7 resultados para visual methods

em Universidad Politécnica de Madrid


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Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

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Non-destructive, visual evaluation and mechanical testing techniques were used to assess the structural properties of 374 samples of chestnut (Castanea sativa). The principal components method was applied to establish and interpret correlations between variables obtained of modulus of elasticity, bending strength and density. The static modulus of elasticity presented higher correlation values than those obtained using non-destructive methods. Bending strength presented low correlations with the non-destructive parameters, but there was some relation to the different knot ratios defined. The relationship was stronger with the most widely used ratio, CKDR. No significant correlations were observed between any of the variables and density.

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In this paper, we seek to expand the use of direct methods in real-time applications by proposing a vision-based strategy for pose estimation of aerial vehicles. The vast majority of approaches make use of features to estimate motion. Conversely, the strategy we propose is based on a MR (Multi-Resolution) implementation of an image registration technique (Inverse Compositional Image Alignment ICIA) using direct methods. An on-board camera in a downwards-looking configuration, and the assumption of planar scenes, are the bases of the algorithm. The motion between frames (rotation and translation) is recovered by decomposing the frame-to-frame homography obtained by the ICIA algorithm applied to a patch that covers around the 80% of the image. When the visual estimation is required (e.g. GPS drop-out), this motion is integrated with the previous known estimation of the vehicles' state, obtained from the on-board sensors (GPS/IMU), and the subsequent estimations are based only on the vision-based motion estimations. The proposed strategy is tested with real flight data in representative stages of a flight: cruise, landing, and take-off, being two of those stages considered critical: take-off and landing. The performance of the pose estimation strategy is analyzed by comparing it with the GPS/IMU estimations. Results show correlation between the visual estimation obtained with the MR-ICIA and the GPS/IMU data, that demonstrate that the visual estimation can be used to provide a good approximation of the vehicle's state when it is required (e.g. GPS drop-outs). In terms of performance, the proposed strategy is able to maintain an estimation of the vehicle's state for more than one minute, at real-time frame rates based, only on visual information.

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Virtual reality (VR) techniques to understand and obtain conclusions of data in an easy way are being used by the scientific community. However, these techniques are not used frequently for analyzing large amounts of data in life sciences, particularly in genomics, due to the high complexity of data (curse of dimensionality). Nevertheless, new approaches that allow to bring out the real important data characteristics, arise the possibility of constructing VR spaces to visually understand the intrinsic nature of data. It is well known the benefits of representing high dimensional data in tridimensional spaces by means of dimensionality reduction and transformation techniques, complemented with a strong component of interaction methods. Thus, a novel framework, designed for helping to visualize and interact with data about diseases, is presented. In this paper, the framework is applied to the Van't Veer breast cancer dataset is used, while oncologists from La Paz Hospital (Madrid) are interacting with the obtained results. That is to say a first attempt to generate a visually tangible model of breast cancer disease in order to support the experience of oncologists is presented.

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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%.

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In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.

