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First Principles of Computer Vision

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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What's inside

Five courses

Camera and Imaging

(5 hours)

This course covers the fundamentals of imaging, the creation of an image ready for consumption or processing by a human or machine. Imaging has a long history, spanning several centuries, but advances in the last three decades have revolutionized the camera and dramatically improved the robustness and accuracy of computer vision systems.

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Features and Boundaries

(5 hours)

This course focuses on detecting features and boundaries in images. Feature and boundary detection is a critical preprocessing step for various vision tasks, including object detection, recognition, and metrology. We'll explore methods for detecting features and boundaries and how extracted features can solve vision tasks.

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3D Reconstruction - Single Viewpoint

(89 hours)

This course focuses on recovering the 3D structure of a scene from its 2D images, particularly from a stationary camera viewpoint. We explore capturing images that provide complementary information about the scene. To estimate scene properties, we define radiometric concepts and tackle shape from shading. We also discuss photometric stereo, depth from defocus, and active illumination techniques for precise 3D reconstruction.

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3D Reconstruction - Multiple Viewpoints

(5 hours)

This course focuses on recovering the 3D structure of a scene from images taken from different viewpoints. We start by building a comprehensive geometric model of a camera and then develop a method for finding (calibrating) the internal and external parameters of the camera model. Then, we show how two such calibrated cameras, whose relative positions and orientations are known, can be used to recover the 3D structure of the scene.

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Visual Perception

(5 hours)

The ultimate goal of a computer vision system is to generate a detailed symbolic description of each image shown. This course focuses on the all-important problem of perception. We first describe the problem of tracking objects in complex scenes. We look at two key challenges in this context. The first is the separation of an image into object and background using a technique called change detection. The second is the tracking of one or more objects in a video.

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Learning objectives

Master the working principles of a digital camera and learn the fundamentals of imaging processing
Create a theory of feature detection and develop algorithms for extracting features from images

Explore novel methods for using visual cues (shading, defocus, etc.) to recover the 3d shape of an object from multiple images or viewpoints
Get exposed to fundamental perceptions tasks such as image segmentation, object tracking, and object recognition

Master the working principles of a digital camera and learn the fundamentals of imaging processing
Create a theory of feature detection and develop algorithms for extracting features from images
Explore novel methods for using visual cues (shading, defocus, etc.) to recover the 3d shape of an object from multiple images or viewpoints
Get exposed to fundamental perceptions tasks such as image segmentation, object tracking, and object recognition

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Time

6540 hours

Via

Coursera

Institution

Columbia University

Instructor

Shree Nayar

Language

English

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