Computer Science / Computer Vision

Introduction to Computer Vision

Brought by: 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

Computer Science / Computer Vision

Introduction to Computer Vision

Introduction to Computer Vision

Brought by: Coursera

Coursera
Free
English
Certificate Available
Available at any time
intermediate
English, Kazakh, German, Hindi, Russian, Swedish, Korean, Portuguese, Greek, Italian, French, Chinese, Spanish, Arabic, Thai, Ukrainian, Japanese, Indonesian, Polish, Dutch, Turkish

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