In this chapter, we discussed different image processing techniques based on mathematical morphology. We discussed morphological binary operations such as erosion, dilation, opening, closing, skeletonizing, and white and black top-hats. Then we discussed some applications such as computing the convex hull, removing small objects, extracting the boundary, fingerprint cleaning with opening and closing, filling holes in binary objects, and using opening and closing to remove noise. After that, we discussed extension of the morphological operations to grayscale operations and applications of morphological contrast enhancement, noise removal with the median filter, and computing local entropy. Also, we discussed how to compute the morphological (Beucher) gradient and the morphological Laplace. By the end of this chapter, the reader should be able to write Python code for morphological image processing (for example, opening, closing, skeletonizing, and computing the convex hull).
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Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.
Read more about Sandipan Dey
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Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.
Read more about Sandipan Dey