Úlceras de pele são causadas devido à deficiência na circulação sanguínea. O diagnóstico é feito pela análise visual das regiões afetadas. A quantificação da distribuição de cores da lesão, por meio de técnicas de processamento de imagens pode auxiliar na caracterização e análise da dinâmica do processo patológico e resposta ao tratamento. O processamento de imagens de úlceras dermatológicas envolve etapas relacionadas a segmentação, caracterização e indexação. Esta análise é importante para classificação, recuperação de imagens similares e acompanhamento da evolução de uma lesão. Este trabalho apresenta um estudo sobre técnicas de segmentação e caracterização de imagens coloridas de úlceras de pele, baseadas nos modelos de cores RGB, HSV, L*a*b* e L*u*v*, utilizando suas componentes na extração de informações de textura e cor. Foram utilizadas técnicas de Aprendizado de Máquina e algoritmos matemáticos para a segmentação e extração de atributos, utilizando uma base de dados com 172 imagens. Nos testes de recuperação, foram utilizadas diferentes métricas de distância para avaliação do desempenho e técnicas de seleção de atributos. Os resultados obtidos evidenciam bom potencial para apoio ao diagnóstico e acompanhamento da evolução do tratamento com valores de até 75% de precisão para as técnicas de recuperação, 0,9 de área embaixo da curva receiver-operating-characteristic na classificação e 0,04 de erro médio quadrático entre a composição de cores da imagem segmentada automaticamente e a segmentada manualmente. Nos testes utilizando seleção de atributos, foi observado uma redução nos valores de precisão de recuperação (60%) e valores similares nos tetes de classificação (0,85). / Skin ulcers are caused due to deficiency in the bloodstream. The diagnosis is made by a visual analysis of the affected area. Quantification of color distribution of the lesion by image processing techniques can aid in the characterization and response to treatment. The image processing steps involves skin ulcers related to segmentation, characterization and indexing. This analysis is important for classification, image retrieval and similar tracking the evolution of an injury. This project presents a study of segmentation techniques and characterization of color images of dermatological skin ulcers, based on the color models RGB, HSV, L*a*b* and L*u*v*, using their components in the extraction of texture and color information. Were used Machine Learning techniques, mathematical algorithms for segmentation and extraction of attributes, using a database containing 172 images in two versions. In recovery tests were used different distance metrics for performance evaluation and techniques of features selection. The results show good potential to support the diagnosis and monitoring of treatment progress with values up to 75% precision in recovery techniques, 0.9 area under the curve receiver-operating-characteristic) in classification, and 0.04 mean square error between the color composition of the automatically segmented image and the manually segmented image. In tests utilizing feature selection was observed a decrease in precision values of image retrieval (60%) and similar values in the classification\'s tests (0.85).

A document image, beside text, may contain pictures, graphs, signatures, logos, barcodes, hand-drawn sketches and/or seals. Further, the text blocks in an image may be in Manhattan or any complex layout. Document Layout Analysis is an important preprocessing step before subjecting any such image to OCR. Here, the image with complex layout and content is segmented into its constituent components. For many present day applications, separating the text from the non-text blocks is sufficient. This enables the conversion of the text elements present in the image to their corresponding editable form. In this work, an effort has been made to separate the text areas from the various kinds of possible non-text elements. The document images may have been obtained from a scanner or camera. If the source is a scanner, there is control on the scanning resolution, and lighting of the paper surface. Moreover, during the scanning process, the paper surface remains parallel to the sensor surface. However, when an image is obtained through a camera, these advantages are no longer available. Here, an algorithm is proposed to separate the text present in an image from the clutter, irrespective of the imaging technology used. This is achieved by using both the structural and textural information of the text present in the gray image. A bank of Gabor filters characterizes the statistical distribution of the text elements in the document. A connected component based technique removes certain types of non-text elements from the image. When a camera is used to acquire document images, generally, along with the structural and textural information of the text, color information is also obtained. It can be assumed that text present in an image has a certain amount of color homogeneity. So, a graph-theoretical color clustering scheme is employed to segment the iso-color components of the image. Each iso-color image is then analyzed separately for its structural and textural properties. The results of such analyses are merged with the information obtained from the gray component of the image. This helps to separate the colored text areas from the non-text elements. The proposed scheme is computationally intensive, because the separation of the text from non-text entities is performed at the pixel level Since any entity is represented by a connected set of pixels, it makes more sense to carry out the separation only at specific points, selected as representatives of their neighborhood. Harris' operator evaluates an edge-measure at each pixel and selects pixels, which are locally rich on this measure. These points are then employed for separating text from non-text elements. Many government documents and