The Brain's view of the natural world in motion: Computing structure from function using directional Fourier transformations

Attractive possibilities exist for the brain to solve important problems of visual processing in Fourier space. First, we describe a wave-interference model for computing motion contrast directly from the moving intensity distribution, without the need for directionally selective motion sensors. We then propose a global method for motion-based image segmentation based on unsupervised clustering in a three-dimensional Fourier space. The Fourier components of coherently moving entities are segregated from the remainder by means of a simple velocity proximity measure. This is accomplished without altering the spatial frequency components encoding the object, thereby ensuring that the spatiotemporal features of the segregated object can be reconstructed.