Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results.

Toward reduction of artifacts in fused images

Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e.artifacts, in the resulting fusedimages. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fusedimages and their classification results when compared with the original WAT method

Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection

Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier

Efficient methodologies for system matrix modelling in iterative image reconstruction for rotating high-resolution PET

A fully 3D iterative image reconstruction algorithm has been developed for high-resolution PET cameras composed of pixelated scintillator crystal arrays and rotating planar detectors, based on the ordered subsets approach. The associated system matrix is precalculated with Monte Carlo methods that incorporate physical effects not included in analytical models, such as positron range effects and interaction of the incident gammas with the scintillator material. Custom Monte Carlo methodologies have been developed and optimized for modelling of system matrices for fast iterative image reconstruction adapted to specific scanner geometries, without redundant calculations. According to the methodology proposed here, only one-eighth of the voxels within two central transaxial slices need to be modelled in detail. The rest of the system matrix elements can be obtained with the aid of axial symmetries and redundancies, as well as in-plane symmetries within transaxial slices. Sparse matrix techniques for the non-zero system matrix elements are employed, allowing for fast execution of the image reconstruction process. This 3D image reconstruction scheme has been compared in terms of image quality to a 2D fast implementation of the OSEM algorithm combined with Fourier rebinning approaches. This work confirms the superiority of fully 3D OSEM in terms of spatial resolution, contrast recovery and noise reduction as compared to conventional 2D approaches based on rebinning schemes. At the same time it demonstrates that fully 3D methodologies can be efficiently applied to the image reconstruction problem for high-resolution rotational PET cameras by applying accurate pre-calculated system models and taking advantage of the system's symmetries.

Characterization of vineyard's canopy through fuzzy clustering and svm over color images

In this work we propose an image acquisition and processing methodology (framework) developed for performance in-field grapes and leaves detection and quantification, based on a six step methodology: 1) image segmentation through Fuzzy C-Means with Gustafson Kessel (FCM-GK) clustering; 2) obtaining of FCM-GK outputs (centroids) for acting as seeding for K-Means clustering; 3) Identification of the clusters generated by K-Means using a Support Vector Machine (SVM) classifier. 4) Performance of morphological operations over the grapes and leaves clusters in order to fill holes and to eliminate small pixels clusters; 5)Creation of a mosaic image by Scale-Invariant Feature Transform (SIFT) in order to avoid overlapping between images; 6) Calculation of the areas of leaves and grapes and finding of the centroids in the grape bunches. Image data are collected using a colour camera fixed to a mobile platform. This platform was developed to give a stabilized surface to guarantee that the images were acquired parallel to de vineyard rows. In this way, the platform avoids the distortion of the images that lead to poor estimation of the areas. Our preliminary results are promissory, although they still have shown that it is necessary to implement a camera stabilization system to avoid undesired camera movements, and also a parallel processing procedure in order to speed up the mosaicking process.

Análisis y clasificación de maderas mediante técnicas no invasivas de baja energía

