The [course_title] course covers the use of pattern recognition techniques to solve problems of machine vision. At first, a general overview of problems of machine vision and pattern classification will be discussed in the course. Then you will learn about, image formation and processing, feature extraction from images, biological object recognition, Bayesian decision theory, Object etection or Recognition and clustering. You will also learn Morphable models, Tracking, and Gesture Recognition.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer need to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: MIT
|class1 Course Introduction||01:05:00|
|class1_2 Feature Extraction Overview||00:40:00|
|class1_3 Quick MATLAB® Tutorial||00:25:00|
|class2 Feature Extraction I||01:35:00|
|class3_1 Part 1 Bayesian Decision Theory||01:45:00|
|class3_2 Principal and Independent Component Analysis||01:05:00|
|class4 Density Estimation||00:45:00|
|class6 Techniques for Clustering||01:05:00|
|class7 An Application of Clusterin||01:15:00|
|class8 Object Detection Recognition||01:25:00|
|class10 Gesture and Action Recognition||01:55:00|
|Submit Your Assignment||00:00:00|
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