By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks.
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Университет штата Нью-Йорк в Баффало
Университет штата Нью-Йорк
Computer Vision Basics
Университет штата Нью-Йорк в БаффалоОб этом курсе
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Промежуточный уровень
Basic programming skills & experience; familiarity with basic linear algebra, calculus & probability, and 3D co-ordinate systems & transformations
Прибл. 10 часов на выполнение
Английский
Субтитры: Английский
Чему вы научитесь
Understand what computer vision is and its goals
Identify some of the key application areas of computer vision
Understand the digital imaging process
Apply mathematical techniques to complete computer vision tasks
Приобретаемые навыки
Computer VisionComputer ProgrammingMatlab
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Промежуточный уровень
Basic programming skills & experience; familiarity with basic linear algebra, calculus & probability, and 3D co-ordinate systems & transformations
Прибл. 10 часов на выполнение
Английский
Субтитры: Английский
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Университет штата Нью-Йорк в Баффало
The University at Buffalo (UB) is a premier, research-intensive public university and the largest, most comprehensive institution of the State University of New York (SUNY) system. UB offers more than 100 undergraduate degrees and nearly 300 graduate and professional programs.
Университет штата Нью-Йорк
The State University of New York, with 64 unique institutions, is the largest comprehensive system of higher education in the United States. Educating nearly 468,000 students in more than 7,500 degree and certificate programs both on campus and online, SUNY has nearly 3 million alumni around the globe.
Программа курса: что вы изучите
3 ч. на завершение
Computer Vision Overview
In this module, we will discuss what computer vision is, the fields related to it, the history and key milestones of it, and some of its applications.
3 ч. на завершение
13 видео ((всего 42 мин.)), 2 материалов для самостоятельного изучения, 4 тестов
13 видео
Meet Jeff Bier1мин
Meet Jungsong Yuan, Ph.D.24
What is Computer Vision?6мин
Why Computer Vision?40
Related Fields of Computer Vision4мин
Relevant Fields56
Computer Programming & Computer Vision1мин
Computer Vision Awareness1мин
Timelines & Milestones8мин
Computer Vision Progression1мин
Computer Vision Applications8мин
CV Applications4мин
CV Impact in the Field of Augmented Reality1мин
2 материала для самостоятельного изучения
Resources (Optional): Computer Vision Overview30мин
REQUIRED - MATLAB Resources10мин
3 практических упражнения
What is Computer Vision?2мин
Related Fields of Computer Vision2мин
MATLAB Basics5мин
3 ч. на завершение
Color, Light, & Image Formation
In this module, we will discuss color, light sources, pinhole and digital cameras, and image formation.
3 ч. на завершение
4 видео ((всего 32 мин.)), 1 материал для самостоятельного изучения, 5 тестов
1 материал для самостоятельного изучения
Resources (Optional): Color, Light, & Image Formation30мин
3 практических упражнения
Light Sources4мин
Pinhole Camera Model2мин
Digital Camera2мин
2 ч. на завершение
Low-, Mid- & High-Level Vision
In this module, we will discuss the three-level paradigm of computer vision that was proposed by David Marr. We will also discuss low, mid, and high level vision.
2 ч. на завершение
5 видео ((всего 32 мин.)), 1 материал для самостоятельного изучения, 3 тестов
5 видео
Low-, Mid-, High-Level Vision1мин
Low-Level Vision8мин
Mid-Level Vision7мин
High-Level Vision6мин
1 материал для самостоятельного изучения
Resources (Optional): Low-, Mid- and High-Level Vision30мин
2 практических упражнения
Three-Level Paradigm2мин
Low-Level Vision2мин
2 ч. на завершение
Mathematics for Computer Vision
In this lecture, we will discuss the Mathematics used in Computer Vision, which includes linear algebra, calculus, probability, and much more.
2 ч. на завершение
8 видео ((всего 10 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
8 видео
Mathematical Preliminaries27
Linear Algebra1мин
Calculus45
Probability Theory35
Algorithms38
Using Algorithms1мин
Aligning RGB channels2мин
2 материала для самостоятельного изучения
Resources (Optional): Mathematics for Computer Vision30мин
Computer Vision Basics - Key Takeaways10мин
1 практическое упражнение
Algorithms2мин
Рецензии
Лучшие отзывы о курсе COMPUTER VISION BASICS
от партнера LM10 авг. 2019 г.
It is a good introduction course but I think some more demo coding for matlab in the first assignments will be a good thing so we don´t have to spend a lot of time on google and on trial and error.
от партнера PS12 окт. 2019 г.
Course was great ! And explained well but Matlab was not explained well. It's hard to complete week-4 assignment if you are not good at Matlab. Please provide python language for solving problems.
от партнера PW22 мая 2020 г.
The course is fine, but it's too fundamental. Overall, it is more suit to personal who already had the fundamental in image processing knowledge. Else the course is a but higher level for others.
от партнера AO28 апр. 2020 г.
Lays a good foundation for Computer Vision. There should be more programming examples as some of the labs were beyond the scope of what was taught in the videos, especially the last one.
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Are there any prerequisites?
Learners should have basic programming skills and experience (understanding of for loops, if/else statements). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration), basic probability (random variables), and 3D co-ordinate systems & transformations.
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