Image Processing I + II
M. Unser and D. Van de Ville/i>
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.
- 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
- 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.
© 2011 EPFL • • 11.10.2011