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Master's Dissertation
DOI
10.11606/D.76.1998.tde-23092008-110948
Document
Author
Full name
Andrea Gomes Campos Bianchi
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1998
Supervisor
Committee
Costa, Luciano da Fontoura (President)
Koberle, Roland
Lotufo, Roberto de Alencar
Title in Portuguese
Detecção e Análise de Contornos em Imagens 2D.
Keywords in Portuguese
Simulação
Sistemas de computação
Abstract in Portuguese
Neste trabalho apresentamos o desenvolvimento e a implementação de diversas técnicas de segmentação de imagens em termos de detecção de bordas, com um destaque especial para a segmentação não-linear. Os métodos considerados foram: o Gradiente, o Laplaciano da Gaussiana, a Regularização linear, e a segmentação não-linear usando o algoritmo Graduated Non Convexity, baseado na minimização de um funcional de energia associado à imagem. O tratamento matemático do funcional foi realizada segundo o paradigma do cálculo variacional. A sua principal vantagem é evidenciada durante o tratamento de bordas e descontinuidades, pois como a segmentação atua de forma não uniforme na imagem, apenas as regiões mais uniformes são suavizadas, preservando as descontinuidades, o que possibilita a conservação mais precisa dos contornos. Nos capítulos destinados a introdução das técnicas computacionais, apresentamos alguns exemplos das segmentações obtidas, possibilitando uma avaliação comparativa e qualitativa dos resultados. Aplicações em micrografias de cristais de KBr e de minerais serviram como um ensaio para a investigação da validação da segmentação através do algoritmo graduated Non Convexity.
Title in English
Detection and analysis of contours on 2D images.
Keywords in English
Computer systems
Simulation
Abstract in English
In this work we describe the development and implementation of several image segmentation techniques, with special attention focused on non linear segmentation. The considered edge detection methods are: Gradient, Laplacian of Gaussian, linear regularization, and the non-linear Graduate Non Convexity segmentation algorithm based on the minimization of the energy functional associated with the image contour. The mathematical treatment was done according to the variational calculus paradigm. The major advantage of such an approach is noted during the treatment of borders and discontinuities, since this method causes the segmentation to act non-uniformelly on the image, in such a way that just the homogeneus regions are smoothed, while preserving discontinuities and enabling more exact localization of the contours. Along the charpters dedicated to introducing the techniques, we present some examples of segmented images, enabling the qualitative and quantitative evaluation of the results. Applications to micrographies of KB4 crystals and minerals in soil provide a possibility to investigate and validate the Graduate Non Convexity segmentation methods.
 
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Publishing Date
2009-09-03
 
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