Shape basis interpretation for monocular deformable 3D reconstruction

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft

Observacions sobre la informació científica referent a les drogues en alguns llibres de text d'E.S.O.

Aquest article inclou comentaris dels autors extrets del que han pogut observar, en diversos llibres de text de l’àrea de Ciències de la Naturalesa d’ESO, errors de diverses categories que fan referència a les drogues. Alguns d’aquests errors palesen que els autors no han copsat prou ni els conceptes fonamentals del tema ni la seva complexitat real

El Treball de Fi de Grau a Treball Social: Orientacions per a la seva qualitat

Conferència presentada al seminari «El TFG a Treball Social: modelant la seva implementació» del departament de Treball Social i Serveis Socials de la Universitat de Barcelona (13 de novembre de 2013

Comunicació local i de proximitat

Research on local Valencian communication is, analytically speaking, heter - ogeneous and patchy. General overviews on the entire Valencian area are lacking. Predominating the literature are books summarizing conclusions of diverse regional communication conferences held from 1987 to 2005. Noteworthy are the volumes published by the València Generalitat, the Federació Valenciana de Municipis i Províncies, the Unió de Periodistes Valencians, and the Universitat Jaume I in Castelló, comprising studies on the communication experiences and tendencies of numerous local and regional media. These studies, carried out with analytical rigor by journalism specialists and professionals, give the authors¿ own visions of the structural deficits and strong points of local Valencian media of the last 30 years. La investigació de la comunicació local valenciana es caracteritza per la heterogeneïtat i la dispersió analítica. Manquen les monografies generals que abasten tot el territori. La producció bibliogràfica predominant són llibres que recullen les conclusions de diverses jornades de comunicació de proximitat realitzades durant els anys 1987-2005. Destaquen els volums editats per la Generalitat Valenciana, la Federació Valenciana de Municipis i Províncies, la Unió de Periodistes Valencians i la Universitat Jaume I de Castelló. Aquests compendis analitzen experiències i tendències comunicatives de bastants mitjans locals i comarcals. Són estudis fets amb rigor analític per especialistes i professionals del periodisme, els quals donen la seua visió particular sobre els dèficits estructurals i punts forts dels mitjans locals valencians dels últims trenta anys

Substrate melting during laser heating of nanoscale metal films

We investigate heat transfer mechanisms relevant to metal films of nanoscale thickness deposited on a silicon (Si) substrate coated by a silicon oxide (SiO2) layer and exposed to laser irradiation. Such a setup is commonly used in the experiments exploring self and directed assembly of metal films that melt when irradiated by laser and then evolve on time scale measured in nanoseconds. We show that in a common experimental setting, not only the metal but also the SiO2 layer may melt. Our study of the effect of the laser parameters, including energy density and pulse duration, shows that melting of the substrate occurs on spatial and temporal scales that are of experimental relevance. Furthermore, we discuss how the thicknesses of metal and of the substrate itself influence the maximum depth and liquid lifetime of the melted SiO2 layer. In particular, we find that there is a minimum thickness of SiO2 layer for whichthis layer melts and furthermore, the melting occurs only for metal films of thickness in a specified range. In the experiments, substrate melting is of practical importance since it may significantly modify the evo-lution of the deposited nanoscale metal films or other geometries on nanoscale.Peer ReviewedPostprint (published version

Apunts d'avaluació de serveis socioeducatius

Podeu consultar la 2a edició a: http://hdl.handle.net/2445/101624Podeu consultar la 3a edició a: http://hdl.handle.net/2445/124743S'expliquen teòricament i amb exemples els següents aspectes relacionats amb l'avaluació de serveis socioeducatius: models i dimensions, procés avaluatiu d'un servei, i documents tècnics de proposta i difusió de l'avaluació

Vehicle pose estimation using G-Net: multi-class localization and depth estimation

In this paper we present a new network architecture, called G-Net, for 3D pose estimation on RGB images which is trained in a weakly supervised manner. We introduce a two step pipeline based on region-based Convolutional neural networks (CNNs) for feature localization, bounding box refinement based on non-maximum-suppression and depth estimation. The G-Net is able to estimate the depth from single monocular images with a self-tuned loss function. The combination of this predicted depth and the presented two-step localization allows the extraction of the 3D pose of the object. We show in experiments that our method achieves good results compared to other state-of-the-art approaches which are trained in a fully supervised manner.Peer ReviewedPostprint (author's final draft