Abstract
There are many computational models for the spatial statistics of grayscale (luminance) images, and in computer vision we rely heavily on these for denoising, deblurring, distinguishing shading from material boundaries, and so on. There are also models for the statistics of spectral point samples, and these are useful for tasks like computational color constancy. What we know less about is whether there are significant correlations between the spatial and spectral dimensions of natural images and, if there are, whether these correlations are useful for vision. I'll describe some attempts at answering these questions, including our collection and analysis of a database of hyperspectral real-world images, and our development of computer vision techniques that leverage spatio-spectral image models.
Meeting abstract presented at OSA Fall Vision 2012