Abstract: We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition pipeline, where Dynamic Time Warping (DTW) algorithm is used to perform prior segmentation of gestures. Results of the method in public data sets, within our gesture recognition pipeline, show better performance in comparison to a standard BoVW model.

Abstract: In this thesis we tackle the problem of automatic characterization of human coronary vessel in Intravascular Ultrasound (IVUS) image modality. The basis for the whole characterization process is machine learning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes (ECOC) framework is used as central element for the design of multi-class classifiers.
Two main topics are tackled in this thesis. First, the automatic detection of the vessel borders is presented. For this purpose, a novel context-aware classifier for multi-class classification of the vessel morphology is presented, namely ECOC-DRF. Based on ECOC-DRF, the lumen border and the media-adventitia border in IVUS are robustly detected by means of a novel holistic approach, achieving an error comparable with inter-observer variability and with state of the art methods.
The two vessel borders define the atheroma area of the vessel. In this area, tissue characterization is required. For this purpose, we present a framework for automatic plaque characterization by processing both texture in IVUS images and spectral information in raw Radio Frequency data. Furthermore, a novel method for fusing in-vivo and in-vitro IVUS data for plaque characterization is presented, namely pSFFS. The method demonstrates to effectively fuse data generating a classifier that improves the tissue characterization in both in-vitro and in-vivo datasets.
A novel method for automatic video summarization in IVUS sequences is also presented. The method aims to detect the key frames of the sequence, i.e., the frames representative of morphological changes. This novel method represents the basis for video summarization in IVUS as well as the markers for the partition of the vessel into morphological and clinically interesting events.
Finally, multi-class learning based on ECOC is applied to lung tissue characterization in Computed Tomography. The novel proposed approach, based on supervised and unsupervised learning, achieves accurate tissue classification on a large and heterogeneous dataset.

Abstract: In the recent years commercial eye-tracking hardware has become more common, with the introduction of new models from several brands that have better performance and easier setup procedures. A cause and at the same time a result of this phenomenon is the popularity of eye-tracking research directed at marketing, accessibility and usability, among others.
One problem with these hardware components is scalability, because both the price and the necessary expertise to operate them makes it practically impossible in the large scale. In this work, we analyze the feasibility of a software eye-tracking system based on a single, ordinary webcam. Our aim is to discover the limits of such a system and to see whether it provides acceptable performances.
The significance of this setup is that it is the most common setup found in consumer environments, off-the-shelf electronic devices such as laptops, mobile phones and tablet computers. As no special equipment such as infrared lights, mirrors or zoom lenses are used; setting up and calibrating the system is easier compared to other approaches using these components.
Our work is based on the open source application Opengazer, which provides a good starting point for our contributions. We propose several improvements in order to push the system's performance further and make it feasible as a robust, real-time device. Then we carry out an elaborate experiment involving 18 human subjects and 4 different system setups. Finally, we give an analysis of the results and discuss the effects of setup changes, subject differences and modifications in the software.

Abstract: Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Although striate cortex is traditionally regarded as an area mostly responsive to ensory (i.e. retinal) information,
neurophysiological evidence suggests that perceived brightness information mightbe explicitly represented in V1.
Such evidence has been observed both in anesthetised cats where neuronal response modulations have been found to follow luminance changes outside the receptive felds and in human fMRI measurements. In this work, possible neural mechanisms that ofer a plausible explanation for such phenomenon are investigated. To this end, we consider the model proposed by Z.Li (Li, Network:Comput. Neural Syst., 10 (1999)) which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual infuences, i.e. layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has reproduced other phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant efect of contextual infuences. We have extended the original model such that the input to the network is obtained from a complete multiscale and multiorientation wavelet decomposition, thereby allowing the recovery of an image refecting the perceived intensity. The proposed model successfully accounts for well known psychophysical efects for static contexts (among them: the White's and modifed White's efects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction efects) and also for brigthness induction in dynamic contexts defned by modulating the luminance of surrounding areas (e.g. the brightness of a static central area is perceived to vary in antiphase to the sinusoidal luminance changes of its surroundings). This work thus suggests that intra-cortical interactions in V1 could partially explain perceptual brightness induction efects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual processing pathway.

Abstract: El color y la textura son dos estímulos visuales importantes para la interpretación de las imágenes. La definición de descriptores computacionales que combinan estas dos características es aún un problema abierto. La dificultad se deriva esencialmente de la propia naturaleza de ambas, mientras que la textura es una propiedad de una región, el color es una propiedad de un punto.
Hasta ahora se han utilizado tres los tipos de aproximaciones para la combinación, (a) se describe la textura directamente en cada uno de los canales color, (b) se describen textura y color por separado y se combinan al final, y (c) la combinación se realiza con técnicas de aprendizaje automático. Considerando que este problema se resuelve en el sistema visual humano en niveles muy tempranos, en esta tesis se propone estudiar el problema a partir de la implementación directa de una teoría perceptual, la teoría de los textons, y explorar así su extensión a color.
Puesto que la teoría de los textons se basa en la descripción de la textura a partir de las densidades de los atributos locales, esto se adapta perfectamente al marco de trabajo de los descriptores holísticos (bag-of-words). Se han estudiado diversos descriptores basados en diferentes espacios de textons, y diferentes representaciones de las imágenes. Asimismo se ha estudiado la viabilidad de estos descriptores en una representación conceptual de nivel intermedio.
Los descriptores propuestos han demostrado ser muy eficientes en aplicaciones de recuperación y clasificación de imágenes, presentando ventajas en la generación de vocabularios. Los vocabularios se obtienen cuantificando directamente espacios de baja dimensión y la perceptualidad de estos espacios permite asociar semántica de bajo nivel a las palabras visuales. El estudio de los resultados permite concluir que si bien la aproximación holística es muy eficiente, la introducción de co-ocurrencia espacial de las propiedades de forma y color de los blobs de la imagen es un elemento clave para su combinación, hecho que no contradice las evidencias en percepción

Abstract: The release of challenging datasets with a vast number of images, requires the development of efficient image representations and algorithms which are able to manipulate these large-scale datasets efficiently. Nowadays the Bag-of-Words (BoW) is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based image representation. Observing the remarkable performance of spatial pyramids, their growing number of applications to a broad range of vision problems, and finally its geometry inclusion, a question can be asked what are the limits of spatial pyramids. Within the SP framework, the optimal way for obtaining an image spatial representation, which is able to cope with it’s most foremost shortcomings, concretely, it’s high dimensionality and the rigidity of the resulting image representation, still remains an active research domain. In summary, the main concern of this thesis is to search for the limits of spatial pyramids and try to figure out solutions for them.