Understanding basic terms and the connection between camera, object and its image. Learning how to interpret simple scientific papers and how to chose algorithms in order to recognize objects in a digital image
* Digital Images: Image resolution, sampling, quantization, color images
* Image preprocessing: smoothing, edge detection, multiscale (paramids), image restauration
* Segmentation: Thresholding, edge-based segmentation, region-based segmentation
* Repräsentation: contour-based, region-based (moments, graphs)
* Mathematical Morphology: Binary morphology, grey image morphology, watersheds
* Texture: statistical texture description, syntactical texture description
* Linear image transformations: Fourier-Transform, DCT, Wavelets
* Geometric transformations
* Image compression: Predictive Coding; Vector quantization, JPEG, MPEG
Lecture notes for this course are available.
M. Sonka, V. Hlavac, R. Boyle: Image Processing, Analysis and Machine Vision. Second Edition, 1999, Brooks/Cole, ITP.