Most digital cameras capture images through Color Filter Arrays (CFA), and reconstruct the full color image from the CFA image. Each CFA pixel only captures one primary color component at each pixel location; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. Some other CFAs contain four color filters. The additional filter is a panchromatic/white filter, and it usually receives the full light spectrum. In this research, we studied and compared different four channel CFAs with panchromatic/white filter, and compared them with three channel CFAs. An appropriate demosaicking algorithm has been developed for each CFA. The most well-known three-channel CFA is Bayer. The Fujifilm X-Trans pattern has been studied in this work as another three-channel CFA with a different structure. Three different four-channel CFAs have been discussed in this research: RGBW-Kodak, RGBW-Bayer and RGBW- $5 \times 5$. The structure and the number of filters for each color are different for these CFAs. Since the Least-Square Luma-Chroma Demultiplexing method is a state of the art demosaicking method for the Bayer CFA, we designed the Least-Square method for RGBW CFAs. The effect of noise on different CFA patterns will be discussed for four channel CFAs. The Kodak database has been used to evaluate our non-adaptive and adaptive demosaicking methods as well as the optimized algorithms with the least square method. The captured values of white (panchromatic/clear) filters in RGBW CFAs have been estimated using red, green and blue filter values. Sets of optimized coefficients have been proposed to estimate the white filter values accurately. The results have been validated using the actual white values of a hyperspectral image dataset. A new denoising-demosaicking method for RGBW-Bayer CFA has been presented in this research. The algorithm has been tested on the Kodak dataset using the estimated value of white filters and a hyperspectral image dataset using the actual value of white filters, and the results have been compared. The results in both cases have been compared with the previous works on RGB-Bayer CFA, and it shows that the proposed algorithm using RGBW-Bayer CFA is working better than RGB-Bayer CFA in presence of noise.

Doctor of Philosophy / Curriculum and Instruction / Thomas Vontz / This phenomenological study hermeneutically explores law professors’ felt experiences within online existential lifeworld spheres. Prose, poetry, color images, and virtual journeying provide descriptive and interpretive text suggesting expansion of Gadamer’s fusion of horizonal understanding. Law professors who teach asynchronously online selected five color images from pixabay.com corresponding with the five universal existential themes: body, space, time, relationships and material things/technology (van Manen, 2014) as catalysts to conversationally explore what it feels like to transition from classroom to online instruction. Multiple phenomenological, artistic, and scientific theories prismatically amplify and explain the study’s design: Gadamer’s hermeneutical circle of understanding (1960/2006), Termes six-point spherical perspective (2016), Einstein’s closed yet unbounded universe (Egdall, 2014), and Seamon’s concept of “at homeness” (2012). Dialogical understanding of Self and Other(s) through Gadamer’s call for festival and serious play (1960/2006) is activated: The reader is invited to interact with the study text through visual and auditory web experiences. Researcher’s hermeneutic and existential retelling of the professors’ conversations begins to unfold metaphorically around a table within a virtual forest. When researcher’s previously bracketed-away prejudice for incorporating synchronous modalities into online learning erupts, professors’ longing felt for classroom home actualizes and ultimately emerges as a sixth existential dimension proposed by the researcher. A culminating journey through virtual desert in search of online home continues the retelling and metaphorically incorporates all six existential themes. Dramatic changes in researcher’s lifeworld view, ways of knowing and being, self view, self action and pedagogical development as a result of conducting the study are summarized. Future research is implicated including exploration of professors’ existentially felt experiences while teaching synchronously online and deep-mining professorial empathy toward students. Factors that impinge on all law professors’ transitioning to online instruction contextually anchor the study: 1) Legal pedagogy’s evolution from 18th Century professional skills training through the late 19th Century intrusion of legal doctrine instruction, and 20th Century paralegal skills training; 2) The American Bar Association’s 21st century mandates for graduating students with both legal skills and legal doctrine training; 3) 21st Century pedagogical Immutables (teaching online, teaching legal job skills, teaching legal doctrine, teaching to standardized tests); and 4) 21st Century Protean Challenges (institution and student demand for technology-based instruction, the Global Legal Services Industry’s hierarchical control over legal education and practice, enrollment and tuition crises, multi-cultural limitations, and the pedagogical conundrum of choosing among multiple online design and delivery modalities).

