Computer Vision: Algorithms and Applications, 2nd Ed
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The second edition of 'Computer Vision: Algorithms and Applications' by Richard Szeliski is shared, sparking discussion on its relevance and usefulness in the field, particularly with the rise of AI and deep learning.
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Object detection / segmentation, human pose (2D/3D), 3D human motion tracking and modeling, multi-object tracking, re-identification and metric learning, action recognition, OCR, handwriting, face and biometrics, open-vocabulary recognition, 3D geometry and vision-language-action models, autonomous driving, epipolar geometry, triangulation, SLAM, PnP, bundle adjustment, structure-from-motion, 3D reconstruction (meshes, NeRFs, Gaussian splatting, point clouds), depth/normal/optical flow estimation, 3D scene flow, recovering material properties, inverse rendering, differentiable rendering, camera calibration, sensor fusion, IMUs, LiDAR, birds eye view perception. Generative modeling, text-to-image diffusion, video generation and editing, question answering, un- and self-supervised representation learning (contrastive, masked modeling), semi/weak supervision, few-shot and meta-learning, domain adaptation, continual learning, active learning, synthetic data, test-time augmentation strategies, low-level image processing and computational photography, event cameras, denoising, deblurring, super-resolution, frame-interpolation, dehazing, HDR, color calibration, medical imaging, remote sensing, industrial inspection, edge deployment, quantization, distillation, pruning, architecture search, auto-ML, distributed training, inference systems, evaluation/benchmarking, metric design, explainability etc.
You can't put all that into a single generic textbook.
Computer Vision: Algorithms and Applications, 2nd ed (szeliski.org)
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https://news.ycombinator.com/item?id=24945823
But anyway; why not? Yes, add (2020) to the title, by all means.
Would love to see / hear if there are any undergrad/grad-level courses that follow this book (or others) that cover computer vision - from basic-to-advanced.
Thanks!
I must be blind
Perhaps this
In fact, illumination and hardware setup are often more important than complex algorithms. Classical techniques remain highly relevant, especially when speed and accuracy are critical.