Técnicas de modelagem baseada em imagens têm recebido considerável atenção da comunidade de visualização computacional devido ao potencial de criar cenas realistas a partir de um pequeno conjunto de imagens bi-dimensionais. Entretanto, a qualidade dos modelos gerados pelas ferramentas atualmente disponíveis é extremamente dependente de entradas fornecidas pelo usuário. Este trabalho propõe a execução do projeto de uma ferramenta de auxílio para sistemas de modelagem baseada em imagens que utiliza o conceito de contornos ativos para aumentar o grau de automação do processo de localização do contorno do objeto presente na fotografia, que servirá de guia para a posterior localização dos vértices desse objeto. Através desta abordagem, figuras geométricas mais simples, como pirâmides e hexaedros, puderam ser reconstruídas após a recuperação das coordenadas de seus vértices / Image Based Modelling techniques has received considerable attention from the computer vision community due to the potential to create realistic scenes from some bi-dimensional images. However, the model?s quality generated by the tools available nowadays is extremely dependent on entries provided by the user. This work proposes the execution of a help tool project for image based modelling systems that uses the active contours concept to increase the process automation degree of locating the contour of an object in the image, which will guide the vertex location process of this object. Through this approach, simple geometric figures, as pyramids and squares, could be reconstructed after the vertex coordinates recuperation

Técnicas de modelagem baseada em imagens têm recebido considerável atenção da comunidade de visualização computacional devido ao potencial de criar cenas realistas a partir de um pequeno conjunto de imagens bi-dimensionais. Entretanto, a qualidade dos modelos gerados pelas ferramentas atualmente disponíveis é extremamente dependente de entradas fornecidas pelo usuário. Este trabalho propõe a execução do projeto de uma ferramenta de auxílio para sistemas de modelagem baseada em imagens que utiliza o conceito de contornos ativos para aumentar o grau de automação do processo de localização do contorno do objeto presente na fotografia, que servirá de guia para a posterior localização dos vértices desse objeto. Através desta abordagem, figuras geométricas mais simples, como pirâmides e hexaedros, puderam ser reconstruídas após a recuperação das coordenadas de seus vértices / Image Based Modelling techniques has received considerable attention from the computer vision community due to the potential to create realistic scenes from some bi-dimensional images. However, the model?s quality generated by the tools available nowadays is extremely dependent on entries provided by the user. This work proposes the execution of a help tool project for image based modelling systems that uses the active contours concept to increase the process automation degree of locating the contour of an object in the image, which will guide the vertex location process of this object. Through this approach, simple geometric figures, as pyramids and squares, could be reconstructed after the vertex coordinates recuperation

