CSE 473/573: Computer Vision and Image Processing

CSE 473/573: Computer Vision and Image Processing

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🙋‍♀️ Syllabus for Fall 2026 🙌

This course is an introduction to those areas of Artificial Intelligence that deal with fundamental issues and techniques of computer vision and image processing. The emphasis is on physical, mathematical, and information-processing aspects of the vision. Topics to be covered include image formation, edge detection and segmentation, convolution, image enhancement techniques, extraction of features such as color, texture, and shape, object detection, 3-D vision, and computer vision system architectures and applications. Together, we will explore fascinating topics related to Computer Vision and Image Processing, including Optical Image Formation, Feature Extraction, Classification, and Recognition.

Instructor

Name Title

Chen Wang

Assistant Professor

   

Fall 2026 Schedule Download ALL Slides

Date Topic Note
8/25/2026 L1: Introduction Quiz 0
8/27/2026 L2: Camera Model P1 Assigned
9/1/2026 L3: Coloring & Warping
9/3/2026 L4: Filtering Quiz 1
9/8/2026 L5: Morphology
9/10/2026 L6: Edge Detection
9/15/2026 L7: Pyramids & Histogram
9/17/2026 L8: Feature P1 Due
9/22/2026 L9: Optical Flow
9/24/2026 L10: Hough Transform Quiz 2
9/29/2026 Midterm Exam
10/1/2026 No lecture
10/6/2026 L11: Alignment and Fitting
10/8/2026 L12: Blending & RANSAC P2 Assigned; Quiz 3
10/13/2026 Fall break
10/15/2026 L13: Epipolar Geometry & Stereo Vision
10/20/2026 L14: Texture & Segmentation
10/22/2026 L15: Classification P2 Due
10/27/2026 L16: Recognition & Retrieval Quiz 4
10/29/2026 L17: Face Detection P3 Assigned
11/3/2026 Midterm Exam
11/5/2026 L18: Multi-layer Perceptron
11/10/2026 L19: Deep Learning Quiz 5
11/12/2026 L20: Object Detection/Segmentation Instructor: Bharat Yalavarthi
11/17/2026 L21: Interpretability in vision models
11/19/2026 L22: Diffusion models Instructor: Vikram Velankar
11/24/2026 L23: Multi-task learning P3 Due
11/26/2026 Thanksgiving break
12/9/2026 Final Exam Hoch 114

Acknowledgement

Many materials are derived from Prof. David Doermann’s course slides. We give special thanks to Prof. Junsong Yuan and Prof. Nalini Ratha for providing fruitful suggestions. We also thank numerous generous researchers for contributing to the contents, which include but are not limited to K. Kitani, K. Grauman, S. Seitz, S. Marschner, M. Hebert, Fei-Fei Li, L. Lazebnik, R. Szeliski, A. Efros, A. Oliva, B. Leibe, D. Hoiem, A. Moore, and D. Lowe, etc.

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