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Explore fundamental computer vision concepts and techniques through this comprehensive university-level course from the University of Central Florida. Master essential topics including digital image processing, linear algebra transformations, and image filtering techniques such as histogram analysis, noise reduction, and image derivatives. Learn edge detection methods including Prewitt, Sobel, Marr-Hildreth, and Canny algorithms for identifying object boundaries in images. Dive deep into neural networks and convolutional neural networks (CNNs), covering basic architectures, non-linearity concepts, fundamental operations, and practical implementation considerations through case studies. Develop practical skills in training neural networks using backpropagation, understanding CNN variants, and implementing models with PyTorch through hands-on tutorials. Study feature extraction techniques including keypoint detection, Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT) for robust image analysis. Examine autoencoder architectures for unsupervised learning and dimensionality reduction. Master classification algorithms including nearest neighbor classifiers, maximum margin classifiers, and support vector machines for pattern recognition tasks. Advance to object detection using sliding window approaches and evaluation metrics for assessing detection performance. Conclude with image segmentation techniques including clustering-based methods, semantic segmentation for pixel-level classification, and instance segmentation for distinguishing individual objects within the same class.
