Introduction to Computer Vision
Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will learn modern deep learning methods, such as neural networks and specific models designed for image recognition, and how it can be used to perform more complex tasks like object detection and image segmentation.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- المدة 8 ساعات hours
- الطبع بواسطة University of Colorado Boulder
-
Offered by
عن
In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene. Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same scene taken at different times or combine images acquired from different scientific instruments, as is common with hyperspectral and medical images. You will use MATLAB throughout this course. MATLAB is the go-to choice for millions of people working in engineering and science, and provides the capabilities you need to accomplish your computer vision tasks. You will be provided free access to MATLAB for the course duration to complete your work. To be successful in this course, it will help to have some prior image processing experience. If you are new to image data, it’s recommended to first complete the Image Processing for Engineering and Science specialization.الوحدات
About this Course
-
1
Videos
-
4
Readings
1
Videos
- Meet Your Instructor
4
Readings
- Earn Academic Credit for your Work!
- Course Support
- Course Introduction
- About the Assessments in this Course
Image
-
3
Videos
-
2
Assignment
2
Assignment
- Review: Image Function
- Review: Image Function Operations
3
Videos
- Common Image Types
- Image Function
- Image Function Operations
Transform
-
3
Videos
-
2
Assignment
2
Assignment
- Review: Linear Transform
- Review: Homogeneous Coordinate
3
Videos
- Linear Transform
- Homogeneous Coordinate
- Homogeneous Transformation
Week 1: Graded Quiz
-
1
Assignment
1
Assignment
- Week 1: Graded Quiz
Compare
-
6
Videos
-
4
Assignment
4
Assignment
- Review: L1 Distance
- Review: L2 Distance
- Review: Image Moment
- Review: Cross Entropy
6
Videos
- Compare Pixels
- Compare Many by Features
- Image Moment
- Similarity vs Distance
- Pixels to Distributions
- Cross Entropy
Filter
-
4
Videos
-
4
Assignment
4
Assignment
- Review: Cross Correlation in 1D
- Review: 2D Neighborhood
- Review: 2D Cross Correlation
- 1D Filter Scaling
4
Videos
- Cross Correlation in 1D
- Cross Correlation as Matrix Multiplication
- Up vs Down
- Math of Cross Correlation
Week 2: Graded Quiz
-
1
Assignment
1
Assignment
- Week 2: Graded Quiz
Multiview
-
4
Videos
-
2
Assignment
2
Assignment
- Review: Multiple Coordinate Systems
- Review: Multiview Projection
4
Videos
- Motivation
- Multiple Coordinate Systems
- Multiple Viewing Planes
- Multiview Projection
Camera Movement
-
4
Videos
-
3
Assignment
3
Assignment
- Review: 3D Translation
- Review: 3D Rotation
- Review: Camera Translation
4
Videos
- 3D Translation
- 3D Rotation
- Camera Translation
- Camera Rotation
Week 3: Graded Quiz
-
1
Assignment
1
Assignment
- Week 3: Graded Quiz
Camera Model
-
3
Videos
-
2
Assignment
2
Assignment
- Review: Intrinsic Matrix
- Review: Extrinsic Matrix
3
Videos
- Extrinsic Matrix
- Pinhole Camera Model
- Intrinsic Matrix
Epipolar
-
3
Videos
-
3
Assignment
3
Assignment
- Review: Cross-Product Matrix
- Review: Epipolar Constraint
- Review: Essential Matrix
3
Videos
- Motivation for Epipolar Geometry
- Basic Components of Epipolar Geometry
- Essential Matrix
Week 4: Graded Quiz
-
1
Assignment
1
Assignment
- Week 4: Graded Quiz
Auto Summary
Dive into the world of computer vision with this engaging course, designed to teach you essential algorithms and modern deep learning methods for image recognition, object detection, and image segmentation. Taught by experts from CU Boulder, this professional-level course is part of their MS in Computer Science degree on Coursera, offering flexible 8-week sessions and pay-as-you-go tuition. Ideal for recent graduates or working professionals, explore the ethical considerations of AI-generated images and videos while earning academic credit. Available through Starter and Professional subscriptions.
Tom Yeh