2D markov-switching autoregressive (MS AR) models for image segmentation
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Maltepe Üniversitesi
Erişim Hakkı
CC0 1.0 Universal
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Özet
2D autoregressive (AR) models have been successfully used in several applications in signal and image processing. See [1], [2] and [3] for their use in image restoration, [6], [7] and [8] for their contribution to textural images analysis and synthesis. However, 2D AR models do not allow to describe spatial series with fundamental changes and structural breaks in the dynamic of the data. For instance, when the pixels grey levels of a richly textured image are observed the resulting spatial series exhibit an alternance of different spatial dynamics corresponding to texture regimes. To take into account structural breaks occurring across 2D data, we introduce in this work the 2D Markov-switching autoregressive model that allows for the possibility of sudden changes in the values of the parameters of a 2D AR process. This model can capture various key features of image data, such as similar properties of neighboring pixels, a mean level growth of regional volatility or regional asymmetry.
Açıklama
Anahtar Kelimeler
Spatial models, Marko switching, Image segmentation
Kaynak
International Conference of Mathematical Sciences (ICMS 2019)
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Kharfouchi, S. ve Djafri, H. (2019). 2D markov-switching autoregressive (MS AR) models for image segmentation. International Conference of Mathematical Sciences (ICMS 2019). s. 192.
