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Image Segmentation in Machine Learning: A Step-by-Step Guide
If you’ve ever wondered how self-driving cars recognize objects on the road or how medical imaging software detects tumors, the answer often lies in image segmentation. This critical component of machine learning enables computers to break down and understand the visual world by dividing images into meaningful parts.
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In this article, we’ll walk through the different types of image segmentation, exploring what they are, why they matter, and how you can start using them in your projects.
What is Image Segmentation?
At its core, image segmentation is the process of partitioning an image into segments or regions that are more meaningful and easier to analyze. Think of it as slicing an image into pieces where each piece corresponds to a particular object or region of interest. This technique is crucial in applications ranging from medical diagnostics to autonomous vehicles and beyond.