No description available.

[ES] La visión artificial es uno de los campos en mayor crecimiento en la actualidad que, junto con otras tecnologías como la Biometría o el Big Data, se ha convertido en el foco de interés de numerosas investigaciones y es considerada como una de las tecnologías del futuro. Este amplio campo abarca diversos métodos entre los que se encuentra el procesamiento y análisis de imágenes digitales. El éxito del análisis de imágenes y otras tareas de procesamiento de alto nivel, como pueden ser el reconocimiento de patrones o la visión 3D, dependerá en gran medida de la buena calidad de las imágenes de partida. Hoy en día existen multitud de factores que dañan las imágenes dificultando la obtención de imágenes de calidad óptima, esto ha convertido el (pre-) procesamiento digital de imágenes en un paso fundamental previo a la aplicación de cualquier otra tarea de procesado. Los factores más comunes son el ruido y las malas condiciones de adquisición: los artefactos provocados por el ruido dificultan la interpretación adecuada de la imagen y la adquisición en condiciones de iluminación o exposición deficientes, como escenas dinámicas, causan pérdida de información de la imagen que puede ser clave para ciertas tareas de procesamiento. Los pasos de (pre-)procesamiento de imágenes conocidos como suavizado y realce se aplican comúnmente para solventar estos problemas: El suavizado tiene por objeto reducir el ruido mientras que el realce se centra en mejorar o recuperar la información imprecisa o dañada. Con estos métodos conseguimos reparar información de los detalles y bordes de la imagen con una nitidez insuficiente o un contenido borroso que impide el (post-)procesamiento óptimo de la imagen. Existen numerosos métodos que suavizan el ruido de una imagen, sin embargo, en muchos casos el proceso de filtrado provoca emborronamiento en los bordes y detalles de la imagen. De igual manera podemos encontrar una enorme cantidad de técnicas de realce que intentan combatir las pérdidas de información, sin embargo, estas técnicas no contemplan la existencia de ruido en la imagen que procesan: ante una imagen ruidosa, cualquier técnica de realce provocará también un aumento del ruido. Aunque la idea intuitiva para solucionar este último caso será el previo filtrado y posterior realce, este enfoque ha demostrado no ser óptimo: el filtrado podrá eliminar información que, a su vez, podría no ser recuperable en el siguiente paso de realce. En la presente tesis doctoral se propone un modelo basado en teoría de grafos para el procesamiento de imágenes en color. En este modelo, se construye un grafo para cada píxel de tal manera que sus propiedades permiten caracterizar y clasificar dicho pixel. Como veremos, el modelo propuesto es robusto y capaz de adaptarse a una gran variedad de aplicaciones. En particular, aplicamos el modelo para crear nuevas soluciones a los dos problemas fundamentales del procesamiento de imágenes: suavizado y realce. Se ha estudiado el modelo en profundidad en función del umbral, parámetro clave que asegura la correcta clasificación de los píxeles de la imagen. Además, también se han estudiado las posibles características y posibilidades del modelo que nos han permitido sacarle el máximo partido en cada una de las posibles aplicaciones. Basado en este modelo se ha diseñado un filtro adaptativo capaz de eliminar ruido gaussiano de una imagen sin difuminar los bordes ni perder información de los detalles. Además, también ha permitido desarrollar un método capaz de realzar los bordes y detalles de una imagen al mismo tiempo que se suaviza el ruido presente en la misma. Esta aplicación simultánea consigue combinar dos operaciones opuestas por definición y superar así los inconvenientes presentados por el enfoque en dos etapas. / [CAT] La visió artificial és un dels camps en major creixement en l'actualitat que, junt amb altres tecnlogies com la Biometria o el Big Data, s'ha convertit en el focus d'interés de nombroses investigacions i és considerada com una de les tecnologies del futur. Aquest ampli camp comprén diversos m`etodes entre els quals es troba el processament digital d'imatges i anàlisis d'imatges digitals. L'èxit de l'anàlisis d'imatges i altres tasques de processament d'alt nivell, com poden ser el reconeixement de patrons o la visió 3D, dependrà en gran manera de la bona qualitat de les imatges de partida. Avui dia existeixen multitud de factors que danyen les imatges dificultant l'obtenció d'imatges de qualitat òptima, açò ha convertit el (pre-) processament digital d'imatges en un pas fonamental previa la l'aplicació de qualsevol altra tasca de processament. Els factors més comuns són el soroll i les males condicions d'adquisició: els artefactes provocats pel soroll dificulten