Remote sensing imaging is a tool for collecting information about the Earth's surface such as soil, vegetation and water. Recent progress in electronics, telecommunications and sensor developments have resulted in the launch of many satellites in the past three decades. Different sensors in remote sensing systems capture a variety of images with differing characteristics. Image fusion has been used to integrate two or more images and provides output images with better accuracy. This research provides a new technique for image fusion using the contourlet transform in combination with the YCbCr color space. The output images preserve both the spectral and spatial characteristics of the input images and they are better for human and machine interpretation. This technique provides solutions to some problems (\emph{i.e.}, ghosting effect, and blocking artifacts) which the traditional image fusion techniques fail to address. The proposed technique is tested on both classical and remote sensing images. Quality metrics are used to evaluate the results of the proposed technique. The results proved significant enhancement of the quality of the output images. More fine details are successfully captured and the original chromaticity information is preserved as well. The proposed technique eliminates the blocking artifacts in the output images. Also, a new metric is presented to measure the blocking artifacts in the fused image. The results showed that increasing the number of contourlet decomposition levels does not degrade the quality of the output image. Therefore, the output images do not lose their chromaticity information when the number of contourlet decomposition levels increases. The proposed technique is tested on a variety of the remote sensing images that have large resolution ratios (\emph{i.e.}, 1:8, 1:16 and 1:32). The proposed technique is robust and suitable for many image applications. The detection of the concealed objects is an example of such applications, where the proposed technique is tested to measure its capability to fuse images with different features. The results of the Contourlet-YCbCr fusion technique are compared with the conventional fusion methods, where the proposed technique is more capable in detecting the hidden objects and preserving the original color components of the input image.

Video surveillance systems have been popular as a security tool for years, and the technological development helps monitoring accident-prone areas with the help of digital image processing.A thermal and a visual camera are being used in the surveillance project. The thermal camera is sensitive to the heat emitted by objects, and it is essential to employ the thermal camera as the visual camera is only useful in the presence of light. These cameras do not provide images of the same resolution. In order to extract the region of interest (ROI) of the visual camera, the images of these cameras need to have the same resolution; therefore the thermal images are processed in order to have the same size as the visual image.The ROI extraction is needed in order to reduce the data that needs to be transmitted. The region of interest is extracted from the visual image and the required processes are mostly done on the thermal image as it has lower resolution and therefore requires less computational processing. The image taken from the thermal camera is up scaled by using the nearest neighbor algorithm and it is zero-padded to make the resolutions of the two images equal, and then the region of interest is extracted by masking the result with the related converted version of visual image to YCbCr color space.

Human skin is made of a stack of different layers, each of which reflects a portion of impinging light, after absorbing a certain amount of it by the pigments which lie in the layer. The main pigments responsible for skin color origins are melanin and hemoglobin. Skin segmentation plays an important role in a wide range of image processing and computer vision applications. In short, there are three major approaches for skin segmentation: rule-based, machine learning and hybrid. They differ in terms of accuracy and computational efficiency. Generally, machine learning and hybrid approaches outperform the rule-based methods but require a large and representative training dataset and, sometimes, costly classification time as well, which can be a deal breaker for real-time applications. In this work, we propose an improvement, in three distinct versions, of a novel method for rule-based skin segmentation that works in the YCbCr color space. Our motivation is based on the hypotheses that: (1) the original rule can be complemented and, (2) human skin pixels do not appear isolated, i.e. neighborhood operations are taken into consideration. The method is a combination of some correlation rules based on these hypotheses. Such rules evaluate the combinations of chrominance Cb, Cr values to identify the skin pixels depending on the shape and size of dynamically generated skin color clusters. The method is very efficient in terms of computational effort as well as robust in very complex images. / A pele humana é constituída de uma série de camadas distintas, cada uma das quais reflete uma porção de luz incidente, depois de absorver uma certa quantidade dela pelos pigmentos que se encontram na camada. Os principais pigmentos responsáveis pela origem da cor da pele são a melanina e a hemoglobina. A segmentação de pele desempenha um papel importante em uma ampla gama de aplicações em processamento de imagens e visão computacional. Em suma, existem três abordagens principais para segmentação de pele: baseadas em regras, aprendizado de máquina e híbridos. Elas diferem em termos de precisão e eficiência computacional. Geralmente, as abordagens com aprendizado de máquina e as híbridas superam os métodos baseados em regras, mas exigem um conjunto de dados de treinamento grande e representativo e, por vezes, também um tempo de classificação custoso, que pode ser um fator decisivo para aplicações em tempo real. Neste trabalho, propomos uma melhoria, em três versões distintas, de um novo método de segmentação de pele baseado em regras que funciona no espaço de cores YCbCr. Nossa motivação baseia-se nas hipóteses de que: (1) a regra original pode ser complementada e, (2) pixels de pele humana não aparecem isolados, ou seja, as operações de vizinhança são levadas em consideração. O método é uma combinação de algumas regras de correlação baseadas nessas hipóteses. Essas regras avaliam as combinações de valores de crominância Cb, Cr para identificar os pixels de pele, dependendo da forma e tamanho dos agrupamentos de cores de pele gerados dinamicamente. O método é muito eficiente em termos de esforço computacional, bem como robusto em imagens muito complexas.

