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Shape analysis through the ultimate levelings

Abstract

In the last years efforts in research in computer vision field has been growing, it enabled the development of numerous applications in various field of science and industry. This increasing was motivated, in part, by the spread of sensors able to acquire high-quality images and the emergence of computers with more processing power, making it possible to solve problems more complex, such as: analyze the shape of objects in images.In general, in a typical problem of shape analysis, an image of a scene containing the object of interest is acquired and segmented to isolate the shape of the object of interest. Thereafter, the shape of the object of interest is represented so that we can extract features which describe the shape and so recognize it through of a classifier.In this context, we intend to explore a theory based on residual operators defined in the context of Mathematical Morphology allowing us to reduce an image analysis problem in n shape analysis problems. More precisely, this project seek to make theoretical and methodological contributions in the class of ultimate leveling operators. Furthermore, we aim to build and provide practical applications of this operator class in image analysis problems. (AU)

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VEICULO: TITULO (DATA)

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
DIAS, CLEBER GUSTAVO; DA SILVA, LUIZ CARLOS; LUZ ALVES, WONDER ALEXANDRE. A Histogram of Oriented Gradients Approach for Detecting Broken Bars in Squirrel-Cage Induction Motors. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, v. 69, n. 9, p. 6968-6981, . (16/02547-5, 16/02525-1, 18/05214-2)
SILVA, LUIZ C.; DIAS, CLEBER G.; ALVES, WONDER A. L.; KURKOVA, V; MANOLOPOULOS, Y; HAMMER, B; ILIADIS, L; MAGLOGIANNIS, I. A Histogram of Oriented Gradients for Broken Bars Diagnosis in Squirrel Cage Induction Motors. ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT I, v. 11139, p. 10-pg., . (16/02525-1, 16/02547-5)
ALVES, WONDER A. L.; GOBBER, CHARLES F.; HASHIMOTO, RONALDO F.; CAMPILHO, A; KARRAY, F; ROMENY, BT. Plant Bounding Box Detection from Desirable Residues of the Ultimate Levelings. IMAGE ANALYSIS AND RECOGNITION (ICIAR 2018), v. 10882, p. 8-pg., . (16/02547-5)
ALVES, WONDER A. L.; HASHIMOTO, RONALDO F.; MARCOTEGUI, BEATRIZ. Ultimate levelings. COMPUTER VISION AND IMAGE UNDERSTANDING, v. 165, p. 60-74, . (16/02547-5)

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