El estudio de materiales, especialmente biológicos, por medios no destructivos está adquiriendo una importancia creciente tanto en las aplicaciones científicas como industriales. Las ventajas económicas de los métodos no destructivos son múltiples. Existen numerosos procedimientos físicos capaces de extraer información detallada de las superficie de la madera con escaso o nulo tratamiento previo y mínima intrusión en el material. Entre los diversos métodos destacan las técnicas ópticas y las acústicas por su gran versatilidad, relativa sencillez y bajo coste. Esta tesis pretende establecer desde la aplicación de principios simples de física, de medición directa y superficial, a través del desarrollo de los algoritmos de decisión mas adecuados basados en la estadística, unas soluciones tecnológicas simples y en esencia, de coste mínimo, para su posible aplicación en la determinación de la especie y los defectos superficiales de la madera de cada muestra tratando, en la medida de lo posible, no alterar su geometría de trabajo. Los análisis desarrollados han sido los tres siguientes: El primer método óptico utiliza las propiedades de la luz dispersada por la superficie de la madera cuando es iluminada por un laser difuso. Esta dispersión produce un moteado luminoso (speckle) cuyas propiedades estadísticas permiten extraer propiedades muy precisas de la estructura tanto microscópica como macroscópica de la madera. El análisis de las propiedades espectrales de la luz laser dispersada genera ciertos patrones mas o menos regulares relacionados con la estructura anatómica, composición, procesado y textura superficial de la madera bajo estudio que ponen de manifiesto características del material o de la calidad de los procesos a los que ha sido sometido. El uso de este tipo de láseres implica también la posibilidad de realizar monitorizaciones de procesos industriales en tiempo real y a distancia sin interferir con otros sensores. La segunda técnica óptica que emplearemos hace uso del estudio estadístico y matemático de las propiedades de las imágenes digitales obtenidas de la superficie de la madera a través de un sistema de scanner de alta resolución. Después de aislar los detalles mas relevantes de las imágenes, diversos algoritmos de clasificacion automatica se encargan de generar bases de datos con las diversas especies de maderas a las que pertenecían las imágenes, junto con los márgenes de error de tales clasificaciones. Una parte fundamental de las herramientas de clasificacion se basa en el estudio preciso de las bandas de color de las diversas maderas. Finalmente, numerosas técnicas acústicas, tales como el análisis de pulsos por impacto acústico, permiten complementar y afinar los resultados obtenidos con los métodos ópticos descritos, identificando estructuras superficiales y profundas en la madera así como patologías o deformaciones, aspectos de especial utilidad en usos de la madera en estructuras. La utilidad de estas técnicas esta mas que demostrada en el campo industrial aun cuando su aplicación carece de la suficiente expansión debido a sus altos costes y falta de normalización de los procesos, lo cual hace que cada análisis no sea comparable con su teórico equivalente de mercado. En la actualidad gran parte de los esfuerzos de investigación tienden a dar por supuesto que la diferenciación entre especies es un mecanismo de reconocimiento propio del ser humano y concentran las tecnologías en la definición de parámetros físicos (módulos de elasticidad, conductividad eléctrica o acústica, etc.), utilizando aparatos muy costosos y en muchos casos complejos en su aplicación de campo. Abstract The study of materials, especially the biological ones, by non-destructive techniques is becoming increasingly important in both scientific and industrial applications. The economic advantages of non-destructive methods are multiple and clear due to the related costs and resources necessaries. There are many physical processes capable of extracting detailed information on the wood surface with little or no previous treatment and minimal intrusion into the material. Among the various methods stand out acoustic and optical techniques for their great versatility, relative simplicity and low cost. This thesis aims to establish from the application of simple principles of physics, surface direct measurement and through the development of the more appropriate decision algorithms based on statistics, a simple technological solutions with the minimum cost for possible application in determining the species and the wood surface defects of each sample. Looking for a reasonable accuracy without altering their work-location or properties is the main objetive. There are three different work lines: Empirical characterization of wood surfaces by means of iterative autocorrelation of laser speckle patterns: A simple and inexpensive method for the qualitative characterization of wood surfaces is presented. it is based on the iterative autocorrelation of laser speckle patterns produced by diffuse laser illumination of the wood surfaces. The method exploits the high spatial frequency content of speckle images. A similar approach with raw conventional photographs taken with ordinary light would be very difficult. A few iterations of the algorithm are necessary, typically three or four, in order to visualize the most important periodic features of the surface. The processed patterns help in the study of surface parameters, to design new scattering models and to classify the wood species. Fractal-based image enhancement techniques inspired by differential interference contrast microscopy: Differential interference contrast microscopy is a very powerful optical technique for microscopic imaging. Inspired by the physics of this type of microscope, we have developed a series of image processing algorithms aimed at the magnification, noise reduction, contrast enhancement and tissue analysis of biological samples. These algorithms use fractal convolution schemes which provide fast and accurate results with a performance comparable to the best present image enhancement algorithms. These techniques can be used as post processing tools for advanced microscopy or as a means to improve the performance of less expensive visualization instruments. Several examples of the use of these algorithms to visualize microscopic images of raw pine wood samples with a simple desktop scanner are provided. Wood species identification using stress-wave analysis in the audible range: Stress-wave analysis is a powerful and flexible technique to study mechanical properties of many materials. We present a simple technique to obtain information about the species of wood samples using stress-wave sounds in the audible range generated by collision with a small pendulum. Stress-wave analysis has been used for flaw detection and quality control for decades, but its use for material identification and classification is less cited in the literature. Accurate wood species identification is a time consuming task for highly trained human experts. For this reason, the development of cost effective techniques for automatic wood classification is a desirable goal. Our proposed approach is fully non-invasive and non-destructive, reducing significantly the cost and complexity of the identification and classification process.

