Image Processing, Features & Segmentation
University at Buffalo and State University of New York via Coursera
Overview
Class Central Tips
This course empowers learners to develop image processing programs and leverage MATLAB functionalities to implement sophisticated image applications. It provides a rich explanation of the fundamentals of computer vision’s lower- and mid-level tasks by examining several principle approaches and their historical roots. By the end of the course, learners are prepared to analyze images in frequency domain. Topics include image filters, image features and matching, and image segmentation.
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.
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
Image Processing Fundamentals
-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.
-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
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