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mlampros Organizing and Sharing thoughts, Receiving constructive feedback

Image segmentation based on Superpixels and Clustering

In this blog post, I’ll explain the new functionality of the OpenImageR package, SLIC and SLICO superpixels (Simple Linear Iterative Clustering) and their applicability based on an IJSR article. The author of the article uses superpixel (SLIC) and Clustering (Affinity Propagation) to perform image segmentation. The article was reproduced (and extended with Kmeans) using the latest versions of the OpenImageR and ClusterR packages.


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Extending the OpenImageR package with Gabor feature extraction

This blog post illustrates the new functionality of the OpenImageR package (Gabor Feature Extraction). The Gabor features have been used extensively in image analysis and processing (Character and Face recognition). Gabor (Nobel prize winner, an electrical engineer, and physicist) used the following wording which I think it’s worth mentioning in this blog post, “You can’t predict the future, but you can invent it.” (source).

In the following lines I’ll describe the GaborFeatureExtract R6 class which includes the following methods,


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Singularity as a software distribution / deployment tool

In this blog post, I’ll explain how someone can take advantage of Singularity to make R or Python packages available as an image file to users. This is a necessity if the specific R or Python package is difficult to install across different operating systems making that way the installation process cumbersome. Lately, I’ve utilized the reticulate package in R (it provides an interface between R and Python) and I realized from first hand how difficult it is, in some cases, to install R and Python packages and make them work nicely together in the same operating system. This blog post by no means presents the potential of Singularity or containerization tools, such as docker, but it’s mainly restricted to package distribution / deployment.


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Extreme Learning Machine

As of 2018-06-17 the elmNN package was archived and due to the fact that it was one of the machine learning functions that I used when I started learning R (it returns the output results pretty fast too) plus that I had to utilize the package last week for a personal task I decided to reimplement the R code in Rcpp. It didn’t take long because the R package was written, initially by the author, in a clear way. In the next lines I’ll explain the differences and the functionality just for reference.


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New functionality for the textTinyR package

This blog post discuss the new functionality, which is added in the textTinyR package (version 1.1.0). I’ll explain some of the functions by using the data and pre-processing steps of this blog-post.


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