Python Automation and Machine Learning for EM and ICs

In this script (Code), an image is processed using a series of image analysis steps to enhance and visualize its edge features. The image is first read and converted to a grayscale format to simplify processing. A binary threshold is then applied to segment the image into foreground and background regions. Morphological dilation is performed to expand the bright regions, and the difference between the dilated and the original thresholded images is computed to highlight the transitional edges. An inversion operation is subsequently applied to produce a clear edge map. Throughout this process, intermediate results—namely, the binary thresholded image, the dilated image, the absolute difference image, and the final edge map—are saved to disk and displayed for inspection. This approach facilitates the extraction and visualization of interface or boundary features within the image, which is particularly useful for failure analysis and materials characterization in advanced semiconductor and nanostructured materials.

Another similar script is Code:

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Edge finder with Canny method. code:              
               
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More scripts for similar functions described above are code and code.

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