opencv.org
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OpenCV.org is the official home of the OpenCV library, a leading open-source toolkit for real-time computer vision and image processing. It offers downloads, documentation, tutorials, samples, and community resources to help users learn, develop, and deploy CV applications.
More resources on Computer Vision
ImageNet Classification with Deep Convolutional Neural Networks
This paper introduced AlexNet, a groundbreaking convolutional neural network that significantly outperformed previous methods in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012. It marked a turning point for deep learning's resurgence and practical application.
cs231n.stanford.edu
Stanford's CS231n course site for Convolutional Neural Networks for Visual Recognition, offering lecture notes, slide decks, assignments, and project materials that teach deep learning techniques for computer vision.
PyImageSearch
Adrian Rosebrock's tutorial site for practical computer vision, with step-by-step OpenCV and deep learning walkthroughs in Python covering image processing, object detection, and face recognition. Many tutorials are free; longer courses and books are paid.
Computer Vision: Algorithms and Applications
Richard Szeliski's standard graduate survey of the field, moving from image formation and feature detection through stereo, motion, structure from motion, segmentation, and recognition. Grounds each technique in the underlying mathematics and cites the original papers.
Convolutional Neural Networks for Visual Recognition
Course four of DeepLearning.AI's Deep Learning Specialization, taught by Andrew Ng. Covers convolution and pooling layers, classic architectures like ResNet, object detection with YOLO, face recognition, and neural style transfer, with programming assignments in Python.
Computer Vision: Models, Learning, and Inference
Simon Prince's textbook treating vision as probabilistic inference: generative and discriminative models, graphical models, and learning algorithms applied to tracking, segmentation, and recognition. Mathematically demanding, with worked derivations and exercises for graduate study.