本論文的目的是利用人臉在彩色影像中所提供的多色彩空間資訊，來達成在變異度較大的光源中即時偵測人臉的任務。彩色影像所擁有的原始RGB色彩資訊，經過轉化到正規RGB以及HSV (色調、飽合、明度)等色彩空間後，擁有對光源變化反應減緩的特性。以此特性為基礎，在４個選定的色彩空間中定義８種不同的類赫爾特徵(Haar-like feature)，再利用推進演算法（Boosting algorithm）選出重要性最高的幾組特徵來進行對人臉的特徵。實驗結果顯示依此方法所產生的辨識器可在２點多秒內處理近百萬個次窗口(sub-window)，並對光源變化有相當程度的抵抗力。 / The main goal of this thesis is to detect human face under varying lighting condition by utilizing multiple color space information in real-time. Images of RGB color space can be converted into normalized RGB and HSV color spaces and thus reduce the interference of lighting condition. Base on this mechanism, we define 8 Haar-like features inside 4 selected color spaces, and then select the important features with boosting algorithm. Experimental results show that detectors constructed with our approach are able to process nearly one million sub-windows within 2.4 seconds, being robust to the changes of lighting conditions.

Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science, particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J. Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes the ability to solve complex problems. The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm intelligence. The process developed for this purpose consists of a combination of various known structures, which are then adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example application program.

Wireless capsule endoscopy (WCE) is a state-of-the-art technology to receive images of human intestine for medical diagnostics. In WCE, the patient ingests a specially designed electronic capsule which has imaging and wireless transmission capabilities inside it. While the capsule travels through the gastrointestinal (GI) tract, it captures images and sends them wirelessly to an outside data logger unit. The data logger stores the image data and then they are transferred to a personal computer (PC) where the images are reconstructed and displayed for diagnosis. The key design challenge in WCE is to reduce the area and power consumption of the capsule while maintaining acceptable image reconstruction. In this research, the unique properties of WCE images are identified by analyzing hundreds of endoscopic images and video frames, and then these properties are used to develop novel and low complexity compression algorithms tailored for capsule endoscopy. The proposed image compressor consists of a new YEF color space converter, lossless prediction coder, customizable chrominance sub-sampler and an efficient Golomb-Rice encoder. The scheme has both lossy and lossless modes and is further customized to work with two lighting modes – conventional white light imaging (WLI) and emerging narrow band imaging (NBI). The average compression ratio achieved using the proposed lossy compression algorithm is 80.4% for WBI and 79.2% for NBI with high reconstruction quality index for both bands. Two surveys have been conducted which show that the reconstructed images have high acceptability among medical imaging doctors and gastroenterologists. The imaging algorithms have been realized in hardware description language (HDL) and their functionalities have been verified in field programmable gate array (FPGA) board. Later it was implemented in a 0.18 μm complementary metal oxide semiconductor (CMOS) technology and the chip was fabricated. Due to the low complexity of the core compressor, it consumes only 43 µW of power and 0.032 mm2 of area. The compressor is designed to work with commercial low-power image sensor that outputs image pixels in raster scan fashion, eliminating the need of significant input buffer memory. To demonstrate the advantage, a prototype of the complete WCE system including an FPGA based electronic capsule, a microcontroller based data logger unit and a Windows based image reconstruction software have been developed. The capsule contains the proposed low complexity image compressor and can generate both lossy and lossless compressed bit-stream. The capsule prototype also supports both white light imaging (WLI) and narrow band imaging (NBI) imaging modes and communicates with the data logger in full duplex fashion, which enables configuring the image size and imaging mode in real time during the examination. The developed data logger is portable and has a high data rate wireless connectivity including Bluetooth, graphical display for real time image viewing with state-of-the-art touch screen technology. The data are logged in micro SD cards and can be transferred to PC or Smartphone using card reader, USB interface, or Bluetooth wireless link. The workstation software can decompress and show the reconstructed images. The images can be navigated, marked, zoomed and can be played as video. Finally, ex-vivo testing of the WCE system has been done in pig's intestine to validate its performance.