Advanced Fresnel Köhler concentrators for photovoltaic applications

La concentración fotovoltaica (CPV) es una de las formas más prometedoras de reducir el coste de la energía proveniente del sol. Esto es posible gracias a células solares de alta eficiencia y a una significativa reducción del tamaño de la misma, que está fabricada con costosos materiales semiconductores. Ambos aspectos están íntimamente ligados ya que las altas eficiencias solamente son posibles con materiales y tecnologías de célula caros, lo que forzosamente conlleva una reducción del tamaño de la célula si se quiere lograr un sistema rentable. La reducción en el tamaño de las células requiere que la luz proveniente del sol ha de ser redirigida (es decir, concentrada) hacia la posición de la célula. Esto se logra colocando un concentrador óptico encima de la célula. Estos concentradores para CPV están formados por diferentes elementos ópticos fabricados en materiales baratos, con el fin de reducir los costes de producción. El marco óptimo para el diseño de concentradores es la óptica anidólica u óptica nonimaging. La óptica nonimaging fue desarrollada por primera vez en la década de los años sesenta y ha ido evolucionando significativamente desde entonces. El objetivo de los diseños nonimaging es la transferencia eficiente de energía entre la fuente y el receptor (sol y célula respectivamente, en el caso de la CPV), sin tener en cuenta la formación de imagen. Los sistemas nonimaging suelen ser simples, están compuestos de un menor número de superficies que los sistemas formadores de imagen y son más tolerantes a errores de fabricación. Esto hace de los sistemas nonimaging una herramienta fundamental, no sólo en el diseño de concentradores fotovoltaicos, sino también en el diseño de otras aplicaciones como iluminación, proyección y comunicaciones inalámbricas ópticas. Los concentradores ópticos nonimaging son adecuados para aplicaciones CPV porque el objetivo no es la reproducción de una imagen exacta del sol (como sería el caso de las ópticas formadoras de imagen), sino simplemente la colección de su energía sobre la célula solar. Los concentradores para CPV pueden presentar muy diferentes arquitecturas y elementos ópticos, dando lugar a una gran variedad de posibles diseños. El primer elemento óptico que es atravesado por la luz del sol se llama Elemento Óptico Primario (POE en su nomenclatura anglosajona) y es el elemento más determinante a la hora de definir la forma y las propiedades del concentrador. El POE puede ser refractivo (lente) o reflexivo (espejo). Esta tesis se centra en los sistemas CPV que presentan lentes de Fresnel como POE, que son lentes refractivas delgadas y de bajo coste de producción que son capaces de concentrar la luz solar. El capítulo 1 expone una breve introducción a la óptica geométrica y no formadora de imagen (nonimaging), explicando sus fundamentos y conceptos básicos. Tras ello, la integración Köhler es presentada en detalle, explicando sus principios, válidos tanto para aplicaciones CPV como para iluminación. Una introducción a los conceptos fundamentales de CPV también ha sido incluida en este capítulo, donde se analizan las propiedades de las células solares multiunión y de los concentradores ópticos empleados en los sistemas CPV. El capítulo se cierra con una descripción de las tecnologías existentes empleadas para la fabricación de elementos ópticos que componen los concentradores. El capítulo 2 se centra principalmente en el diseño y desarrollo de los tres concentradores ópticos avanzados Fresnel Köhler que se presentan en esta tesis: Fresnel-Köhler (FK), Fresnel-Köhler curvo (DFK) y Fresnel-Köhler con cavidad (CFK). Todos ellos llevan a cabo integración Köhler y presentan una lente de Fresnel como su elemento óptico primario. Cada uno de estos concentradores CPV presenta sus propias propiedades y su propio procedimiento de diseño. Además, presentan todas las características que todo concentrador ha de tener: elevado factor de concentración, alta tolerancia de fabricación, alta eficiencia óptica, irradiancia uniforme sobre la superficie de la célula y bajo coste de producción. Los concentradores FK y DFK presentan una configuración de cuatro sectores para lograr la integración Köhler. Esto quiere decir que POE y SOE se dividen en cuatro sectores simétricos cada uno, y cada sector del POE trabaja conjuntamente con su correspondiente sector de SOE. La principal diferencia entre los dos concentradores es que el POE del FK es una lente de Fresnel plana, mientras que una lente curva de Fresnel es empleada como POE del DFK. El concentrador CFK incluye una cavidad de confinamiento externo integrada, que es un elemento óptico capaz de recuperar los rayos reflejados por la superficie de la célula con el fin de ser reabsorbidos por la misma. Por tanto, se aumenta la absorción de la luz, lo que implica un aumento en la eficiencia del módulo. Además, este capítulo también explica un método de diseño alternativo para los elementos faceteados, especialmente adecuado para las lentes curvas