CS 5540 Computer Vision 3.0 Credits
This course covers principles and practice of computer vision: how to acquire, improve, and interpret images and video. Coverage includes computational imaging for capture and enhancement (sensing, lightness, radiometry and color, sampling, convolution and filtering, denoising and deblurring, HDR and panorama stitching), core representations for visual analysis, geometric methods for understanding cameras and scene structure, and neural network approaches for recognition (classification, detection, segmentation). Depending on the offering, modules may also include biometric recognition, generative models, vision with language, among other topics. Emphasis is on computational methods and their underlying mathematical ideas through practical programming work.
Repeat Status: Not repeatable for credit
Prerequisites: (CS 5010 [Min Grade: D] or CS 501 [Min Grade: D]) and (CS 5030 [Min Grade: D] or CS 502 [Min Grade: D])