Image Processing I + II

M. Unser and D. Van de Ville

Master level

Goals

Understanding the basic techniques of image processing. Introduction to image processing software development and prototyping in JAVA; application to real-world examples in industrial vision and biomedical imaging.

Contents I

Introduction. Image processing versus image analysis.Applications. System components.
Characterization of continuous images. Image classes. 2D Fourier transform. Shift-invariant systems.
Image acquisition. Sampling theory. Acquisition systems. Histogram and simple statistics. Linear and Max-Lloyd Quantization.
Characterization of discrete images and linear filtering. z-transform. Convolution. Separability. FIR and IIR filters.
Image processing operations. Point operators (thresholding, histogram modification). Spatial operators (smoothing, enhancement, non-linear filtering). Morphological operators.
Introduction to image analysis and computer vision. Segmentation, edge detection, objet detection, image comparison.v

Contents II

Review of fundamental notions. Multi-dimensional Fourier transform. Convolution. Sampling theory. Z transform. Digital filters.
Continuous representation of discrete data. Splines. Interpolation. Geometric transformations. Multi-scale decomposition (pyramids and wavelets).
Image transforms. Karhunen-Loève transform (KLT). Discrete cosine transform (DCT). JPEG coding. Image pyramids. Wavelet decomposition.
Reconstruction from projections. X-ray scanners. Radon transform. Central slice theorem. Filtered backprojection. Iterative methods.
Image restoration. Inverse and Wiener filtering. Weighted and regularized least squares. Maximum likelihood and Bayesian approaches.
Statistical pattern classification. Decision making. Bayesian classification. Parameter estimation. Supervised learning. Clustering.
Image analysis. Pixel classification. Contour extraction and representation. Shape. Texture. Snakes and active contours.