Triangle mesh skeletonization using non-deterministic voxel thinning and graph spectrum segmentation

In the context of shape processing, the estimation of the medial axis is relevant for the simplification and re-parameterization of 3D bodies. The currently used methods are based on (1) General fields, (2) Geometric methods and (3) voxel-based thinning. They present shortcomings such as (1) overrepresentation and non-smoothness of the medial axis due to high frequency nodes and (2) biased-skeletons due to skewed thinning. To partially overcome these limitations, this article presents a non-deterministic algorithm for the estimation of the 1D skeleton of triangular B-Reps or voxel-based body representations. Our method articulates (1) a novel randomized thinning algorithm that avoids possible skewings in the final skeletonization, (2) spectral-based segmentation that eliminates short dead-end branches, and (3) a maximal excursion method for reduction of high frequencies. The test results show that the randomized order in the removal of the instantaneous skin of the solid region eliminates bias of the skeleton, thus respecting features of the initial solid. An Alpha Shape-based inversion of the skeleton encoding results in triangular boundary Representations of the original body, which present reasonable quality for fast non-minute scenes. Future work is needed to (a) tune the spectral filtering of high frequencies off the basic skeleton and (b) extend the algorithm to solid regions whose skeletons mix 1D and 2D entities.

Impact of microsaccades on visual shape processing

Sensitivity to subtle changes in the shape of visual objects has been attributed to the existence of global pooling mechanisms that integrate local form information across space. While global pooling is typically demonstrated under steady fixation, other work suggests prolonged fixation can lead to a collapse of global structure. Here we ask whether small ballistic eye movements that naturally occur during periods of fixation affect the global processing of radial frequency (RF) patterns - closed contours created by sinusoidally modulating the radius of a circle. Observers were asked to discriminate the shapes of circular and RF modulated patterns while fixational eye movements were recorded binocularly at 500Hz. Microsaccades were detected using a velocity-based algorithm, allowing trials to be sorted according to the relative timing of stimulus and microsaccade onset. Results revealed clear peri-saccadic changes in shape discrimination thresholds. Performance was impaired when microsaccades occurred close to stimulus onset, but facilitated when they occurred shortly afterwards. In contrast, global integration of shape was unaffected by the timing of microsaccades. These findings suggest that microsaccades alter the discrimination sensitivity to briefly presented shapes but do not disrupt the spatial pooling of local form signals.

Joint Representation of Color and Shape in Convolutional Neural Networks: A Stimulus-rich Network Perspective

To interact with real-world objects, any effective visual system must jointly code the unique features defining each object. Despite decades of neuroscience research, we still lack a firm grasp on how the primate brain binds visual features. Here we apply a novel network-based stimulus-rich representational similarity approach to study color and shape binding in five convolutional neural networks (CNNs) with varying architecture, depth, and presence/absence of recurrent processing. All CNNs showed near-orthogonal color and shape processing in early layers, but increasingly interactive feature coding in higher layers, with this effect being much stronger for networks trained for object classification than untrained networks. These results characterize for the first time how multiple visual features are coded together in CNNs. The approach developed here can be easily implemented to characterize whether a similar coding scheme may serve as a viable solution to the binding problem in the primate brain.

Multivariate Medians for Image and Shape Analysis

Having been studied since long by statisticians, multivariate medianconcepts found their way into the image processing literature in thecourse of the last decades, being used to construct robust and efficientdenoising filters for multivariate images such as colour images but alsomatrix-valued images.Based on the similarities between image and geometric data as results ofthe sampling of continuous physical quantities, it can be expected that theunderstanding of multivariate median filters for images provides a startingpoint for the development of shape processing techniques.This paper presents an overview of multivariate median concepts relevantfor image and shape processing. It focusses on their mathematical principlesand discusses important properties especially in the context of imageprocessing.