This course is an introduction to digital image processing. The course introduces the idea of what digital image processing is and where digital images come from, how images and colors are preceived by the human eye, coding for lossless and lossy image compression, and restoration of images among other topics.
Intro to Digital Image Processing Lectures, Spring 2015
Rensselaer Polytechnic Institute via YouTube
Overview
Syllabus
DIP Lecture 1: Digital Image Modalities and Processing.
DIP Lecture 2: The human visual system, perception, and color.
DIP Lecture 3: Image acquisition and sensing.
DIP Lecture 4: Histograms and point operations.
DIP Lecture 5: Geometric operations.
DIP Lecture 6: Spatial filters.
DIP Lecture 7: The 2D Discrete Fourier Transform.
DIP Lecture 8: Frequency domain filtering; sampling and aliasing.
DIP Lecture 9: Unitary image transforms.
DIP Lecture 10: Edge detection.
DIP Lecture 11: Edge linking and line detection.
DIP Lecture 12: Thresholding.
DIP Lecture 12a: Image Segmentation.
DIP Lecture 12b: Snakes, active contours, and level sets.
DIP Lecture 13: Morphological image processing.
DIP Lecture 13a: Region description and filtering.
DIP Lecture 14: Object and feature detection.
DIP Lecture 15: Lossless image coding.
DIP Lecture 16: Lossy image compression.
DIP Lecture 17: Image restoration and the Wiener filter.
DIP Lecture 18: Reconstruction from parallel projections and the Radon transform.
DIP Lecture 19: Fan-beam reconstruction.
DIP Lecture 20: Dithering and halftoning.
DIP Lecture 21: Digital watermarking.
DIP Lecture 22: Image blending.
DIP Lecture 23: Photomontage and inpainting.
DIP Lecture 24: Image retargeting.
DIP Lecture 24a: Digital Image Forensics.
DIP Lecture 25: Active shape models.
Taught by
Rich Radke
