Improving Hardware Reuse through XML-based Interface Encapsulation

This work proposes an encapsulation scheme aimed at simplifying the reuse process of hardware cores. This hardware encapsulation approach has been conceived with a twofold objective. First, we look for the improvement of the reuse interface associated with the hardware core description. This is carried out in a first encapsulation level by improving the limited types and configuration options available in the conventional HDLs interface, and also providing information related to the implementation itself. Second, we have devised a more generic interface focused on describing the function avoiding details from a particular implementation, what corresponds to a second encapsulation level. This encapsulation allows the designer to define how to configure and use the design to implement a given functionality. The proposed encapsulation schemes help improving the amount of information that can be supplied with the design, and also allow to automate the process of searching, configuring and implementing diverse alternatives.

Video-based tasks for emotional processing rehabilitation in schizophrenia

Schizophrenia is a mental disorder characterized by a breakdown of cognitive processes and by a deficit of typi-cal emotional responses. Effectiveness of computerized task has been demonstrated in the field of cognitive rehabilitation. However, current rehabilitation programs based on virtual environments normally focus on higher cognitive functions, not covering social cognition training. This paper presents a set of video-based tasks specifically designed for the rehabilita-tion of emotional processing deficits in patients in early stages of schizophrenia or schizoaffective disorders. These tasks are part of the Mental Health program of Guttmann NeuroPer-sonalTrainer® cognitive tele-rehabilitation platform, and entail innovation both from a clinical and technological per-spective in relation with former traditional therapeutic con-tents.

Depth perceptual video coding for free viewpoint video based on H.264/AVC

A novel scheme for depth sequences compression, based on a perceptual coding algorithm, is proposed. A depth sequence describes the object position in the 3D scene, and is used, in Free Viewpoint Video, for the generation of synthetic video sequences. In perceptual video coding the human visual system characteristics are exploited to improve the compression efficiency. As depth sequences are never shown, the perceptual video coding, assessed over them, is not effective. The proposed algorithm is based on a novel perceptual rate distortion optimization process, assessed over the perceptual distortion of the rendered views generated through the encoded depth sequences. The experimental results show the effectiveness of the proposed method, able to obtain a very considerable improvement of the rendered view perceptual quality.

Efficient hybrid monocular-stereo approach to on-board, video-based traffic sign detection and tracking

In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion

Optimized HOG for on-road video based vehicle verification

Vision-based object detection from a moving platform becomes particularly challenging in the field of advanced driver assistance systems (ADAS). In this context, onboard vision-based vehicle verification strategies become critical, facing challenges derived from the variability of vehicles appearance, illumination, and vehicle speed. In this paper, an optimized HOG configuration for onboard vehicle verification is proposed which not only considers its spatial and orientation resolution, but descriptor processing strategies and classification. An in-depth analysis of the optimal settings for HOG for onboard vehicle verification is presented, in the context of SVM classification with different kernels. In contrast to many existing approaches, the evaluation is realized in a public and heterogeneous database of vehicle and non-vehicle images in different areas of the road, rendering excellent verification rates that outperform other similar approaches in the literature.

HOG-Like gradient-based descriptor for visual vehicle detection

One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.

High-quality region-based foreground segmentation using a spatial grid of SVM classifiers

This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms.

Conceptual framework for the design, implementation and validation of psychomotor skills’ assessment systems in minimally invasive surgery

