Although the analysis of data is a task that has gained the interest of the statistical community in recent years and whose familiarity with the statistical computing environment, they encourage the current statistical community (to students and teachers of the area) to complete statistical analysis reproducible by means of the tool R. However for years there has been a gap between the calculation of matrices on a large scale and the term "big data", in this work the Normalized Cut algorithm for images is applied. Despite the expected, the R environment to do image analysis is poorly, in comparison with other computing platforms such as the Python language or with specialized software such as OpenCV.
Being well known the absence of such function, in this work we share an implementation of the Normalized Cut algorithm in the R environment with extensions to programs and processes performed in C ++, to provide the user with a friendly interface in R to segment images. The article concludes by evaluating the current implementation and looking for ways to generalize the implementation for a large scale context and reuse the developed code.
Key words: Normaliced Cut, image segmentation, Lanczos algorithm, eigenvalues and eigenvectors, graphs, similarity matrix, R (the statistical computing environment), open source, large scale and big data.
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This paper provides a sample of a LATEX document for the NIME conference series. It conforms, somewhat loosely, to the formatting guidelines for ACM SIG Proceedings. It is an alternate style which produces a tighter-looking paper and was designed in response to concerns expressed, by authors, over page-budgets. It complements the document Author’s (Alternate) Guide to Preparing ACM SIG Proceedings Using LATEX2ε and BibTEX. This source file has been written with the intention of being compiled under LATEX2ε and BibTeX.
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This paper describes the use of the LaTeX2e sagej.cls class file for setting papers to be submitted to a SAGE Publications journal. The template can be downloaded here.
v1.2, 14 Jan 2017