como el POE del DFK. El capítulo 3 se centra en la caracterización y medidas experimentales de los concentradores ópticos presentados en el capítulo 2, y describe sus procedimientos. Estos procedimientos son en general aplicables a cualquier concentrador basado en una lente de Fresnel, e incluyen tres tipos principales de medidas experimentales: eficiencia eléctrica, ángulo de aceptancia y uniformidad de la irradiancia en el plano de la célula. Los resultados que se muestran a lo largo de este capítulo validarán a través de medidas a sol real las características avanzadas que presentan los concentradores Köhler, y que se demuestran en el capítulo 2 mediante simulaciones de rayos. Cada concentrador (FK, DFK y CFK) está diseñado y optimizado teniendo en cuenta condiciones de operación realistas. Su rendimiento se modela de forma exhaustiva mediante el trazado de rayos en combinación con modelos distribuidos para la célula. La tolerancia es un asunto crítico de cara al proceso de fabricación, y ha de ser máxima para obtener sistemas de producción en masa rentables. Concentradores con tolerancias limitadas generan bajadas significativas de eficiencia a nivel de array, causadas por el desajuste de corrientes entre los diferentes módulos (principalmente debido a errores de alineación en la fabricación). En este sentido, la sección 3.5 presenta dos métodos matemáticos que estiman estas pérdidas por desajuste a nivel de array mediante un análisis de sus curvas I-V, y por tanto siendo innecesarias las medidas a nivel de mono-módulo. El capítulo 3 también describe la caracterización indoor de los elementos ópticos que componen los concentradores, es decir, de las lentes de Fresnel que actúan como POE y de los secundarios free-form. El objetivo de esta caracterización es el de evaluar los adecuados perfiles de las superficies y las transmisiones ópticas de los diferentes elementos analizados, y así hacer que el rendimiento del módulo sea el esperado. Esta tesis la cierra el capítulo 4, en el que la integración Köhler se presenta como una buena alternativa para obtener distribuciones uniformes en aplicaciones de iluminación de estado sólido (iluminación con LED), siendo particularmente eficaz cuando se requiere adicionalmente una buena mezcla de colores. En este capítulo esto se muestra a través del ejemplo particular de un concentrador DFK, el cual se ha utilizado para aplicaciones CPV en los capítulos anteriores. Otra alternativa para lograr mezclas cromáticas apropiadas está basada en un método ya conocido (deflexiones anómalas), y también se ha utilizado aquí para diseñar una lente TIR aplanética delgada. Esta lente cumple la conservación de étendue, asegurando así que no hay bloqueo ni dilución de luz simultáneamente. Ambos enfoques presentan claras ventajas sobre las técnicas clásicas empleadas en iluminación para obtener distribuciones de iluminación uniforme: difusores y mezcla caleidoscópica mediante guías de luz. ABSTRACT Concentrating Photovoltaics (CPV) is one of the most promising ways of reducing the cost of energy collected from the sun. This is possible thanks to both, very high-efficiency solar cells and a large decrease in the size of cells, which are made of costly semiconductor materials. Both issues are closely linked since high efficiency values are only possible with expensive cell materials and technologies, implying a compulsory area reduction if cost-effectiveness is desired. The reduction in the cell size requires that light coming from the sun must be redirected (i.e. concentrated) towards the cell position. This is achieved by placing an optical concentrator system on top of the cell. These CPV concentrators consist of different optical elements manufactured on cheap materials in order to maintain low production costs. The optimal framework for the design of concentrators is nonimaging optics. Nonimaging optics was first developed in the 60s decade and has been largely developed ever since. The aim of nonimaging devices is the efficient transfer of light power between the source and the receiver (sun and cell respectively in the case of CPV), disregarding image formation. Nonimaging systems are usually simple, comprised of fewer surfaces than imaging systems and are more tolerant to manufacturing errors. This renders nonimaging optics a fundamental tool, not only in the design of photovoltaic concentrators, but also in the design of other applications as illumination, projection and wireless optical communications. Nonimaging optical concentrators are well suited for CPV applications because the goal is not the reproduction of an exact image of the sun (as imaging optics would provide), but simply the collection of its energy on the solar cell. Concentrators for CPV may present very different architectures and optical elements, resulting in a vast variety of possible designs. The first optical element that sunlight goes through is called the Primary Optical Element (POE) and is the most determinant element in order to define the shape and properties of the whole