![]() 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. L07 - Learning in Graphical Models | Slidesĩ.1 - Implicit Neural Representations | Videoĩ.2 - Differentiable Volumetric Rendering | Videoġ0.!(images/title.jpg) ![]() L06 - Applications of Graphical Models | Slides ![]() ![]() L05 - Probabilistic Graphical Models | Slides A strong emphasis of this course is on 3D vision.Ģ.1 Primitives and Transformations | Videoģ.2 - Two-frame Structure-from-Motion| Video This course therefore assumes prior knowledge of deep learning (e.g., deep learning lecture) and introduces the basic concepts of graphical models and structured prediction where needed. The tutorials will deepen the understanding of deep neural networks by implementing and applying them in Python and PyTorch. Modern computer vision relies heavily on machine learning in particular deep learning and graphical models. Applications include building 3D maps, creating virtual avatars, image search, organizing photo collections, human computer interaction, video surveillance, self-driving cars, robotics, virtual and augmented reality, simulation, medical imaging, and mobile computer vision. This course will provide an introduction to computer vision, with topics including image formation, camera models, camera calibration, feature detection and matching, motion estimation, geometry reconstruction, object detection and tracking, and scene understanding. Problems in this field include reconstructing the 3D shape of an object, determining how things are moving and recognizing objects or scenes. The goal of computer vision is to compute geometric and semantic properties of the three-dimensional world from digital images. ![]()
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