Human skin is made of a stack of different layers, each of which reflects a portion of impinging light, after absorbing a certain amount of it by the pigments which lie in the layer. The main pigments responsible for skin color origins are melanin and hemoglobin. Skin segmentation plays an important role in a wide range of image processing and computer vision applications. In short, there are three major approaches for skin segmentation: rule-based, machine learning and hybrid. They differ in terms of accuracy and computational efficiency. Generally, machine learning and hybrid approaches outperform the rule-based methods but require a large and representative training dataset and, sometimes, costly classification time as well, which can be a deal breaker for real-time applications. In this work, we propose an improvement, in three distinct versions, of a novel method for rule-based skin segmentation that works in the YCbCr color space. Our motivation is based on the hypotheses that: (1) the original rule can be complemented and, (2) human skin pixels do not appear isolated, i.e. neighborhood operations are taken into consideration. The method is a combination of some correlation rules based on these hypotheses. Such rules evaluate the combinations of chrominance Cb, Cr values to identify the skin pixels depending on the shape and size of dynamically generated skin color clusters. The method is very efficient in terms of computational effort as well as robust in very complex images. / A pele humana é constituída de uma série de camadas distintas, cada uma das quais reflete uma porção de luz incidente, depois de absorver uma certa quantidade dela pelos pigmentos que se encontram na camada. Os principais pigmentos responsáveis pela origem da cor da pele são a melanina e a hemoglobina. A segmentação de pele desempenha um papel importante em uma ampla gama de aplicações em processamento de imagens e visão computacional. Em suma, existem três abordagens principais para segmentação de pele: baseadas em regras, aprendizado de máquina e híbridos. Elas diferem em termos de precisão e eficiência computacional. Geralmente, as abordagens com aprendizado de máquina e as híbridas superam os métodos baseados em regras, mas exigem um conjunto de dados de treinamento grande e representativo e, por vezes, também um tempo de classificação custoso, que pode ser um fator decisivo para aplicações em tempo real. Neste trabalho, propomos uma melhoria, em três versões distintas, de um novo método de segmentação de pele baseado em regras que funciona no espaço de cores YCbCr. Nossa motivação baseia-se nas hipóteses de que: (1) a regra original pode ser complementada e, (2) pixels de pele humana não aparecem isolados, ou seja, as operações de vizinhança são levadas em consideração. O método é uma combinação de algumas regras de correlação baseadas nessas hipóteses. Essas regras avaliam as combinações de valores de crominância Cb, Cr para identificar os pixels de pele, dependendo da forma e tamanho dos agrupamentos de cores de pele gerados dinamicamente. O método é muito eficiente em termos de esforço computacional, bem como robusto em imagens muito complexas.

