94 research outputs found
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
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