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Coursera

Introduction to Computer Vision

MathWorks via Coursera

Overview

In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene. Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same scene taken at different times or combine images acquired from different scientific instruments, as is common with hyperspectral and medical images. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this course, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.

Syllabus

  • Introduction to Features
  • Working With Features
  • Image Registration
  • Image Stitching

Taught by

Amanda Wang, Isaac Bruss, Matt Rich, Megan Thompson and Brandon Armstrong

Reviews

4.0 rating, based on 2 Class Central reviews

4.5 rating at Coursera based on 37 ratings

Start your review of Introduction to Computer Vision

  • Anonymous
    This a great course to start with computer vision. you will learn how to extract features and match for image registration, also for developping many applications in computer vision task. I recommend you this course.
  • Anonymous
    This is a very brief and very introductory course. It doesn't explain CV in any way. It is more like "look what exists in Matlab for this task". Still, it is better than nothing and you can get some value out of this class if you genuinely will stretch yourself, do all the labs and walk extra mile.

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