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Combination of local and global features in image operator learning


The problem of designing morphological operators can be modeled in the context of machine learning as a problem of learning a local function that maps the pattern observed in each point of the image to an output value. An interesting characteristic of morphological operators is the fact that they allow an intuitive interpretation of their effects since their conception is strongly based on exploring shape and topological information. Moreover, they are formally well characterized by a sound theoretical foundation. However, by construction, morphological operators do not possess interesting properties such as scale or rotation invariance and they also do not take global or context information into consideration. In this project, the main aim is to advance existing morphological operator learning methods in order to make them able to process objects of different scales and also to take global and context information into consideration. To that end, the main idea to be explored is the use of a variety of feature descriptors cited in literature, coupled to the operator combination framework. Associated theoretical, statistical and practical aspects will be studied. Applications in document image processing are planned as a means to validate the methods to be developed. (AU)

Scientific publications
(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)
MONTAGNER, IGOR S.; HIRATA, NINA S. T.; HIRATA, JR., ROBERTO. Staff removal using image operator learning. PATTERN RECOGNITION, v. 63, p. 310-320, MAR 2017. Web of Science Citations: 4.

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