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Radhakrishna Dasari and Junsong Yuan
This specialization provides a foundation in the rapidly expanding research field of computer vision, laying the groundwork necessary for designing sophisticated vision applications. Learners explore the integral elements that enable vision applications,...
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This specialization provides a foundation in the rapidly expanding research field of computer vision, laying the groundwork necessary for designing sophisticated vision applications. Learners explore the integral elements that enable vision applications, ranging from editing images to reading traffic signs in self-driving cars to factory robots navigating around human co-workers. Content includes image processing and state-of-the-art vision techniques, augmented by insights from top leaders in the computer vision field. Learners gain hands-on experience writing computer vision programs through online labs using MATLAB and supporting toolboxes. The specialization is taught in MATLAB* using computer vision and supporting toolboxes. 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). To learn more, 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.
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What's inside

Four courses

Image Processing, Features & Segmentation

(0 hours)
This course empowers learners to develop image processing programs and leverage MATLAB functionalities to implement sophisticated image applications.

Computer Vision Basics

By the end of this course, learners will understand computer vision and its mission of making computers see and interpret the world as humans do. They will be able to identify key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence.

Visual Recognition & Understanding

(0 hours)
This course immerses learners in deep learning for computer vision problems. Topics include object detection, face detection and recognition, and deep learning techniques like CNNs and Generative Models.

Stereo Vision, Dense Motion & Tracking

(0 hours)
This course introduces stereo vision theory, dense motion and visual tracking. Learners will discuss techniques used to obtain the 3D structure of objects.

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