We introduce an image model for color texture based on spatial correlation functions defined both within and between color bands. We show that three dimensional geometric transformations of a surface in the scene produce corresponding transformations in these correlation functions. From this analysis, we derive invariants of color correlation functions that can be computed efficiently and used for geometry invariant recognition. We show experimentally that these invariants are effective for recognition in situations where neither color distributions nor gray scale texture are sufficient. Following recognition, the estimated color correlation functions can be used to recover the three dimensional location and orientation of surfaces. The color texture invariants can be used in the recognition of three dimensional objects, the segmentation of images of three dimensional scenes, and the searching of image databases.>

Changes in measured image irradiance have many physical causes and are the primary cue for several visual processes, such as edge detection and shape from shading. Using physical models for charged-coupled device (CCD) video cameras and material reflectance, we quantify the variation in digitized pixel values that is due to sensor noise and scene variation. This analysis forms the basis of algorithms for camera characterization and calibration and for scene description. Specifically, algorithms are developed for estimating the parameters of camera noise and for calibrating a camera to remove the effects of fixed pattern nonuniformity and spatial variation in dark current. While these techniques have many potential uses, we describe in particular how they can be used to estimate a measure of scene variation. This measure is independent of image irradiance and can be used to identify a surface from a single sensor band over a range of situations. Experimental results confirm that the models presented in this paper are useful for modeling the different sources of variation in real images obtained from video cameras.>

A model that characterizes the appearance of textured color surfaces in three dimensions is developed. The model is derived from properties of surfaces and the physics of image formation. The color texture model describes the dependence of the spatial correlations within and between bands of a color image on surface reflectance, illumination, and the scene geometry. It is shown that there are important advantages in using color information for texture analysis. From the authors' model, an algorithm for recognizing instances of color textures independent of scene geometry is derived. This algorithm is useful for the recognition of three-dimensional objects and the segmentation of color images of three-dimensional scenes. Experimental results are provided to confirm the model and to illustrate the performance of the algorithm.>

The variation in digitized pixel values that is due to sensor noise and scene variation is quantified, using physical models for CCD (charge-coupled device) video cameras and material reflectance. This analysis forms the basis of algorithms for camera characterization and calibration and for surface description. The use of these techniques for estimating a measure of scene variation is described. This measure is independent of image irradiance and can be used to identify a surface from a single sensor band over a range of situations. Experimental results verify the models presented.>