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La iluminación con diodos emisores de luz (LED) está reemplazando cada vez en mayor medida a las fuentes de luz tradicionales. La iluminación LED ofrece ventajas en eficiencia, consumo de energía, diseño, tamaño y calidad de la luz. Durante más de 50 años, los investigadores han estado trabajando en mejoras LED. Su principal relevancia para la iluminación está aumentando rápidamente. Esta tesis se centra en un campo de aplicación importante, como son los focos. Se utilizan para enfocar la luz en áreas definidas, en objetos sobresalientes en condiciones profesionales. Esta iluminación de alto rendimiento requiere una calidad de luz definida, que incluya temperaturas ajustables de color correlacionadas (CCT), de alto índice de reproducción cromática (CRI), altas eficiencias, y colores vivos y brillantes. En el paquete LED varios chips de diferentes colores (rojo, azul, fósforo convertido) se combinan para cumplir con la distribución de energía espectral con alto CRI. Para colimar la luz en los puntos concretos deseados con un ángulo de emisión determinado, se utilizan blancos sintonizables y diversos colores de luz y ópticas secundarias. La combinación de una fuente LED de varios colores con elementos ópticos puede causar falta de homogeneidad cromática en la distribución espacial y angular de la luz, que debe resolverse en el diseño óptico. Sin embargo, no hay necesidad de uniformidad perfecta en el punto de luz debido al umbral en la percepción visual del ojo humano. Por lo tanto, se requiere una descripción matemática del nivel de uniformidad del color con respecto a la percepción visual. Esta tesis está organizada en siete capítulos. Después de un capítulo inicial que presenta la motivación que ha guiado la investigación de esta tesis, en el capítulo 2 se presentan los fundamentos científicos de la uniformidad del color en luces concentradas, como son: el espacio de color aplicado CIELAB, la percepción visual del color, los fundamentos de diseño de focos respecto a los motores de luz y ópticas no formadoras de imágenes, y los últimos avances en la evaluación de la uniformidad del color en el campo de los focos. El capítulo 3 desarrolla diferentes métodos para la descripción matemática de la distribución espacial del color en un área definida, como son la diferencia de color máxima, la desviación media del color, el gradiente de la distribución espacial de color, así como la suavidad radial y axial. Cada función se refiere a los diferentes factores que influyen en la visión, los cuales necesitan un tratamiento distinto que el de los datos que se tendrán en cuenta, además de funciones de ponderación que pre- y post-procesan los datos simulados o medidos para la reducción del ruido, la luminancia de corte, la aplicación de la ponderación de luminancia, la función de sensibilidad de contraste, y la función de distribución acumulativa. En el capítulo 4, se obtiene la función de mérito Usl para la estimación de la uniformidad del color percibida en focos. Se basó en los resultados de dos conjuntos de experimentos con factor humano realizados para evaluar la percepción visual de los sujetos de los patrones de focos típicos. El primer experimento con factor humano dio lugar al orden de importancia percibida de los focos. El orden de rango percibido se utilizó para correlacionar las descripciones matemáticas de las funciones básicas y la función ponderada sobre la distribución espacial del color, que condujo a la función Usl. El segundo experimento con factor humano probó la percepción de los focos bajo condiciones ambientales diversas, con el objetivo de proporcionar una escala absoluta para Usl, para poder así sustituir la opinión subjetiva personal de los individuos por una función de mérito estandarizada. La validación de la función Usl se presenta en relación con el alcance de la aplicación y condiciones, así como las limitaciones y restricciones que se realizan en el capítulo 5. Se compararon los datos medidos y simulados de varios sistemas ópticos. Se discuten los campos de aplicación , así como validaciones y restricciones de la función. El capítulo 6 presenta el diseño del sistema de focos y su optimización. Una evaluación muestra el análisis de sistemas basados en el reflector y la lente TIR. Los sistemas ópticos simulados se comparan en la uniformidad del color Usl, sensibilidad a las sombras coloreadas, eficiencia e intensidad luminosa máxima. Se ha comprobado que no hay un sistema único que obtenga los mejores resultados en todas las categorías, y que una excelente uniformidad de color se pudo alcanzar por la conjunción de dos sistemas diferentes. Finalmente, el capítulo 7 presenta el resumen de esta tesis y la perspectiva para investigar otros aspectos. ABSTRACT Illumination with light-emitting diodes (LED) is more and more replacing traditional light sources. They provide advantages in efficiency, energy consumption, design, size and light quality. For more than 50 years, researchers have been working on LED improvements. Their main relevance for illumination is rapidly increasing. This thesis is focused on one important field of application which are spotlights. They are used to focus light on defined areas, outstanding objects in professional conditions. This high performance illumination required a defined light quality including tunable correlated color temperatures (CCT), high color rendering index (CRI), high efficiencies and bright, vivid colors. Several differently colored chips (red, blue, phosphor converted) in the LED package are combined to meet spectral power distribution with high CRI, tunable white and several light colors and secondary optics are used to collimate the light into the desired narrow spots with defined angle of emission. The combination of multi-color LED source and optical elements may cause chromatic inhomogeneities in spatial and angular light distribution which needs to solved at the optical design. However, there is no need for perfect uniformity in the spot light due to threshold in visual perception of human eye. Therefore, a mathematical description of color uniformity level with regard to visual perception is required. This thesis is organized seven seven chapters. After an initial one presenting the motivation that has guided the research of this thesis, Chapter 2 introduces the scientific basics of color uniformity in spot lights including: the applied color space CIELAB, the visual color perception, the spotlight design fundamentals with regards to light engines and nonimaging optics, and the state of the art for the evaluation of color uniformity in the far field of spotlights. Chapter 3 develops different methods for mathematical description of spatial color distribution in a defined area, which are the maximum color difference, the average color deviation, the gradient of spatial color distribution as well as the radial and axial smoothness. Each function refers to different visual influencing factors, and they need different handling of data be taken into account, along with weighting functions which pre- and post-process the simulated or measured data for noise reduction, luminance cutoff, the implementation of luminance weighting, contrast sensitivity function, and cumulative distribution function. In chapter 4, the merit function Usl for the estimation of the perceived color uniformity in spotlights is derived. It was based on the results of two sets of human factor experiments performed to evaluate the visual perception of typical spotlight patterns by subjects. The first human factor experiment resulted in the perceived rank order of the spotlights. The perceived rank order was used to correlate the mathematical descriptions of basic functions and weighted function concerning the spatial color distribution, which lead to the Usl function. The second human factor experiment tested the perception of spotlights under varied environmental conditions, with to objective to provide an absolute scale for Usl, so the subjective personal opinion of individuals could be replaced by a standardized merit function. The validation of the Usl function is presented concerning the application range and conditions as well as limitations and restrictions in carried out in chapter 5. Measured and simulated data of various optical several systems were compared. Fields of applications are discussed as well as validations and restrictions of the function. Chapter 6 presents spotlight system design and their optimization. An evaluation shows the analysis of reflector-based and TIR lens systems. The simulated optical systems are compared in color uniformity Usl , sensitivity to colored shadows, efficiency, and peak luminous intensity. It has been found that no single system which performed best in all categories, and that excellent color uniformity could be reached by two different system assemblies. Finally, chapter 7 summarizes the conclusions of the present thesis and an outlook for further investigation topics.