Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores image compression principles, focusing on JPEG 2000, covering transform-based coding, quantization, entropy coding, region of interest, error resilience, and software implementations.
Covers the concepts of compression and prediction using prefix-free codes and distributions.
