ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ - ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ SITE MAP | uoi ΕΛΛΗΝΙΚΑ | uoi ENGLISH
Digital Image Processing

(Undergraduate Course)

 

INSTRUCTOR:
Michalis Vrigkas
COURSE_ID:
MYE037
WEEKLY HOURS:
3, 0, 2
SEMESTER:
-
COURSE UNITS:
4
ECTS CREDITS:
5.00
YEAR 2018/2019:
YES
PREREQUISITE:
-
DESCRIPTION:
Introduction to digital images, applications of digital image processing. Elements of visual perception, image acquisition, sampling and quantization. Intensity transformations, histogram processing, spatial filtering, smoothing and sharpening filters, fuzzy set techniques for intensity transformation. Filtering in the frequency domain, 2D sampling and 2D Fourier transform, 2D convolution, aliasing, 1D and 2D discrete Fourier transform (DFT), circulant matrices and convolution. Image restoration, noise models, inverse and pseudo-inverse filter, Wiener filter, regularized least squares filter. Tomographic image reconstruction, the Radon transform, the Fourier-slice (central slice) theorem, reconstruction by filtered back-projections. Color image processing, RGB, CMY, CMYK color models, smoothing and sharpening of color images, color edge detection, noise in color images, Grassman’s laws, chromaticity diagram, color perception and reproduction. Wavelets and multiresolution processing, image pyramids, the Haar transform, sub-band coding, scaling functions, wavelet functions, wavelet series, discrete wavelet transform, continuous wavelet transform, fast wavelet transform, wavelet packets. Morphological image processing, image erosion and dilation, opening and closing, morphological image reconstruction. Image segmentation, edge detection, thresholding, Hough transform, segmentation by watersheds. Boundary and region representation and description, chain codes, minimum perimeter polygon, skeletons, Fourier descriptors, statistical texture description, principal component analysis (PCA). Object recognition, patterns and pattern classes, introduction to classifiers, minimum distance classifier, correlation coefficient, optimum statistical classifier, Bayes classifier for Gaussian pattern classes.
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