Отрывок: The image segmentation algorithm based on MsRMRF model can be decomposed into the combination of RMRF segmentation for each scale, and can transfer the correlation between scales through tagged results of the scales. On the minimum resolution scale, we use the pixel level ICM algorithm to obtain the segmentation results. On other scales, the segmentation results are obtained by the minimum energy function expressed by the formula (16). { ( ) 1 , ( ) ( ) log (1 ( , )) . d d ddv vv d ...
Название : | Unsupervised color texture segmentation based on multi-scale region-level Markov random field models |
Авторы/Редакторы : | Song, X. Wu, L. Liu, G. |
Ключевые слова : | region-level Markov random field model color texture image image segmentation wavelet transformation multi-scale |
Дата публикации : | Апр-2019 |
Издательство : | Новая техника |
Библиографическое описание : | Song X, Wu L, Liu G. Unsupervised color texture segmentation based on multi-scale region-level Markov random field models. Computer Optics 2019; 43(2): 264-269. DOI: 10.18287/2412-6179-2019-43-2-264-269. |
Серия/номер : | 43;2 |
Аннотация : | In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors. |
URI (Унифицированный идентификатор ресурса) : | https://dx.doi.org/10.18287/2412-6179-2019-43-2-264-269 http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Unsupervised-color-texture-segmentation-based-on-multiscale-regionlevel-Markov-random-field-models-77077 |
Другие идентификаторы : | Dspace\SGAU\20190524\77077 |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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