Image Segmentation with Python and Unsupervised Learning

Offered By
Coursera Project Network
In this Guided Project, you will:

Display an image in a viewable frame, and in RGB space.

Use K-means to partition the pixels into relevant colour clusters and segment an image.

Find the best K value according to an objective criterion.

Clock1 hour
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this one hour long project-based course, you will tackle a real-world problem in computer vision called segmentation. Segmentation means taking an image and partitioning it into different regions that capture the different elements of interest in the scene. We will tackle this problem using an unsupervised learning technique called K-means. By the end of this project, you will have segmented an image with unsupervised learning, using code you will write in Python.

Skills you will develop

Machine LearningUnsupervised LearningMatplotlibNumpyComputer Vision

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Load an image from file

  2. Display an image in frame and RGB space

  3. Find colour clusters using K-means

  4. Display colour clusters and segmented image

  5. Optimize K

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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