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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Staff removal using image operator learning

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Montagner, Igor S. ; Hirata, Nina S. T. ; Hirata, Jr., Roberto
Total Authors: 3
Document type: Journal article
Source: PATTERN RECOGNITION; v. 63, p. 310-320, MAR 2017.
Web of Science Citations: 4

Staff removal is an image processing task that aims to facilitate further analysis of music score images. Even when restricted to images in specific domains such as music score recognition, solving image processing problems usually requires the design of customized algorithms. To cope with image variabilities and the growing amount of data, machine learning based techniques emerge as a natural approach to be employed in image processing problems. In this sense, image operator learning methods are concerned with estimating, from sample pairs of input-output images of a transformation, a local function that characterizes the image transformation. These methods require the definition of some parameters, including the local information to be considered in the processing which is defined by a window. In this work we show how to apply the image operator learning technique to the staff line removal problem. We present an algorithm for window determination and show that it captures visual information relevant for staff removal. We also present a reference window set to be used in cases where the training set is not sufficiently large. Experimental results obtained with respect to synthetic and handwritten music scores under varying image conditions show that the learned image operators are comparable with especially designed state-of-the-art heuristic algorithms. (AU)

FAPESP's process: 11/23310-0 - Automatic design of image operators: extension and contextualization to not necessarily boolean lattices
Grantee:Igor dos Santos Montagner
Support type: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 15/17741-9 - Combination of local and global features in image operator learning
Grantee:Nina Sumiko Tomita Hirata
Support type: Regular Research Grants
FAPESP's process: 14/21692-0 - Exploring high-level representations in image operator learning
Grantee:Igor dos Santos Montagner
Support type: Scholarships abroad - Research Internship - Doctorate (Direct)
FAPESP's process: 11/00325-1 - Challenges in multilevel design of morphological operators
Grantee:Nina Sumiko Tomita Hirata
Support type: Regular Research Grants