nÃo hà / O mapeamento da poluiÃÃo de Ãleo no mar utilizando imagens de Radar de Abertura SintÃtica (SAR, do inglÃs Synthetic Aperture Radar) à uma importante Ãrea de interesse na Ãrea da vigilÃncia ambiental. Pode-se utilizar imagens SAR para extrair caracterÃsticas atravÃs de diferentes mÃtodos com o objetivo de predizer atravÃs de Redes Neurais Artificiais (RNAs) se uma regiÃo especÃfica contÃm ou nÃo uma mancha de Ãleo. O principal problema dessa abordagem à a ocorrÃncia de excessivos alarmes falsos decorrentes de erros de classificaÃÃo. Manchas de Ãleo sÃo eventos raros e a pequena disponibilidade de imagens contendo manchas à um fator limitante do desempenho dos classificadores. Este trabalho propÃe a utilizaÃÃo de mÃltiplos conjuntos de caracterÃsticas e mÃtodos de combinaÃÃo de classificadores para minimizar o nÃmero de alarmes falsos a fim de possibilitar a reduÃÃo de custos operacionais de sistemas automÃticos de deteÃÃo de manchas de Ãleo. As imagens SAR utilizadas neste trabalho nÃo estÃo limitadas a um Ãnico sistema de imageamento e diferentes conjuntos de caracterÃsticas baseados na geometria e textura das manchas foram testados. Os desempenhos de generalizaÃÃo de mÃtodos de combinaÃÃo de classificadores, como boosting e bagging, foram comparados com aqueles obtidos com classificadores individuais, como Perceptron Multi-Camadas (MLP, do inglÃs Multi-Layer Perceptron) e MÃquina de Vetor de Suporte (SVM, do inglÃs Support Vector Machine). Os resultados experimentais sugerem que a caracterizaÃÃo das manchas de Ãleo pode ser significativamente melhorada atravÃs do uso do boosting,mesmo quando poucas imagens amostrais estÃo disponÃveis. / Mapping ocean oil pollution by using Synthetic Aperture Radar (SAR) images is an important area of interest for environmental surveillance. One can make use of the SAR images to extract features by using different methods in order to predict if a specific region contains an oil spill or not using Artificial Neural Networks (ANN). A major problem in this approach is the number of false alarms due to misclassification. Oil spills are rare events and the number of available images containing spills is atistically small which is a limitation for the classifier performance. This work proposes the use of multiple feature sets and classifier combining methods to minimize the number of false alarms and thus, reduce the operational costs of automatic oil spill detection systems. The SAR images used in this work are not limited to a specific imaging system and different feature sets based on geometry and texture of the spills were tested. The generalization performances of classifier combination methods as boosting and bagging were compared with those resulting from single classifiers as Multilayer Perceptron (MLP) and Support Vector Machines (SVM). The experimental results suggest that oil spill characterization can be significantly improved using boosting even when few image samples are available and the feature sets have high dimensionality.

Gastric and esophagogastric junction cancers are heterogeneous and aggressive tumors with an unpredictable response to cytotoxic treatment. New methods allowing for the analysis of drug resistance are needed. Here, we describe a novel technique by which human tumor specimens can be cultured ex vivo, preserving parts of the natural cancer microenvironment. Using a tissue chop- per, fresh surgical tissue samples were cut in 400 μm slices and cultivated in 6-well plates for up to 6 days. The slices were processed for routine histopa- thology and immunohistochemistry. Cytokeratin stains (CK8, AE1/3) were ap- plied for determining tumor cellularity, Ki-67 for proliferation, and cleaved caspase-3 staining for apoptosis. The slices were analyzed under naive conditions and following 2–4 days in vitro exposure to 5-FU and cisplatin. The slice culture technology allowed for a good preservation of tissue morphology and tumor cell integrity during the culture period. After chemotherapy exposure, a loss of tumor cellularity and an increase in apoptosis were observed. Drug sensitivity of the tumors could be assessed. Organotypic slice cultures of gastric and es- ophagogastric junction cancers were successfully established. Cytotoxic drug effects could be monitored. They may be used to examine mechanisms of drug resistance in human tissue and may provide a unique and powerful ex vivo platform for the prediction of treatment response.

Gastric and esophagogastric junction cancers are heterogeneous and aggressive tumors with an unpredictable response to cytotoxic treatment. New methods allowing for the analysis of drug resistance are needed. Here, we describe a novel technique by which human tumor specimens can be cultured ex vivo, preserving parts of the natural cancer microenvironment. Using a tissue chop- per, fresh surgical tissue samples were cut in 400 μm slices and cultivated in 6-well plates for up to 6 days. The slices were processed for routine histopa- thology and immunohistochemistry. Cytokeratin stains (CK8, AE1/3) were ap- plied for determining tumor cellularity, Ki-67 for proliferation, and cleaved caspase-3 staining for apoptosis. The slices were analyzed under naive conditions and following 2–4 days in vitro exposure to 5-FU and cisplatin. The slice culture technology allowed for a good preservation of tissue morphology and tumor cell integrity during the culture period. After chemotherapy exposure, a loss of tumor cellularity and an increase in apoptosis were observed. Drug sensitivity of the tumors could be assessed. Organotypic slice cultures of gastric and es- ophagogastric junction cancers were successfully established. Cytotoxic drug effects could be monitored. They may be used to examine mechanisms of drug resistance in human tissue and may provide a unique and powerful ex vivo platform for the prediction of treatment response.