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Orientador: Teresa Cristina Tarlé Pissarra / Coorientador: David Luciano Rosalen / Banca: Luiz Henrique da Silva Rotta / Banca: Marcílio Vieira Martins Filho / Banca: Rejane Ennes Cicerelli / Banca: Newton La Scxala Junior / Resumo: A voçoroca é o estágio mais avançado da erosão hídrica, causando inúmeros prejuízos para o meio ambiente e para o homem. Devido à extensão desse fenômeno e a dificuldade de acesso em campo, as técnicas de detecção automática de voçorocas têm despertado interesse, especialmente por meio da Análise de Imagens Baseada em Objetos Geográficos (GEOBIA). O objetivo desse trabalho foi mapear voçorocas utilizando a GEOBIA a partir de imagens RapidEye e dados SRTM, em duas regiões localizadas em Uberaba, Minas Gerais. Para isso, foi proposto aplicar o Índice de Avaliação da Segmentação (SEI) na etapa de segmentação da imagem. A criação das regras para detecção das voçorocas foi realizada de forma empírica, no software InterIMAGE, e de forma automática, a partir do algoritmo de árvore de decisão. A avaliação da acurácia foi realizada por meio dos coeficientes de concordância extraídos da matriz de confusão e, adicionalmente, a partir da sobreposição com dados de referência vetorizados manualmente. O índice SEI proporcionou a criação de objetos semelhantes às voçorocas, permitindo a extração de atributos específicos desses alvos. As regras de classificação do modelo empírico permitem detectar voçorocas nas duas áreas de estudos, ainda que essas feições ocupem uma pequena porção da cena. Os modelos empíricos alcançaram resultados satisfatórios: índice Kappa de 0,74 e F-measure de 53,46% na área 1, e índice Kappa de 0,73 e F-measure de 55,95% na área 2. A informação altimétrica mostrou ser... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Gully is the most advanced stage of water erosion, causing numerous damages to the environment and man. Due to the extension of this phenomenon and the difficulty of access in the field, automatic gully detection techniques have aroused interest, especially through Geographic Object Based Image Analysis (GEOBIA). The objective of this work was to map gullies using GEOBIA from RapidEye images and SRTM data, in two regions located in Uberaba, Minas Gerais. It was proposed to apply the Segmentation Evaluation Index (SEI) in the image segmentation stage. The rule set creation for gully detection was made empirically in the InterIMAGE software, and automatically, from the decision tree algorithm. The accuracy assessment was performed based on concordance coefficients extracted from the confusion matrix and, additionally, overlapping manually digitized reference data. The SEI index allowed the creation of objects similar to real gullies, providing the extraction of specific attributes of these targets. Empirical model rule set allowed gully detection on both study areas, although these features occupied a small portion of the scene. Empirical models have achieved very good results: Kappa index of 0.74 and F-measure of 53.46% in area 1, and Kappa index of 0.73 and F-measure of 55.95% in area 2. Altimetric information proved to be an important parameter for gully detection, since slope removal from the empirical models reduced the F-measure index by 34,90% in area 1 and 28,65% in are... (Complete abstract click electronic access below) / Doutor

