Segmentation and depth perception of naturalistic textures by human observers

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From moment to moment in everyday life, our visual system unconsciously and effortlessly parses images of cluttered visual scenes into discrete 3D surfaces and objects. Exactly how our brains achieve this amazing feat is remarkably poorly understood, in spite of a wealth of knowledge about how neurons in early stages of the visual pathways extract and represent simple image features. Natural images are often comprised of richly textured surfaces, whose many small features greatly compound these computational challenges. In particular, we poorly understand how texture features are integrated and/or segregated into distinct regions of the visual image, and how their depth relationships are inferred. The long-term goal of my research is to achieve a well-defined understanding of how the visual system is able to encode and segregate image features belonging to distinct surfaces and to ascertain their depth relationships. The resultant knowledge will be a major advance in our fundamental understanding of the human visual system, and will be invaluable in the design of future computer vision and artificial intelligence technology. The proposed experiments will build upon our recent progress using texture stimuli in three kinds of human psychophysical task: simultaneous contrast of perceived texture density, segmentation ("figure-ground segregation") between adjacent textures, and depth perception from motion parallax and stereopsis. We will use more naturalistic texture stimuli to bring out the actual complexities of real-world images and to insure the applicability of the results to realistic situations. The work will also involve computational modeling, based on biologically realistic components and operations, to predict psychophysical responses - thus providing a rigorous, quantitative test of how well the model summarizes or captures human performance. All of the proposed projects will provide highly technical training to graduate students and post-doc's. To pursue this research, they will have to learn and use theoretical knowledge about linear and nonlinear systems and signals (e.g. spatial convolution, linear filtering, Fourier spectra, nonlinearities, etc), computer programming (Matlab) both for creating and presenting visual stimuli (Psychophysics Toolbox), and in some cases for simulating models. Aim 3 will involve machine learning, and Aim 4, advanced computer graphics.