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.
oferecido por
Universidade de Buffalo
Universidade Estadual de Nova York
Computer Vision Basics
Universidade de BuffaloInformações sobre o curso
267,853 visualizações recentes
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Basic programming skills & experience; familiarity with basic linear algebra, calculus & probability, and 3D co-ordinate systems & transformations
Aprox. 10 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
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
Habilidades que você terá
Computer VisionComputer ProgrammingMatlab
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Basic programming skills & experience; familiarity with basic linear algebra, calculus & probability, and 3D co-ordinate systems & transformations
Aprox. 10 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade de Buffalo
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.
Universidade Estadual de Nova York
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.
Programa - O que você aprenderá com este curso
3 horas para concluir
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 horas para concluir
13 vídeos (Total 42 mín.), 2 leituras, 4 testes
13 videos
Meet Jeff Bier1min
Meet Jungsong Yuan, Ph.D.24s
What is Computer Vision?6min
Why Computer Vision?40s
Related Fields of Computer Vision4min
Relevant Fields56s
Computer Programming & Computer Vision1min
Computer Vision Awareness1min
Timelines & Milestones8min
Computer Vision Progression1min
Computer Vision Applications8min
CV Applications4min
CV Impact in the Field of Augmented Reality1min
2 leituras
Resources (Optional): Computer Vision Overview30min
REQUIRED - MATLAB Resources10min
3 exercícios práticos
What is Computer Vision?2min
Related Fields of Computer Vision2min
MATLAB Basics5min
3 horas para concluir
Color, Light, & Image Formation
In this module, we will discuss color, light sources, pinhole and digital cameras, and image formation.
3 horas para concluir
4 vídeos (Total 32 mín.), 1 leitura, 5 testes
1 leituras
Resources (Optional): Color, Light, & Image Formation30min
3 exercícios práticos
Light Sources4min
Pinhole Camera Model2min
Digital Camera2min
2 horas para concluir
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 horas para concluir
5 vídeos (Total 32 mín.), 1 leitura, 3 testes
5 videos
Low-, Mid-, High-Level Vision1min
Low-Level Vision8min
Mid-Level Vision7min
High-Level Vision6min
1 leituras
Resources (Optional): Low-, Mid- and High-Level Vision30min
2 exercícios práticos
Three-Level Paradigm2min
Low-Level Vision2min
2 horas para concluir
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 horas para concluir
8 vídeos (Total 10 mín.), 2 leituras, 2 testes
8 videos
Mathematical Preliminaries27s
Linear Algebra1min
Calculus45s
Probability Theory35s
Algorithms38s
Using Algorithms1min
Aligning RGB channels2min
2 leituras
Resources (Optional): Mathematics for Computer Vision30min
Computer Vision Basics - Key Takeaways10min
1 exercício prático
Algorithms2min
Avaliações
Principais avaliações do COMPUTER VISION BASICS
por LM10 de Ago de 2019
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.
por PS12 de Out de 2019
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.
por PW22 de Mai de 2020
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.
por AO28 de Abr de 2020
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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