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A new approach is described for treating and understanding multispectral images and multisensor imagery. The approach relies on first-order contrast information.

We compute the sign of Gaussian curvature using a purely geometric definition. Consider a point p on a smooth surface S and a closed curve y on S which encloses p. The image of [Gamma] on the unit normal Gaussian sphere is a new curve [Beta]. The Gaussian curvature at p is defined as the ratio of the area enclosed by [Gamma] over the area enclosed by [Beta] as [Gamma] contracts to p. The sign of Gaussian curvature at p is determined by the relative orientations of the closed curves [Gamma] and [Beta]. We directly compute the relative orientation of two such curves from intensity data. We employ three unknown illumination conditions to create a photometric scatter plot. This plot is in one-to-one correspondence with the subset of the unit Gaussian sphere containing the mutually illuminated surface normals. This permits direct computation of the sign of Gaussian curvature without the recovery of surface normals. Our method is albedo invariant. We assume diffuse reflectance, but the nature of the diffuse reflectance can be general and unknown. Error analysis on simulated images shows the accuracy of our technique. We also demonstrate the performance of this methodology on empirical data. Index Terms - Gaussian curvature, differential geometry, photometric invariant, photometric data, shape recovery, curve orientation.

We present a fully automated system which unites CCD camera technology with liquid crystal technology to create a polarization camera capable of sensing the partial linear polarization of reflected light from objects at pixel resolution. As polarization sensing not only measures intensity but also additional physical parameters of light, it can therefore provide a richer set of descriptive physical constraints for the understanding of images. Recently it has been shown that polarization cues can be used to perform dielectric/metal material identification, specular and diffuse reflection component analysis, as well as complex image segmentations that would be significantly more complicated or even infeasible using intensity and color alone. Such analysis has so far been done with a linear polarizer mechanically rotated in front of a CCD camera. The full automation of resolving polarization components using liquid crystals not only affords an elegant application, but significantly speeds up the sensing of polarization components and reduces the amount of optical distortion present in the wobbling of a mechanically rotating polarizer. In our system two twisted nematic liquid crystals are placed in front of a fixed linear polarizer placed in front of a CCD camera. The application of a series of electrical pulses to the liquid crystals in synchronization with the CCD camera video frame rate produces a controlled sequence of polarization component images that are stored and processed on Datacube boards. We present a scheme for mapping a partial linear polarization state measured at a pixel into hue, saturation and intensity producing a representation for a partial linear polarization image. Our polarization camera currently senses partial linear polarization and outputs such a color representation image at 5 Hz. The unique vision understanding capabilities of our polarization camera system are demonstrated with experimental results showing polarization-based dielectric/metal material classification, specular reflection and occluding contour segmentations in a fairly complex scene, and surface orientation constraints.

Polarization vision is fast developing as a practical technique for outdoor and underwater imaging, owing to remarkable advancements in polarization camera technology which open up an entirely new visual domain. Despite the incapability of human vision to sense components of light polarization, polarization parameters can be visualized in terms of hue and saturation. Portable liquid crystal polarization cameras have applications in natural object recognition, automatic target detection, inspection of ship hull damage and marine biology.