la inter- pretació adequada de la imatge i l'adquisició en condicions d'il·luminació o exposició deficients, com a escenes dinàmiques, causen pèrdua d'informació de la imatge que pot ser clau per a certes tasques de processament. Els passos de (pre-) processament d'imatges coneguts com suavitzat i realç s'apliquen comunament per a resoldre aquests problemes: El suavitzat té com a objecte reduir el soroll mentres que el real se centra a millorar o recuperar la informació imprecisa o danyada. Amb aquests mètodes aconseguim reparar informació dels detalls i bords de la imatge amb una nitidesa insuficient o un contingut borrós que impedeix el (post-)processament òptim de la imatge. Existeixen nombrosos mètodes que suavitzen el soroll d'una imatge, no obstant això, en molts casos el procés de filtrat provoca emborronamiento en els bords i detalls de la imatge. De la mateixa manera podem trobar una enorme quantitat de tècniques de realç que intenten combatre les pèrdues d'informació, no obstant això, aquestes tècniques no contemplen l'existència de soroll en la imatge que processen: davant d'una image sorollosa, qualsevol tècnica de realç provocarà també un augment del soroll. Encara que la idea intuïtiva per a solucionar aquest últim cas seria el previ filtrat i posterior realç, aquest enfocament ha demostrat no ser òptim: el filtrat podria eliminar informació que, al seu torn, podria no ser recuperable en el seguënt pas de realç. En la present Tesi doctoral es proposa un model basat en teoria de grafs per al processament d'imatges en color. En aquest model, es construïx un graf per a cada píxel de tal manera que les seues propietats permeten caracteritzar i classificar el píxel en quëstió. Com veurem, el model proposat és robust i capaç d'adaptar-se a una gran varietat d'aplicacions. En particular, apliquem el model per a crear noves solucions als dos problemes fonamentals del processament d'imatges: suavitzat i realç. S'ha estudiat el model en profunditat en funció del llindar, paràmetre clau que assegura la correcta classificació dels píxels de la imatge. A més, també s'han estudiat les possibles característiques i possibilitats del model que ens han permés traure-li el màxim partit en cadascuna de les possibles aplicacions. Basat en aquest model s'ha dissenyat un filtre adaptatiu capaç d'eliminar soroll gaussià d'una imatge sense difuminar els bords ni perdre informació dels detalls. A més, també ha permés desenvolupar un mètode capaç de realçar els bords i detalls d'una imatge al mateix temps que se suavitza el soroll present en la mateixa. Aquesta aplicació simultània aconseguix combinar dues operacions oposades per definició i superar així els inconvenients presentats per l'enfocament en dues etapes. / [EN] Computer vision is one of the fastest growing fields at present which, along with other technologies such as Biometrics or Big Data, has become the focus of interest of many research projects and it is considered one of the technologies of the future. This broad field includes a plethora of digital image processing and analysis tasks. To guarantee the success of image analysis and other high-level processing tasks as 3D imaging or pattern recognition, it is critical to improve the quality of the raw images acquired. Nowadays all images are affected by different factors that hinder the achievement of optimal image quality, making digital image processing a fundamental step prior to the application of any other practical application. The most common of these factors are noise and poor acquisition conditions: noise artefacts hamper proper image interpretation of the image; and acquisition in poor lighting or exposure conditions, such as dynamic scenes, causes loss of image information that can be key for certain processing tasks. Image (pre-) processing steps known as smoothing and sharpening are commonly applied to overcome these inconveniences: Smoothing is aimed at reducing noise and sharpening at improving or recovering imprecise or damaged information of image details and edges with insufficient sharpness or blurred content that prevents optimal image (post-)processing. There are many methods for smoothing the noise in an image, however in many cases the filtering process causes blurring at the edges and details of the image. Besides, there are also many sharpening techniques, which try to combat the loss of information due to blurring of image texture and need to contemplate the existence of noise in the image they process. When dealing with a noisy image, any sharpening technique may amplify the noise. Although the intuitive idea to solve this last case would be the previous filtering and later sharpening, this approach has proved not to be optimal: the filtering could remove information that, in