This course covers the basic concepts of computer vision. This course has two parts. The first part of classical computer vision discusses camera model, image processing, corner detection, edges, interest point, image warping, homography, model fitting, stereo and optical flow. The second part of modern computer vision with deep learning includes topics for convolutional neural network, transformer network, image classification, segmentation, and object detection.
Lectures
- Monday and Wednesday 1:30 pm to 2:45 pm, COB1 Room 120
Labs
- CSE-185-04L, Wednesday 7:30 pm to 10:20 pm, SE1 Room 100
- Instructor: Meng Tang
- Email: mtang4@umcerced.edu
- Office hours: Online every Tuesday from 10AM to 11AM, Zoom link
- Teaching assistant: Weijie Lyu (wlyu3@ucmerced.edu)
Topics
- Camera model
- Image processing and filtering
- Corner detection
- Interest point
- Image warping
- Homograph and multi-view geometry
- Stereo and optical flow
- Convolutional neural network
- Segmentation
- Object detection
- Transformer network
Reading list
- All lecture slides will be available on the course website
Prerequisites
- Upper class standing. Basic
knowledge of calculus, linear algebra
Grading
- 40% Assignments
- 20% Midterm exam
- 20% Final exam
- 20% Labs