Image Granularity Analysis Algorithm Using Mathematical Morphology
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The image granularity analysis algorithm using mathematical morphology first applies noise removal preprocessing to the image through morphological filtering operations such as opening or closing. The algorithm then calculates the area of particles in the image using connected component analysis and morphological area measurements. Finally, it plots the granularity distribution function by analyzing particle size distributions through morphological granulometry. This algorithm plays a significant role in image processing applications, helping researchers better understand and analyze image data through quantitative granularity metrics. Key implementation steps typically involve morphological operations like erosion, dilation, and watershed transforms for accurate particle segmentation and measurement.
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