turn, may not be recoverable in the later sharpening step. In the present PhD dissertation we propose a model based on graph theory for color image processing from a vector approach. In this model, a graph is built for each pixel in such a way that its features allow to characterize and classify the pixel. As we will show, the model we proposed is robust and versatile: potentially able to adapt to a variety of applications. In particular, we apply the model to create new solutions for the two fundamentals problems in image processing: smoothing and sharpening. To approach high performance image smoothing we use the proposed model to determine if a pixel belongs to a at region or not, taking into account the need to achieve a high-precision classification even in the presence of noise. Thus, we build an adaptive soft-switching filter by employing the pixel classification to combine the outputs from a filter with high smoothing capability and a softer one to smooth edge/detail regions. Further, another application of our model allows to use pixels characterization to successfully perform a simultaneous smoothing and sharpening of color images. In this way, we address one of the classical challenges within the image processing field. We compare all the image processing techniques proposed with other state-of-the-art methods to show that they are competitive both from an objective (numerical) and visual evaluation point of view. / Pérez Benito, C. (2019). Color Image Processing based on Graph Theory [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/123955 / TESIS

Cette thèse a pour objectif de développer un système de reconnaissance de structures logique des documents d'entreprises sans modèle. Il s'agit de reconnaître la fonction logique de blocs de textes qui sont importants à localiser et à identifier. Ce problème est identique à celui de la détection d'objets dans une scène naturelle puisqu'il faut à la fois reconnaître les objets et les localiser dans une image. A la différence de la reconnaissance d'objets, les documents d'entreprises doivent être interprétés sans aucune information a priori sur leurs modèles de structures. La seule solution consiste à développer une approche non supervisée basée principalement sur les relations spatiales et sur les informations textuelles et images. Les documents d'entreprises possèdent des contenus et des formes très hétérogènes car chaque entreprise et chaque administration créent son propre formulaire ou ses propres modèles de factures. Nous faisons l'hypothèse que toute structure logique de document est constituée de morceaux de micro-structures déjà observées dans d'autres documents. Cette démarche est identique en détection d'objets dans les images naturelles. Tout modèle particulier d'objet dans une scène est composé de morceaux d'éléments déjà vu sur d'autres exemples d'objets de même classe et qui sont reliés entre eux par des relations spatiales déjà observées. Notre modèle est donc basé sur une reconnaissance partie par partie et sur l'accumulation d'évidences dans l'espace paramétrique et spatial. Notre solution a été testée sur des applications de détection d'objets dans les scènes naturelles et de reconnaissance de structure logique de documents d'entreprises. Les bonnes performances obtenues valident les hypothèses initiales. Ces travaux contiennent aussi de nouvelles méthodes de traitement et d'analyse d'image couleurs de documents et d'images naturelles. / This digital revolution introduces new services and new usages in numerous domains. The advent of the digitization of documents and the automatization of their processing constitutes a great cultural and economic revolution. In this context, computer vision provides numerous applications and impacts our daily lives and businesses. Behind computer-vision technology, fundamental concepts, methodologies, and algorithms have been developed worldwide in the last fifty years. Today, computer vision technologies arrive to maturity and become a reality in many domains. Computer-vision systems reach high performance thanks to the large amount of data and the increasing performance of the hardware. Despite the success of computer-vision applications, however, numerous other applications require more research, new methodologies, and novel algorithms. Among the difficult problems encountered in the computer-vision domain, detection remains a challenging task. Detection consists of localizing and recognizing an object in an image. This problem is far more difficult than the problem of recognition alone. Among the numerous applications based on detection, object detection in a natural scene is the most popular application in the computer-vision community. This work is about the detection tasks and its applications.

