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Fundamentals

In: Multi-Level Bayesian Models for Environment Perception

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  • Csaba Benedek

    (Institute for Computer Science and Control (SZTAKI))

Abstract

This chapter presents the main mathematical foundations of the problems, concepts, and methods covered by the book. First, a formal description is given for 2D image and 3D point cloud-based measurement representation, then various Markovian data analysis frameworks are discussed, which implement image segmentationImage segmentation, and geometric object population extraction tasks. The chapter covers state-of-the-art methodologies of graph-based scene representation, probabilistic modeling of prior knowledge-based and image data-based information, Bayesian inference, parameter estimation, and various energy optimization approaches. Special focus is devoted to established techniques such as Markov Random FieldsMarkov Random Field (MRF), mixed Markov models, and Marked Point ProcessMarked Point Process (MPP) frameworks. Finally, based on the presented fundamentals, the methodological contributions of the book are summarized.

Suggested Citation

  • Csaba Benedek, 2022. "Fundamentals," Springer Books, in: Multi-Level Bayesian Models for Environment Perception, chapter 0, pages 9-23, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-83654-2_2
    DOI: 10.1007/978-3-030-83654-2_2
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