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Visual Perception
  • Level Foundation
  • Duration 10 hours
  • Course by Columbia University
  • Offered by

About

The ultimate goal of a computer vision system is to generate a detailed symbolic description of each image shown. This course focuses on the all-important problem of perception. We first describe the problem of tracking objects in complex scenes. We look at two key challenges in this context. The first is the separation of an image into object and background using a technique called change detection. The second is the tracking of one or more objects in a video. Next, we examine the problem of segmenting an image into meaningful regions. In particular, we take a bottom-up approach where pixels with similar attributes are grouped together to obtain a region. Finally, we tackle the problem of object recognition. We describe two approaches to the problem. The first directly recognize an object and its pose using the appearance of the object. This method is based on the concept of dimension reduction, which is achieved using principal component analysis. The second approach is to use a neural network to solve the recognition problem as one of learning a mapping from the input (image) to the output (object class, object identity, activity, etc.). We describe how a neural network is constructed and how it is trained using the backpropagation algorithm.

Modules

Welcome to First Principles of Computer Vision: Visual Perception

    • 6 Readings
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6 Readings

  • Course Syllabus
  • About the Instructor
  • Course Information and Support
  • Academic Honesty Policy
  • Discussion Forum Etiquette
  • Frequently Asked Questions

Pre-Course Survey

    • 1 Readings
    • 1 Plugin
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1 Readings

  • Pre-Course Survey

Week 1: Introduction to First Principles of Computer Vision

    • 7 Plugin
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Week 2: Object Tracking

    • 1 Readings
    • 1 Discussion
    • 5 Plugin
    • 4 Assignment
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4 Assignment

  • 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

1 Discussions

  • Week 2 Object Tracking

1 Readings

  • 2.4 Video Correction

Week 2 Quiz

    • 1 Assignment
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1 Assignment

  • Week 2 Object Tracking

Week 3: Image Segmentation

    • 6 Plugin
    • 5 Assignment
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5 Assignment

  • 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

Week 3 Quiz

    • 1 Assignment
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1 Assignment

  • Week 3 Image Segmentation

Week 4: Appearance Matching

    • 2 Readings
    • 1 Discussion
    • 8 Plugin
    • 7 Assignment
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7 Assignment

  • 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

1 Discussions

  • Week 4 Smartphones using 3D Sensors

2 Readings

  • 4.4 Video Correction
  • 4.7 Video Correction

Week 4 Quiz

    • 1 Assignment
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1 Assignment

  • Week 4 Appearance Matching

Week 5: Neural Networks

    • 1 Discussion
    • 9 Plugin
    • 8 Assignment
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8 Assignment

  • 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

1 Discussions

  • Week 5 Neural Networks

Week 5 Quiz

    • 1 Assignment
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1 Assignment

  • Week 5 Neural Networks

Post-Course Survey

    • 1 Readings
    • 1 PeerReview
    • 1 Plugin
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1 Peer Review

  • Peer Review (Test)

1 Readings

  • Post-Course Survey

Auto Summary

Dive into the realm of Visual Perception, a foundational course in Data Science & AI, guided by Coursera. This course delves into critical aspects of computer vision, including object tracking, image segmentation, and object recognition. Learners will explore techniques like change detection, principal component analysis, and neural networks, gaining comprehensive insights into image processing and symbolic description. With a duration of 600 minutes, the course offers Starter and Professional subscription options, making it ideal for those eager to build foundational skills in visual data interpretation.

Shree Nayar
Instructor

Shree Nayar