CompVision GenImage Modeling

3 Credits · Level: Graduate

Computer Vision & Generative Image Modeling - Computer Vision Analytics & Generative Image Modeling offers a comprehensive introduction to image mining and computer vision. The course covers image acquisition, representation, and processing, including convolution, Fourier transforms, filters, and feature generation. Advanced topics include classification, segmentation, spatial relations, deep-fake detection, object tracking, and image sentiment analysis. Students will explore cutting-edge techniques such as diffusion models, Vision Transformers, advanced GANs (StyleGAN, CycleGAN), Neural Radiance Fields (NeRFs), zero-shot and few-shot learning, image-to-image translation, self-supervised learning, 3D generative models, cross-modal generation, and adversarial robustness. Practical applications include facial recognition, OpenCV in Python, deep fake detection, gesture analysis, and object/scene categorization. This course equips students with the skills to develop sophisticated computer vision and generative image solutions.