O mamão (Carica papaya L.) cv. \"Sunrise Solo\" é um fruto que apresenta mudança gradual e desuniforme da cor da casca, que vai de verde para amarela. Isso faz com que a metodologia instrumental para avaliação da cor, por meio de um colorímetro, seja subjetiva, devido ao número de pontos observados, bem como às localizações deles no fruto. Como alternativa, foi proposta a utilização de imagens digitalizadas de toda região da casca do fruto, obtidas por meio de um scanner de mesa. Para avaliar a precisão desses métodos, foi conduzido um experimento com 20 repetições. Cada repetição era constituída de um fruto de mamoeiro cv. \"Sunrise Solo\", mantido sob temperatura e umidade relativa controladas. A cor da casca dos frutos foi avaliada, diariamente, utilizando um colorímetro e um scanner. Com o scanner, foram digitalizadas as duas faces do fruto e, com o colorímetro, foram observados quatro pontos equidistantes, na região equatorial do mesmo. Como a avaliação para cada fruto foi feita ao longo do tempo, os dados são classificados como longitudinais. Assim, utilizaram-se modelos lineares de efeitos mistos para estudar o comportamento da tonalidade média, pois essa técnica permite o uso de diferentes estruturas de variâncias e covariâncias para as matrizes dos efeitos aleatórios e dos erros. O processo de seleção do modelo foi realizado por meio do teste da razão de verossimilhanças e dos critérios de informação AIC e BIC, resultando no mesmo preditor linear e matrizes de variâncias e covariâncias para ambas as metodologias de quantificação da cor. O modelo final apresentou um preditor linear quadrático com efeitos aleatórios para o intercepto e para os termos linear e quadrático com matriz de variâncias e covariâncias dos efeitos aleatórios não estruturada e componentes de variância com heterocedasticidade para os erros. A utilização do scanner revelou dois grupos de maturação fisiológica distintos, que podem estar relacionados ao ponto de colheita do fruto, fato que não ficou evidente ao utilizar um colorímetro. De forma geral, o uso de um scanner possibilitou obter uma avaliação precisa da maturação do fruto, além de ser mais consistentes e eficiente do que o uso de um colorímetro para estudar a tonalidade média da casca de frutos que apresentam coloração desuniforme. / Papayas (Carica papaya L.) of \"Sunrise solo\" variety are fruits that present gradual and uneven changes in the peel color, which goes from green to yellow. As a consequence, when using a colorimeter to quantify their color, the results are subjective because of the number of observed points, as well as because of their position on the fruit. A proposed alternative was to use scanned images of the whole fruit peel to quantify color. To assess the precision of these methods, an experiment with 20 replicates was carried out. Each replicate consisted of a papaya fruit, kept under controlled temperature and relative humidity. The fruits\' peel colors were assessed, daily, using a colorimeter and a scanner. With the scanner, both sides of the fruit were scanned and, with the colorimeter, four equidistant points at the equatorial region of the fruit were observed. As the assessment was made through time for a same fruit, the data are classified as longitudinal. Therefore, linear mixed effect models were used to study the behavior of the average fruit color tonality through time, as this technique allows usage of different random effects and error covariance structures. Model selection was made using likelihood-ratio tests and the Akaike and Bayesian information criteria, which resulted in the selection of the same linear predictor and covariance matrices for both color quantification methods. The final model presented a quadractic linear predictor with random effects for the intercept, linear and quadractic terms with an unstructured variance-covariance matrix for the random effects and a variance components with heterogeneity matrix for the residuals. The use of a scanner revealed two distinct phisiological maturation groups, which may be related to the harvesting time. This was not observed when using a colorimeter. In general, using a scanner made possible to obtain more consistent observations, which makes it a more efficient methodology to study the average fruit peel color tonality.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-08-08T13:18:51Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO DO MESTRADO - RENAN.pdf: 2412478 bytes, checksum: 4cfaf4abac06be681bb16b4db23db3a2 (MD5) / Made available in DSpace on 2017-08-08T13:18:51Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO DO MESTRADO - RENAN.pdf: 2412478 bytes, checksum: 4cfaf4abac06be681bb16b4db23db3a2 (MD5) Previous issue date: 2016-08-29 / A busca de métodos que possam auxiliar na atualização de produtos cartográficos existentes e propiciar uma base cartográfica atual é de fundamental importância para o planejamento urbano e consequentemente para o gerenciamento de todo o território nacional. As feições que devem ser atualizadas nos produtos cartográficos podem ser obtidas a partir de imagens de sensoriamento remoto e fotografias aéreas. E um dos atributos das feições que pode ser usado é a cor que é amplamente utilizado na extração pela análise visual e também pode auxiliar na extração automática das feições. Nessa dissertação, tem-se uma abordagem levando em consideração a extração pela cor usando a morfologia matemática e espaço de cores RGB e HSV. Denota-se Morfologia, por ajudar na análise de formas e objetos, e Matemática, por esta análise se basear na teoria de conjuntos, topologia, reticulados. A morfologia matemática foi inicialmente desenvolvida para análise de imagens binárias, onde a abordagem linear não se mostrava eficiente, e depois foi estendida para níveis de cinza. Nessa dissertação trabalha-se com as imagens no espaço de cores. Utiliza-se as ferramentas da morfologia matemática denotadas de erosão, dilatação e gradiente para a segmentação e detecção de bordas das imagens. Essas ferramentas e os espaços de cores RBG e HSV são a base dos algoritmos para a obtenção das bordas das feições na imagem, obtendo-se como resultados as feições do tipo estradas, edificações e vegetações existentes na imagem de alta resolução (ortofoto). A metodologia com gradiente no espaço HSV gera o melhor resultado e semelhante com fotointerpretação (interpretação visual). / Methods that assist a cartographic updating products and provide a current map base is fundamental importance for urban planning and therefore for managing the entire national territory. Features that must be updated in cartographic products can be obtained from remote sensing images and aerial photographs. And one of the attributes of the features that can be used is the color that is widely used in extraction by visual analysis and can also assist in the selfextracting features. In this dissertation, there is an approach considering the color extraction using mathematical morphology, RGB and HSV color space. It denotes Morphology, because of the analysis of shapes and objects, and mathematics, because this analysis is based on set theory, topology, lattices. Mathematical morphology was initially developed for the analysis of binary images, where the linear approach showed no efficient and was then extended to gray levels. In this dissertation, it works with images in color space. It uses the tools of mathematical morphology denoted erosion, dilation and gradient for segmentation and detection of images of edges. These tools and RBG color space and HSV are the basis of algorithms for obtaining the edges of the features in the image, obtaining thus the features of the type roads, existing buildings and vegetation in high resolution image (orthophoto). Methodology with gradient with HSV space is shown the best result and similar with photointerpretation (visual interpretation).

