Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Columbia University

Features and Boundaries

Columbia University via Coursera

Overview

Prepare for a new career with $100 off Coursera Plus
Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.
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.

Syllabus

  • Getting Started: Features and Boundaries
  • Edge Detection
  • Boundary Detection
  • SIFT Detector
  • Image Stitching
  • Face Detection

Taught by

Shree Nayar

Reviews

4.8 rating at Coursera based on 30 ratings

Start your review of Features and Boundaries

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.