Image processing is a fundamental aspect of computer science and engineering, focusing on the analysis and manipulation of digital images using algorithms and mathematical techniques. This course provides students with a comprehensive understanding of image processing concepts, techniques, and applications. In addition, this course covers an introduction about computer vision goals, challenges and applications and an intro to deep learning.
Syllabus
L1: Introduction to computer vision, intro to image processing and its applications L2: Image types, color systems, operations L3: Filters: Mean, Gaussian, median filters. Blurring images, and other operations L4: Fourier transform, Law pass filters, high pass filters, deblurring, noise elimination L5: Frequency filters, comparison between spatial domain and frequency domain L6: Morphological operations L7: Edge detection L8: Segmentation L9: Intro to deep learning, neural networks L10: intro to CNN for computer vision, Object segmentation and classification using CNN. PW1: image basics using OpenCV library PW2: Blinding and pasting images, some operations, CV functions. PW3: Image Thresholding PW4: Blurring and smoothing PW5: Morphological operations PW6: Gradients PW7: Corner detection PW8: Edge detection PW9: Licence plate detection and blurring PW10: Face detection