Course Description
This course introduces the foundations and techniques for 2D and 3D imaging. From classical Fourier analysis to modern deep-learning models, we explore a broad spectrum of image-based technologies, including segmentation, recognition, volumetric capture, depth estimation, clustering, convolutional neural networks, generative models, etc. By the end of the semester, students will be equipped with fundamental knowledge and hands-on experience to pursue research or industry careers across Computer Vision, AI, HCI, robotics, or medical imaging. (Note: This course can be counted toward CS 422/522 Digital Image Processing for the corresponding undergraduate and graduate program requirements.)
Course Information
Time: Tuesdays & Thursdays, 12:30 pm – 1:45 pm, Spring 2026
Location: TBD
Instructor: Prof. Zhu Wang
Email: zhuwang at unm dot edu
Website: https://cs.unm.edu/~zhuwang
Office Hours: 11:30 am – 12:30 pm Tuesdays & Thursdays (Spring 2026)
Office 3130, Farris Engineering Center
Textbook / Recommended Reading (Optional)
- Digital Image Processing, 4th Edition, Gonzalez & Woods, 2017
- Computer Vision: Algorithms and Applications, 2nd Edition, Szeliski, 2022
Prerequisites
This course requires students to have working knowledge of linear algebra and calculus, and prior programming experience. Assignments must be implemented in Python. Students should also be familiar with basic usage of PyTorch (PyTorch Tutorials) and NumPy (NumPy Tutorials) by the 4th week of the semester. MATLAB is acceptable for early assignments, but not for the entire course. If you are already familiar with JavaScript, Java, C++, or a similar high-level language, you should be able to handle Python quickly and complete the course successfully.
Weekly Topic Breakdown (Tentative)
- Introduction, overview, history, ethics
- Image formation (cameras, pixels, sampling, color, 2D/3D transformations)
- Classic filtering & lab session 1
- Fourier analysis
- Eigen-based image representation (i.e. Principal Component Analysis (PCA) and Linear Discriminant Analysis(LDA), etc.)
- Morphological operations
- Detection and recognition
- Segmentation & lab session 2
- classification
- Computational photography
- Depth estimation, stereo vision
- Simultaneous Localization and Mapping (SLAM)
- Convolutional neural networks (CNNs) & lab session 3
- Generative models (GANs, diffusion models) (advanced and optional)
- Final project presentation
Grading
6~8 Project Assignments – 60%
Midterm – 15%
Final Project – 25%
Late work loses 30% per week and receives zero more than two weeks.
ST 491 and ST 591 Digital Image Processing (CS 422/522) cover the same content; however, students in ST 491 (CS 422) will either be graded under a less stringent standard or will have fewer project assignments.