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We propose the use of ground surface segmentation to enhance the perception of obstacles in low to medium resolution prosthetic visual representations. We apply a recently proposed algorithm for segmenting traversable space in stereo disparity data, and show how such a scheme may be utilised to enhance the distinction between the ground surface and(More)
In this article, we present a framework to perform statistical shape analysis for segmented hippocampi, including an efficient permutation test for detecting subtle class differences, and a regularized discriminative direction method for visualizing the shape discrepancy. Fisher permutation and bootstrap tests are preferred to traditional hypothesis tests(More)
Navigation and way finding including obstacle avoidance is difficult when visual perception is limited to low resolution, such as is currently available on a bionic eye. Depth visualisation may be a suitable alternative. Such an approach can be evaluated using simulated phosphenes with a wearable mobile virtual reality kit. In this paper, we present two(More)
Matching and registration of shapes is a key issue in Computer Vision, Pattern Recognition, and Medical Image Analysis. This paper presents a shape representation framework based on Gaussian curvature and Markov random fields (MRFs) for the purpose of shape matching. The method is based on a surface mesh model in R<sup>3</sup>, which is projected into a(More)
3D shape matching based on meshed surfaces can be formulated as an energy function minimisation problem under a Markov random field (MRF) framework. However, to solve such a global optimisation problem is NP-hard. So research mainly focuses on approximation algorithms. One of the best known is belief propagation (BP), which has shown success in early vision(More)
A dense point-based registration is an ideal starting point for detailed comparison between two neuroanatomical objects. This paper presents a new algorithm for global dense point-based registration between anatomical objects without assumptions about their shape. We represent mesh models of the surfaces of two similar 3D anatomical objects using a Markov(More)
Identifying the shape difference between two groups of anatomical objects is important for medical image analysis and computer-aided diagnosis. A method called "discriminative direction" in the literature has been proposed to solve this problem. In that method, the shape difference between groups is identified by deforming a shape along the discriminative(More)