The University of Texas at Dallas

CS 4391: Introduction to Computer Vision

Fall 2023

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Course Overview

Key Topics: Computer Vision, Deep Learning.

Description: This undergraduate-level computer vision course focuses on theory and practice of computer vision (CV). We will provide in-depth overview of CV, including image formulation, image filtering and transformation, feature detection and matching, modern neural networks, various recognition problems such as image classification, object detection, semantic segmentation, visual motion, 3D vision, and several advanced topics such as NeRF, visual representation learning, vision+language, and vision+audio.

Course Relevance: The course is relevant to students who want to understand and implement computer vision and deep learning techniques.

Course Goals: There are three primary course goals. First, the course aims to familiarize students with the fundamental concepts of image formulation, image processing, visual motion, classical feature detection and 3D vision. Second, the course helps students understand key components in modern deep learning-based computer vision. Third, through the assignments and final project, students have an opportunity to learn how to build practical computer vision systems.

Course Information



Assignment Structure

Students will be expected to:

Late Policy

For the assignments (not including your final project report), students will be allowed a total of five late days per semester. After you use up the free late days, your late submissions will be penalized as follows. Assignments turned in within 24 hours of the due date will receive 90% of its score. Assignments turned in within 48 hours of the due date will receive 70% of its score. Assignments more than 48 hours late will not be accepted.


This course assumes familiarity with Linear Algebra and Python Programming. If you have not taken courses covering this material, consult with the instructor.


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