Course Description

This course covers fundamental topics of computer vision. Topics include elementary image operations and transformations, feature extraction, model fitting, object recognition, classification and tracking, deep learning, camera models and stereo vision.

Course Outline

•    The modules are designed to be taken in the following order0) Elementary image operations; 1) Image transformations and filters; 2) Feature extraction and dimensionality reduction; 3) Model fitting, object detection and recognition; 4) Image segmentation (Optional module);5) Image search (Optional module)

•    Delivery: Asynchronous, self-paced, 10-12 hours a week.
•    Sequence for each module: Instructional videos, suggested readings, video embedded quizzes, short quiz after each module milestone, a final quiz for each module.
•    Students who are confident about the material can test out of the module and advance to the next one by completing the final quiz for that module.
•    Failing the final quiz three times for a module will result in recycling the entire module.

Learner Outcomes

Students successfully completing this course will be able to:
•    List, explain and discuss the most important concepts of image processing, perception, analysis, and computer vision.
•    Demonstrate a variety of computer techniques for the design of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, and human activity recognition.
•    Use of machine learning and artificial neural networks on computer vision-related problems.
•    Develop, use, and evaluate practical applications through computer and programming exercises.
•    Identify and apply fundamental knowledge to comprehend and appraise image processing & computer vision literature
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