Almost all countries around the world are struggling against the novel coronavirus (Covid-19) pandemic. In this paper, a nonlinear Markov chains model is proposed in order to analyse and to understand the behaviour of the Covid-19 pandemic. The data from China was used to build up the presented model. Thereafter, the nonlinear Markov chain model is employed to estimate the daily new Covid-19 cases in some countries including Italy, Spain, France, UK, the USA, Germany, Turkey, and Kuwait. In addition, the correlation between the daily new Covid-19 cases and the daily number of deaths is examined.
Özetçe-Kenar belirlemenin görüntü işleme alanında önemli uygulamaları vardır. Yaygın olarak kullanılan rastsal-olmayan (deterministic) kenar belirleme yöntemlerinin yanısıra, stokastik yöntemler de geliştirilmiş ve başarıları onaylanmıştır. Bu çalışmada kenar belirleme için stokastik özbağlanımlı süreç yöntemi sunulmuş ve yöntem gri ölçekli ve renkli ölçekli imgelerde denenmiştir. Sonuçlar diğer bilinen kenar belirleme yöntemleri ile karşılaştırılmış ve yöntemin uygulanabilirliği gösterilmiştir.
Anahtar Kelimeler -kenar belirleme, özbağlanımlı süreç, renkli imge işleme.Abstract-Edge detection has important applications in image processing area. In addition to well-known deterministic approaches, stochastic models have been developed and validated on edge detection. In this study, a stochastic auto-regressive process method has been presented and this method applied to gray scale and color scale images. Results have been compared to other well-recognized edge detectors, then applicability of the developed method is pointed out.
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