Image Segmentation with Python and Unsupervised Learning

提供方
Coursera Project Network
在此指导项目中,您将:

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
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

  • Machine Learning
  • Unsupervised Learning
  • Matplotlib
  • Numpy
  • Computer Vision

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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常见问题

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