Image Analysis and Object Recognition

Introduction

1. Organization, motivation, definition, application examples (slides, print)

Image processing

2. Image representation, basic enhancement, binary images, thresholding, morphological operators
3. Boundaries, filling, hit-or-miss, thinning, thickening, connected components, region properties
4. Invariant descriptors, (non-)linear filtering, noise, local operators, Gaussian, image derivatives, Sobel
5. Laplacian, ...

Feature extraction

5. Gaussian derivatives, GoG, LoG, DoG, edge detection, corner detection, interest point operator
6. Discrete 2D Fourier transform, FFT, spectrum, convolution theorem, filtering in frequency domain, ideal high- and low-pass filter, Butterworth

Shape detection

7. Hough transform, fitting of straight lines, line segments, circles with (un)known radius, generalized Hough transform, Fourier descriptors, normalization

Object recognition

8. Sliding window, template matching, image pyramids, Viola-Jones face detection, Haar features, integral images, boosting, classifier cascade, local image features
9. Scale invariant extraction, scale-space, unique feature description and matching, SIFT
10. Part-based voting, vocabulary tree, bag of visual words, ...

Image regions

10. Segmentation, clustering, k-means
11. Mean shift, region growing, watershed, graph-cut segmentation, affinity matrix, normalized-cut, soft segmentation, grab-cut, ...

Machine learning

11. Pattern recognition systems, supervision, generalization
12. Deep learning for visual recognition, artificial neural networks, supervised convolutional nets
13. Classifiers, k-nearest neighbor, boosted decision tree, Bayesian decision theory, expectation maximization, support vector machine, summary