A tarefa de encontrar faces em imagens é extremamente complexa, pois pode ocorrer variação de luminosidade, fundos extremamente complexos e objetos que podem se sobrepor parcialmente à face que será localizada, entre outros problemas. Com o avanço na área de visão computacional técnicas mais recentes de processamento de imagens e inteligência artificial têm sido combinadas para desenvolver algoritmos mais eficientes para a tarefa de detecção de faces. Este trabalho apresenta uma metodologia de visão computacional que utiliza redes neurais MLP (Perceptron Multicamadas) para segmentar a cor da pele e a textura da face, de outros objetos presentes em uma imagem de fundo complexo. A imagem resultante é dividida em regiões, e para cada região são extraídas características que são aplicadas em outra rede neural MLP para identificar se naquela região contem face ou não. Para avaliação do software implementado foram utilizados dois banco de imagens, um com imagens padronizadas (Banco AR) e outro banco com imagens adquiridas na Internet contendo faces com diferentes tons de pele e fundo complexo. Os resultados finais obtidos foram de 83% de faces detectadas para o banco de imagens da Internet e 88% para o Banco AR, evidenciando melhores resultados para as imagens deste banco, pelo fato de serem padronizadas, não conterem faces inclinadas e fundo complexo. A etapa de segmentação apesar de reduzir a quantidade de informação a ser processada para os demais módulos foi a que contribuiu para o maior número de falsos negativos. / The task of finding faces in images is extremely complex, as there is variation in brightness, backgrounds and highly complex objects that may overlap partially in the face to be found, among other problems. With the advancement in the field of computer vision techniques latest image processing and artificial intelligence have been combined to develop more efficient algorithms for the task of face detection. This work presents a methodology for computer vision using neural networks MLP (Multilayer Perceptron) to segment the skin color and texture of the face, from other objects present in a complex background image. The resulting image is divided into regions and from each region are extracted features that are applied in other MLP neural network to identify whether this region contains the face or not. To evaluate the software two sets of images were used, images with a standard database (AR) and another database with images acquired from the Internet, containing faces with different skin tones and complex background. The final results were 83% of faces detected in the internet database of images and 88% for the database AR. These better results for the database AR is due to the fact that they are standardized, are not rotated and do not contain complex background. The segmentation step, despite reducing the amount of information being processed for the other modules contributed to the higher number of false negatives.

