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

Subjectively interpreted shape dimensions as privileged and orthogonal axes in mental shape space

The shape of an object is fundamental in object recognition but it is still an open issue to what extent shape differences are perceived analytically (i.e., by the dimensional structure of the shapes) or holistically (i.e., by the overall similarity of the shapes). The dimensional structure of a stimulus is available in a primary stage of processing for separable dimensions, although it can also be derived cognitively from a perceived stimulus consisting of integral dimensions. Contrary to most experimental paradigms, the present study asked participants explicitly to analyze shapes according to two dimensions. The dimensions of interest were aspect ratio and medial axis curvature, and a new procedure was used to measure the participants' interpretation of both dimensions (Part I, Experiment 1). The subjectively interpreted shape dimensions showed specific characteristics supporting the conclusion that they also constitute perceptual dimensions with objective behavioral characteristics (Part II): (1) the dimensions did not correlate in overall similarity measures (Experiment 2), (2) they were more separable in a speeded categorization task (Experiment 3), and (3) they were invariant across different complex 2-D shapes (Experiment 4). The implications of these findings for shape-based object processing are discussed

The Sunyaev Zel'dovich effect: simulation and observation

The Sunyaev Zel'dovich effect (SZ effect) is a complete probe of ionized baryons, the majority of which are likely hiding in the intergalactic medium. We ran a $512^3$ $\Lambda$CDM simulation using a moving mesh hydro code to compute the statistics of the thermal and kinetic SZ effect such as the power spectra and measures of non-Gaussianity. The thermal SZ power spectrum has a very broad peak at multipole $l\sim 2000-10^4$ with temperature fluctuations $\Delta T \sim 15\mu$K. The power spectrum is consistent with available observations and suggests a high $\sigma_8\simeq 1.0$ and a possible role of non-gravitational heating. The non-Gaussianity is significant and increases the cosmic variance of the power spectrum by a factor of $\sim 5$ for $l<6000$. We explore optimal driftscan survey strategies for the AMIBA CMB interferometer and their dependence on cosmology. For SZ power spectrum estimation, we find that the optimal sky coverage for a 1000 hours of integration time is several hundred square degrees. One achieves an accuracy better than 40% in the SZ measurement of power spectrum and an accuracy better than 20% in the cross correlation with Sloan galaxies for $2000<l<5000$. For cluster searches, the optimal scan rate is around 280 hours per square degree with a cluster detection rate 1 every 7 hours, allowing for a false positive rate of 20% and better than 30% accuracy in the cluster SZ distribution function measurement.Comment: 34 pages, 20 figures. Submitted to ApJ. Simulation maps have been replaced by high resolution images. For higher resolution color images, please download from http://www.cita.utoronto.ca/~zhangpj/research/SZ/ We corrected a bug in our analysis. the SZ power spectrum decreases 50% and y parameter decrease 25

Perception and intelligent localization for autonomous driving

Mestrado em Engenharia de Computadores e TelemáticaVisão por computador e fusão sensorial são temas relativamente recentes, no entanto largamente adoptados no desenvolvimento de robôs autónomos que exigem adaptabilidade ao seu ambiente envolvente. Esta dissertação foca-se numa abordagem a estes dois temas para alcançar percepção no contexto de condução autónoma. O uso de câmaras para atingir este fim é um processo bastante complexo. Ao contrário dos meios sensoriais clássicos que fornecem sempre o mesmo tipo de informação precisa e atingida de forma determinística, as sucessivas imagens adquiridas por uma câmara estão repletas da mais variada informação e toda esta ambígua e extremamente difícil de extrair. A utilização de câmaras como meio sensorial em robótica é o mais próximo que chegamos na semelhança com aquele que é o de maior importância no processo de percepção humana, o sistema de visão. Visão por computador é uma disciplina científica que engloba àreas como: processamento de sinal, inteligência artificial, matemática, teoria de controlo, neurobiologia e física. A plataforma de suporte ao estudo desenvolvido no âmbito desta dissertação é o ROTA (RObô Triciclo Autónomo) e todos os elementos que consistem o seu ambiente. No contexto deste, são descritas abordagens que foram introduzidas com fim de desenvolver soluções para todos os desafios que o robô enfrenta no seu ambiente: detecção de linhas de estrada e consequente percepção desta, detecção de obstáculos, semáforos, zona da passadeira e zona de obras. É também descrito um sistema de calibração e aplicação da remoção da perspectiva da imagem, desenvolvido de modo a mapear os elementos percepcionados em distâncias reais. Em consequência do sistema de percepção, é ainda abordado o desenvolvimento de auto-localização integrado numa arquitectura distribuída incluindo navegação com planeamento inteligente. Todo o trabalho desenvolvido no decurso da dissertação é essencialmente centrado no desenvolvimento de percepção robótica no contexto de condução autónoma.Computer vision and sensor fusion are subjects that are quite recent, however widely adopted in the development of autonomous robots that require adaptability to their surrounding environment. This thesis gives an approach on both in order to achieve perception in the scope of autonomous driving. The use of cameras to achieve this goal is a rather complex subject. Unlike the classic sensorial devices that provide the same type of information with precision and achieve this in a deterministic way, the successive images acquired by a camera are replete with the most varied information, that this ambiguous and extremely dificult to extract. The use of cameras for robotic sensing is the closest we got within the similarities with what is of most importance in the process of human perception, the vision system. Computer vision is a scientific discipline that encompasses areas such as signal processing, artificial intelligence, mathematics, control theory, neurobiology and physics. The support platform in which the study within this thesis was developed, includes ROTA (RObô Triciclo Autónomo) and all elements comprising its environment. In its context, are described approaches that introduced in the platform in order to develop solutions for all the challenges facing the robot in its environment: detection of lane markings and its consequent perception, obstacle detection, trafic lights, crosswalk and road maintenance area. It is also described a calibration system and implementation for the removal of the image perspective, developed in order to map the elements perceived in actual real world distances. As a result of the perception system development, it is also addressed self-localization integrated in a distributed architecture that allows navigation with long term planning. All the work developed in the course of this work is essentially focused on robotic perception in the context of autonomous driving

Optimal boundary conditions at the staircase-shaped coastlines

A 4D-Var data assimilation technique is applied to the rectangular-box configuration of the NEMO in order to identify the optimal parametrization of boundary conditions at lateral boundaries. The case of the staircase-shaped coastlines is studied by rotating the model grid around the center of the box. It is shown that, in some cases, the formulation of the boundary conditions at the exact boundary leads to appearance of exponentially growing modes while optimal boundary conditions allow to correct the errors induced by the staircase-like appriximation of the coastline.Comment: Submitted to Ocean Dynamics. (27/02/2014

Seismic Fault Preserving Diffusion

This paper focuses on the denoising and enhancing of 3-D reflection seismic data. We propose a pre-processing step based on a non linear diffusion filtering leading to a better detection of seismic faults. The non linear diffusion approaches are based on the definition of a partial differential equation that allows us to simplify the images without blurring relevant details or discontinuities. Computing the structure tensor which provides information on the local orientation of the geological layers, we propose to drive the diffusion along these layers using a new approach called SFPD (Seismic Fault Preserving Diffusion). In SFPD, the eigenvalues of the tensor are fixed according to a confidence measure that takes into account the regularity of the local seismic structure. Results on both synthesized and real 3-D blocks show the efficiency of the proposed approach.Comment: 10 page