DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis

Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutional neural networks (CNNs) in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based computer-aided diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs.This work was supported in part by the National Grant TEC2014-53390-P and National Grant TEC2014-61729-EXP of the Spanish Ministry of Economy and Competitiveness, and in part by NVIDIA Corporation with the donation of the TITAN X GPUPublicad

A region-centered topic model for object discovery and category-based image segmentation

Latent topic models have become a popular paradigm in many computer vision applications due to their capability to unsupervisely discover semantics in visual content. Relying on the Bag-of-Words representation, they consider images as mixtures of latent topics that generate visual words according to some specific distributions. However, the performance of these methods is still limited by the way in which they take into account the spatial distribution of visual words and, what is even more important, the currently used appearance distributions. In this paper, we propose a novel region-centered latent topic model that introduces two main contributions: first, an improved spatial context model that allows for considering inter-topic inter-region influences; and second, an advanced region-based appearance distribution built on the Kernel Logistic Regressor. It is worth highlighting that the proposed contributions have been seamlessly integrated in the model, so that all the parameters are concurrently estimated using a unified inference process. Furthermore, the proposed model has been extended to work in both unsupervised and supervised modes. Our results for unsupervised mode improve 30% those of previous latent topic models. For supervised mode, where discriminative approaches are preponderant, our results are quite close to those of discriminative state-of-the-art methods.This work has been partially supported by the project AFICUS, co-funded by the Spanish Ministry of Industry, Trade and Tourism, and the European Fund for Regional Development, with Ref.: TSI-020110-2009-103, and the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad

Adaptive Multi-Pattern Fast Block-Matching Algorithm Based on Motion Classification Techniques

Motion estimation is the most time-consuming subsystem in a video codec. Thus, more efficient methods of motion estimation should be investigated. Real video sequences usually exhibit a wide-range of motion content as well as different degrees of detail, which become particularly difficult to manage by typical block-matching algorithms. Recent developments in the area of motion estimation have focused on the adaptation to video contents. Adaptive thresholds and multi-pattern search algorithms have shown to achieve good performance when they success to adjust to motion characteristics. This paper proposes an adaptive algorithm, called MCS, that makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits to them. Specifically, a hierarchical structure of binary linear classifiers is proposed. Our experimental results show that MCS notably reduces the computational cost with respect to an state-of-the-art method while maintaining the qualityPublicad

Colección de prácticas de tratamiento digital de audio para telecomunicaciones

Grado en Ingeniería de Sistemas Audiovisuales. Asignatura: Tratamiento digital de audio para Telecomunicacione

Generative models for image segmentation and representation

This PhD. Thesis consists of two well differentiated parts, each of them focusing on one particular field of Computer Vision. The first part of the document considers the problem of automatically generating image segmentations in video sequences in the absence of any kind of semantic knowledge or labeled data. To that end, a blind spatio-temporal segmentation algorithm is proposed that fuses motion, color and spatial information to produce robust segmentations. The approach follows an iterative splitting process in which well known probabilistic techniques such as Gaussian Mixture Models are used as a core technique. At each iteration of the segmentation process, some regions are split into new ones, so that the number of mixture components is automatically set depending on the image content. Furthermore, in order to keep in memory valuable information from previous iterations, prior distributions are applied to the mixture components so that areas of the image that remain unchanged are fixed during the learning process. Additionally, in order to make decisions about whether or not to split regions at the end of one iteration, we propose the use of novel spatio-temporal mid-level features. These features model properties that are usually found in real-world objects so that the resulting segmentations are closer to the human perception. Examples of spatial mid-level features are regularity or adjacency, whereas the temporal ones relate to well known motion patterns such as translation or rotation. The proposed algorithm has been assessed in comparison to some state-of-the-art spatio-temporal segmentation algorithms, taking special care of showing the influence of each of the original contributions. The second part of the thesis studies the application of generative probabilistic models to the image representation problem. We consider “image representation” as a concurrent process that helps to understand the contents in an image and covers several particular tasks in computer vision as image recognition, object detection or image segmentation. Starting from the well-known bag-of-words paradigm we study the application of Latent Topic Models. These models were initially proposed in the text retrieval field, and consider a document as generated by a mixture of latent topics that are hopefully associated to semantic concepts. Each topic generates in turn visual local descriptors following a specific distribution. Due to the bag-of-words representation, Latent Topic Models exhibit an important limitation when applied to vision problems: they do not model the distribution of topics along the images. The benefits of this spatial modeling are twofold: first, an improved performance of these models in tasks such as image classification or topic discovery; and second, an enrichment of such models with the capability of generating robust image segmentations. However, modeling the spatial location of visual words under this framework is not longer straightforward since one must ensure that both appearance and spatial models are jointly trained using the same learning algorithm that infers the latent topics. We have proposed two Latent Topic Models, Region-Based Latent Topic Model and Region-Based Latent Dirichlet Allocation that extend basic approaches to model the spatial distribution of topics along images. For that end, previous blind segmentations provide a geometric layout of an image and are included in the model through cooperative distributions that allow regions to influence each other. In addition, our proposals tackle several other aspects in topic models that enhance the image representation. It is worth to mention one contribution that explores the use of advanced appearance models, since it has shown to notably improve the performance in several tasks. In particular, a distribution based on the Kernel Logistic Regression has been proposed that takes into account the nonlinear relations of visual descriptors that lie in the same image region. Our proposals have been evaluated in three important tasks towards the total scene understanding: image classification, category-based image segmentation and unsupervised topic discovery. The obtained results support our developments and compare well with several state-of-the-art algorithms and, even more, with more complex submissions to international challenges in the vision field

