Concluding Remarks

Csaba Benedek ()
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Csaba Benedek: Institute for Computer Science and Control (SZTAKI)

Chapter Chapter 7 in Multi-Level Bayesian Models for Environment Perception, 2022, pp 187-187 from Springer

Abstract: Abstract This book has focused on various region level and object-based pattern recognition problems, which raise nowadays important challenges to experts in computer vision and machine perception. Stochastic Bayesian energy minimization techniques have been chosen as bases for the introduced new methods, and improvements versus state-of-the-art approaches have been proposed in various aspects, including observation processing, combination of different model structures, and new spatial and temporal interpretation of up to-date sensor measurements. While in several real time applications, the high computation cost of energy minimization methods may mean bottleneck of applying complex models, we have shown that with using appropriate dimension reducing techniques, combining stochastic and deterministic relaxation approaches, and the utilization of prior knowledge-based rules we can often obtain high-quality solutions in a computationally efficient manner.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-83654-2_7

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DOI: 10.1007/978-3-030-83654-2_7

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