Columbia University
First Principles of Computer Vision Specialization
Columbia University

First Principles of Computer Vision Specialization

Master the First Principles of Computer Vision. Advance the mathematical and physical algorithms empowering computer vision

Taught in English

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Shree Nayar

Instructor: Shree Nayar

7,701 already enrolled

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Specialization - 5 course series

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(156 reviews)

Beginner level

Recommended experience

4 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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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Specialization - 5 course series

Get in-depth knowledge of a subject


(156 reviews)

Beginner level

Recommended experience

4 months at 10 hours a week
Flexible schedule
Learn at your own pace

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Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
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Specialization - 5 course series

Camera and Imaging

Course 119 hours4.7 (118 ratings)

What you'll learn

  • Learn how a camera works and how an image is formed using a lens

  • Understand how an image sensor works and its key characteristics

  • Design cameras that capture high dynamic range and wide angle images

  • Learn to create binary images and use them to build a simple object recognition system

Skills you'll gain

Category: Scale Space
Category: SIFT Detector
Category: Edge and Corner Detection
Category: Active Contours
Category: Image Transformations

Features and Boundaries

Course 223 hours4.8 (35 ratings)

What you'll 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.

Skills you'll gain

Category: Image Segmentation
Category: Computer Vision
Category: Artificial Neural Network
Category: Tracking
Category: apperance matching

3D Reconstruction - Single Viewpoint

Course 389 hours4.9 (26 ratings)

What you'll learn

  • Learn radiometric concepts related to light and how it interacts with scenes.

  • Understand reflectance models and the different physical mechanisms that determine the appearance of a surface.

  • Develop a method for recovering the shape of a surface from its shading.

  • Understand the principle of photometric stereo where a dense surface normal map of the scene is obtained by varying the illumination direction.

Skills you'll gain

Category: Photometric Stereo
Category: Structed Light Methods
Category: Depth from Focus and Defocus
Category: Reflectance Models
Category: Radiometry

3D Reconstruction - Multiple Viewpoints

Course 48 hours4.7 (31 ratings)

What you'll learn

  • Develop a comprehensive model of a camera and learn how to calibrate a camera by estimating its parameters.

  • Develop a simple stereo system that uses two cameras of known configuration to estimate the 3D structure of a scene.

  • Design an algorithm for recovering both the structure of the scene and the motion of the camera from a video.

  • Develop optical flow algorithms for estimating the motion of points in a video sequence.

Skills you'll gain

Category: Camera Model
Category: Camera Calibration
Category: Epipolar Geometry
Category: Simple Stereo
Category: Structure from Motion

Visual Perception

Course 510 hours4.5 (22 ratings)

What you'll learn

  • Design algorithms for detecting meaningful changes in a scene

  • Develop methods for tracking objects in a video while the object undergoes changes in pose and illumination

  • Learn several approaches to segmenting an image into meaningful regions

  • Create an end-to-end pipeline for learning and recognizing objects based on their visual appearance

Skills you'll gain

Category: High-Dynamic-Range (HDR) Imaging
Category: Image Formation
Category: Convolution and Deconvolution
Category: Working Principles of a Camera
Category: Fourier Transform


Shree Nayar
Columbia University
5 Courses15,427 learners

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