Introduction to Computer Vision from Udacity

What's inside

Syllabus

1A-L1 Introduction

2A-L1 Images as functions

2A-L2 Filtering

2A-L3 Linearity and convolution

2A-L4 Filters as templates

2A-L5 Edge detection: Gradients

2A-L6 Edge detection: 2D operators

2B-L1 Hough transform: Lines

2B-L2 Hough transform: Circles

2B-L3 Generalized Hough transform

2C-L1 Fourier transform

2C-L2 Convolution in frequency domain

2C-L3 Aliasing

3A-L1 Cameras and images

3A-L2 Perspective imaging

3B-L1 Stereo geometry

3B-L2 Epipolar geometry

3B-L3 Stereo correspondence

3C-L1 Extrinsic camera parameters

3C-L2 Instrinsic camera parameters

3C-L3 Calibrating cameras

3D-L1 Image to image projections

3D-L2 Homographies and mosaics

3D-L3 Projective geometry

3D-L4 Essential matrix

3D-L5 Fundamental matrix

4A-L1 Introduction to "features"

4A-L2 Finding corners

4A-L3 Scale invariance

4B-L1 SIFT descriptor

4B-L2 Matching feature points (a little)

4C-L1 Robust error functions

4C-L2 RANSAC

5A-L1 Photometry

5B-L1 Lightness

5C-L1 Shape from shading

6A-L1 Introduction to motion

6B-L1 Dense flow: Brightness constraint

6B-L2 Dense flow: Lucas and Kanade

6B-L3 Hierarchical LK

6B-L4 Motion models

7A-L1 Introduction to tracking

7B-L1 Tracking as inference

7B-L2 The Kalman filter

7C-L1 Bayes filters

7C-L2 Particle filters

7C-L3 Particle filters for localization

7C-L4 Particle filters for real

7D-L1 Tracking considerations

8A-L1 Introduction to recognition

8B-L1 Classification: Generative models

8B-L2 Principle Component Analysis

8B-L3 Appearance-based tracking

8C-L1 Discriminative classifiers

8C-L2 Boosting and face detection

8C-L3 Support Vector Machines

8C-L4 Bag of visual words

8D-L1 Introduction to video analysis

8D-L2 Activity recognition

8D-L3 Hidden Markov Models

9A-L1 Color spaces

9A-L2 Segmentation

9A-L3 Mean shift segmentation

9A-L4 Segmentation by graph partitioning

9B-L1 Binary morphology

9C-L1 3D perception

10A-L1 The retina

10B-L1 Vision in the brain

We're Done!

Sandbox

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces basic image concepts, making it suitable for beginners in the field

Covers a range of techniques used in computer vision, providing a well-rounded foundation for learners

Emphasizes practical applications, ensuring its relevance to industry requirements

Taught by reputable instructors in the field, enhancing the course's credibility

Provides hands-on experience through labs and interactive materials, fostering practical understanding

Reviews summary

Well-explained computer vision basics course

The "Introduction to Computer Vision" course, taught by Professor Aaron Bobick, is a graduate-level course with rigorous mathematics. Students describe the course as interesting and engaging, with intuitive explanations. The course includes practical Matlab/Octave exercises to check understanding of lecture materials and give practice in applying knowledge. While it covers traditional computer vision techniques rather than deep learning, students find it comprehensive. It is recommended that students jump to topics of interest after the first few lessons to ensure they finish the course. Overall, this course is highly recommended for those seeking a solid foundation in computer vision.

Students can jump to topics of interest after the first few lessons.

"My advice for or students planning to take this course is, after the first few lessons, you don't need necessarily need to follow the course order, just jump the the topics you're interested in or you might not finish the whole thing."

Engaging instructor with a sense of humor.

"Prof. Aaron Bobick makes the class interesting with his humor."

Concepts explained clearly and intuitively.

"The concepts are explained clearly and intuitively."

Matlab/Octave exercises for practical application.

"Mixed through the lectures are matlab/octave exercises which are really great for checking understanding of the lecture material and give a good amount of practice in the practical application of the knowledge you are getting."

Graduate-level course with rigorous mathematics.

"This is a proper graduate-level course with rigorous mathematics."

Covers traditional computer vision techniques comprehensively.

"I think this is the best computer vision MOOC as it covers almost all the traditional computer vision techniques ..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Computer Vision with these activities:

Practice Image Filtering

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Filter images to understand convolution and edge detection.

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Apply different filters (Gaussian, Sobel, Laplace) to images.
Observe how different filters affect the image.
Experiment with different filter parameters.

Learn about Stereo Vision

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Delve deeper into stereo vision and explore techniques for stereo correspondence.

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Follow online tutorials on stereo vision.
Understand the principles of stereo correspondence.
Implement stereo correspondence algorithms.

Practice Feature Matching

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Practice matching features between images to strengthen understanding of feature extraction and matching.

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Use a feature matching library (e.g., OpenCV).
Experiment with different feature matching parameters.
Evaluate the performance of different feature matching algorithms.

Four other activities

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Practice Dense Optical Flow

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Practice calculating dense optical flow to enhance understanding of motion estimation techniques.

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Use an optical flow library (e.g., OpenCV).
Apply optical flow to different videos.
Analyze the results and identify patterns of motion.