Las técnicas de cirugía de mínima invasión (CMI) se están consolidando hoy en día como alternativa a la cirugía tradicional, debido a sus numerosos beneficios para los pacientes. Este cambio de paradigma implica que los cirujanos deben aprender una serie de habilidades distintas de aquellas requeridas en cirugía abierta. El entrenamiento y evaluación de estas habilidades se ha convertido en una de las mayores preocupaciones en los programas de formación de cirujanos, debido en gran parte a la presión de una sociedad que exige cirujanos bien preparados y una reducción en el número de errores médicos. Por tanto, se está prestando especial atención a la definición de nuevos programas que permitan el entrenamiento y la evaluación de las habilidades psicomotoras en entornos seguros antes de que los nuevos cirujanos puedan operar sobre pacientes reales. Para tal fin, hospitales y centros de formación están gradualmente incorporando instalaciones de entrenamiento donde los residentes puedan practicar y aprender sin riesgos. Es cada vez más común que estos laboratorios dispongan de simuladores virtuales o simuladores físicos capaces de registrar los movimientos del instrumental de cada residente. Estos simuladores ofrecen una gran variedad de tareas de entrenamiento y evaluación, así como la posibilidad de obtener información objetiva de los ejercicios. Los diferentes estudios de validación llevados a cabo dan muestra de su utilidad; pese a todo, los niveles de evidencia presentados son en muchas ocasiones insuficientes. Lo que es más importante, no existe un consenso claro a la hora de definir qué métricas son más útiles para caracterizar la pericia quirúrgica. El objetivo de esta tesis doctoral es diseñar y validar un marco de trabajo conceptual para la definición y validación de entornos para la evaluación de habilidades en CMI, en base a un modelo en tres fases: pedagógica (tareas y métricas a emplear), tecnológica (tecnologías de adquisición de métricas) y analítica (interpretación de la competencia en base a las métricas). Para tal fin, se describe la implementación práctica de un entorno basado en (1) un sistema de seguimiento de instrumental fundamentado en el análisis del vídeo laparoscópico; y (2) la determinación de la pericia en base a métricas de movimiento del instrumental. Para la fase pedagógica se diseñó e implementó un conjunto de tareas para la evaluación de habilidades psicomotoras básicas, así como una serie de métricas de movimiento. La validación de construcción llevada a cabo sobre ellas mostró buenos resultados para tiempo, camino recorrido, profundidad, velocidad media, aceleración media, economía de área y economía de volumen. Adicionalmente, los resultados obtenidos en la validación de apariencia fueron en general positivos en todos los grupos considerados (noveles, residentes, expertos). Para la fase tecnológica, se introdujo el EVA Tracking System, una solución para el seguimiento del instrumental quirúrgico basado en el análisis del vídeo endoscópico. La precisión del sistema se evaluó a 16,33ppRMS para el seguimiento 2D de la herramienta en la imagen; y a 13mmRMS para el seguimiento espacial de la misma. La validación de construcción con una de las tareas de evaluación mostró buenos resultados para tiempo, camino recorrido, profundidad, velocidad media, aceleración media, economía de área y economía de volumen. La validación concurrente con el TrEndo® Tracking System por su parte presentó valores altos de correlación para 8 de las 9 métricas analizadas. Finalmente, para la fase analítica se comparó el comportamiento de tres clasificadores supervisados a la hora de determinar automáticamente la pericia quirúrgica en base a la información de movimiento del instrumental, basados en aproximaciones lineales (análisis lineal discriminante, LDA), no lineales (máquinas de soporte vectorial, SVM) y difusas (sistemas adaptativos de inferencia neurodifusa, ANFIS). Los resultados muestran que en media SVM presenta un comportamiento ligeramente superior: 78,2% frente a los 71% y 71,7% obtenidos por ANFIS y LDA respectivamente. Sin embargo las diferencias estadísticas medidas entre los tres no fueron demostradas significativas. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la definición de sistemas de evaluación de habilidades para cirugía de mínima invasión, a la utilidad del análisis de vídeo como fuente de información y a la importancia de la información de movimiento de instrumental a la hora de caracterizar la pericia quirúrgica. Basándose en estos cimientos, se han de abrir nuevos campos de investigación que contribuyan a la definición de programas de formación estructurados y objetivos, que puedan garantizar la acreditación de cirujanos sobradamente preparados y promocionen la seguridad del paciente en el quirófano. Abstract Minimally invasive surgery (MIS) techniques have become a standard in many surgical sub-specialties, due to their many benefits for patients. However, this shift in paradigm implies that surgeons must acquire a complete different set of skills than those normally attributed to open surgery. Training and assessment of these skills has become a major concern in surgical learning programmes, especially considering the social demand for better-prepared professionals and for the decrease of medical errors. Therefore, much effort is being put in the definition of structured MIS learning programmes, where practice with real patients in the operating room (OR) can be delayed until the resident can attest for a minimum level of psychomotor competence. To this end, skills’ laboratory settings are being introduced in hospitals and training centres where residents may practice and be assessed on their psychomotor skills. Technological advances in the field of tracking technologies and virtual reality (VR) have enabled the creation of new learning systems such as VR simulators or enhanced box trainers. These systems offer a wide range of tasks, as well as the capability of registering objective data on the trainees’ performance. Validation studies give proof of their usefulness; however, levels of evidence reported are in many cases low. More importantly, there is still no clear consensus on topics such as the optimal metrics that must be used to assess competence, the validity of VR simulation, the portability of tracking technologies into real surgeries (for advanced assessment) or the degree to which the skills measured and obtained in laboratory environments transfer to the OR. The purpose of this PhD is to design and validate a conceptual framework for the definition and validation of MIS assessment environments based on a three-pillared model defining three main stages: pedagogical (tasks and metrics to employ), technological (metric acquisition technologies) and analytical (interpretation of competence based on metrics). To this end, a practical implementation of the framework is presented, focused on (1) a video-based tracking system and (2) the determination of surgical competence based on the laparoscopic instruments’ motionrelated data. The pedagogical stage’s results led to the design and implementation of a set of basic tasks for MIS psychomotor skills’ assessment, as well as the definition of motion analysis parameters (MAPs) to measure performance on said tasks. Validation yielded good construct results for parameters such as time, path length, depth, average speed, average acceleration, economy of area and economy of volume. Additionally, face validation results showed positive acceptance on behalf of the experts, residents and novices. For the technological stage the EVA Tracking System is introduced. EVA provides a solution for tracking laparoscopic instruments from the analysis of the monoscopic video image. Accuracy tests for the system are presented, which yielded an average RMSE of 16.33pp for 2D tracking of the instrument on the image and of 13mm for 3D spatial tracking. A validation experiment was conducted using one of the tasks and the most relevant MAPs. Construct validation showed significant differences for time, path length, depth, average speed, average acceleration, economy of area and economy of volume; especially between novices and residents/experts. More importantly, concurrent validation with the TrEndo® Tracking System presented high correlation values (>0.7) for 8 of the 9 MAPs proposed. Finally, the analytical stage allowed comparing the performance of three different supervised classification strategies in the determination of surgical competence based on motion-related information. The three classifiers were based on linear (linear discriminant analysis, LDA), non-linear (support vector machines, SVM) and fuzzy (adaptive neuro fuzzy inference systems, ANFIS) approaches. Results for SVM show slightly better performance than the other two classifiers: on average, accuracy for LDA, SVM and ANFIS was of 71.7%, 78.2% and 71% respectively. However, when confronted, no statistical significance was found between any of the three. Overall, this PhD corroborates the investigated research hypotheses regarding the definition of MIS assessment systems, the use of endoscopic video analysis as the main source of information and the relevance of motion analysis in the determination of surgical competence. New research fields in the training and assessment of MIS surgeons can be proposed based on these foundations, in order to contribute to the definition of structured and objective learning programmes that guarantee the accreditation of well-prepared professionals and the promotion of patient safety in the OR.

