RGB2X: An RGB Image Data Extract-Export Tool for Digital Image Processing and Analysis in Microsoft Excel

2018 ◽  
Vol 34 (2) ◽  
pp. 263-276 ◽  
Author(s):  
Peter Ako Larbi

Abstract. Microsoft Excel is not considered a typical software for digital image processing and analysis. However, based on its large data handling and graphing capabilities, as well as its widespread usage, it presents a good opportunity for use as a tool for teaching image data processing or use in demonstrations requiring little training. It also lends itself well as a potentially useful research tool that can benefit a wide range of users including those with little or no computer programming knowledge. This article demonstrates a new method which can be adopted for teaching concepts of image processing and analysis, consisting of systematic procedures for implementing typical operations in Excel. Categories of operations demonstrated using this method include image preprocessing, image enhancement, image classification, analysis of change over time, and image data fusion. Examples of outputs resulting from using this new method are discussed in the article. The success of this proposed method is hinged on the availability of the required image data, based on which a simple graphical user interface (GUI) application was developed in MATLAB. That application, RGBExcel or the later RGB2X, extracts RGB image data from image files of any format and file size, and exports to Excel for processing. Deployed as standalone applications, both versions can be installed on a 64-bit windows computer and run without MATLAB. Keywords: Color images, Multispectral imagery, Remote sensing, RGB image data, RGB2X, RGBExcel.


Author(s):  
Scott A. Raschke ◽  
Roman D. Hryciw ◽  
Gregory W. Donohoe

Laboratory experiments are typically performed on particulate media to study stress-deformation behavior and to verify or calibrate computer models from controlled or measured boundary stresses and displacements. However, such data do not permit the formation of shear bands, displacement fields within flowing granular media, and other small-scale localized deformation phenomena to be identified. Described are two semiautomated computer vision techniques for accurately determining the two-dimensional displacement field in granular soils from video images obtained through a transparent planar viewing window. The techniques described are applicable for studying the behavior of particulate media under plane strain and certain axisymmetric test conditions. Digital image processing and analysis routines are used in two different computer programs, Tracker and Tracer, Tracker uses a graphical user interface that allows individual particles to be selected and tracked through a sequence of digital video images. A contrast edge detection algorithm delineates the two-dimensional projected boundaries of particles. The location of the centroid of each particle selected for tracking is determined from the boundary to quantify the trajectory of each particle. Tracer maps the trace or trajectory of specially dyed fluorescent particles in a sequence of video frames. A thresholding technique segments individual particle trajectories. Together, Tracker and Tracer provide a set of tools for identifying small-scale displacement fields in particulate assemblies deforming under either quasi-static or rapid loading (such as gravity flow).


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