Electrophysiological Evidence of the Amodal Representation of Symmetry in Extrastriate Areas

Extrastriate visual areas are strongly activated by image symmetry. Less is known about symmetry representation at object-, rather than image-, level. Here we investigated electrophysiological responses to symmetry, generated by amodal completion of partially-occluded polygon shapes. We used a similar paradigm in four experiments (N=112). A fully-visible abstract shape (either symmetric or asymmetric) was presented for 250ms (t0). A large rectangle covered it entirely for 250ms (t1) and then moved to one side to reveal half of the shape hidden behind (t2, 1000ms). Note that at t2 no symmetry could be inferred from retinal image information. In half of the trials the shape was the same as previously presented, in the other trials it was replaced by a novel shape. Participants matched shapes similarity (Exp. 1 and Exp. 2), or their colour (Exp. 3) or the orientation of a triangle superimposed to the shapes (Exp. 4). The fully-visible shapes (t0-t1) elicited automatic symmetry-specific ERP responses in all experiments. Importantly, there was an exposure-dependent symmetry-response to the occluded shapes that were recognised as previously seen (t2). Exp. 2 and Exp.4 confirmed this second ERP (t2) did not reflect a reinforcement of a residual carry-over response from t0. We conclude that the extrastriate symmetry-network can achieve amodal representation of symmetry from occluded objects that have been previously experienced as wholes.

Constant curvature modeling of abstract shape representation

How abstract shape is perceived and represented poses crucial unsolved problems in human perception and cognition. Recent findings suggest that the visual system may encode contours as sets of connected constant curvature segments. Here we describe a model for how the visual system might recode a set of boundary points into a constant curvature representation. The model includes two free parameters that relate to the degree to which the visual system encodes shapes with high fidelity vs. the importance of simplicity in shape representations. We conducted two experiments to estimate these parameters empirically. Experiment 1 tested the limits of observers’ ability to discriminate a contour made up of two constant curvature segments from one made up of a single constant curvature segment. Experiment 2 tested observers’ ability to discriminate contours generated from cubic splines (which, mathematically, have no constant curvature segments) from constant curvature approximations of the contours, generated at various levels of precision. Results indicated a clear transition point at which discrimination becomes possible. The results were used to fix the two parameters in our model. In Experiment 3, we tested whether outputs from our parameterized model were predictive of perceptual performance in a shape recognition task. We generated shape pairs that had matched physical similarity but differed in representational similarity (i.e., the number of segments needed to describe the shapes) as assessed by our model. We found that pairs of shapes that were more representationally dissimilar were also easier to discriminate in a forced choice, same/different task. The results of these studies provide evidence for constant curvature shape representation in human visual perception and provide a testable model for how abstract shape descriptions might be encoded.

EDC-Net: Edge Detection Capsule Network for 3D Point Clouds

Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.

The Fragmented Frame 1

The aesthetic of the fragment is examined in detailed analyses of the Hitchcockian frame. The frame is both the formal composition underpinning mise en scène and the opening into the infinite play of fragmented images within visual, aural, and narrative form. The frame is a site of formal “expressivity,” “abstraction,” “topographic representation,” and “schematization.” The fragmented frame is revealed in the modernist experimentation of form through color, line, and shape in North by Northwest, the topographic frame in The Birds, and the canting of the visual frame in Shadow of a Doubt. The chapter concludes that the representational image forming the diegesis is overwhelmed in Hitchcock’s experimental works by the formal potential of abstract shape and pattern.

A new workflow for the automated measurement of shape descriptors of rocks

Shape properties of rocks carry important geological information about their origin, and they may also provide a window to study the abrasion processes forming their geometry. The number of mechanical equilibria is a significant property with a profound mathematical background that could reveal the secrets hidden in the artifacts of Nature. Although it is easy to count by hand, the automation of its measurement is not a straightforward task. A new workflow is introduced for the fast and efficient measurement of geometrical properties, including the number and location of stable and unstable equilibrium points of rocks based on a portable 3D scanner combined with computer software that can analyze the resulting point cloud. The technique allows for the fast examination of statistically sufficient sample sizes without the need for transportation or storage of the specimens. A previously hand-measured set of pebbles and fragments was used as a reference for the validation of the method, and its effectiveness is demonstrated through the examination of beach pebbles carried out in Kawakawa Bay, New Zealand.

Color Photometric Stereo Using Multi-Band Camera Constrained by Median Filter and Occluding Boundary

One of the main problems faced by the photometric stereo method is that several measurements are required, as this method needs illumination from light sources from different directions. A solution to this problem is the color photometric stereo method, which conducts one-shot measurements by simultaneously illuminating lights of different wavelengths. However, the classic color photometric stereo method only allows measurements of white objects, while a surface-normal estimation of a multicolored object using this method is theoretically impossible. Therefore, it is necessary to add some constraints to estimate the surface normal of a multicolored object using the framework of the color photometric stereo method. In this study, a median filter is employed as the constraint condition of albedo, and the surface normal of the occluding boundary is employed as the constraint condition of the surface normal. By employing a median filter as the constraint condition, the smooth distribution of the albedo and normal is calculated while the sharp features at the boundary of different albedos and normals are preserved. The surface normal at the occluding boundary is propagated into the inner part of the object region, and forms the abstract shape of the object. Such a surface normal gives a great clue to be used as an initial guess to the surface normal. To demonstrate the effectiveness of this study, a measurement device that can realize the multispectral photometric stereo method with seven colors is employed instead of the classic color photometric stereo method with three colors.

Shared understanding is correlated with shared neural responses in the default mode network

Humans have a striking ability to infer meaning from even the sparsest and most abstract forms of narratives. At the same time, flexibility in the form of a narrative is matched by inherent ambiguity in the interpretation of it. How does the brain represent subtle, idiosyncratic differences in the interpretation of abstract and ambiguous narratives? In this fMRI study, we scanned subjects watching a 7-min original animation that depicts a complex narrative through the movement of simple geometric shapes. We additionally scanned two separate groups listening to concrete verbal descriptions of either the social narrative or the physical properties of the movie. After scanning, all subjects freely recalled their interpretation of the stimulus. Using an intersubject representational similarity analysis, we compared the similarity of narrative interpretation across subjects, as measured using text analysis, with the similarity of neural responses, as measured using intersubject correlation (ISC). We found that the more similar two people’s interpretations of the abstract shape movie were, the more similar their neural responses were in the default mode network (DMN). Moreover, these shared responses were modality invariant: despite vast differences in stimulus properties, we found that the shapes movie and the verbal interpretation of the movie elicited shared responses in linguistic areas and a subset of the DMN when subjects shared interpretations. Together, these results suggest that the DMN is not only sensitive to subtle individual differences in narrative interpretation, but also resilient to large differences in the modality of the narrative.Significance statementThe same narrative can be both communicated in different ways and interpreted in different ways. How are subtle, idiosyncratic differences in the interpretation of complex narratives presented in different forms represented in the brain? In this fMRI study, we show that the more similarly two people interpreted an ambiguous animation composed of moving shapes, the more similar their neural responses were in the Default Mode Network. In addition, by presenting the same narrative in a different form, we found shared responses across modalities when subjects shared interpretations despite the vast differences in modality of the stimuli. Together, these results suggest that the DMN is at once sensitive to individual differences in narrative interpretation and resilient to differences narrative modality.