AMELIE: Authoring Multimedia-Enhanced Learning Interactive Environment for medical contents

This paper presents the AMELIE Authoring Tool for medical e-learning applications. The tool allows for the creation of enhanced-video based didactic contents, and can be adjusted to any number of platforms and applications. Validation provides preliminary good results on its acceptance and usefulness.

AMELIE: Authoring Multimedia-Enhanced Learning Interactive Environment for e-Health Contents

This paper presents the AMELIE Authoring Tool for e-health applications. AMELIE provides the means for creating video-based contents with a focus on e-learning and telerehabilitation processes. The main core of AMELIE lies in the efficient exploitation of raw multimedia resources, which may be already available at clinical centers or recorded ad hoc for learning purposes by health professionals. Three real use cases scenarios involving different target users are presented: (1) cognitive skills? training of surgeons in minimally invasive surgery (medical professionals), (2) training of informal carers for elderly home assistance and (3) cognitive rehabilitation of patients with acquired brain injury. Preliminary validation in the field of surgery hints at the potential of AMELIE; and its versatility in different medical applications is patent from the use cases described. Regardless, new validation studies are planned in the three main application areas identified in this work.

Region-dependent vehicle classification using PCA features

Video-based vehicle detection is the focus of increasing interest due to its potential towards collision avoidance. In particular, vehicle verification is especially challenging due to the enormous variability of vehicles in size, color, pose, etc. In this paper, a new approach based on supervised learning using Principal Component Analysis (PCA) is proposed that addresses the main limitations of existing methods. Namely, in contrast to classical approaches which train a single classifier regardless of the relative position of the candidate (thus ignoring valuable pose information), a region-dependent analysis is performed by considering four different areas. In addition, a study on the evolution of the classification performance according to the dimensionality of the principal subspace is carried out using PCA features within a SVM-based classification scheme. Indeed, the experiments performed on a publicly available database prove that PCA dimensionality requirements are region-dependent. Hence, in this work, the optimal configuration is adapted to each of them, rendering very good vehicle verification results.

Fast human pose estimation using 3D Zernike descriptors

Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.

