Statistical properties of natural 3D scenes and their role in visual perception

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What could be easier than seeing? We see without trying, and usually without thinking about it. In fact, seeing is a remarkable feat. It seems easy because the part of our brain responsible for vision is a powerful computer with a lifetime of experience at seeing things. One reason why vision is challenging is that all visual images are highly ambiguous, and any given image could conceivably be seen as depicting a wide range of shapes, colours, and lights. Our visual system needs to overcome this ambiguity somehow, in order to perceive scenes and objects correctly. One way our visual system overcomes ambiguity is by relying on assumptions about what shapes, colours, and lights are most likely to occur. For example, previous experiments have shown that starting from infancy, our visual system slowly learns that light usually shines from above us, and our visual system uses this knowledge to perceive objects and scenes correctly. The main question addressed by the present research project is, what other facts about real-world shapes, colours, and lighting does the human visual system rely on in order to perceive the world correctly? We will investigate this question in two ways. First, using novel measurement tools, we will measure three perceptually important characteristics of the world: surface shape, surface colour, and illumination. We will do statistical analyses of these measurements, in order to see what reliable properties real-world shapes, colours, and lights have that the visual system can use in order to perceive scenes and objects correctly. Second, using perceptual experiments we will investigate what assumptions about shape, surface colour, and illumination guide human perception of scenes and objects. Observers will judge the shapes, colours, and lighting of carefully designed, computer-generated images of ambiguous objects, and using computational and mathematical models we will deduce what assumptions the visual system relies on to see scenes and objects correctly from these ambiguous images. This research will help us understand normal and abnormal human vision, and it will also be useful for designing computer vision systems that see scenes and objects correctly.