Syllabus

1. Introduction to image analysis.
   • Fundamental concepts of image processing: point-based, adjacency-based, and connectivity-based transforms.
   • Extending concepts to multiband and multidimensional images.
2. Image representation.
   • Point, region, and shape-based approaches.
   • Superpixel and hierarchical segmentation.
   • Iso-contours, multiscale skeletons, and shape saliences.
   • Space-frequency transforms: from Fourier to wavelets.
3. Image description.
   • Color, texture, shape decriptors and their combination.
   • Popular image descriptors.
   • Data clustering and bag of visual words.
   • Convolutional layers.
4. Image classification.
   • Fundamentals of machine learning.
   • From perceptron to deep convolutional neural networks.
5. Image segmentation.
   • From image-based to model-based approaches.
   • Semantic and instance segmentation.