Introduction to Computer Vision

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9/10 stars
based on  1 review
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Cost FREE
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Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Udacity online courses
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and ...
Udacity gives students the opportunity to create hands-on projects that can be put into their portfolios and used to demonstrate their skills to future employers. You'll have a personal coach who helps provide feedback on your assignments and projects to assist you in reaching your goals and staying on track in your online classes. Throughout your education experience, you'll be able to track your development, complete in-class projects, have access to interactive exercises and videos and earn a verified certificate at the end of the course as proof of all that you've learned. You'll be learning from knowledgeable professors across various schools and parts of the globe. Learn about computer science from Dave Evans, an instructor at the University of Virginia, or delve into app development with Samantha Ready, a Developer Evangelist at Salesforce.com.

Provider Subject Specialization
Sciences & Technology
102 reviews

Course Description

This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry including stereo, motion estimation and tracking, and classification. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really classification theory best learned in an ML course. The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the problem sets. All algorithms work perfectly in the slides. But remember what [Yogi Berra](http://yogiberramuseum.org/just-for-fun/yogisms/) said: In theory there is no difference... This course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry including stereo, motion estimation and tracking, and classification. We’ll develop basic methods for applications that include finding known models in images, depth recovery from stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really classification theory best learned in an ML course. The focus of the course is to develop the intuitions and mathematics of the methods in lecture, and then to learn about the difference between theory and practice in the problem sets. All algorithms work perfectly in the slides. But remember what [Yogi Berra](http://yogiberramuseum.org/just-for-fun/yogisms/) said: In theory there is no difference between theory and practice. In practice there is. (Einstein said something similar but who knows more about real life?) In this course you do not, for the most part, apply high-level library functions but use low to mid level algorithms to analyze images and extract structural information.
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Reviews 9/10 stars
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10/10 starsTaking Now
  • 1 review
  • 0 completed
3 years, 10 months ago
Really thorough course with a lot of theory. Covers everything from image filtering and edge detection, to feature detection and description, to image transformations, stereo imaging and camera calibration, to photometry using reflection models and BRDFs, to parameter estimation with RANSAC and tracking with Kalman filters, Bayes filters and particle filters, to Image flow, to color spaces and image segmentation, to image classification, recognition, boosting and face detection, to 3D perception, passive and active 3D sensing and the Kinect, to how vision works on the retina and in the brain. I probably missed a few. Just go check it out.
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