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In this thesis, we tackle the problem of designing and implementing a wireless video sensor network for a surveillance application. The goal was to design a low power content aware system that is able to take an image from an image sensor, determine blocks in the image that contain important information and encode those block for transmission thus reducing the overall transmission effort. At the same time, the encoder and the preprocessor must not consume so much computation power that the utility of this system is lost. We have implemented such a system which uses a combination of Edge Detection and Frame Differencing to determine useful information within an image. A JPEG encoder then encodes the important blocks for transmission. An implementation on a FPGA is presented in this work. This work demonstrates that preprocessing gives us a 48.6 % reduction in power for a single frame while maintaining a delivery ratio of above 85 % for the given set of test frames.

This work deals with image processing and face detection. Includes approaches to the problems of image processing. Furthermore, it focuses mainly on the choice of appropriate libraries and implementation of algorithms able to detect faces from the input image data.

With arrival of affordable digital technology we are increasingly coming into contact with digital images. Cameras are no longer dedicated devices, but part of almost every mobile phone, PDA and laptop. This paper discusses methods for enhancing quality of digital images with focus on removing noise, creating high dynamic range (HDR) images and extending depth of field (DOF). It contains familiarization with technical means for acquiring digital image, explains origin of image noise. Further attention is drawn to HDR, from explaining the term, physical basis, difference between HDR sensing and HDR displaying, to survey and historical development of methods dealing with creating HDR images. The next part is explaining DOF when displaying, physical basis of this phenomenon and review of methods used for DOF extension. The paper mentions problem of acquiring images needed for solving given tasks and designs method for acquiring images. Using it a database of test images for each task was created. Part of the paper also deals with design of a program, that implements discussed methods, for solving the given tasks. With help of proposed class imgmap, quality of output images is improved, by modifying maps of input images. The paper describes methods, improvements, means of setting parameters and their effects on algorithms and control of program using proposed GUI. Finally, comparison with free software for extending DOF takes place. The proposed software provides at least comparable results, the correct setting of parameters for specific cases allows to achieve better properties of the resulting image. Time requirements of image processing are worse because designed software was not optimised.

This thesis deals with design and realization of methods of preprocessing of X-ray images and its storage. In the first part of this thesis, there were designed and implemented methods for preprocessing of series of X-ray images such as averaging after image registration or merging of images to a HDR image using Debevec method. In the following part of the thesis, there was done a literary research of data formats based on which was implemented a library for x-ray images storage. Both implemented methods allow to reduce a random noise by merging a series of images. Application of the Debevec method also allow to increase a dynamic range of image.

The development in modern medicine has allowed to study human genome and detect predispositions to several diseases. One of very promising techniques is the analysis of human karyotype, i.e., the number and appearance of chromosomes in the cell nucleus. The most important step in the karyotype analysis is the chromosome detection and categorization. In this work, a new algorithm for detection of chromosomes from an image of microscopic DNA sample and their categorization into seven groups was developed. The algorithm was implemented in Matlab. The accuracy of segmentation and classification was tested on a set of images from two databases with 117 and 38 images, respectively. The sensitivity of the developed segmentation reached 88% while the value of positive predictivity of segmentation reached 92%. The success rate of chromosome pairing achieves 77%.

A textura de uma imagem apresenta informações importantes sobre as características de um objeto. Usar essa informação para reconhecimento de padrões vem sendo uma tarefa bastante pesquisada na área de processamento de imagens e aplicado em atividades como indústria têxtil, biologia, análise de imagens médicas, imagens de satélite, análise de peças industriais, entre outros. Muitos pesquisadores focam em criar mecanismos que convertam a imagem em um vetor de características a fim de utilizar um classificador sobre esse vetores. No entanto, as imagens podem ser transformadas para que que características peculiares sejam evidenciadas fazendo com que extratores de características já existentes explorem melhor as imagens. Esse trabalho tem como objetivo estudar a influência da aplicação de métodos de pré-processamento em imagens de textura para a posterior análise das imagens. Os métodos escolhidos são seis: difusão isotrópica, difusão anisotrópica clássica, dois métodos de regularização da difusão anisotrópica, um método de difusão morfológica e a transformada de distância. Além disso, os métodos foram aliados a sete descritores já conhecidos da literatura para que as características das imagens tranformadas sejam extraídas. Resultados mostram um aumento significativo no desempenho dos classificadores KNN e Naive Bayes quando utilizados nas imagens transformadas de quatro bases de textura: Brodatz, Outex, Usptex e Vistex. / The texture of an image plays an important source of information of the image content. The use of this information to pattern recognition became very popular in image processing area and has applications such in textile industry, biology, medical image analysis, satelite images analysis, industrial equipaments analysis, among others. Many researchers focus on creating different methods to convert the input image to a feature vector to the able to classify the image based on these vectors. However, images can be modified in different ways such that important features are enhanced. Therefore, descriptors are able to extract features easily to perform a better representation of the image. This project aims to apply six different preprocessing methods to analyze their power of enhancement on the texture extraction. The methods are: isotropic diffusion, the classic anisotropic diffusion, two regularizations of the anisotropic diffusion, a morphologic diffusion and the distance transform. To extract the features of these modified images, seven texture analysis algorithms are used along KNN and Naive Bayes to classify the textures. Results show a significant increase when datasets Brodatz, Vistex, Usptex and Outex are transformed prior to texture analysis and classification.