Inspirado nas técnicas utilizadas por pintores que sobrepõem camadas de tintas de diversos matizes na geração de uma tela artística e também observando-se a distribuição da quantidade dos cones na retina do olho humano na interpretação destas cores, este trabalho propõe uma técnica de processamento de imagens baseada na mistura de cores. Trata-se de um método de quantização de cores estático que expressa a proporção das cores preto, azul, verde, ciano, vermelho, magenta, amarelo e branco obtida pela representação binária da cor que compõe os pixels de uma imagem RGB com 8 bits por canal. O histograma da mistura é denominado de misturograma e gera planos que interceptam o espaço RGB, definindo o espaço de cor HSM (Hue, Saturation and Mixture). A posição destes planos dentro do cubo RGB é modelada por meio da distribuição dos cones sensíveis aos comprimentos de onda curta (Short), média (Middle) e longa (Long) consideradas para a retina humana. Para demonstrar a aplicabilidade do espaço de cor HSM, é proposta, neste trabalho, a segmentação dos pixels de uma imagem digital em pele humana ou não pele com o uso dessa nova abordagem. Para análise de desempenho da mistura de cores foi implementado um método tradicional no espaço de cor RGB e também usando uma distribuição Gaussiana nos espaços de cores HSV e HSM. Os resultados obtidos demonstram o potencial da técnica que emprega a mistura de cores para a segmentação de imagens digitais coloridas. Verificou-se também que, baseando-se apenas na camada mais significativa da mistura de cores, gera-se a imagem esboço de uma imagem facial denominada esboço da face. Os resultados obtidos comprovam o bom desempenho do esboço da face em aplicações CBIR. / Inspired on the techniques used by painters to overlap layers of various hues of paint to create oil paintings, and also on observations of the distribution of cones in human retina for the interpretation of these colors, this thesis proposes an image processing technique based on color mixing. This is a static color quantization method that expresses the mixture of black, blue, green, cyan, red, magenta, yellow and white colors quantified by the binary weight of the color that makes up the pixels of an RGB image with 8 bits per channel. The mixture histogram, called a mixturegram, generates planes that intersect the RGB color space, defining the HSM (Hue, Saturation and Mixture) color space. The position of these planes inside the RGB cube is modeled by the distribution of cones sensitive to the short (S), middle (M) and long (L) wave lengths of the human retina. To demonstrate the applicability of the HSM color space, this thesis proposes the segmentation of the pixels of a digital image of human skin or non-skin using this new approach. The performance of the color mixture is analyzed by implementing a traditional method in the RGB color space and by a Gaussian distribution in the HSV and HSM color spaces. The results demonstrate the potential of the proposed technique for color image segmentation. It was also noted that, based only on the most significant layer of the colors mixture, it is possible generates the face sketch image. The results show the performance of the face sketch image in CBIR applications.

A tarefa de encontrar faces em imagens é extremamente complexa, pois pode ocorrer variação de luminosidade, fundos extremamente complexos e objetos que podem se sobrepor parcialmente à face que será localizada, entre outros problemas. Com o avanço na área de visão computacional técnicas mais recentes de processamento de imagens e inteligência artificial têm sido combinadas para desenvolver algoritmos mais eficientes para a tarefa de detecção de faces. Este trabalho apresenta uma metodologia de visão computacional que utiliza redes neurais MLP (Perceptron Multicamadas) para segmentar a cor da pele e a textura da face, de outros objetos presentes em uma imagem de fundo complexo. A imagem resultante é dividida em regiões, e para cada região são extraídas características que são aplicadas em outra rede neural MLP para identificar se naquela região contem face ou não. Para avaliação do software implementado foram utilizados dois banco de imagens, um com imagens padronizadas (Banco AR) e outro banco com imagens adquiridas na Internet contendo faces com diferentes tons de pele e fundo complexo. Os resultados finais obtidos foram de 83% de faces detectadas para o banco de imagens da Internet e 88% para o Banco AR, evidenciando melhores resultados para as imagens deste banco, pelo fato de serem padronizadas, não conterem faces inclinadas e fundo complexo. A etapa de segmentação apesar de reduzir a quantidade de informação a ser processada para os demais módulos foi a que contribuiu para o maior número de falsos negativos. / The task of finding faces in images is extremely complex, as there is variation in brightness, backgrounds and highly complex objects that may overlap partially in the face to be found, among other problems. With the advancement in the field of computer vision techniques latest image processing and artificial intelligence have been combined to develop more efficient algorithms for the task of face detection. This work presents a methodology for computer vision using neural networks MLP (Multilayer Perceptron) to segment the skin color and texture of the face, from other objects present in a complex background image. The resulting image is divided into regions and from each region are extracted features that are applied in other MLP neural network to identify whether this region contains the face or not. To evaluate the software two sets of images were used, images with a standard database (AR) and another database with images acquired from the Internet, containing faces with different skin tones and complex background. The final results were 83% of faces detected in the internet database of images and 88% for the database AR. These better results for the database AR is due to the fact that they are standardized, are not rotated and do not contain complex background. The segmentation step, despite reducing the amount of information being processed for the other modules contributed to the higher number of false negatives.

