Colour image processing

In the context of the round table the following topics related to image colour processing will be discussed: historical point of view. Studies of Aguilonius, Gerritsen, Newton and Maxwell. CIE standard (Commission International de lpsilaEclaraige). Colour models. RGB, HIS, etc. Colour segmentation based on HSI model. Industrial applications. Summary and discussion. At the end, video images showing the robustness of colour in front of B/W images will be presented

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

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

Argument omissions in preschool Catalan and Spanish speaking children with SLI

El presente artículo describe tres estudios sobre la producción del verbo y la estructura argumental en niños con Trastorno Específico del Lenguaje (TEL) usando diferentes metodologías. El primero es un estudio observacional que usa una muestra de habla espontánea. El segundo usa una tarea experimental de denominación de oraciones como resultado de la observación de videos de acciones. El tercero comprende la tarea de denominación de oraciones con imágenes estáticas en eventos con diferente complejidad argumental. Aunque los datos concretos varían en función de la metodología usada, hay una clara evidencia de que los niños de habla catalana y española con TEL presentan especiales dificultades en la producción de verbos con una alta complejidad en relación a la estructura argumental y cometen errores en la especificación de los argumentos obligatorios. Se concluye que tanto limitaciones en el procesamiento como déficits en la representación semántica de los verbos pueden estar implicados en estas dificultades

Visualitzation and processing of diffusion tensor MRI

Diffusion Tensor Imaging (DTI) is a new magnetic resonance imaging modality capable of producing quantitative maps of microscopic natural displacements of water molecules that occur in brain tissues as part of the physical diffusion process. This technique has become a powerful tool in the investigation of brain structure and function because it allows for in vivo measurements of white matter fiber orientation. The application of DTI in clinical practice requires specialized processing and visualization techniques to extract and represent acquired information in a comprehensible manner. Tracking techniques are used to infer patterns of continuity in the brain by following in a step-wise mode the path of a set of particles dropped into a vector field. In this way, white matter fiber maps can be obtained.

Signal processing of microstructure profiles: integrating turbulent spatial scales in aquatic ecological modeling

The main objective of this thesis was the integration of microstructure information in synoptic descriptors of turbulence, that reflects the mixing processes. Turbulent patches are intermittent in space and time, but they represent the dominant process for mixing. In this work, the properties of turbulent patches were considered the potential input for integrating the physical microscale measurements. The development of a method for integrating the properties of the turbulent patches required solving three main questions: a) how can we detect the turbulent patches from he microstructure measurements?; b) which are the most relevant properties of the turbulent patches?; and ) once an interval of time has been selected, what kind of synoptic parameters could better reflect the occurrence and properties of the turbulent patches? The answers to these questions were the final specific objectives of this thesis.

Advances in animal blood processing: development of a biopreservation system and insights on the functional properties of plasma

La sang és un subproducte amb un alt potencial de valorització que s'obté en quantitats importants en els escorxadors industrials. Actualment, la majoria de sistemes de recollida de la sang no segueixen unes mesures d'higiene estrictes, pel que esdevé un producte de baixa qualitat microbiològica. Conseqüentment, l'aprofitament de la sang és una sortida poc estimulant des del punt de vista econòmic, ja que acostuma a perdre les qualitats que permetrien l'obtenció de productes d'alt valor afegit. El capítol I del present treball s'inclou dins d'un projecte que proposa la inoculació de bacteris de l'àcid làctic (LAB) com un cultiu bioconservador de la sang, un sistema senzill i de baix cost que cerca l'estabilitat de la sang, tant microbiològica com fisicoquímica, durant el període del seu emmagatzematge. El capítol II s'emmarca dins d'un projecte que cerca la millora de l'aprofitament integral de la sang que, en el cas de la fracció plasmàtica, es centra en l'estudi de la funcionalitat dels seus principals constituents. Conèixer la contribució dels components majoritaris ha de permetre la millora de la funcionalitat dels ingredients alimentaris derivats. Els resultats presentats en aquesta tesi poden ajudar a la valorització de la sang porcina d'escorxadors industrials, mitjançant els coneixements adquirits pel que fa a la millora del seu sistema de recollida i del desenvolupament d'ingredients alimentaris amb interessants propietats funcionals.