forms in India are bi-lingual or tri-lingual in nature. Further, in school text books, it is common to find English words interspersed within sentences in the main Indian language of the book. In such documents, successive words in a line of text may be of different scripts (languages). Hence, for OCR of these documents, the script must be recognized at the level of words, rather than lines or paragraphs. A database of about 20,000 words each from 11 Indian scripts1 is created. This is so far the largest database of Indian words collected and deployed for script recognition purpose. Here again, a bank of 36 Gabor filters is used to extract the feature vector which represents the script of the word. The effectiveness of Gabor features is compared with that of DCT and it is found that Gabor features marginally outperform the DOT. Simple, linear and non-linear classifiers are employed to classify the word in the feature space. It is assumed that a scheme developed to recognize the script of the words would work equally fine for sentences and paragraphs. This assumption has been verified with supporting results. A systematic study has been conducted to evaluate and compare the accuracy of various feature-classifier combinations for word script recognition. We have considered the cases of bi-script and tri-script documents, which are largely available. Average recognition accuracies for bi-script and tri-script cases are 98.4% and 98.2%, respectively. A hierarchical blind script recognizer, involving all eleven scripts has been developed and evaluated, which yields an average accuracy of 94.1%. The major contributions of the thesis are: • A graph theoretic color clustering scheme is used to segment colored text. • A scheme is proposed to separate text from the non-text content of documents with complex layout and content, captured by scanner or camera. • Computational complexity is reduced by performing the separation task on a selected set of locally edge-rich points. • Script identification at word level is carried out using different feature classifier combinations. Gabor features with SVM classifier outperforms any other feature-classifier combinations. A hierarchical blind script recognition algorithm, involving the recognition of 11 Indian scripts, is developed. This structure employs the most efficient feature-classifier combination at each individual nodal point of the tree to maximize the system performance. A sequential forward feature selection algorithm is employed to. select the most discriminating features, in a case by case basis, for script-recognition. The 11 scripts are Bengali, Devanagari, Gujarati, Kannada, Malayalam, Odiya, Puniabi, Roman. Tamil, Telugu and Urdu.

Úlceras de pele são causadas devido à deficiência na circulação sanguínea. O diagnóstico é feito pela análise visual das regiões afetadas. A quantificação da distribuição de cores da lesão, por meio de técnicas de processamento de imagens pode auxiliar na caracterização e análise da dinâmica do processo patológico e resposta ao tratamento. O processamento de imagens de úlceras dermatológicas envolve etapas relacionadas a segmentação, caracterização e indexação. Esta análise é importante para classificação, recuperação de imagens similares e acompanhamento da evolução de uma lesão. Este trabalho apresenta um estudo sobre técnicas de segmentação e caracterização de imagens coloridas de úlceras de pele, baseadas nos modelos de cores RGB, HSV, L*a*b* e L*u*v*, utilizando suas componentes na extração de informações de textura e cor. Foram utilizadas técnicas de Aprendizado de Máquina e algoritmos matemáticos para a segmentação e extração de atributos, utilizando uma base de dados com 172 imagens. Nos testes de recuperação, foram utilizadas diferentes métricas de distância para avaliação do desempenho e técnicas de seleção de atributos. Os resultados obtidos evidenciam bom potencial para apoio ao diagnóstico e acompanhamento da evolução do tratamento com valores de até 75% de precisão para as técnicas de recuperação, 0,9 de área embaixo da curva receiver-operating-characteristic na classificação e 0,04 de erro médio quadrático entre a composição de cores da imagem segmentada automaticamente e a segmentada manualmente. Nos testes utilizando seleção de atributos, foi observado uma redução nos valores de precisão de recuperação (60%) e valores similares nos tetes de classificação (0,85). / Skin ulcers are caused due to deficiency in the bloodstream. The diagnosis is made by a visual analysis of the affected area. Quantification of color distribution of the lesion by image processing techniques can aid in the characterization and response to treatment. The image processing steps involves skin ulcers related to segmentation, characterization and indexing. This analysis is important for classification, image retrieval and similar tracking the evolution of an injury. This project presents a study of segmentation techniques and characterization of color images of dermatological skin ulcers, based on the color models RGB, HSV, L*a*b* and L*u*v*, using their components in the extraction of texture and color information. Were used Machine Learning techniques, mathematical algorithms for segmentation and extraction of attributes, using a database containing 172 images in two versions. In recovery tests were used different distance metrics for performance evaluation and techniques of features selection. The results show good potential to support the diagnosis and monitoring of treatment progress with values up to 75% precision in recovery techniques, 0.9 area under the curve receiver-operating-characteristic) in classification, and 0.04 mean square error between the color composition of the automatically segmented image and the manually segmented image. In tests utilizing feature selection was observed a decrease in precision values of image retrieval (60%) and similar values in the classification\'s tests (0.85).