Estudio del efecto de los ácidos fenílicos y P-cumarínico obtenidos de Solanum lycopersicum sobre la expresión de interleuquina 4

65 p.Introducción: En los últimos años el consumo de frutas y verduras ha aumentado en la población tanto joven como adulta. El consumo de estos productos tiene efectos beneficiosos sobre todo en la prevención de enfermedades crónicas pero se sabe poco acerca de los beneficios específicos que tienen sobre el sistema inmune. Es aquí donde toman importancia las denominadas Citoquinas las cuales participan en la regulación de la respuesta inmune innata. Una de ellas es la Interleuquina 4 (IL-4) la cual posee propiedades antiinflamatorias e induce a la diferenciación de los Linfocitos B para producir Inmunoglobulinas. En los estudios que han incluido encuestas sobre el consumo de frutas y verduras en distintos grupos de la población chilena, se ha encontrado que éste es muy inferior al recomendado por la OMS, tanto en niños como en adultos, sin diferencias según nivel socioeconómico. En los últimos años los científicos comenzaron a desarrollar un gran interés en el tomate por el efecto beneficioso que parece tener sobre el organismo, y son cada vez más los estudios que parecen confirmar que este vegetal es una fuente inagotable de propiedades preventivas y curativas. Las primeras investigaciones se centraron en las virtudes que parecían tener en la prevención de ciertos cánceres, al mostrar que aquellas personas que lo consumían con frecuencia estaban menos expuestas a cánceres de colon y de próstata. Luego se descubrieron las propiedades antienvejecimiento de una sustancia únicamente presente en el tomate, el licopeno, ya que aquellas con índices mayores de licopeno en la sangre tenían una mayor agilidad a la hora de realizar todo tipo de actividades. Objetivo general: Determinar los efectos inmunoestimulantes de compuestos fenólicos derivados del tomate (Solanum lycopersicum) sobre la expresión de IL-

A Generative Model for Concurrent Image Retrieval and ROI Segmentation

This paper proposes a probabilistic generative model that concurrently tackles the problems of image retrieval and region-of-interest (ROI) segmentation. Specifically, the proposed model takes into account several properties of the matching process between two objects in different images, namely: objects undergoing a geometric transformation, typical spatial location of the region of interest, and visual similarity. In this manner, our approach improves the reliability of detected true matches between any pair of images. Furthermore, by taking advantage of the links to the ROI provided by the true matches, the proposed method is able to perform a suitable ROI segmentation. Finally, the proposed method is able to work when there is more than one ROI in the query image. Our experiments on two challenging image retrieval datasets proved that our approach clearly outperforms the most prevalent approach for geometrically constrained matching and compares favorably to most of the state-of-the-art methods. Furthermore, the proposed technique concurrently provided very good segmentations of the ROI. Furthermore, the capability of the proposed method to take into account several objects-of-interest was also tested on three experiments: two of them concerning image segmentation and object detection in multi-object image retrieval tasks, and another concerning multiview image retrieval. These experiments proved the ability of our approach to handle scenarios in which more than one object of interest is present in the query.This work has been partially supported by the project AFICUS, co-funded by the Spanish Ministry of Industry, Trade and Tourism, and the European Fund for Regional Development, with Ref.: TSI-020110-2009-103, and the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad

A probabilistic topic approach for context-aware visual attention modeling

Proceedings of: 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)The modeling of visual attention has gained much interest during the last few years since it allows to efficiently drive complex visual processes to particular areas of images or video frames. Although the literature concerning bottom-up saliency models is vast, we still lack of generic approaches modeling top-down task and context-driven visual attention. Indeed, many top-down models simply modulate the weights associated to low-level descriptors to learn more accurate representations of visual attention than those ones of the generic fusion schemes in bottom-up techniques. In this paper we propose a hierarchical generic probabilistic framework that decomposes the complex process of context-driven visual attention into a mixture of latent subtasks, each of them being in turn modeled as a combination of specific distributions of low-level descriptors. The inclusion of this intermediate level bridges the gap between low-level features and visual attention and enables more comprehensive representations of the later. Our experiments on a dataset in which videos are organized by genre demonstrate that, by learning specific distributions for each video category, we can notably enhance the system performance

Goal-oriented top-down probabilistic visual attention model for recognition of manipulated objects in egocentric videos

We propose a new top down probabilistic saliency model for egocentric video content. It aims to predict top-down visual attention maps focused on manipulated objects, that are then used for psycho-visual weighting of features in the problem of manipulated object recognition. The model is probabilistically defined using both global and local appearance features extracted from automatically segmented arm areas and objects. A psycho-visual experiment has been conducted in a guided framework that compares our proposal and other popular state-of-the-art models with respect to human gaze fixations. The obtained results show that our approach outperforms several popular bottom-up saliency approaches in a well-known egocentric dataset Furthermore, an additional task-driven assessment for object recognition in egocentric video reveals that the proposed method improves the performance of several state-of-the-art techniques for object detection