We will not accept your submission after 72 hours over the deadline. For example, you submit HW1 12 hours passing the deadline, then the max score you will get is 88% of that assignment. Late Work Policy: Deduct 1% of total score of that assignment per hour passing the deadline for each assignment. The project will generate a 6-page (excluding references) double-column report and a presentation at the end of the term. Final Project (40%): Students will form groups with up to two members to implement a final course project.They will be done independently by each student. Each (worth 15%) will be a mix of analytical questions and programming questions. Homework Assignments (60%): There will be four homework assignments over the term. Please note that the instructor owns the copyright of all recorded lectures and course materials, You should not post them on the Internet or sharing with a friend without my consent. The recordings along with any other course materials will be posted on Blackboard for asynchronous viewing by students. Please note, all online classes will be recorded. Most lectures will be streamed synchronously via Zoom. This is a tentative schedule of the course and is subject to modifications over the term. Renting a cloud GPU via cloud service providers e.g., Google Cloud.īy taking this course, you confirm that you have met the system requirements.Owning an NVIDIA GPU system with at least 6GB GPU memory or.Using our provided CSUG account to ssh to shared GPU resources or.Besides, many of the course assignments require you to have access to a powerful GPU system. You need a computer and a reliable network to access online lectures and course materials. For assignments and projects, the most common programming language is Python. Prerequisitesīasic knowledge of probability, linear algebra (MTH 165 Strongly Recommended) data structures, algorithms programming experience. The students will develop a strong understanding of formulating and solving problems in computer vision. The course is designed as an upper-level elective for Computer Science undergraduate students and an AI-area breath course for graduate students. Image Formation and Multiple View Geometry: camera models, light and color, camera calibration, image alignment, stereo visionĪn introduction to computer vision.Recognition: feature learning, convolutional and recurrent neural networks, advanced CNN architectures, attention mechanisms, feature visualization, image classification, object detection, instance segmentation, scene parsing, image captioning, face recognition, style transfer, visual and sound, video analysis, generative adversarial networks, image synthesis.Segmentation and Fitting: Gestalt principles, segmentation by clustering, image as graphs, interactive segmentation, robust fitting and RANSAC, Hough transform.
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