This course is part of the First Principles of Computer Vision Specialization

Shree Nayar

Offered By

First Principles of Computer Vision SpecializationColumbia University

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Course 2 of 5 in the

First Principles of Computer Vision Specialization

Beginner Level

Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.

Approx. 10 hours to complete

English

Subtitles: English

What you will learn

Learn how to detect edges and corners in images.

Develop active contours (snakes) to find complex object boundaries.

Learn about the Hough Transform for finding simple parametric shapes in images.

Learn about image transformations and how to estimate the homography between two images.

Skills you will gain

Scale SpaceActive ContoursImage TransformationsEdge and Corner DetectionSIFT Detector

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

First Principles of Computer Vision Specialization

Beginner Level

Students should know the fundamentals of linear algebra and calculus. The knowledge of any programming language is beneficial, though not required.

Approx. 10 hours to complete

English

Subtitles: English

Instructor

Shree Nayar

T. C. Chang Professor

Computer Science

235 Learners

5 Courses

Offered by

Columbia University

For more than 250 years, Columbia has been a leader in higher education in the nation and around the world. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds in pursuit of greater human understanding, pioneering new discoveries and service to society.

Syllabus - What you will learn from this course

Week

Week 1

2 hours to complete

Getting Started: Features and Boundaries

2 hours to complete

8 readings

Week

Week 2

2 hours to complete

Edge Detection

2 hours to complete

Week

Week 3

2 hours to complete

Boundary Detection

2 hours to complete

Week

Week 4

2 hours to complete

SIFT Detector

2 hours to complete

About the First Principles of Computer Vision Specialization

This specialization presents the first comprehensive treatment of the foundations of computer vision. It focuses on the mathematical and physical underpinnings of vision and has been designed for learners, practitioners and researchers who have little or no knowledge of computer vision. The program includes a series of 5 courses. Any learner who completes this specialization has the potential to build a successful career in computer vision, a thriving field that is expected to increase in importance in the coming decades.

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Features and Boundaries