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Features and Boundaries

First Principles of Computer Vision,

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.

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Length 7 weeks
Effort 4-6 hours/week
Starts Jan 31 (earlier today)
Cost $49
From Columbia University via Coursera
Instructor Shree Nayar
Download Videos On all desktop and mobile devices
Language English
Subjects Programming
Tags Computer Science Algorithms

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Rating Not enough ratings
Length 7 weeks
Effort 4-6 hours/week
Starts Jan 31 (earlier today)
Cost $49
From Columbia University via Coursera
Instructor Shree Nayar
Download Videos On all desktop and mobile devices
Language English
Subjects Programming
Tags Computer Science Algorithms

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