關於此 專項課程
25,235 次近期查看

100% 在線課程

立即開始,按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

完成時間大約為2 個月

建議 14 小時/週

英語(English)

字幕:英語(English)

您將學到的內容有

  • Check

    Design computer vision application programs from scratch

  • Check

    Leverage MATLAB functionalities to implement sophisticated vision applications

  • Check

    Discern the level of complexity of vision algorithms

  • Check

    Understand the limitations of vision algorithms

您將獲得的技能

MatlabMachine LearningImage ProcessingComputer ProgrammingComputer Vision

100% 在線課程

立即開始,按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

完成時間大約為2 個月

建議 14 小時/週

英語(English)

字幕:英語(English)

專項課程 的運作方式

加入課程

Coursera 專項課程是幫助您掌握一門技能的一系列課程。若要開始學習,請直接註冊專項課程,或預覽專項課程並選擇您要首先開始學習的課程。當您訂閱專項課程的部分課程時,您將自動訂閱整個專項課程。您可以只完成一門課程,您可以隨時暫停學習或結束訂閱。訪問您的學生面板,跟踪您的課程註冊情況和進度。

實踐項目

每個專項課程都包括實踐項目。您需要成功完成這個(些)項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程,則需要在開始之前完成其他所有課程。

獲得證書

在結束每門課程並完成實踐項目之後,您會獲得一個證書,您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。

how it works

此專項課程包含 4 門課程

課程1

Computer Vision Basics

4.1
32 個評分
16 個審閱

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.

...
課程2

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.

...
課程3

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.

...
課程4

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.

...

講師

Avatar

Radhakrishna Dasari

Instructor
Department of Computer Science
Avatar

Junsong Yuan

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

關於 纽约州立大学布法罗分校

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....

關於 纽约州立大学纽约州立大学

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....

常見問題

  • 可以!点击您感兴趣的课程卡开始注册即可。注册并完成课程后,您可以获得可共享的证书,或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分,系统会自动为您订阅完整的专项课程。访问您的学生面板,跟踪您的进度。

  • 此课程完全在线学习,无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

  • 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.

  • No

  • 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

還有其他問題嗎?請訪問 學生幫助中心