Dans cette thèse, nous nous intéressons à la reconnaissance d’images couleur à l’aide d’une nouvelle approche géométrique du domaine fréquentiel. La plupart des méthodes existantes ne traitent que les images en niveaux de gris au travers de descripteurs issus de la transformée de Fourier usuelle. L’extension de telles méthodes aux images multicanaux, comme par exemple les images couleur, consiste généralement à reproduire un traitement identique sur chacun des canaux. Afin d’éviter ce traitement marginal, nous étudions et mettons en perspective les différentes généralisations de la transformée de Fourier pour les images couleur. Ce travail nous oriente vers la transformée de Fourier Clifford pour les images couleur définie dans le cadre des algèbres géométriques. Une étude approfondie de celle-ci nous conduit à définir un algorithme de calcul rapide et à proposer une méthode de corrélation de phase pour les images couleur. Dans un deuxième temps, nous cherchons à généraliser à travers cette transformée de Fourier les définitions des descripteurs de Fourier de la littérature. Nous étudions ainsi les propriétés, notamment l’invariance à la translation, rotation et échelle, des descripteurs existants. Ce travail nous mène à proposer trois nouveaux descripteurs appelés “descripteurs de Fourier couleur généralisés”(GCFD) invariants en translation et en rotation.Les méthodes proposées sont évaluées sur des bases d’images usuelles afin d’estimer l’apport du contenu fréquentiel couleur par rapport aux méthodes niveaux de gris et marginales. Les résultats obtenus à l’aide d’un classifieur SVM montrent le potentiel des méthodes proposées ; les descripteurs GCFD se révèlent être plus compacts, de complexité algorithmique moindre pour des performances de classification au minimum équivalentes. Nous proposons également des heuristiques pour le choix du paramètre de la transformée de Fourier Clifford.Cette thèse constitue un premier pas vers une généralisation des méthodes fréquentielles aux images multicanaux. / In this thesis, we focus on color image recognition using a new geometric approach in the frequency domain. Most existing methods only process grayscale images through descriptors defined from the usual Fourier transform. The extension of these methods to multichannel images such as color images usually consists in reproducing the same processing for each channel. To avoid this marginal processing,we study and compare the different generalizations of color Fourier transforms. This work leads us to use the Clifford Fourier transform for color images defined in the framework of geometric algebra. A detailed study of it leads us to define a fast algorithm and to propose a phase correlation for colorimages. In a second step, with the aim of generalizing Fourier descriptors of the literature with thisFourier transform, we study their properties, including invariance to translation, rotation and scale.This work leads us to propose three new descriptors called “generalized color Fourier descriptors”(GCFD) invariant in translation and in rotation.The proposed methods are evaluated on usual image databases to estimate the contribution of color frequency content compared with grayscale and marginal methods. The results obtained usingan SVM classifier show the potential of the proposed methods ; the GCFD are more compact, have less computational complexity and give better recognition rates. We also propose heuristics for choosing the parameter of the color Clifford Fourier transform.This thesis is a first step towards a generalization of frequency methods to multichannel images.

High quality image /video has become an integral part in our day-to-day life ranging from many areas of science, engineering and medical diagnosis. All these imaging applications call for high resolution, properly focused and crisp images. However, in real situations obtaining such a high quality image is expensive, and in some cases it is not practical. In imaging systems such as digital camera, blur and noise degrade the image quality. The recorded images look blurred, noisy and unable to resolve the finer details of the scene, which are clearly notable under zoomed conditions. The post processing techniques based on computational methods extract the hidden information and thereby improve the quality of the captured images. The study in this thesis focuses on deconvolution and eventually blind de-convolution problem of a single frame captured at low light imaging conditions arising from digital photography/surveillance imaging applications. Our intention is to restore a sharp image from its blurred and noisy observation, when the blur is completely known/unknown and such inverse problems are ill-posed/twice ill-posed. This thesis consists of two major parts. The first part addresses deconvolution/blind deconvolution problem using Bayesian approach with fuzzy logic based gradient potential as a prior functional. In comparison with analog cameras, artifacts are visible in digital cameras when the images are enlarged and there is a demand to enhance the resolution. The increased resolution can be in spatial, temporal or even in both the dimensions. Super resolution reconstruction methods reconstruct images/video containing spectral information beyond that is available in the captured low resolution images. The second part of the thesis addresses resolution enhancement of observed monochromatic/color images using multiple frames of the same scene. This reconstruction problem is formulated in Bayesian domain with an aspiration of reducing blur, noise, aliasing and increasing the spatial resolution. The image is modeled as Markov random field and a fuzzy logic filter based gradient potential is used to differentiate between edge and noisy pixels. Suitable priors are adaptively applied to obtain artifact free/reduced images. In this work, all our approaches are experimentally validated using standard test images. The Matlab based programming tools are used for carrying out the validation. The performance of the approaches are qualitatively compared with results of recently proposed methods. Our results turn out to be visually pleasing and quantitatively competitive.