Ramon Carbó i la semblança molecular quàntica

Entrevista en Ramon Carbó-Dorca sobre la semblança molecular quantica

Emili Ros Rahola, gastroenteròleg

Entrevista a Emili Ros Rahola, gastroenteròleg gironí

Enric Canadell i la química teòrica

Entrevista a Enric Canadell, fisicoquímic gironí

Lluís Oliver i els components fonamentals de la matèria

Entrevista en Lluís Oliver, químic gironí

Tecnologia: el factor temps

Conjunt d'articles amb anàlisis i reflexions sobre el factor temps l'àmbit de la tècnica des de diferents punts de vista

Catàleg: nou WebOpac

Conferència emmarcada dins el marc del III Workshop Biblioteca UdG, on es presenta la nova imatge de l'OPAC

Conversa a Utopia

Joan Manuel del Pozo acaba de publicar la primera traducció catalana directa d’Utopia. Apareguda per primer cop en llatí el 1516, és l’obra més destacable de l’humanista anglès Tomas More, decapitat per protestar contra la pretensió d’Enric VIII d’envair un espai de poder que fins aleshores li havia estat aliè: l’espiritual. Utopia és una illa, i More la va imaginar com un paradís de tolerància religiosa, on no faltava la feina ni els recursos per a tothom. Amb el pretext de l’edició del llibre, Engega ha convidat el filòsof Joan Manuel del Pozo, el químic Miquel Costas i la doctora en Medicina Marta Aymerich, a mantenir una conversa gens utòpica, que us presentem a continuació

Anna Maria Geli: "La Universitat és un servei públic i té l'obligació de retre comptes"

La revista Engega entrevista la Dra. Anna M. Geli, rectora de la Universitat de Girona

Perfils. Montserrat Tura

Montserrat Tura és consellera de Justícia de la Generalitat. Del 2003 al 2006, ho va ser d’Interior i, abans d’entrar al Govern, va ser alcaldessa de Mollet durant 16 anys. Destaca pel seu coratge i decisió a l’hora d’afrontar reptes polítics, per la seva capacitat de gestió i pel seu compromís ineludible amb la democràcia i el país. Ha estat present en la signatura del conveni de col·laboració entre la Càtedra de Cultura Jurídica de la UdG i el Consell General del Poder Judicial que ha tingut lloc a la Facultat de Dret

Differential epipolar constraint in mobile robot egomotion estimation

The estimation of camera egomotion is a well established problem in computer vision. Many approaches have been proposed based on both the discrete and the differential epipolar constraint. The discrete case is mainly used in self-calibrated stereoscopic systems, whereas the differential case deals with a unique moving camera. The article surveys several methods for mobile robot egomotion estimation covering more than 0.5 million samples using synthetic data. Results from real data are also given

Local map update for large scale SLAM

A technique for simultaneous localisation and mapping (SLAM) for large scale scenarios is presented. This solution is based on the use of independent submaps of a limited size to map large areas. In addition, a global stochastic map, containing the links between adjacent submaps, is built. The information in both levels is corrected every time a loop is closed: local maps are updated with the information from overlapping maps, and the global stochastic map is optimised by means of constrained minimisation

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos