In addition, our system based onĪR technology can support the work of the lecturer, which is difficult to do by Mechanism as a lecture recording environment. Other recording contents used by the lecturer. In particular, we use augmented reality (AR) technology toĭigitize and display in real-time lecture materials, assistant agents, and This study, we aim to reduce the cumbersome task of lecture video editing byĭeveloping a system that enables the addition of visual effects in the video It is therefore imperative to edit the lecture video after recording. Time to put those convolutions to use! For week 3 we delve in to what makes images interesting, what makes them unique, how to find correspondences between images, and how to fit models with a large number of outliers in the data.Download a PDF of the paper titled Developing a Lecture Video Recording System Using Augmented Reality, by Yuma Ito and 3 other authors Download PDF Abstract: Assistive technology is a prerequisite for making a high-quality lecture # Lecture 4: Resizing, Filters, Convolutions You'll apply this knowledge as you get started on (). You learn how to manipulate images and perform operations like resizing, sharpening, smoothing, and more. In week 2 we start to dive into low-level vision and image processing. # Lecture 2: Human Vision, Color Spaces, Transforms # All fun stuff! Once you've learned the basics you should be ready for (), which is mostly an introduction to the codebase we'll be using for the assignments. There's background information on the human visual system, color, light, what an image actually is, and how it's stored in a computer. There's an introduction to the three levels of vision, **low-level** vision mostly concerns the pixels or groups of nearby pixels, **mid-level** vision starts to connect images to each other and the real world, and **high-level** vision connects images to semantics and meaning. This week we cover the basics of computer vision. If you don't have an idea you can train a classifier on birds and compete in the Kaggle competition posted on the Ed discussion board. Projects can focus on developing new techniques or tools in computer vision or applying existing tools to a new domain. Each project should have a significant technical component, software implementation, or large-scale study. Pick any area of computer vision that interests you and pursue some independent work in that area. There is a final project worth 20% of the final grade. You will not be penalized for turning in assingments late due to COVID (or if you're having trouble getting caught back up afterward). Once you are well please reach out to the course staff and we can figure out how to get you back on track with assingments and any missed classes. If you feel like doing computer vision while sick go for it but also know you can take some time off. **COVID Policy:** If you get COVID don't worry about doing your homework, rest, recover, do what you need to do to get better. After you have used your late days late assingments will be penalized up to 10% per day late. Any number can be used on any assingment. You have 8 late day to use throughout the quarter. **Note:** due date subject to change if we haven't covered relevant material in time for the assignment. The individual homeworks can be found in the `src/` folder. We cover basic image manipulations, filtering, features, stitching, optical flow, machine learning, and convolutional neural networks. The class has 6 homeworks where you will build out a computer vision library in C. Just make your own copy of the slides on Google Docs, don't ask to modify mine! Lectures 8 and 9 on Flow, 3d, and stereo are by ().Īll of the slides, videos, and homeworks are free to use, modify, redistribute as you like without permission. Special thanks to: Rob Fergus, Linda Shapiro, Harvey Rhody, Rick Szeliski, Ali Farhadi, Robert Collins. Slides are a mishmash of lots of other people's work. Participate in whatever way best suits your needs this quarter! **Please do not come to in-person class if you are sick or have reason to suspect you may be sick.** Asynchronously: See below for lecture recordings (old-school vision), as well as newer, machine-learning based computer vision.Ĭourse will be offered in a variety of modalities: It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. This class is a general introduction to computer vision.
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