Brief Introduction
Images have become ubiquitous in computing. Sometimes we forget that images often capture the light reflected from a physical scene. This course gives you both insight into the fundamentals of image formation and analysis, as well as the ability to extract information much above the pixel level. These skills are useful for anyone interested in operating on images in a context-aware manner or where images from multiple scenarios need to be combined or organized in an appropriate way.Course Summary
In this course, you will learn the basics of computer vision and image processing, including image formation, feature detection and matching, stereo vision, motion estimation and tracking, and object recognition. You will also learn about deep learning and convolutional neural networks for computer vision applications.Key Learning Points
- Understand the fundamentals of image formation and processing
- Learn about feature detection, stereo vision, and object recognition
- Explore deep learning and convolutional neural networks for computer vision applications
Related Topics for further study
Learning Outcomes
- Understand the basics of image formation and processing
- Develop knowledge of feature detection, stereo vision, and object recognition
- Learn about deep learning and convolutional neural networks for computer vision applications
Prerequisites or good to have knowledge before taking this course
- Familiarity with linear algebra and calculus
- Basic programming skills in Python
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
Similar Courses
- Applied Computer Vision
- Deep Learning
- Machine Learning Engineer
Related Education Paths
- Artificial Intelligence Nanodegree Program
- Computer Vision Nanodegree Program
- Robotics Software Engineer Nanodegree Program
Related Books
Description
This course provides an introduction to computer vision including fundamentals, methods for application and machine learning classification.Requirements
- Data structures: You'll be writing code that builds representations of images, features, and geometric constructions. A good working knowledge of Matlab and/or Python with NumPy. The lecture videos use Matlab for occasional demonstration because the instructor is too old to change. Problem sets will be done in Matlab or Python. As mentioned in the resources note below, you can use either Matlab or the open source version Octave. This course has more math than many CS courses: Linear algebra, vector calculus, and linear algebra (that is not a typo). No prior knowledge of vision is assumed though any experience with Signal Processing is helpful. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Introduction Introduction lesson 2 Image Processing for Computer Vision Linear image processing Model fitting Frequency domain analysis lesson 3 Camera Models and Views Camera models Stereo geometry Camera calibration Multiple views lesson 4 Image Features Feature detection Feature descriptors Model fitting lesson 5 Lighting Photometry Lightness Shape from shading lesson 6 Image Motion Overview Optical flow lesson 7 Tracking Introduction to tracking Parametric models Non-parametric models Tracking considerations lesson 8 Classification and Recognition Introduction to recognition Classification: Generative models Classification: Discriminative models Action recognition lesson 9 Useful Methods Color spaces and segmentation Binary morphology 3D perception lesson 10 Human Visual System The retina Vision in the brain
Summary of User Reviews
Learn the basics of computer vision with this comprehensive course on Udacity. Students praise the hands-on approach and detailed explanations provided by the instructors.Key Aspect Users Liked About This Course
The course offers practical examples and exercises that help students understand complex concepts.Pros from User Reviews
- Instructors are knowledgeable and provide clear explanations
- Hands-on exercises reinforce concepts learned in lectures
- Course materials are well-organized and easy to follow
- Course covers a wide range of topics in computer vision
- Great introduction to computer vision for beginners
Cons from User Reviews
- Some sections may be too technical for beginners
- Lack of interaction with instructors or peers
- Limited real-world applications discussed
- Exercises can be challenging and time-consuming
- Course may not be suitable for those without a strong math background
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