Специализация: общие сведения

Только онлайн-курсы

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Промежуточный уровень

Английский

Субтитры: Английский

Чему вы научитесь

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    Design computer vision application programs from scratch

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    Leverage MATLAB functionalities to implement sophisticated vision applications

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    Discern the level of complexity of vision algorithms

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    Understand the limitations of vision algorithms

Приобретаемые навыки

MatlabMachine LearningImage ProcessingComputer ProgrammingComputer Vision

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.

Гибкий график

Установите гибкие сроки сдачи заданий.

Промежуточный уровень

Английский

Субтитры: Английский

Специализация: принцип работы

Пройти курсы

Специализация Coursera — это серия курсов, помогающих в совершенстве овладеть определенным навыком. Можно сразу записаться на специализацию или просмотреть курсы, из которых она состоит и выбрать тот, с которого вы хотите начать. Подписываясь на курс, который входит в специализацию, вы автоматически подписываетесь на всю специализацию. Можно завершить всего один курс, а потом сделать паузу в обучении или в любой момент отменить подписку. Отслеживайте свои курсы и прогресс на панели управления учащегося.

Практический проект

В каждой специализации есть практический проект, который нужно успешно выполнить, чтобы завершить специализацию и получить сертификат. Если для практического проекта в специализации предусмотрен отдельный курс, прежде чем начать его, необходимо завершить все остальные курсы.

Получите сертификат

Когда вы пройдете все курсы и завершите практический проект, вы получите сертификат, которым можно поделиться с потенциальными работодателями и коллегами.

how it works

Специализация включает несколько курсов: 4

Курс1

Computer Vision Basics

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. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). 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) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. This is the first course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0. * A free license to install MATLAB for the duration of the course is available from MathWorks....
Курс2

Image Processing, Features & Segmentation

This course empowers learners to develop image processing programs and leverage MATLAB functionalities to implement sophisticated image applications. It provides a rich explanation of the fundamentals of computer vision’s lower- and mid-level tasks by examining several principle approaches and their historical roots. By the end of the course, learners are prepared to analyze images in frequency domain. Topics include image filters, image features and matching, and image segmentation. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). 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) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. This is the second course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0. * A free license to install MATLAB for the duration of the course is available from MathWorks....
Курс3

Stereo Vision, Dense Motion & Tracking

This course enables learners to develop 3D vision applications using a stereo imaging system. They are introduced to stereo vision theory, dense motion and visual tracking. They are able to discuss techniques used to obtain the 3D structure of objects. Topics include epipolar geometry, optical flow, structure from motion, multi-object tracking, 3D vision and visual odometry. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). 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) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. This is the third course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0. * A free license to install MATLAB for the duration of the course is available from MathWorks....
Курс4

Visual Recognition & Understanding

This course immerses learners in deep learning, preparing them to solve computer vision problems. Learners plunge into the field of computer vision that deals with recognizing, identifying and understanding visual information from visual data, whether the information is from a single image or video sequence. Topics include object detection, face detection and recognition (using Adaboost and Eigenfaces), and the progression of deep learning techniques (CNN, AlexNet, REsNet, and Generative Models.) This course is ideal for anyone curious about or interested in exploring the concepts of visual recognition and deep learning computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (free introductory tutorial: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). 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) and basic probability (random variables). It is highly recommended that learners take the “Deep Learning Onramp” course available at https://matlabacademy.mathworks.com/. Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. This is the fourth course in the Computer Vision specialization that lays the groundwork necessary for designing sophisticated vision applications. To learn more about the specialization, check out a video overview at https://youtu.be/OfxVUSCPXd0. * A free license to install MATLAB for the duration of the course is available from MathWorks....

Преподаватели

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Radhakrishna Dasari

Instructor
Department of Computer Science
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Junsong Yuan

Associate Professor and Director of Visual Computing Lab
Computer Science and Engineering

О 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....

О 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....

Часто задаваемые вопросы

  • Да! Чтобы начать, нажмите карточку интересующего вас курса и зарегистрируйтесь. Зарегистрировавшись, вы можете пройти курс и получить сертификат, ссылкой на который можно делиться с другими людьми. Просто ознакомиться с содержанием курса можно бесплатно. При подписке на курс, входящий в специализацию, вы автоматически подписываетесь на всю специализацию. Ход учебы можно отслеживать в панели управления учащегося.

  • Это полностью дистанционный курс, потому вам не нужно ничего посещать. Все лекции, материалы для самостоятельного изучения и задания доступны всегда и везде по Интернету и с мобильных устройств.

  • Time to completion can vary based on your schedule, but learners can expect to complete the specialization in 3 to 6 months.

  • This specialization is taught in MATLAB using computer vision and supporting toolboxes. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). 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) and basic probability (random variables).

  • It is important that learners take the courses in order, since the concepts and projects are developed based on the previous course, as described below.

    · The first course focuses on providing the mathematical foundations for the entire specialization and introduces the majority of concepts covered in the next three courses.

    · The second course explores the concepts of image processing, which are used in courses 3 and 4.

    · The third course covers the concepts of dense motion and tracking, which are used in course 4.

    · The fourth course builds upon the concepts in courses 1, 2 and 3, and focuses on higher-level, sophisticated computer vision concepts and visual understanding.

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  • On completion of this specialization, a learner will be able to:

    · Recognize foundational concepts of computer vision

    · Develop computer vision application programs from scratch

    · Leverage MATLAB functionalities to implement sophisticated vision applications

    · Discern the level of complexity of vision algorithms

    · Understand the limitations of vision algorithms

    · Design and build image processing applications

    · Develop 3D vision applications using a stereo imaging system

    · Implement a recognition system using machine learning algorithms

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