Abstract
This paper presents a new texture segmentation technique for both supervised and unsupervised segmentation. The textured images under study are modeled by a proposed hierarchical Markov random field (MRF) model. This model is formed by combining the binomial model for textures and the multi-level logistic model for region distributions. The supervised segmentation is achieved by a new algorithm which can reach the global maxima of the posteriori distribution even if the textures are modeled by an MRF model. For unsupervised segmentation, a new parameter estimation scheme is proposed to estimate the model parameters directly from a given image. The new technique is verified by a variety of textured images, such as synthesized textures, natural textures and aerial images, in both supervised and unsupervised segmentation cases.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
