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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

Course Curriculum

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
class5 Classification 01:55:00
class6 Techniques for Clustering 01:05:00
class7 An Application of Clusterin 01:15:00
class8 Object Detection Recognition 01:25:00
class9 Tracking 14:10:00
class10 Gesture and Action Recognition 01:55:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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