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.
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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.
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.
Course 2 of 5 in the First Principles of Computer Vision Specialization
Syllabus
WEEK 1: Getting Started: Features and Boundaries
WEEK 2: Edge Detection
WEEK 3: Boundary Detection
WEEK 4: SIFT Detector
WEEK 5: Image Stitching
WEEK 6: Face Detection
