Estudi i implementació d’un mètode de reconstrucció 3D basat en SfM i registre de vistes 3D parcials

Aquest projecte es basarà en reconstruir una imatge 3D gran a partir d’una seqüència d’imatges 2D capturades per una càmera. Ens centrem en l’estudi de les bases matemàtiques de la visió per computador així com en diferents mètodes emprats en la reconstrucció 3D d’imatges. Per portar a terme aquest estudi s’utilitza la plataforma de desenvolupament MatLab ja que permet tractar operacions matemàtiques, imatges i matrius de gran tamany amb molta senzillesa, rapidesa i eficiència, per aquesta raó s’usa en moltes recerques sobre aquest tema. El projecte aprofundeix en el tema descrit anteriorment estudiant i implementant un mètode que consisteix en aplicar Structure From Motion (SFM) a pocs frames seguits obtinguts d’una seqüència d’imatges 2D per crear una reconstrucció 3D. Quan s’han creat dues reconstruccions 3D consecutives i fent servir un frame com a mínim en comú entre elles, s’aplica un mètode de registre d’estructures 3D, l’Iterative Closest Point (ICP), per crear una reconstrucció 3D més gran a través d’unir les diferents reconstruccions obtingudes a partir de SfM. El mètode consisteix en anar repetint aquestes operacions fins al final dels frames per poder aconseguir una reconstrucció 3D més gran que les petites imatges que s’aconsegueixen a través de SfM. A la Figura 1 es pot veure un esquema del procés que es segueix. Per avaluar el comportament del mètode, utilitzem un conjunt de seqüències sintètiques i un conjunt de seqüències reals obtingudes a partir d’una càmera. L’objectiu final d’aquest projecte és construir una nova toolbox de MatLab amb tots els mètodes per crear reconstruccions 3D grans per tal que sigui possible tractar amb facilitat aquest problema i seguir-lo desenvolupant en un futur

On optimal computation of MPLS label binding for multipoint-to-point connections

Most network operators have considered reducing Label Switched Routers (LSR) label spaces (i.e. the number of labels that can be used) as a means of simplifying management of underlaying Virtual Private Networks (VPNs) and, hence, reducing operational expenditure (OPEX). This letter discusses the problem of reducing the label spaces in Multiprotocol Label Switched (MPLS) networks using label merging - better known as MultiPoint-to-Point (MP2P) connections. Because of its origins in IP, MP2P connections have been considered to have tree- shapes with Label Switched Paths (LSP) as branches. Due to this fact, previous works by many authors affirm that the problem of minimizing the label space using MP2P in MPLS - the Merging Problem - cannot be solved optimally with a polynomial algorithm (NP-complete), since it involves a hard- decision problem. However, in this letter, the Merging Problem is analyzed, from the perspective of MPLS, and it is deduced that tree-shapes in MP2P connections are irrelevant. By overriding this tree-shape consideration, it is possible to perform label merging in polynomial time. Based on how MPLS signaling works, this letter proposes an algorithm to compute the minimum number of labels using label merging: the Full Label Merging algorithm. As conclusion, we reclassify the Merging Problem as Polynomial-solvable, instead of NP-complete. In addition, simulation experiments confirm that without the tree-branch selection problem, more labels can be reduced

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Point sampling with uniformly distributed lines

In this paper we address the problem of extracting representative point samples from polygonal models. The goal of such a sampling algorithm is to find points that are evenly distributed. We propose star-discrepancy as a measure for sampling quality and propose new sampling methods based on global line distributions. We investigate several line generation algorithms including an efficient hardware-based sampling method. Our method contributes to the area of point-based graphics by extracting points that are more evenly distributed than by sampling with current algorithms