Image Processing, Features & Segmentation
University at Buffalo and State University of New York via Coursera
-
35
-
- Write review
Overview
Class Central Tips
This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables).
Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes.
This is the second course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0.
* A free license to install MATLAB for the duration of the course is available from MathWorks.
Syllabus
-In this module, we will discuss the basics and applications of digital image processing, including intensity transformations and color image processing.
Image Filters
-In this module, we will discuss image filtering as well as some advanced image processing methods.
Image Features & Matching
-In this module, we will discuss image features, feature matching, texture matching, and how to create a panorama.
Image Segmentation
-This module describes what image segmentation is and provides information on the different techniques used to perform image segmentation.
Taught by
Radhakrishna Dasari and Junsong Yuan
Related Courses
-
Computer Vision and Image Processing - Fundamentals and Applications
Indian Institute of Technology Guwahati, NPTEL
-
OpenCV for Python Developers
-
Image Processing and Analysis for Life Scientists
École Polytechnique Fédérale de Lausanne
-
Introduction to Computer Vision
Georgia Institute of Technology
4.7 -
Biomedical Image Analysis in Python
-
Robotics: Vision Intelligence and Machine Learning
University of Pennsylvania
Reviews
0.0 rating, based on 0 reviews