Practice Image Segmentation

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Practice image segmentation to improve understanding of image partitioning and region grouping techniques.

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Implement an image segmentation algorithm (e.g., mean shift).
Apply image segmentation to different images.
Evaluate the performance of the image segmentation algorithm.

Practice Object Detection with Boosting

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Practice object detection using boosting algorithms to reinforce concepts of classification and feature selection.

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Implement a boosting algorithm (e.g., AdaBoost).
Use boosting for object detection.
Evaluate the performance of the object detector.

Build a Camera Calibration Tool

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Create a tool that can calibrate cameras and understand the concepts involved in camera calibration.

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Research camera calibration techniques.
Design and implement the tool.
Test the tool on different cameras.
Write documentation for the tool.

Career center

Learners who complete Introduction to Computer Vision will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

Computer Vision Engineers help build and train computer systems to interpret and understand visual data, such as images and videos. They apply algorithms and machine learning techniques to develop solutions for various industries, including healthcare, manufacturing, and autonomous vehicles. Taking this course would be particularly beneficial for those interested in the image processing, object recognition, and motion analysis aspects of computer vision.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models to solve complex problems in various domains. They have expertise in data analysis, model building, and deployment. This course would provide a strong foundation for those interested in applying machine learning techniques to computer vision tasks, such as image classification, object detection, and motion tracking.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. They work in various industries, helping organizations make data-driven decisions. This course would be helpful for those interested in applying computer vision techniques to analyze and interpret visual data, a growing area in data science.

See salaries and explore the career path for Data Scientist

Robotics Engineer

Robotics Engineers design, build, and maintain robots for various applications, including manufacturing, healthcare, and space exploration. They have expertise in mechanics, electronics, and computer science. This course would be relevant for those interested in developing computer vision systems for robots, enabling them to perceive and interact with their environment.

See salaries and explore the career path for Robotics Engineer

Autonomous Vehicle Engineer

Autonomous Vehicle Engineers design and develop self-driving cars and other autonomous vehicles. They have expertise in computer vision, sensor fusion, and control systems. This course would provide a strong foundation for those interested in developing computer vision systems for autonomous vehicles, enabling them to navigate and perceive their surroundings.

See salaries and explore the career path for Autonomous Vehicle Engineer

Medical Imaging Analyst

Medical Imaging Analysts use computer vision techniques to analyze medical images, such as X-rays, CT scans, and MRIs. They help diagnose diseases, plan treatments, and monitor patient progress. This course would be helpful for those interested in applying computer vision to healthcare, particularly in the analysis and interpretation of medical images.

See salaries and explore the career path for Medical Imaging Analyst

Computer Graphics Artist

Computer Graphics Artists create and manipulate digital images and animations for various applications, including movies, video games, and advertising. They have expertise in computer graphics software and techniques. This course would be relevant for those interested in using computer vision techniques to enhance computer graphics, such as in image manipulation, object tracking, and motion capture.

See salaries and explore the career path for Computer Graphics Artist

Video Game Developer

Video Game Developers design and develop video games for various platforms. They have expertise in game design, programming, and computer graphics. This course would be helpful for those interested in applying computer vision techniques to video games, such as in character recognition, motion tracking, and scene analysis.

See salaries and explore the career path for Video Game Developer

Surveillance Engineer

Surveillance Engineers design, install, and maintain surveillance systems for various applications, including security and law enforcement. They have expertise in camera technology, video analytics, and network security. This course would be useful for those interested in applying computer vision techniques to surveillance systems, enabling them to detect and track objects, recognize faces, and analyze behavior.

See salaries and explore the career path for Surveillance Engineer

Forensic Analyst

Forensic Analysts examine and analyze evidence to help solve crimes and legal disputes. They have expertise in various scientific and technical fields, including computer science and image analysis. This course would be may be useful for those interested in using computer vision techniques in forensic analysis, such as in image enhancement, facial recognition, and document analysis.

See salaries and explore the career path for Forensic Analyst

Architect

Architects design and supervise the construction of buildings and other structures. They have expertise in structural engineering, building materials, and aesthetics. This course would be may be useful for those interested in using computer vision techniques in architecture, such as in 3D modeling, building inspection, and interior design.

See salaries and explore the career path for Architect

Industrial Designer

Industrial Designers design and develop products, taking into account both functionality and aesthetics. They have expertise in ergonomics, materials science, and manufacturing processes. This course would be may be useful for those interested in using computer vision techniques in industrial design, such as in product modeling, quality control, and user experience research.

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User Experience Designer

User Experience Designers design and evaluate the interactions between users and products or services. They have expertise in human-computer interaction, psychology, and design principles. This course would be may be useful for those interested in using computer vision techniques in user experience design, such as in eye tracking, facial expression analysis, and gesture recognition.

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Photographer

Photographers capture and edit images for various purposes, such as art, journalism, and advertising. They have expertise in camera techniques, composition, and post-processing software. This course would be may be useful for those interested in using computer vision techniques in photography, such as in image enhancement, object recognition, and color correction.

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Astronomer

Astronomers study the universe and its contents, including stars, galaxies, and planets. They have expertise in physics, mathematics, and computer modeling. This course would be may be useful for those interested in using computer vision techniques in astronomy, such as in image processing, object detection, and data analysis.

See salaries and explore the career path for Astronomer