RESUMOO presente trabalho tem como objetivo determinar o número de nódulos de grafita em amostra de ferro fundido nodular, de acordo com a norma NBR 6913, utilizando técnicas de processamento e análise de imagens digitais. Esta contagem determina propriedades mecânicas do ferro fundido nodular que são utilizados para projetos para aplicações na área. Deste modo, esta contagem é feita, tradicionalmente, de forma visual por um operador através do auxílio de um microscópio óptico. Com o objetivo de reduzir o tempo de contagem da quantidade de nódulos e eliminar o máximo possível de erros na contagem, foi desenvolvido um software, utilizando a Linguagem C e com auxílio da biblioteca OpenCV. Esta biblioteca é livre e agrupa técnicas otimizadas de Visão Computacional através de técnicas de Processamento Digital de Imagens. Foram utilizadas duas abordagens para contabilizar os nódulos, uma através do método Crescimento de Regiões e outra utilizando Watershed. Testes são realizados e comparados com o resultado obtido por seis especialistas utilizando a técnica visual tradicional visando avaliar os métodos propostos. Os resultados obtidos demonstram que os métodos podem ser incorporados a um sistema para cálculo do número de nódulos em ferro fundido nodular. Ao fim deste trabalho, é possível confirmar a praticidade e a confiabilidade na contagem de nódulos utilizando o software desenvolvido, em comparação ao método tradicional de contagem, demonstrando sua importância para auxílio nesta área do conhecimento. Palavras-chave:Processamento Digital de Imagens, OpenCV, ferro fundido, cálculo de nódulos de grafita. ABSTRACTThis study aims to determine the number of graphite nodules in ductile iron sample according to NBR 6913 and using techniques process and analysis in digital images. This count determines the mechanical properties of the nodular cast iron, which are used for project designs in the field. Therefore, an operator with the aid of an optical microscope does this count traditionally visually. Aiming to reduce the time of determining the amount of nodes and eliminate the maximum possible error in counting, a software was developed using the C language and with the help of the OpenCV library. This library is open source and incorporates optimized Computer Vision techniques. Two approaches were used to count the nodes, one by Region Growing and another using Watershed. Tests were performed and compared with the results obtained by six experts using traditional visual technique to evaluate the proposed methods. The results show that the methods can be incorporated into a system to calculate the number of nodules in nodular cast iron. At the end of this work, it is possible to confirm that the developed software is useful and reliable to nodule count. Therefore is demonstrated its importance to aid this knowledge area.

In many applications in metallography and analysis, many regions need to be considered and not only the current region. In cases where there are analyses with multiple images, the specialist should also evaluate neighboring areas. For example, in metallurgy, welding technology is derived from conventional testing and metallographic analysis. In welding, these tests allow us to know the features of the metal, especially in the Heat-Affected Zone (HAZ); the region most likely for natural metallurgical problems to occur in welding. The expanse of the Heat-Affected Zone exceeds the size of the area observed through a microscope and typically requires multiple images to be mounted on a larger picture surface to allow for the study of the entire heat affected zone. This image stitching process is performed manually and is subject to all the inherent flaws of the human being due to results of fatigue and distraction. The analyzing of grain growth is also necessary in the examination of multiple regions, although not necessarily neighboring regions, but this analysis would be a useful tool to aid a specialist. In areas such as microscopic metallography, which study metallurgical products with the aid of a microscope, the assembly of mosaics is done manually, which consumes a lot of time and is also subject to failures due to human limitations. The mosaic technique is used in the construct of environment or scenes with corresponding characteristics between themselves. Through several small images, and with corresponding characteristics between themselves, a new model is generated in a larger size. This article proposes the use of Digital Image Processing for the automatization of the construction of these mosaics in metallographic images. The use of this proposed method is meant to significantly reduce the time required to build the mosaic and reduce the possibility of failures in assembling the final image; therefore increasing efficiency in obtaining results and expediting the decision making process. Two different methods are proposed: One using the transformed Scale Invariant Feature Transform (SIFT), and the second using features extractor Speeded Up Robust Features (SURF). Although slower, the SIFT method is more stable and has a better performance than the SURF method and can be applied to real applications. The best results were obtained using SIFT with Peak Signal-to-Noise Ratio = 61.38, Mean squared error = 0.048 and mean-structural-similarity = 0.999, and processing time of 4.91 seconds for mosaic building. The methodology proposed shows be more promissory in aiding specialists during analysis of metallographic images.