Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Visual Perception is an online course for beginners that is flexibly designed and can be taken by learners at their own pace and convenience. The certification programme by Coursera will guide the learners on computer vision systems and all of the major problems of perception. Visual Perception Certification Course, provided by Coursera, is part of the First Principles of Computer Vision Specialization that is offered by Columbia University.
Visual Perception Training is developed and taught by Shree Nayar who is a professor of computer science at Columbia University. Visual Perception Certification by Coursera can be completed by the students within 2 months at the least. To be eligible for the online course, the learners must have a basic understanding of calculus and linear algebra.
The Highlights
- Provided by Coursera
- Offered by Columbia University
- Beginner-level course
- Self-Paced Learning Option
- 100% Online Course
- Around 2 months to Complete
- Flexible Deadlines
- Shareable Certificate
- Financial Aid Available
Programme Offerings
- English videos with multiple subtitles
- Shareable Certificate
- Financial aid available
- Shareable Certificates
- Self-Paced Learning Option
- Course Videos & Readings
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Columbia University, New YorkCoursera |
Visual Perception Fee Structure
Duration | Amount in INR |
1 Month | INR 1,699 |
3 Months | INR 3,499 (INR 1166 /month) |
6 Months | INR 5,199 (INR 867 /month) |
Eligibility Criteria
Academic Qualifications
Visual Perception online courses can be taken only by learners who have a basic understanding of linear algebra and calculus. Plus, it is also recommended that the learners have the knowledge of any programming language that will help them to get more benefits out of the online course.
What you will learn
Visual Perception Certification Syllabus will give the learners a sheer glimpse to :
- Appearance matching
- Tracking
- Object tracking methods.
- Separation of an image into an object
- Image Segmentation
- Computer Vision
Who it is for
For any professional and learners who want to learn visual perception, especially photographers, Visual Perception Classes is a very recommended programme.
Admission Details
Step 1 - Browse the official URL
https://www.coursera.org/learn/perception
Step 2 - Click on the option of ‘Enroll Now’ and join the online course.
The Syllabus
Readings
- Course Syllabus
- About the Instructor
- Course Information and Support
- Academic Honesty Policy
- Discussion Forum Etiquette
- Frequently Asked Questions
- Pre-Course Survey
Plugins
- Pre-Course Survey
- 1.1 Overview of Introduction
- 1.2 What is Computer Vision?
- 1.3 What is Vision Used For?
- 1.4 How Do Humans Do It?
- 1.5 Topics Covered
- 1.6 About the Lecture Series
- 1.7 References and Credits
Quiz
- Week 2 Object Tracking
Assignments
- 2.2 Change Detection Self-Check Quiz
- 2.3 Gaussian Mixture Model Self-Check Quiz
- 2.4 Object Tracking using Templates Self-Check Quiz
- 2.5 Tracking by Feature Detection Self-Check Quiz
Discussion Prompt
- Week 2 Object Tracking
Plugins
- 2.1 Overview of Object Tracking
- 2.2 Change Detection
- 2.3 Gaussian Mixture Model
- 2.4 Object Tracking using Templates
- 2.5 Tracking by Feature Detection
Quiz
- Week 3 Image Segmentation
Assignments
- 3.2 Segmentation by Humans Self-Check Quiz
- 3.3 Segmentation as Clustering Self-Check Quiz
- 3.4 k-Means Segmentation Self-Check Quiz
- 3.5 Mean-Shift Segmentation Self-Check Quiz
- 3.6 Graph Based Segmentation Self-Check Quiz
Plugins
- 3.1 Overview of Image Segmentation
- 3.2 Segmentation by Humans
- 3.3 Segmentation as Clustering
- 3.4 k-Means Segmentation
- 3.5 Mean-Shift Segmentation
- 3.6 Graph Based Segmentation
Quiz
- Week 4 Appearance Matching
Assignments
- 4.2 Shape vs. Appearance Self-Check Quiz
- 4.3 Learning Appearance Self-Check Quiz
- 4.4 Principal Component Analysis Self-Check Quiz
- 4.5 Finding Principal Components Self-Check Quiz
- 4.6 PCA and SVD Self-Check Quiz
- 4.7 Parametric Appearance Representation Self-Check Quiz
- 4.8 Appearance Matching Self-Check Quiz
Discussion Prompt
- Week 4 Smartphones using 3D Sensors
Plugins
- 4.1 Overview of Appearance Matching
- 4.2 Shape vs. Appearance
- 4.3 Learning Appearance
- 4.4 Principal Component Analysis
- 4.5 Finding Principal Components
- 4.6 PCA and SVD
- 4.7 Parametric Appearance Representation
- 4.8 Appearance Matching
Reading
- Post-Course Survey
Quiz
- Week 5 Neural Networks
Reading
- Post-Course Survey
Assignments
- 5.2 Perceptron Self-Check Quiz
- 5.3 Perceptron Network Self-Check Quiz
- 5.4 Activation Function Self-Check Quiz
- 5.5 Neural Network Self-Check Quiz
- 5.6 Gradient Descent Self-Check Quiz
- 5.7 Backpropagation Algorithm Self-Check Quiz
- 5.8 Example Applications Self-Check Quiz
- 5.9 When to Use Machine Learning? Self-Check Quiz
Peer Review
- Peer Review (Test)
Discussion Prompt
- Week 5 Neural Networks
Plugins
- 5.1 Overview of Neural Networks
- 5.2 Perceptron
- 5.3 Perceptron Network
- 5.4 Activation Function
- 5.5 Neural Network
- 5.6 Gradient Descent
- 5.7 Backpropagation Algorithm
- 5.8 Example Applications
- 5.9 When to Use Machine Learning?
- Post-Course Survey
Instructors
Columbia University, New York Frequently Asked Questions (FAQ's)
Columbia University is the collaborator of Coursera to administer the online certification programme.
The professor at Columbia University who is in charge of the online programme is Shree Nayar.
The online course on Visual Perception is developed and designed for beginner-level learners.
The specialization in which the online certification programme is a part is the First Principles of Computer Vision Specialization.
The learners can cover the online course fully within approximately 2 months at the least.