ENGR3421 Image Processing, Reconstruction & Analysis
Imaging, Imaging algorithms and imaging systems are being used every day to analyze and interact with the world around us, from facial recognition to medical data collection, from search & rescue to surveillance, from autonomous vehicles to assistive devices. In this course, we will learn about the basic concepts of image processing, image reconstruction from incomplete data and image analysis to obtain meaningful information from imaging data. We will also study how and where there is a possibility of biases being introduced into the entire imaging process - from acquisition to interpretation. The specific topics (as they apply to imaging) that we will cover include but are not limited to sampling, linear transformation, geometric transformation, convolution, change detection, edge detection, quantization, filtering, compression, color spaces, image segmentation, image reconstruction, classification, feature extraction.
Note about conceptual overlap with DSP: Since images are signals that have two spatial domains, image processing is an application of digital signal processing. If you want to learn concepts from DSP, you can take image processing and learn not all but quite a few of those. Some of these concepts are: Linear time-invariant systems, Fourier transforms, sampling & aliasing, convolution & deconvolution, filtering, data compression, feature detection, histogram processing and analysis, representation of signals in frequency domain or other transform domains.