Accurately quantifying the human perception of color is an unsolved prob- lem. There are dozens of numerical systems for quantifying colors and how we as humans perceive them, but as a whole, they are far from perfect. The ability to accurately measure color for reproduction and verification is critical to indus- tries that work with textiles, paints, food and beverages, displays, and media compression algorithms. Because the science of color deals with the body, mind, and the subjective study of perception, building models of color requires largely empirical data over pure analytical science. Much of this data is extremely dated, from small and/or homogeneous data sets, and is hard to compare. While these studies have somewhat advanced our understanding of color adequately, mak- ing significant, further progress without improved datasets has proven dicult if not impossible. I propose new methods of crowdsourcing color experiments through color-accurate mobile devices to help develop a massive, global set of color perception data to aid in creating a more accurate model of human color perception.

[pt] Nos últimos anos, diversas fórmulas de diferença de cores foram desenvolvidas para o espaço CIELAB, tais como CMC, CIE94 e CIEDE2000. Embora essas fórmulas obtenham maior precisão na medida perceptual entre cores, muitas aplicações não podem usufruir desta maior precisão, pois as distâncias euclidianas no CIELAB não são isométricas de acordo com essas novas fórmulas. Com isso, aplicações como gamut mapping e interpolação de cores precisam de um espaço de cores que seja isométrico em relação as fórmulas mais recentes de medição de diferenças de cores. Esse trabalho estuda o mapeamento do espaço CIELAB, em particular do plano ab deste espaço, sob a métrica da fórmula CIEDE2000, por meio de técnicas de escalonamento multidimensional, ou Multidimensional Scaling (MDS), tais como o ISOMAP e uma otimizaçãobaseada em Sammon Mapping. / [en] In recent years, various color difference formulas were developed for the CIELAB space, such as CMC, CIE94 and CIEDE2000. Although these formulas have achieved greater accuracy in perceptual measurement between colors, many applications cannot take advantage of this greater precision, because the Euclidean distances in CIELAB are not isometric in accordance with these new formulas. Thus, applications such as gamut mapping and color interpolation need a color space that is isometric in relation to the latest color difference formulas. This paper studies the mapping of the CIELAB space, particularly the ab plane of this space according to the metrics of the CIEDE2000 formula, through multidimensional scaling (MDS) techniques, more specifically ISOMAP and an optimization based on Sammon Mapping.

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.

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.

Tracking and understanding human gait is an important step towards improving elderly mobility and safety. This thesis presents a vision-based tracking system that estimates the 3D pose of a wheeled walker user's lower limbs with cameras mounted on the moving walker. The tracker estimates 3D poses from images of the lower limbs in the coronal plane in a dynamic, uncontrolled environment. It employs a probabilistic approach based on particle filtering with three different camera setups: a monocular RGB camera, binocular RGB cameras, and a depth camera. For the RGB cameras, observation likelihoods are designed to compare the colors and gradients of each frame with initial templates that are manually extracted. Two strategies are also investigated for handling appearance change of tracking target: increasing number of templates and using different representations of colors. For the depth camera, two observation likelihoods are developed: the first one works directly in the 3D space, while the second one works in the projected image space. Experiments are conducted to evaluate the performance of the tracking system with different users for all three camera setups. It is demonstrated that the trackers with the RGB cameras produce results with higher error as compared to the depth camera, and the strategies for handling appearance change improve tracking accuracy in general. On the other hand, the tracker with the depth sensor successfully tracks the 3D poses of users over the entire video sequence and is robust against unfavorable conditions such as partial occlusion, missing observations, and deformable tracking target.