Observer strategies in perception of 3-D shape from isotropic textures: developable surfaces

AbstractWe document the limitations of isotropic textures in conveying three-dimensional shape. We measured the perceived shape and pitch of upright and pitched corrugated surfaces overlaid with different classes of isotropic textures: patterns containing isotropic texture elements, isotropically filtered noise patterns, and patterns containing ellipses or lines of all orientations. Frequency modulations arising from surface slant were incorrectly interpreted as changes in surface distance, resulting in concavities being misclassified as convexities, and right and left slants as concavities. In addition, images of pitched surfaces exhibited oriented flows that confound surface shape and surface pitch. Observers related oriented flow patterns to particular surface shapes with a bias for perceiving convex surfaces. When concave and convex curvatures were concurrently visible, the number of correct shape classifications increased slightly. Isotropic textures thus convey correct 3-D shapes of developable surfaces only in some conditions, and the same perceptual strategies lead to non-veridical percepts in other conditions

Object Rigidity: Competition and cooperation between motion-energy and feature-tracking mechanisms and shape-based priors

Why do moving objects appear rigid when projected retinal images are deformed nonrigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly but rigid rotation was reported at slow speeds. When gaps, paint or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds, but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation, gave a high probability of wobbling for the motion-energy flows. However, the CNN gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining CNN outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow, with the shape-based priors for wobbling and rolling, explained rigid and nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate how cooperation and competition between different neuronal classes lead to specific states of visual perception and to transitions between the states.Comment: 36 pages, 11 figures (10 main figures and 1 appendix figure

Tunable and Growing Network Generation Model with Community Structures

Recent years have seen a growing interest in the modeling and simulation of social networks to understand several social phenomena. Two important classes of networks, small world and scale free networks have gained a lot of research interest. Another important characteristic of social networks is the presence of community structures. Many social processes such as information diffusion and disease epidemics depend on the presence of community structures making it an important property for network generation models to be incorporated. In this paper, we present a tunable and growing network generation model with small world and scale free properties as well as the presence of community structures. The major contribution of this model is that the communities thus created satisfy three important structural properties: connectivity within each community follows power-law, communities have high clustering coefficient and hierarchical community structures are present in the networks generated using the proposed model. Furthermore, the model is highly robust and capable of producing networks with a number of different topological characteristics varying clustering coefficient and inter-cluster edges. Our simulation results show that the model produces small world and scale free networks along with the presence of communities depicting real world societies and social networks.Comment: Social Computing and Its Applications, SCA 13, Karlsruhe : Germany (2013