concentrator. The POE can be either refractive (lens) or reflective (mirror). This thesis focuses on CPV systems based on Fresnel lenses as POE, which are thin and inexpensive refractive lenses able to concentrate sunlight. Chapter 1 exposes a short introduction to geometrical and nonimaging optics, explaining their fundamentals and basic concepts. Then, the Köhler integration is presented in detail, explaining its principles, valid for both applications: CPV and illumination. An introduction to CPV fundamental concepts is also included in this chapter, analyzing the properties of multijunction solar cells and optical concentrators employed in CPV systems. The chapter is closed with a description of the existing technologies employed for the manufacture of optical elements composing the concentrator. Chapter 2 is mainly devoted to the design and development of the three advanced Fresnel Köhler optical concentrators presented in this thesis work: Fresnel-Köhler (FK), Dome-shaped Fresnel-Köhler (DFK) and Cavity Fresnel-Köhler (CFK). They all perform Köhler integration and comprise a Fresnel lens as their Primary Optical Element. Each one of these CPV concentrators presents its own characteristics, properties and its own design procedure. Their performances include all the key issues in a concentrator: high concentration factor, large tolerances, high optical efficiency, uniform irradiance on the cell surface and low production cost. The FK and DFK concentrators present a 4-fold configuration in order to perform the Köhler integration. This means that POE and SOE are divided into four symmetric sectors each one, working each POE sector with its corresponding SOE sector by pairs. The main difference between both concentrators is that the POE of the FK is a flat Fresnel lens, while a dome-shaped (curved) Fresnel lens performs as the DFK’s POE. The CFK concentrator includes an integrated external confinement cavity, which is an optical element able to recover rays reflected by the cell surface in order to be re-absorbed by the cell. It increases the light absorption, entailing an increase in the efficiency of the module. Additionally, an alternative design method for faceted elements will also be explained, especially suitable for dome-shaped lenses as the POE of the DFK. Chapter 3 focuses on the characterization and experimental measurements of the optical concentrators presented in Chapter 2, describing their procedures. These procedures are in general applicable to any Fresnel-based concentrator as well and include three main types of experimental measurements: electrical efficiency, acceptance angle and irradiance uniformity at the solar cell plane. The results shown along this chapter will validate through outdoor measurements under real sun operation the advanced characteristics presented by the Köhler concentrators, which are demonstrated in Chapter 2 through raytrace simulation: high optical efficiency, large acceptance angle, insensitivity to manufacturing tolerances and very good irradiance uniformity on the cell surface. Each concentrator (FK, DFK and CFK) is designed and optimized looking at realistic performance characteristics. Their performances are modeled exhaustively using ray tracing combined with cell modeling, taking into account the major relevant factors. The tolerance is a critical issue when coming to the manufacturing process in order to obtain cost-effective mass-production systems. Concentrators with tight tolerances result in significant efficiency drops at array level caused by current mismatch among different modules (mainly due to manufacturing alignment errors). In this sense, Section 3.5 presents two mathematical methods that estimate these mismatch losses for a given array just by analyzing its full-array I-V curve, hence being unnecessary any single mono-module measurement. Chapter 3 also describes the indoor characterization of the optical elements composing the concentrators, i.e. the Fresnel lenses acting as POEs and the free-form SOEs. The aim of this characterization is to assess the proper surface profiles and optical transmissions of the different elements analyzed, so they will allow for the expected module performance. This thesis is closed by Chapter 4, in which Köhler integration is presented as a good approach to obtain uniform distributions in Solid State Lighting applications (i.e. illumination with LEDs), being particularly effective when dealing with color mixing requirements. This chapter shows it through the particular example of a DFK concentrator, which has been used for CPV applications in the previous chapters. An alternative known method for color mixing purposes (anomalous deflections) has also been used to design a thin aplanatic TIR lens. This lens fulfills conservation of étendue, thus ensuring no light blocking and no light dilution at the same time. Both approaches present clear advantages over the classical techniques employed in lighting to obtain uniform illumination distributions: diffusers and kaleidoscopic lightpipe mixing.

