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Bayesian algorithms for adaptive change detection in image sequences using Markov random fields

Aach, Til ; Kaup, André

Signal processing. Image communication, 1995-08, Vol.7 (2), p.147-160 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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  • Título:
    Bayesian algorithms for adaptive change detection in image sequences using Markov random fields
  • Autor: Aach, Til ; Kaup, André
  • Assuntos: Applied sciences ; Context-adaptive change detection ; Exact sciences and technology ; Image analysis ; Image coding ; Image processing ; Information, signal and communications theory ; Markov random fields ; Signal processing ; Telecommunications and information theory
  • É parte de: Signal processing. Image communication, 1995-08, Vol.7 (2), p.147-160
  • Descrição: In many conventional methods for change detection, the detections are carried out by comparing a test statistic, which is computed locally for each location on the image grid, with a global threshold. These ‘nonadaptive’ methods for change detection suffer from the dilemma of either causing many false alarms or missing considerable parts of non-stationary areas. This contribution presents a way out of this dilemma by viewing change detection as an inverse, ill-posed problem. As such, the problem can be solved using prior knowledge about typical properties of change masks. This reasoning leads to a Bayesian formulation of change detection, where the prior knowledge is brought to bear by appropriately specified a priori probabilities. Based on this approach, a new, adaptive algorithm for change detection is derived where the decision thresholds vary depending on context, thus improving detection performance substantially. The algorithm requires only a single raster scan per picture and increases the computional load only slightly in comparison to non-adaptive techniques.
  • Editor: Amsterdam: Elsevier B.V
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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