Inspirado nas técnicas utilizadas por pintores que sobrepõem camadas de tintas de diversos matizes na geração de uma tela artística e também observando-se a distribuição da quantidade dos cones na retina do olho humano na interpretação destas cores, este trabalho propõe uma técnica de processamento de imagens baseada na mistura de cores. Trata-se de um método de quantização de cores estático que expressa a proporção das cores preto, azul, verde, ciano, vermelho, magenta, amarelo e branco obtida pela representação binária da cor que compõe os pixels de uma imagem RGB com 8 bits por canal. O histograma da mistura é denominado de misturograma e gera planos que interceptam o espaço RGB, definindo o espaço de cor HSM (Hue, Saturation and Mixture). A posição destes planos dentro do cubo RGB é modelada por meio da distribuição dos cones sensíveis aos comprimentos de onda curta (Short), média (Middle) e longa (Long) consideradas para a retina humana. Para demonstrar a aplicabilidade do espaço de cor HSM, é proposta, neste trabalho, a segmentação dos pixels de uma imagem digital em pele humana ou não pele com o uso dessa nova abordagem. Para análise de desempenho da mistura de cores foi implementado um método tradicional no espaço de cor RGB e também usando uma distribuição Gaussiana nos espaços de cores HSV e HSM. Os resultados obtidos demonstram o potencial da técnica que emprega a mistura de cores para a segmentação de imagens digitais coloridas. Verificou-se também que, baseando-se apenas na camada mais significativa da mistura de cores, gera-se a imagem esboço de uma imagem facial denominada esboço da face. Os resultados obtidos comprovam o bom desempenho do esboço da face em aplicações CBIR. / Inspired on the techniques used by painters to overlap layers of various hues of paint to create oil paintings, and also on observations of the distribution of cones in human retina for the interpretation of these colors, this thesis proposes an image processing technique based on color mixing. This is a static color quantization method that expresses the mixture of black, blue, green, cyan, red, magenta, yellow and white colors quantified by the binary weight of the color that makes up the pixels of an RGB image with 8 bits per channel. The mixture histogram, called a mixturegram, generates planes that intersect the RGB color space, defining the HSM (Hue, Saturation and Mixture) color space. The position of these planes inside the RGB cube is modeled by the distribution of cones sensitive to the short (S), middle (M) and long (L) wave lengths of the human retina. To demonstrate the applicability of the HSM color space, this thesis proposes the segmentation of the pixels of a digital image of human skin or non-skin using this new approach. The performance of the color mixture is analyzed by implementing a traditional method in the RGB color space and by a Gaussian distribution in the HSV and HSM color spaces. The results demonstrate the potential of the proposed technique for color image segmentation. It was also noted that, based only on the most significant layer of the colors mixture, it is possible generates the face sketch image. The results show the performance of the face sketch image in CBIR applications.

