1- Introductory Concepts:
Introduction to Image, Projective geometry, Images as functions, Image processing, Color Space, 3D Vision
2- Image Histograms & Point Operations:
Image Histograms, Image Brightness, Point Operations, Dynamic Range, Inverting Images, Thresholding, and Histogram Equalization
3- Image Filtering:
Types of image transformations. Point image processing., Linear shift-invariant image filtering. Convolution and Correlation. Image gradients.
4- Image Pyramids and Frequency Domain:
Image downsampling. Aliasing. Gaussian image pyramid. Laplacian image pyramid. Fourier series. Frequency domain. Fourier transform. Frequency-domain filtering. Revisiting sampling.
5- Hough Transform:
Finding boundaries. Line fitting. Line parameterizations. Hough transform. Hough circles. Some applications.
6- Corner Detection:
Why detect corners? Visualizing quadratics., Harris corner detector., Multi-scale detection., Multi-scale blob detection.
7- Feature Detection and Description:
Why do we need feature descriptors? Designing feature descriptors. MOPS descriptor. GIST descriptor. Histogram of Textons descriptor. HOG descriptor. SIFT.
8- 2D transformation: 2D transformations.
Projective geometry. Transformations in projective geometry. Classification of 2D transformations. Determining unknown 2D transformations. Determining unknown image warps.
9- Image Homographies:
Motivation: panoramas. Back to warping: image homographies. Computing with homographies. The direct linear transform (DLT). Random Sample Consensus (RANSAC).
10- Image Classification:
Introduction to learning-based vision. Image classification. Bag-of-words. K-means clustering. Classification. K nearest neighbors. Naïve Bayes. Support vector machine.
11- Neural Networks:
Perceptron. Neural networks. Training perceptrons. Gradient descent. Backpropagation. Stochastic gradient descent.
12- Convolutional Neural Networks:
Architecture of the Convolutional Networks, Convolutional Layers, Pooling Layers, Fully Connected Layers, CNN implementation with TensorFlow, CNN with Own Name, Tools, Practical applications.
13- Object Detection and Classification:
Object Detection and Classification, R-CNN Models, YOLO, Detectron, and SegmenTron