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The automation of information extraction from earth observation imagery has become a field of active research. This is mainly due to the high volumes of remotely sensed data that remain unused and the possible benefits that the extracted information can provide to a wide range of interest groups. In this work an earth observation image processing system is presented and profiled that attempts to streamline the information extraction process, without degradation of the quality of the extracted information, for geographic object anomaly detection. The proposed system, implemented as a software application, combines recent research in automating image segment generation and automatically finding statistical classifier parameters and attribute subsets using evolutionary inspired search algorithms. Exploratory research was conducted on the use of an edge metric as a fitness function to an evolutionary search heuristic to automate the generation of image segments for a region merging segmentation algorithm having six control parameters. The edge metric for such an application is compared with an area based metric. The use of attribute subset selection in conjunction with a free parameter tuner for a one class support vector machine (SVM) classifier, operating on high dimensional object based data, was also investigated. For common earth observation anomaly detection problems using typical segment attributes, such a combined free parameter tuning and attribute subset selection system provided superior statistically significant results compared to a free parameter tuning only process. In some extreme cases, due to the stochastic nature of the search algorithm employed, the free parameter only strategy provided slightly better results. The developed system was used in a case study to map a single class of interest on a 22.5 x 22.5km subset of a SPOT 5 image and is compared with a multiclass classification strategy. The developed system generated slightly better classification accuracies than the multiclass classifier and only required samples from the class of interest. / AFIKAANSE OPSOMMING: Die outomatisering van die verkryging van inligting vanaf aardwaarnemingsbeelde het in sy eie reg 'n navorsingsveld geword as gevolg van die groot volumes data wat nie benut word nie, asook na aanleiding van die moontlike bydrae wat inligting wat verkry word van hierdie beelde aan verskeie belangegroepe kan bied. In hierdie tesis word 'n aardwaarneming beeldverwerkingsstelsel bekend gestel en geëvalueer. Hierdie stelsel beoog om die verkryging van inligting van aardwaarnemingsbeelde te vergemaklik deur verbruikersinteraksie te minimaliseer, sonder om die kwaliteit van die resultate te beïnvloed. Die stelsel is ontwerp vir geografiese voorwerp anomalie opsporing en is as 'n sagteware program geïmplementeer. Die program kombineer onlangse navorsing in die gebruik van evolusionêre soek-algoritmes om outomaties goeie beeldsegmente te verkry en parameters te vind, sowel as om kenmerke vir 'n statistiese klassifikasie van beeld segmente te selekteer. Verkennende navorsing is gedoen op die benutting van 'n rand metriek as 'n passings funksie in 'n evolusionêre soek heuristiek om outomaties goeie parameters te vind vir 'n streeks kombinering beeld segmentasie algoritme met ses beheer parameters. Hierdie rand metriek word vergelyk met 'n area metriek vir so 'n toepassing. Die nut van atribuut substel seleksie in samewerking met 'n vrye parameter steller vir 'n een klas steun vektor masjien (SVM) klassifiseerder is ondersoek op hoë dimensionele objek georiënteerde data. Vir algemene aardwaarneming anomalie opsporings probleme met 'n tipiese segment kenmerk versameling, het so 'n stelsel beduidend beter resultate as 'n eksklusiewe vrye parameter stel stelsel gelewer in sommige uiterste gevalle. As gevolg van die stogastiese aard van die soek algoritme het die eksklusiewe vrye parameter stel strategie effens beter resultate gelewer. Die stelsel is getoets in 'n gevallestudie waar 'n enkele klas op 'n 22.5 x 22.5km substel van 'n SPOT 5 beeld geïdentifiseer word. Die voorgestelde stelsel, wat slegs monsters van die gekose klas gebruik het, het beter klassifikasie akkuraathede genereer as die multi klas klassifiseerder.

A avaliação de qualidade de segmentação de vídeos tem se mostrado um problema pouco investigado no meio científico. Apesar disso, estudos recentes na área resultaram em algumas métricas que têm como finalidade avaliar objetivamente a qualidade da segmentação produzida pelos algoritmos. Tais métricas consideram as diferentes formas em que os erros ocorrem (fatores perceptuais) e seus parâmetros são ajustados de acordo com a aplicação em que se pretende utilizar os vídeos segmentados. Neste trabalho apresentam-se: i) uma avaliação da métrica que representa o estado-da-arte, demonstrando que seu desempenho varia de acordo com o algoritmo; ii) um método subjetivo para avaliação de qualidade de segmentação; e iii) uma nova métrica perceptual objetiva, derivada do método subjetivo aqui proposto, capaz de encontrar o melhor ajuste dos parâmetros de dois algoritmos de segmentação encontrados na literatura, quando os vídeos por eles segmentados são utilizados na composição de cenas em ambientes de Teleconferência Imersiva. / Assessment of video segmentation quality is a problem seldom investigated by the scientific community. Nevertheless, recent studies presented some objective metrics to evaluate algorithms. Such metrics consider different ways in which segmentation errors occur (perceptual factors) and its parameters are adjusted according to the application for which the segmented frames are intended. In this work: i) we demonstrate empirically that the performance of existing metrics changes according to the segmentation algorithm; ii) we developed a subjective method to evaluate segmentation quality; and iii) we contribute with a new objective metric derived on the basis of experiments from subjective method in order to adjust the parameters of two bilayer segmentation algorithms found in the literature when these algorithms are used for compose scenes in Immersive Teleconference environments.

A avaliação de qualidade de segmentação de vídeos tem se mostrado um problema pouco investigado no meio científico. Apesar disso, estudos recentes na área resultaram em algumas métricas que têm como finalidade avaliar objetivamente a qualidade da segmentação produzida pelos algoritmos. Tais métricas consideram as diferentes formas em que os erros ocorrem (fatores perceptuais) e seus parâmetros são ajustados de acordo com a aplicação em que se pretende utilizar os vídeos segmentados. Neste trabalho apresentam-se: i) uma avaliação da métrica que representa o estado-da-arte, demonstrando que seu desempenho varia de acordo com o algoritmo; ii) um método subjetivo para avaliação de qualidade de segmentação; e iii) uma nova métrica perceptual objetiva, derivada do método subjetivo aqui proposto, capaz de encontrar o melhor ajuste dos parâmetros de dois algoritmos de segmentação encontrados na literatura, quando os vídeos por eles segmentados são utilizados na composição de cenas em ambientes de Teleconferência Imersiva. / Assessment of video segmentation quality is a problem seldom investigated by the scientific community. Nevertheless, recent studies presented some objective metrics to evaluate algorithms. Such metrics consider different ways in which segmentation errors occur (perceptual factors) and its parameters are adjusted according to the application for which the segmented frames are intended. In this work: i) we demonstrate empirically that the performance of existing metrics changes according to the segmentation algorithm; ii) we developed a subjective method to evaluate segmentation quality; and iii) we contribute with a new objective metric derived on the basis of experiments from subjective method in order to adjust the parameters of two bilayer segmentation algorithms found in the literature when these algorithms are used for compose scenes in Immersive Teleconference environments.