A textura de uma imagem apresenta informações importantes sobre as características de um objeto. Usar essa informação para reconhecimento de padrões vem sendo uma tarefa bastante pesquisada na área de processamento de imagens e aplicado em atividades como indústria têxtil, biologia, análise de imagens médicas, imagens de satélite, análise de peças industriais, entre outros. Muitos pesquisadores focam em criar mecanismos que convertam a imagem em um vetor de características a fim de utilizar um classificador sobre esse vetores. No entanto, as imagens podem ser transformadas para que que características peculiares sejam evidenciadas fazendo com que extratores de características já existentes explorem melhor as imagens. Esse trabalho tem como objetivo estudar a influência da aplicação de métodos de pré-processamento em imagens de textura para a posterior análise das imagens. Os métodos escolhidos são seis: difusão isotrópica, difusão anisotrópica clássica, dois métodos de regularização da difusão anisotrópica, um método de difusão morfológica e a transformada de distância. Além disso, os métodos foram aliados a sete descritores já conhecidos da literatura para que as características das imagens tranformadas sejam extraídas. Resultados mostram um aumento significativo no desempenho dos classificadores KNN e Naive Bayes quando utilizados nas imagens transformadas de quatro bases de textura: Brodatz, Outex, Usptex e Vistex. / The texture of an image plays an important source of information of the image content. The use of this information to pattern recognition became very popular in image processing area and has applications such in textile industry, biology, medical image analysis, satelite images analysis, industrial equipaments analysis, among others. Many researchers focus on creating different methods to convert the input image to a feature vector to the able to classify the image based on these vectors. However, images can be modified in different ways such that important features are enhanced. Therefore, descriptors are able to extract features easily to perform a better representation of the image. This project aims to apply six different preprocessing methods to analyze their power of enhancement on the texture extraction. The methods are: isotropic diffusion, the classic anisotropic diffusion, two regularizations of the anisotropic diffusion, a morphologic diffusion and the distance transform. To extract the features of these modified images, seven texture analysis algorithms are used along KNN and Naive Bayes to classify the textures. Results show a significant increase when datasets Brodatz, Vistex, Usptex and Outex are transformed prior to texture analysis and classification.

This thesis deals with creation of a mobile library for preprocessing of images with text which represents a part of a system for text recognition. The library is realized with emphasis on generality of use, efficiency and portability. The library providing a set of algorithms primarily for image quality assessment and text detection was created in this thesis. These algorithms enable a substantial decrease in volume of transmitted data and speed up and refinement of the recognition process. An example application for the Android platform able to analyze composition of foods stated on their wrappings was created as well. Overall, the library (system) simplifies development of mobile applications with focus on text extraction and analysis. The mobile application then provides a comfortable way of food harmfulness verification. The thesis offers a reader an overview of current solutions and tools available in this field, it provides a breakdown of important image preprocessing algorithms and guides him through the construction of the library and the application for mobile devices.

This work deal with determination topohraphic parameters of a randomly rough surface by the help of method of shearing interferometry. It is a optical method for determination surface roughness. The basic idea is based of on deformation interference strips which are made by interference of the same mutually translated monochrome luminous wavefronts. The wavefront is created after transit or reflection monochrome lights from the surface of a studied sample. The wavefronts creates picture with deformed interference strips , which carries information about character of the surface. This information can be profited by algorithms of image processing from the picture . The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.