Automatic correction of Ma and Sonka's thinning algorithm using P-simple points

International audienceMa and Sonka proposed a fully parallel 3D thinning algorithm which does not always preserve topology. We propose an algorithm based on P-simple points which automatically corrects Ma and Sonka's Algorithm. As far as we know, our algorithm is the only fully parallel curve thinning algorithm which preserves topology

Improved 3D thinning algorithms for skeleton extraction

In this study, we focused on developing a novel 3D Thinning algorithm to extract one-voxel wide skeleton from various 3D objects aiming at preserving the topological information. The 3D Thinning algorithm was testified on computer-generated and real 3D reconstructed image sets acquired from TEMT and compared with other existing 3D Thinning algorithms. It is found that the algorithm has conserved medial axes and simultaneously topologies very well, demonstrating many advantages over the existing technologies. They are versatile, rigorous, efficient and rotation invariant.<br /

A 3D parallel shrinking algorithm

Shrinking is a frequently used preprocessing step in image processing. This paper presents an efficient 3D parallel shrinking algorithm for transforming a binary object into its topological kernel. The applied strategy is called directional: each iteration step is composed of six subiterations each of which can be executed in parallel. The algorithm makes easy implementation possible, since deletable points are given by 3 x 3 x 3 matching templates. The topological correctness of the algorithm is proved for (26,6) binary pictures

Gap Filling of 3-D Microvascular Networks by Tensor Voting

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated

Features and neural net recognition strategies for hand printed digits

The thesis goal is to develop a computer system for hand printed digit recognition based on an investigation into various feature extractors and neural network strategies. Features such as subwindow pixel summation, moments, and orientation vectors will be among those investigated. Morphological thinning of characters prior to feature extraction will be used to assess the impact on network training and testing. Different strategies for implementing a multilayer perceptron neural network will be investigated. A high-level language called MatLab will be used for neural network algorithm development and quick prototyping. The feature extractors will be developed to operate on small (less than or equal to 256 bits) binary hand printed digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)

An automatic correction of Ma's thinning algorithm based on P -simple points

International audienceThe notion of P -simple points has been introduced by Bertrand to conceive parallel thinning algorithms. In 'A 3D fully parallel thinning algorithm for generating medial faces', Ma has proposed an algorithm for which there exists objects whose topology is not preserved. In this paper, we propose a new application of P -simple points: to automatically correct Ma's algorithm

A 3D Sequential Thinning Scheme Based on Critical Kernels

International audienceWe propose a new generic sequential thinning scheme based on the critical kernels framework. From this scheme, we derive sequential algorithms for obtaining ultimate skeletons and curve skeletons. We prove some properties of these algorithms, and we provide the results of a quantitative evaluation that compares our algorithm for curve skeletons with both sequential and parallel ones