Image Segmentation with Mean Shift Clustering
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Explore image segmentation using Mean Shift Clustering. Learn how this non-parametric clustering technique can be applied to segment and analyze images, ideal for applications in computer vision, medical imaging, and autonomous vehicles.
At a Glance
From image segmentation to anomaly detection, Mean Shift Clustering offers a versatile and powerful solution for a wide range of data analysis challenges. It is no ordinary algorithm – it’s a dynamic and non-parametric technique that can navigate through complex data terrains, finding density peaks that lead to clusters of diverse shapes and sizes and more. In this guided project, you will learn how to identify complex patterns, clusters, and subgroups in your datasets and use it for image segmentation.
In the first part of this guided project, we will focus on the image segmentation, which is used in many object detection and tracking systems, as it makes it easier to detect the contour of each object. In the second part, we will show how to use the Mean Shift Clustering to classify the survivors rates of the Titanic, the most famous shipwreck in history. Based on the passengers’ features (e.g. age, ticket class, fare, etc.) we will classify the passengers into clusters with different survival probabilities.
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