33  References

References

This page lists the main references for The Mathematics of Modern Computer Vision. The bibliography supports the mathematical foundations, classical computer vision methods, geometric vision, optimization, probabilistic modeling, and modern deep/multimodal vision topics developed throughout the book.

33.2 How this bibliography is organized

The references cover the following strands of the book:

  • Image processing and mathematical foundations: pixels, functions on domains, Fourier analysis, convolution, local operators, edges, morphology, and variational models.
  • Classical and geometric computer vision: cameras, calibration, epipolar geometry, stereo, optical flow, structure from motion, and bundle adjustment.
  • Statistical and probabilistic vision: random fields, likelihood models, MAP estimation, robust losses, uncertainty, and calibration.
  • Machine learning for vision: classification, representation learning, CNNs, optimization, detection, segmentation, and evaluation metrics.
  • Modern foundation models: vision transformers, self-supervised learning, vision-language models, promptable segmentation, diffusion models, neural fields, and multimodal systems.

The entries are stored in references.bib. Quarto/Pandoc will automatically format the bibliography below.

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