Image Segmentation with Python and Unsupervised Learning

提供方
Coursera Project Network
在此指導項目中,您將:

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中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

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

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

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