<p>Mammographic screening is an effective way to detect breast cancer. Early detection of breast cancer depends to a high degree on the adequacy of the mammogram from which the diagnosis is made. Today, most of the analysis of the mammogram is performed by radiologists. Computer-aided diagnosis (CAD) systems have been proposed as an aid to increase the efficiency and effectiveness of the screening procedure by automatically indicating abnormalities in the mammograms. However, in order for a CAD system to be stable and efficient, the input images need to be adequate. Criteria for adequacy include: high resolution, low image noise and high image contrast. Additionally, the breast needs to be adequately positioned and compressed to properly visualise the entire breast and especially the glandular tissue.</p><p>This thesis addresses questions regarding the automatic determination of mammogram adequacy with the focus on breast positioning and segmentation evaluation. The goal and, thus, the major technical challenge is to develop algorithms that support fully automatic quality checks. The relevant quality criteria are discussed in Chapter 2. The aim of this discussion is to compile a comprehensive list of necessary criteria that a system for automatic assessment of mammographic adequacy must satisfy. Chapter 3 gives an overview of research performed in computer-aided analysis of mammograms. It also provides basic knowledge about image analysis involved in the research area of computerized mammography in general, and in the papers of this thesis, in particular. In contrast, Chapter 4 describes basic knowledge about segmentation evaluation, which is a highly important component in image analysis. Papers I–IV propose algorithms for measuring the quality of a mammogram according to certain criteria and addresses problems related to them. A method for automatic analysis of the shape of the pectoralis muscle is presented in Paper I. Paper II proposes a fully automatic method for extracting the breast border. A geometric assumption used by radiologists is that the nipple is located at the point on the breast border being furthest away from the pectoralis muscle. This assumption is investigated in Paper III, and a method for automatically restricting the search area is proposed. There has been an increasing need to develop an automated segmentation algorithm for extracting the glandular tissue, where the majority of breast cancer occur. In Paper IV, a novel approach for solving this problem in a robust and accurate way is proposed. Paper V discusses the challenges involved in evaluating the quality of segmentation algorithms based on ground truths provided by an expert panel. A method to relate ground truths provided by several experts to each other in order to establish levels of agreement is proposed. Furthermore, this work is used to develop an algorithm that combines an ensemble of markings into one surrogate ground truth.</p>

Mammographic screening is an effective way to detect breast cancer. Early detection of breast cancer depends to a high degree on the adequacy of the mammogram from which the diagnosis is made. Today, most of the analysis of the mammogram is performed by radiologists. Computer-aided diagnosis (CAD) systems have been proposed as an aid to increase the efficiency and effectiveness of the screening procedure by automatically indicating abnormalities in the mammograms. However, in order for a CAD system to be stable and efficient, the input images need to be adequate. Criteria for adequacy include: high resolution, low image noise and high image contrast. Additionally, the breast needs to be adequately positioned and compressed to properly visualise the entire breast and especially the glandular tissue. This thesis addresses questions regarding the automatic determination of mammogram adequacy with the focus on breast positioning and segmentation evaluation. The goal and, thus, the major technical challenge is to develop algorithms that support fully automatic quality checks. The relevant quality criteria are discussed in Chapter 2. The aim of this discussion is to compile a comprehensive list of necessary criteria that a system for automatic assessment of mammographic adequacy must satisfy. Chapter 3 gives an overview of research performed in computer-aided analysis of mammograms. It also provides basic knowledge about image analysis involved in the research area of computerized mammography in general, and in the papers of this thesis, in particular. In contrast, Chapter 4 describes basic knowledge about segmentation evaluation, which is a highly important component in image analysis. Papers I–IV propose algorithms for measuring the quality of a mammogram according to certain criteria and addresses problems related to them. A method for automatic analysis of the shape of the pectoralis muscle is presented in Paper I. Paper II proposes a fully automatic method for extracting the breast border. A geometric assumption used by radiologists is that the nipple is located at the point on the breast border being furthest away from the pectoralis muscle. This assumption is investigated in Paper III, and a method for automatically restricting the search area is proposed. There has been an increasing need to develop an automated segmentation algorithm for extracting the glandular tissue, where the majority of breast cancer occur. In Paper IV, a novel approach for solving this problem in a robust and accurate way is proposed. Paper V discusses the challenges involved in evaluating the quality of segmentation algorithms based on ground truths provided by an expert panel. A method to relate ground truths provided by several experts to each other in order to establish levels of agreement is proposed. Furthermore, this work is used to develop an algorithm that combines an ensemble of markings into one surrogate ground truth.

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