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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

(0 hours)

This course covers the fundamentals of imaging, the creation of an image for consumption or processing. Imaging has a long history, but advances in the last three decades have revolutionized the camera and improved computer vision systems. We describe the fundamentals of imaging and recent innovations that have had a profound impact on computer vision.

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Features and Boundaries

(0 hours)

This course focuses on detecting 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. 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.

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3D Reconstruction - Single Viewpoint

(0 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

(0 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

(0 hours)

The ultimate goal of a computer vision system is to generate a detailed description of each image shown. This course focuses on perception, including tracking objects in complex scenes, segmenting an image into meaningful regions, and object recognition.

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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

2 hours

Level

Introductory

Via

Coursera

Institution

Columbia University

Instructor

Shree Nayar

Level

Introductory

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