major drawback is its dependence on viewpoint and incident illumination. A novel methodology is presented which extracts reflectivity information of the various materials in the scene independent of incident light and scene geometry. A scene is captured under three different narrow-band color filters and the spectral derivatives of the scene are computed. The resulting spectral derivatives form a spectral gradient at each pixel. This spectral gradient is a surface reflectance descriptor which is invariant to scene geometry and incident illumination for smooth diffuse surfaces. The invariant properties of the spectral gradients make them a particularly appealing tool in many diverse areas of computer vision such as color constancy, tracking, scene classification, material classification, stereo correspondence, even re-illumination of a scene

An experimental comparison of appearance and geometric model based recognition

This paper describes an experimental investigation of the recognition performance of two approaches to the representation of objects for recognition. The first representation, generally known as appearance modelling, describes an object by a set of images. The image set is acquired for a range of views and illumination conditions which are expected to be encountered in subsequent recognition. This image database provides a description of the object. Recognition is carried out by constructing an eigenvector space to compute efficiently the distance between a new image and any image in the database. The second representation is a geometric description based on the projected boundary of an object. General object classes such as planar objects, surfaces of revolution and repeated structures support the construction of invariant descriptions and invariant index functions for recognition. In this paper we present an investigation of the relative performance of the two approaches. Two objects, a planar object and a rotationally symmetric object are modelled using both approaches. In the experiments, each object is intentionally occluded by an unmodelled distractor for a range of viewpoints. The resulting images are submitted to two separate recognition systems. Appearance-based recognition is carried out by SLAM and recognition of invariant geometric classes by Lewis/Morse