Content of lectures and exercises: 1. Image signal processing, image acquisition, radiometry, optical part of the camera, CCD and CMOS sensors, colour spaces, geometric transformations. 2. Pixel brightness transformations, linear discrete image transforms, 2D FFT and DCT, Hadamard transform, Wavelets, discrete linear integral transformation, use of LDT in image processing. 3. Image smoothing, edge detectors - convolution masks approximating the derivative of the image function. 4. Image segmentation, thresholding, automatic threshold finding, edge exploitation, graph exploitation. 5. Image segmentation 2, advanced methods using deeper image analysis, region coloring, image moments, simple recognition features, region, compactness, chain codes. 6. Finding parametrically describable objects, Hough transform, RANSAC method. 7. Object detection and tracking, use of simple operators, Laplace, Harris corner detector, Shi-Tomasi corner detector, other fast algorithms for finding corners. 8. Features for objects describing, SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), BRIEF (Binary Robust Independent Elementary Features). 9. Image comparison (matching) based on the use of image features. 10. Introduction to 3D image reconstruction. 11. Video analysis, object tracking, CamShift algorithm, MeanShift algorithm, KLT algorithm, use of optical flow, background separation. 12. Automatic video segmentation, segmentation algorithms. 13. Simple classifiers for image recognition, PCA (Principal Component Analysis), use of SVM (Support Vector Machines), AdaBoost algorithm, Viola-Jones detector. 14. Artificial neural networks in image recognition, theory and practical applications. Convolutional neural networks (CNN) and recurrent neural networks for image recognition.
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