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Features and Boundaries
  • Level Foundation
  • Duration 23 hours
  • Course by Columbia University
  • Offered by

About

This course focuses on the detection of features and boundaries in images. Feature and boundary detection is a critical preprocessing step for a variety of vision tasks including object detection, object recognition and metrology – the measurement of the physical dimensions and other properties of objects. The course presents a variety of methods for detecting features and boundaries and shows how features extracted from an image can be used to solve important vision tasks. We begin with the detection of simple but important features such as edges and corners. We show that such features can be reliably detected using operators that are based on the first and second derivatives of images. Next, we explore the concept of an “interest point” – a unique and hence useful local appearance in an image. We describe how interest points can be robustly detected using the SIFT detector. Using this detector, we describe an end-to-end solution to the problem of stitching overlapping images of a scene to obtain a wide-angle panorama. Finally, we describe the important problem of finding faces in images and show several applications of face detection.

Modules

Welcome to First Principles of Computer Vision: Features and Boundaries

    • 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

    • 2 Discussion
    • 7 Plugin
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2 Discussions

  • Introductions
  • Week 1 Questions and Feedback

Week 2: Edge Detection

    • 2 Readings
    • 2 Discussion
    • 6 Plugin
    • 5 Assignment
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5 Assignment

  • 2.2 What is an Edge? Self-check Quiz
  • 2.3 Edge Detection Using Gradients Self-check Quiz
  • 2.4 Edge Detection Using Laplacian Self-check Quiz
  • 2.5 Canny Edge Detector Self-check Quiz
  • 2.6 Corner Detection Self-check Quiz

2 Discussions

  • Week 2 Edge Detection Methods
  • Week 2 Questions and Feedback

2 Readings

  • Week 2 Lecture Handout
  • 2.6 Video Correction

Week 2 Quiz

    • 1 Assignment
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1 Assignment

  • Week 2 Edge Detection

Week 3: Boundary Detection

    • 2 Readings
    • 2 Discussion
    • 5 Plugin
    • 4 Assignment
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4 Assignment

  • 3.2 Fitting Lines and Curves Self-check Quiz
  • 3.3 Active Contours Self-check Quiz
  • 3.4 Hough Transform Self-check Quiz
  • 3.5 Generalized Hough Transform Self-check Quiz

2 Discussions

  • Week 3 Edge Detection Algorithms
  • Week 3 Questions and Feedback

2 Readings

  • Week 3 Lecture Handout
  • 3.2 Video Correction

Week 3 Quiz

    • 1 Assignment
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1 Assignment

  • Week 3 Boundary Detection

Week 4: SIFT Detector

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

  • 4.2 What is an Interest Point? Self-check Quiz
  • 4.3 Detecting Blobs Self-check Quiz
  • 4.4 SIFT Detector Self-check Quiz
  • 4.5 SIFT Descriptor Self-check Quiz

2 Discussions

  • Week 4 Active Contours
  • Week 4 Questions and Feedback

1 Readings

  • Week 4 Lecture Handout

Week 4 Quiz

    • 1 Assignment
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1 Assignment

  • Week 4 SIFT Detector

Week 5: Image Stitching

    • 1 Discussion
    • 6 Plugin
    • 5 Assignment
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5 Assignment

  • 5.2 2x2 Image Transformations Self-check Quiz
  • 5.3 3x3 Image Transformations Self-check Quiz
  • 5.4 Computing Homography Self-check Quiz
  • 5.5 Dealing with Outliers: RANSAC Self-check Quiz
  • 5.6 Warping and Blending Images Self-check Quiz

1 Discussions

  • Week 5 Questions and Feedback

Week 5 Quiz

    • 1 Assignment
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1 Assignment

  • Week 5 Image Stitching

Week 6: Face Detection

    • 1 Readings
    • 2 Discussion
    • 6 Plugin
    • 6 Assignment
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6 Assignment

  • 6.1 Overview of Face Detection Self-Check Quiz
  • 6.2 Uses of Face Detection Self-Check Quiz
  • 6.3 Haar Features for Face Detection Self-Check Quiz
  • 6.4 Integral Image Self-Check Quiz
  • 6.5 Nearest Neighbor Classifier Self-Check Quiz
  • 6.6 Support Vector Machine Self-Check Quiz

2 Discussions

  • Week 6 Face Detection App
  • Week 6 Questions and Feedback

1 Readings

  • 6.3 Video Correction

Week 6 Quiz

    • 1 Assignment
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1 Assignment

  • Week 6 Face Detection

Post-Course Survey

    • 1 Readings
    • 1 Plugin
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1 Readings

  • Post-Course Survey

Auto Summary

"Features and Boundaries" is a foundational course in Data Science & AI, designed by Coursera. It delves into detecting features and boundaries in images, crucial for tasks like object detection, recognition, and metrology. Learners will explore edge and corner detection, interest points using the SIFT detector, and face detection applications. The course spans 1380 minutes and is available through Starter and Professional subscriptions, ideal for beginners in the field.

Shree Nayar
Instructor

Shree Nayar