Parallel algorithm for determining motion vectors in ice floe images by matching edge features

A parallel algorithm is described to determine motion vectors of ice floes using time sequences of images of the Arctic ocean obtained from the Synthetic Aperture Radar (SAR) instrument flown on-board the SEASAT spacecraft. Researchers describe a parallel algorithm which is implemented on the MPP for locating corresponding objects based on their translationally and rotationally invariant features. The algorithm first approximates the edges in the images by polygons or sets of connected straight-line segments. Each such edge structure is then reduced to a seed point. Associated with each seed point are the descriptions (lengths, orientations and sequence numbers) of the lines constituting the corresponding edge structure. A parallel matching algorithm is used to match packed arrays of such descriptions to identify corresponding seed points in the two images. The matching algorithm is designed such that fragmentation and merging of ice floes are taken into account by accepting partial matches. The technique has been demonstrated to work on synthetic test patterns and real image pairs from SEASAT in times ranging from .5 to 0.7 seconds for 128 x 128 images

Powerful Parallel Symmetric 3D Thinning Schemes Based on Critical Kernels

The main contribution of the present article consists of new 3D parallel and symmetric thinning schemes which have the following qualities: - They are effective and sound, in the sense that they are guaranteed to preserve topology. This guarantee is obtained thanks to a theorem on critical kernels; - They are powerful, in the sense that they remove more points, in one iteration, than any other symmetric parallel thinning scheme; - They are versatile, as conditions for the preservation of geometrical features (e.g., curve extremities or surface borders) are independent of those accounting for topology preservation; - They are efficient: we provide in this article a small set of masks, acting in the grid Z3, that is sufficient, in addition to the classical simple point test, to straightforwardly implement them

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

A Parallel Thinning Algorithm for Grayscale Images

International audienceGrayscale skeletonization offers an interesting alternative to traditional skeletonization following a binarization. It is well known that parallel algorithms for skeletonization outperform sequential ones in terms of quality of results, yet no general and well defined framework has been proposed until now for parallel grayscale thinning. We introduce in this paper a parallel thinning algorithm for grayscale images, and prove its topological soundness based on properties of the critical kernels framework. The algorithm and its proof, given here in the 2D case, are also valid in 3D. Some applications are sketched in conclusion

A complete hand-drawn sketch vectorization framework

Vectorizing hand-drawn sketches is a challenging task, which is of paramount importance for creating CAD vectorized versions for the fashion and creative workflows. This paper proposes a complete framework that automatically transforms noisy and complex hand-drawn sketches with different stroke types in a precise, reliable and highly-simplified vectorized model. The proposed framework includes a novel line extraction algorithm based on a multi-resolution application of Pearson's cross correlation and a new unbiased thinning algorithm that can get rid of scribbles and variable-width strokes to obtain clean 1-pixel lines. Other contributions include variants of pruning, merging and edge linking procedures to post-process the obtained paths. Finally, a modification of the original Schneider's vectorization algorithm is designed to obtain fewer control points in the resulting Bezier splines. All the proposed steps of the framework have been extensively tested and compared with state-of-the-art algorithms, showing (both qualitatively and quantitatively) its outperformance

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

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source.Comment: 32 pages, 21 figure

Atomic discreteness and the nature of structural equilibrium in conductance histograms of electromigrated Cu-nanocontacts

We investigate the histograms of conductance values obtained during controlled electromigration thinning of Cu thin films. We focus on the question whether the most frequently observed conductance values, apparent as peaks in conductance histograms, can be attributed to the atomic structure of the wire. To this end we calculate the Fourier transform of the conductance histograms. We find all the frequencies matching the highly symmetric crystallographic directions of fcc-Cu. In addition, there are other frequencies explainable by oxidation and possibly formation of hcp-Cu. With these structures we can explain all peaks occurring in the Fourier transform within the relevant range. The results remain the same if only a third of the samples are included. By comparing our results to the ones available in the literature on work-hardened nanowires we find indications that even at low temperatures of the environment, metallic nanocontacts could show enhanced electromigration at low current densities due to defects enhancing electron scattering