Reduction of 4-ethylphenol contents in red wines using HCDC+ yeasts and cinnamyl esterases

In this work we have studied the combined effect of using cinnamyl esterases (CE) and HCDC+ yeast strains of S. cerevisiae to transform TE-HCAs into vinylphenols to condense them with grape anthocyanins forming VPAs. These VPAs improve the color stability of the wine and at the same time remove EP precursors of the wine.

Accurate depth-color scene modeling for 3D contents generation with low cost depth cameras

In this paper, we present a depth-color scene modeling strategy for indoors 3D contents generation. It combines depth and visual information provided by a low-cost active depth camera to improve the accuracy of the acquired depth maps considering the different dynamic nature of the scene elements. Accurate depth and color models of the scene background are iteratively built, and used to detect moving elements in the scene. The acquired depth data is continuously processed with an innovative joint-bilateral filter that efficiently combines depth and visual information thanks to the analysis of an edge-uncertainty map and the detected foreground regions. The main advantages of the proposed approach are: removing depth maps spatial noise and temporal random fluctuations; refining depth data at object boundaries, generating iteratively a robust depth and color background model and an accurate moving object silhouette.

Linear color correction for multiple illumination changes and non-overlapping cameras

Many image processing methods, such as techniques for people re-identification, assume photometric constancy between different images. This study addresses the correction of photometric variations based upon changes in background areas to correct foreground areas. The authors assume a multiple light source model where all light sources can have different colours and will change over time. In training mode, the authors learn per-location relations between foreground and background colour intensities. In correction mode, the authors apply a double linear correction model based on learned relations. This double linear correction includes a dynamic local illumination correction mapping as well as an inter-camera mapping. The authors evaluate their illumination correction by computing the similarity between two images based on the earth mover's distance. The authors compare the results to a representative auto-exposure algorithm found in the recent literature plus a colour correction one based on the inverse-intensity chromaticity. Especially in complex scenarios the authors’ method outperforms these state-of-the-art algorithms.

