Using Fractal Iconography to Emulate Nature's Aesthetics.

This year's cover artists are members of a team of physicists and psy-chologists who create human-centered designs based on psychology experiments that investigate the positive impacts of viewing fractal patterns. These positive impacts include reduced physiological stress levels and enhanced cognitive skills. Here, the team explores the concept of 'fractal iconography' as an approach to employing computers to generate naturalistic art. Adopting this approach, three forms of fractal patterning ('fractal icons') are combined in a variety of ways to generate the rich complexity of nature's scenery. These composite fractals are remarkably effective at conveying nature's aesthetic power.

The relationship between shape perception accuracy and drawing ability.

Accurate shape perception is critical for object perception, identification, manipulation, and recreation. Humans are capable of making judgements of both objective (physical) and projective (retinal) shape. Objective judgements benefit from a global approach by incorporating context to overcome the effects of viewing angle on an object's shape, whereas projective judgements benefit from a local approach to filter out contextual information. Realistic drawing skill requires projective judgements of 3D targets to accurately depict 3D shape on a 2D surface, thus benefiting from a local approach. The current study used a shape perception task that comprehensively tests the effects of context on shape perception, in conjunction with a drawing task and several possible measures of local processing bias, to show that the perceptual basis of drawing skill in neurotypical adults is not due to a local processing bias. Perceptual flexibility, the ability to process local or global information as needed, is discussed as a potential mechanism driving both accurate shape judgements and realistic drawing.

2-D center-surround effects on 3-D structure-from-motion.

This study investigates how mechanisms for amplifying 2-D motion contrast influence the assignment of 3-D depth values. The authors found that the direction of movement of a random-dot conveyor belt strongly inclined observers to report that the front surface of a superimposed, transparent, rotating, random-dot sphere moved in a direction opposite to the belt. This motion-contrast effect was direction selective and demonstrated substantial spatial integration. Varying the stereo depth of the belt did not compromise the main effect, precluding a mechanical interpretation (sphere rolling on belt). Varying the speed of the surfaces of the sphere also did not greatly affect the interpretation of rotation direction. These results suggest that 2-D center-surround interactions influence 3-D depth assignment by differentially modulating the strength of response to the moving surfaces of an object (their prominence) without affecting featural specificity.

Associative learning of scene parameters from images.

An important problem for both biological and machine vision is the construction of scene representations from 2-D image data that are useful for recognition. One problem is that there can be more than one world out there giving rise to the image data at hand. Additional constraints regarding the nature of the environment have to be used to narrow the range of solutions. Although effort has gone into understanding these constraints, relatively little has been done to understand how neurallike learning networks may be used to solve scene-from-image problems. A paradigm is proposed in which stochastic models of scene properties are used to provide samples of image and scene representations. Distributed associative networks are taught, by example, the statistical constraints relating the image to the representation of the scene. This technique is applied to problems in optic flow, shape-from-shading, and stereo.