Made available in DSpace on 2014-12-17T14:56:05Z (GMT). No. of bitstreams: 1 GustavoFS.pdf: 1361196 bytes, checksum: fe1a67dcdb84a334e6c49247c8c68a06 (MD5) Previous issue date: 2005-01-21 / Image compress consists in represent by small amount of data, without loss a visual quality. Data compression is important when large images are used, for example satellite image. Full color digital images typically use 24 bits to specify the color of each pixel of the Images with 8 bits for each of the primary components, red, green and blue (RGB). Compress an image with three or more bands (multispectral) is fundamental to reduce the transmission time, process time and record time. Because many applications need images, that compression image data is important: medical image, satellite image, sensor etc. In this work a new compression color images method is proposed. This method is based in measure of information of each band. This technique is called by Self-Adaptive Compression (S.A.C.) and each band of image is compressed with a different threshold, for preserve information with better result. SAC do a large compression in large redundancy bands, that is, lower information and soft compression to bands with bigger amount of information. Two image transforms are used in this technique: Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). Primary step is convert data to new bands without relationship, with PCA. Later Apply DCT in each band. Data Loss is doing when a threshold discarding any coefficients. This threshold is calculated with two elements: PCA result and a parameter user. Parameters user define a compression tax. The system produce three different thresholds, one to each band of image, that is proportional of amount information. For image reconstruction is realized DCT and PCA inverse. SAC was compared with JPEG (Joint Photographic Experts Group) standard and YIQ compression and better results are obtain, in MSE (Mean Square Root). Tests shown that SAC has better quality in hard compressions. With two advantages: (a) like is adaptive is sensible to image type, that is, presents good results to divers images kinds (synthetic, landscapes, people etc., and, (b) it need only one parameters user, that is, just letter human intervention is required / Comprimir uma imagem consiste, basicamente, em represent?-la atrav?s de uma menor quantidade de dados, sem para tanto comprometer a qualidade da imagem. A grande import?ncia da compress?o de dados fica evidente quando se utiliza quantidade muito grande de informa??es e espa?os pequenos para armazenamento. Com esse objetivo ? que se apresenta esse trabalho no qual desenvolveu-se um m?todo para a compress?o de imagens coloridas e multiespectrais baseado na quantidade de informa??o contida em cada banda ou planos da imagem. Este m?todo foi chamado de Compress?o Auto-Adaptativa (C.A.A.), no qual cada banda da imagem ? comprimida com uma taxa de compress?o diferente, buscando um melhor resultado de forma a manter a maior parte da informa??o. A t?cnica baseia-se na compress?o com maior taxa para a banda com maior redund?ncia, ou seja, menor quantidade de informa??o e com taxas mais amenas ?s bandas com informa??o mais significativa. O CAA utiliza duas transformadas de imagens como elementos ativos da compress?o. A Transformada Cosseno Discreta (DCT) e a An?lise de Componentes Principais (PCA). A Imagem original (sem compress?o) ? processada pelo sistema CAA no espa?o RGB, sob o qual ? aplicado a transformada PCA, que leva a imagem para um novo espa?o (ou planos de dados), no qual as informa??es est?o descorrelacionadas. Neste espa?o gerado pela PCA, realiza-se a DCT em cada um dos planos individualmente, e, atrav?s de um limiar calculado em fun??o do resultado da PCA e de um par?metro de compress?o fornecido pelo usu?rio, ? que alguns elementos da matriz gerada pela DCT s?o descartados. Por fim realiza-se, respectivamente, a DCT e PCA inversas, reconstruindo assim uma aproxima??o da imagem. Quando comparada com a compress?o realizada pela tradicional JPEG (Joint Photographic Experts Group), a CAA apresenta, em m?dia, resultados cerca de 10 % melhores no que diz respeito a MSE (Mean Square Root), com duas grandes vantagens, por ser adaptativa, ? sens?vel ao tipo de imagem, ou seja, apresenta bons resultados em diversos tipos de imagens (sint?tica, paisagens, pessoas, e etc.), e, necessita apenas um par?metro de compress?o determinado pelo usu?rio