The presented master thesis deals with the evaluation of the position of the iris compared with surroundings of the eye. This technique is supposed to be use for computer control. The created software works in real-time mode, the pictures are taken with an ordinary webcam. The first part of the work presents basic algorithms used in computer vision for edit of images. The following part is focused on abilities of methods to find the face, eyes and to detect the iris. The detection and the subsequent separation of the face is based on the recognition of skin colour in the YCbCr color space. The position of eye is then searched in the face by Haar-like features. The darkest part of the eye is found by horizontal projection from surroundings and the seed point is started from this place. From the area which is filled by the seed method (Flood Fill) and which shows the iris, the cursor movement is controlled by obtained x and y position.

<p>I detta examensarbete har olika sätt att implementera färgrymdskonverterare i multipel konstant multiplikationsteknik beskrivits med VHDL, syntetiserats och jämförts med avseende på effektförbrukning.</p>

I detta examensarbete har olika sätt att implementera färgrymdskonverterare i multipel konstant multiplikationsteknik beskrivits med VHDL, syntetiserats och jämförts med avseende på effektförbrukning.

The diploma thesis is dividend into four parts. The first part describes basic terms in video technology (luminance, hue, diagrams CIE). The second part includes types of colour spaces RGB, HSV, CMY(K), YUV, YCbCr, YIQ. In the third and fourth part, these theoretical findings are used to propound laboratory observations. The laboratory observation processes the colour rendition of the colours in video technology. In the Matlab software, a user system environment was developed for operations with measured values. The software is capable of recalculating chromaticity coordinates between different colour spaces, to screen colours into diagrams CIE and to show the vectors of colours. The device used for measuring was Chromametr Konica Minolta CS-100A. A manual for the device was created. The laboratory observation was measured and processed in the form of a laboratory protocol.

Current knowledge about impact of colour models on performance of convolutional neural network is investigated in the first part of this thesis. The experiment based on obtained knowledge is conducted in the second part. Six colour models HSV, CIE 1931 XYZ, CIE 1976 L*a*b*, YIQ a YCbCr and deep convolutional neural network ResNet-101 are used. RGB colour model achieved the highest classification accuracy, whereas HSV color model has the lowest accuracy in this experiment.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-20T20:47:40Z No. of bitstreams: 1 ROBERTO HIGINO PEREIRA DA SILVA - DISSERTAÇÃO PPGEE 2006..pdf: 1429650 bytes, checksum: 8995a1f77f3e3471161b540826a56f6e (MD5) / Made available in DSpace on 2018-08-20T20:47:40Z (GMT). No. of bitstreams: 1 ROBERTO HIGINO PEREIRA DA SILVA - DISSERTAÇÃO PPGEE 2006..pdf: 1429650 bytes, checksum: 8995a1f77f3e3471161b540826a56f6e (MD5) Previous issue date: 2006-05-29 / A extração de objetos em uma imagem tem várias aplicações na área da automação, tais como: reconhecimento de padrões em sistemas de vigilância, visão de robôs e outros. Este trabalho apresenta um algoritmo estatístico de extração de imagens no espaço de cores RGB implementado em uma plataforma DSP e a análise dos resultados obtidos. É proposto um outro método estatístico para extração de imagens em fundo desconhecido, podendo ser homogêneo ou não- homogêneo. O algoritmo proposto tem como base o espaço de cores YCbCr , é destinado a aplicações que exigem performance e tolerância a um determinado intervalo de erro. Utiliza a métrica dos máximos para determinar a distância entre os vetores desse espaço, sendo capaz de suportar pequenas variações de luminosidade. Apresenta- se a simulação que validou a funcionalidade desse algoritmo. / The images objects embedded extraction has several applicationns in theautomation area, such as: patterns recognition in surveillance systems, robots vision and others. An image statistical extraction algorithm in the RGB colors space was implemented in a DSP`plataform and the obtained results analysis are presented in this dissertation. We propose a new statistical method for objects extraction in an unknown background (non-homogenous or homogenous), using the YCbCr space. It uses maximum metric to determine its distances betwenn the space vectors, being capable to suport small global variations and places of brightness. They are presented the simulation results that validate the algorithm functionality for applications that demand performance and be tolerant to a determined error interval.