A technical note on digitizing color mapped spectral power distribution images

Abstract

Data visualization is producing images that communicate data in the form of visual objects like lines, points, bars, or colored areas. Often it is necessary to extract numerical data from these images for further analysis. There are a wide variety of data digitization tools, however, only limited formats of plot images can be digitized using them. There exists many data visualization formats spread across different domains of science. Some of these formats need tailored solutions. One such format is color mapped spectral power distribution images of light sources captured by proprietary spectrometers. In this work, a methodology is discussed to digitize spectral plots of light sources. Unlike typical digitization tools which extract data based on contrast difference, color, or morphological similarities, color mapped spectral plots contain blending colors across the x‐axis, without containing any notable morphological features. In this work, image processing is applied to extract the contour representing a spectral plot. For color mapped spectral plots, the importance of appropriate binary input images for edge detection algorithms is highlighted. There are only a few edge‐detection methods give the desired results except a few. Further, the importance of repeatedly adjusting threshold values in extracting desired contours is also discussed. The estimated and measured spectral parameters are in mutual agreement with each other thereby validating the adopted approach.

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“…It's necessary to use Gray scale pictures, which need 8 bits per pixel, to convert SEM photos of metals.Similarly, a 24-bit pixel-based colour image [32].Then, the median filter is employed to determine the SEM image's surrounding region.In order to reduce noise and improve the quality of binarization, it calculates the neighbour threshold value for each area separately.It aids in the detection of cavities and flocs in SEM photographs.In SEM pictures, it aids in determining the area filled by white and black pixels.The voids or flocs occupied area in the SEM picture may be calculated using this method for both metal and nonmetal.White and black pixels are digitally encoded as 0 and 1 [33] in the form of 0 and 1.…”