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Multi-Level Bayesian Models for Environment Perception (Hardcover, 1st ed. 2022)
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Multi-Level Bayesian Models for Environment Perception (Hardcover, 1st ed. 2022)
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This book deals with selected problems of machine perception, using
various 2D and 3D imaging sensors. It proposes several new original
methods, and also provides a detailed state-of-the-art overview of
existing techniques for automated, multi-level interpretation of
the observed static or dynamic environment. To ensure a sound
theoretical basis of the new models, the surveys and algorithmic
developments are performed in well-established Bayesian frameworks.
Low level scene understanding functions are formulated as various
image segmentation problems, where the advantages of probabilistic
inference techniques such as Markov Random Fields (MRF) or Mixed
Markov Models are considered. For the object level scene analysis,
the book mainly relies on the literature of Marked Point Process
(MPP) approaches, which consider strong geometric and prior
interaction constraints in object population modeling. In
particular, key developments are introduced in the spatial
hierarchical decomposition of the observed scenarios, and in the
temporal extension of complex MRF and MPP models. Apart from
utilizing conventional optical sensors, case studies are provided
on passive radar (ISAR) and Lidar-based Bayesian environment
perception tasks. It is shown, via several experiments, that the
proposed contributions embedded into a strict mathematical toolkit
can significantly improve the results in real world 2D/3D test
images and videos, for applications in video surveillance, smart
city monitoring, autonomous driving, remote sensing, and optical
industrial inspection.
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