scikit-image: segmentation and regionprops
We show how to segment a photo of coins, separating the foreground from the background. Our process is to denoise the image (using a median filter), and to then apply watershed segmentation. The resulting segments are cleaned up, using region properties and K-means clustering. There are certainly many other ways of approaching this problem, e.g. region agglomeration with region adjacency graphs, supervised clustering, etc. Please share your ideas in the comments below!

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Tutorial 55 - Image segmentation followed by measurements, in python

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k-Means Segmentation | Image Segmentation

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K-means & Image Segmentation - Computerphile

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Image Analysis in Python with SciPy and Scikit Image | Scipy 2019 Tutorial | Nunez-Iglesias

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63 - Image Segmentation using traditional machine learning Part1 - FeatureExtraction

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Segmentation as Clustering | Image Segmentation

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51 - Image Segmentation using K-means

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23 - Histogram based image segmentation in Python

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PyTorch Image Segmentation Tutorial with U-NET: everything from scratch baby

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Image Segmentation with K-Means Clustering in Python

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Graph Based Segmentation | Image Segmentation

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Deep Learning in Medical Imaging - Ben Glocker, Imperial College London

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20 - Introduction to image processing using scikit-image in Python

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Tutorial 29 -Basic image processing using scikit-image library

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Fully Convolutional Networks for Image Segmentation | SciPy 2017 | Daniil Pakhomov

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Image Processing in Python with Scikits-image

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Explaining Your Job To Your Boomer Boss | Mr. Robot

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33 - Grain size analysis in Python using watershed

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DIP 09 - Image Segmentation (6) - Watershed implementation in Python (+ Distance Transform)

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