Computer Vision Fundamentals: Algorithms to Deep Learning
Master computer vision essentials, from classical algorithms to deep learning. Apply cutting-edge techniques to real-world image analysis tasks.
Master computer vision essentials, from classical algorithms to deep learning. Apply cutting-edge techniques to real-world image analysis tasks.
This comprehensive course guides learners through the essential algorithms and methods of computer vision, enabling computers to 'see' and interpret visual data. Starting with core concepts and traditional image analysis techniques, the course progresses to modern deep learning methods. Students will explore image types, transformations, and advanced topics like multiview geometry and camera models. The curriculum covers both classical feature detection and neural networks for complex tasks such as object detection and image segmentation. Practical assignments and real-world applications provide hands-on experience, while discussions on AI-generated images explore ethical considerations. This course offers a solid foundation for those pursuing careers in computer vision, robotics, or AI.
Instructors:
English
Powered by
What you'll learn
Understand the fundamental principles and algorithms of classical computer vision
Apply deep learning models to various computer vision tasks
Evaluate and implement computer vision solutions for real-world applications
Master image analysis techniques including feature detection and similarity assessment
Gain proficiency in multiview geometry and 3D scene reconstruction
Understand camera models and their role in computer vision applications
Skills you'll gain
This course includes:
7 Hours PreRecorded video
26 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course offers a comprehensive introduction to computer vision, covering both classical algorithms and modern deep learning methods. The curriculum is structured into four modules, each focusing on key aspects of computer vision. Module 1 introduces foundational concepts of image types, functions, and transformations. Module 2 delves into image analysis techniques, including pixel comparison, feature-based analysis, and cross-correlation. Module 3 explores multiview geometry, essential for 3D modeling and scene reconstruction. The final module covers advanced topics such as camera models, epipolar geometry, and their applications in 3D reconstruction and stereo vision. Throughout the course, students engage with practical assignments, applying theoretical concepts to real-world computer vision tasks.
Introduction to Computer Vision: Foundations
Module 1 · 3 Hours to complete
Image Analysis and Similarity
Module 2 · 4 Hours to complete
Multiview Geometry
Module 3 · 4 Hours to complete
Camera Models and Epipolar Geometry
Module 4 · 7 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Innovator in AI and Human-Computer Interaction
Dr. Tom Yeh is an Associate Professor of Computer Science at the University of Colorado Boulder, where he also serves as the Director of the Center for the Brain, AI, and Child (BAIC) and is a faculty member of the Mortenson Center in Global Engineering. His research interests encompass a broad range of topics including artificial intelligence (AI), the ethics of AI, generative AI, assistive technology, 3D printing, STEM education, computer vision, brain imaging, and citizen science. Dr. Yeh earned his Ph.D. from the Massachusetts Institute of Technology, focusing on vision-based user interfaces, and subsequently completed a postdoctoral fellowship at the University of Maryland Institute for Advanced Computer Studies (UMIACS). With over 70 published articles to his name, he has received multiple best paper awards from prestigious conferences such as CHI and SIGCSE. His research is supported by notable organizations including the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Defense Advanced Research Projects Agency (DARPA). Dr. Yeh's work not only advances technological innovation but also emphasizes ethical considerations in AI, making significant contributions to both academia and society.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.