Methodology of image analysis for study of the vertisols moisture content

The main problem to study vertical drainage from the moisture distribution, on a vertisol profile, is searching for suitable methods using these procedures. Our aim was to design a digital image processing methodology and its analysis to characterize the moisture content distribution of a vertisol profile. In this research, twelve soil pits were excavated on a ba re Mazic Pellic Vertisols ix of them in May 13/2011 and the rest in May 19 /2011 after a moderate rainfall event. Digital RGB images were taken from each vertisol pit using a Kodak? camera selecting a size of 1600x945 pixels. Each soil image was processed to homogenized brightness and then a spatial filter with several window sizes was applied to select the optimum one. The RGB image obtained were divided in each matrix color selecting the best thresholds for each one, maximum and minimum, to be applied and get a digital binary pattern. This one was analyzed by estimating two fractal scaling exponents box counting dimension D BC) and interface fractal dimension (D) In addition, three pre-fractal scaling coefficients were determinate at maximum resolution: total number of boxes intercepting the foreground pattern (A), fractal lacunarity (?1) and Shannon entropy S1). For all the images processed the spatial filter 9x9 was the optimum based on entropy, cluster and histogram criteria. Thresholds for each color were selected based on bimodal histograms.

Color uniformity in spotligths - visual perception and system design

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.

Image enhancement with MatLab algorithms

Nowadays, a lot of applications use digital images. For example in face recognition to detect and tag persons in photograph, for security control, and a lot of applications that can be found in smart cities, as speed control in roads or highways and cameras in traffic lights to detect drivers ignoring red light. Also in medicine digital images are used, such as x-ray, scanners, etc. These applications depend on the quality of the image obtained. A good camera is expensive, and the image obtained depends also on external factor as light. To make these applications work properly, image enhancement is as important as, for example, a good face detection algorithm. Image enhancement also can be used in normal photograph, for pictures done in bad light conditions, or just to improve the contrast of an image. There are some applications for smartphones that allow users apply filters or change the bright, colour or contrast on the pictures. This project compares four different techniques to use in image enhancement. After applying one of these techniques to an image, it will use better the whole available dynamic range. Some of the algorithms are designed for grey scale images and others for colour images. It is used Matlab software to develop and present the final results. These algorithms are Successive Means Quantization Transform (SMQT), Histogram Equalization, using Matlab function and own implemented function, and V transform. Finally, as conclusions, we can prove that Histogram equalization algorithm is the simplest of all, it has a wide variability of grey levels and it is not suitable for colour images. V transform algorithm is a good option for colour images. The algorithm is linear and requires low computational power. SMQT algorithm is non-linear, insensitive to gain and bias and it can extract structure of the data. RESUMEN. Hoy en día incontable número de aplicaciones usan imágenes digitales. Por ejemplo, para el control de la seguridad se usa el reconocimiento de rostros para detectar y etiquetar personas en fotografías o vídeos, para distintos usos de las ciudades inteligentes, como control de velocidad en carreteras o autopistas, cámaras en los semáforos para detectar a conductores haciendo caso omiso de un semáforo en rojo, etc. También en la medicina se utilizan imágenes digitales, como por ejemplo, rayos X, escáneres, etc. Todas estas aplicaciones dependen de la calidad de la imagen obtenida. Una buena cámara es cara, y la imagen obtenida depende también de factores externos como la luz. Para hacer que estas aplicaciones funciones correctamente, el tratamiento de imagen es tan importante como, por ejemplo, un buen algoritmo de detección de rostros. La mejora de la imagen también se puede utilizar en la fotografía no profesional o de consumo, para las fotos realizadas en malas condiciones de luz, o simplemente para mejorar el contraste de una imagen. Existen aplicaciones para teléfonos móviles que permiten a los usuarios aplicar filtros y cambiar el brillo, el color o el contraste en las imágenes. Este proyecto compara cuatro técnicas diferentes para utilizar el tratamiento de imagen. Se utiliza la herramienta de software matemático Matlab para desarrollar y presentar los resultados finales. Estos algoritmos son Successive Means Quantization Transform (SMQT), Ecualización del histograma, usando la propia función de Matlab y una nueva función que se desarrolla en este proyecto y, por último, una función de transformada V. Finalmente, como conclusión, podemos comprobar que el algoritmo de Ecualización del histograma es el más simple de todos, tiene una amplia variabilidad de niveles de gris y no es adecuado para imágenes en color. El algoritmo de transformada V es una buena opción para imágenes en color, es lineal y requiere baja potencia de cálculo. El algoritmo SMQT no es lineal, insensible a la ganancia y polarización y, gracias a él, se puede extraer la estructura de los datos.