ExperNet: An Intelligent Multiagent System for WAN Management

This paper describes ExperNet, an intelligent multi-agent system that was developed under an EU funded project to assist in the management of a large-scale data network. ExperNet assists network operators at various nodes of a WAN to detect and diagnose hardware failures and network traffic problems and suggests the most feasible solution, through a web-based interface. ExperNet is composed by intelligent agents, capable of both local problem solving and social interaction among them for coordinating problem diagnosis and repair. The current network state is captured and maintained by conventional network management and monitoring software components, which have been smoothly integrated into the system through sophisticated information exchange interfaces. For the implementation of the agents, a distributed Prolog system enhanced with networking facilities was developed. The agents’ knowledge base is developed in an extensible and reactive knowledge base system capable of handling multiple types of knowledge representation. ExperNet has been developed, installed and tested successfully in an experimental network zone of Ukraine.

AMELIE: Authoring Multimedia-Enhanced Learning Interactive Environment for medical contents

This paper presents the AMELIE Authoring Tool for medical e-learning applications. The tool allows for the creation of enhanced-video based didactic contents, and can be adjusted to any number of platforms and applications. Validation provides preliminary good results on its acceptance and usefulness.

Caracterización de la agitación local y la respuesta oscilatoria de un buque mediante el uso de técnicas de visión artificial : aplicación al análisis de los umbrales operativos en líneas de atraque y amarre

El desarrollo de actividades de carga y descarga son parte de la esencia de la naturaleza funcional de un puerto, de las cuales derivan en gran medida los ingresos del mismo y la eficiencia de la cadena logística en su conjunto. Las oscilaciones en el interior de una dársena y en un línea de atraque disminuyen la calidad de la estancia de las embarcaciones en puerto, reducen el rendimiento de la estiba de los buques y solicitan y fatigan las estructuras y los cuerpos flotantes amarrados. Si los parámetros que definen la agitación local se aproximan a regiones de fallo 0 parada, el subsistema pierde rendimiento, fiabilidad y finalmente se paralizan las operaciones, produciéndose de este modo tiempos de inactividad. Estas paradas operativas conllevan pérdidas económicas para la terminal y, consecuentemente, para el puerto. Hoy día se dispone vastas redes de monitorización destinadas a la caracterización del medio físico en el entorno de los puertos. Paralelamente, las operaciones de manipulación de cargas en las terminales se están dirigiendo hacia modelos de automatización o semi automatización, que permiten no sólo la sistematización de procesos, sino también un profundo conocimiento del flujo de tareas. En este contexto hay un déficit de información sobre cómo afectan los diferentes forzadores del medio físico al rendimiento, la seguridad funcionalidad del proceso de manipulación de carga y descarga. Esto se debe en gran medida a la falta de registros dilatados en el tiempo que permitan correlacionar todos los aspectos mencionados de un modo particularizado para cada línea de atraque y amarre de un puerto. En esta tesis se desarrolla una metodología de vídeo monitorización no intrusiva y de bajo coste basada en la aplicación de técnicas "pixel tool' y la obtención de los parámetros extrínsecos de una observación monofocal. Con ello pretende poner en valor las infraestructuras de vídeo vigilancia de los puertos y de los laboratorios de experimentación a escala reducida, con el objeto de facilitar el estudio los umbrales operativos de las áreas de atraque y amarre. The development of loading and unloading activities is an essential part of he functional nature of a port, which derive largely from he same income and the efficiency of he supply chain as a whole. The oscillations inside a dock and a mooring line diminish he quality of the stay of vessels in port reducing the performance of the stowage of ship and asking and fatigued structures and moored floating bodies. If the parameters defining the local al agitation regions are close to areas of failure or shutdown, he subsystem looses performance, reliability and eventually paralyzes the operations, thereby producing downtime. These operational stops entail economic 1osses to the terminal and, consequently for the port. Today vast networks of monitoring, aimed at he characterization of the physical environment in the vicinity of he ports, are available. In parallel, the cargo handling operations at terminals are moving towards automation or semi-automation models that allow not only the systematization of processes, but also a deep understanding of he workflow. In this context, there is a lack of information about how the different forcing agents of the physical environment affect the performance and he functional safety of the loading and unloading process. This is due largely to the lack of spread-over-time records which would allow to correlate all aspects mentioned, specifically, for each berthing and mooring of a port. This thesis develops a methodology for non-intrusive and low cost monitoring video based on the application of "pixel tool" techniques and on obtaining the extrinsic parameters of a monofocal observation. It seeks an enhancement of the video monitoring infrastructure at ports and at experimental laboratories of reduced scale, in order to facilitate the study of operational thresholds berthing and mooring areas.