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.

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.