کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4946931 1439561 2017 48 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An efficient level set model with self-similarity for texture segmentation
ترجمه فارسی عنوان
یک مدل کارآمد مجموعه ای با خودپسندیابی برای تقسیم بندی بافت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Textures widely exist in the natural scenes while traditional level set models generally use only intensity information to construct energy and ignore the inherent texture features. Thus these models have difficulty in segmenting texture images especially when the texture objects have similar intensity to the background. To solve this problem, we propose a new level set model for texture segmentation that considers the impact of local Gaussian distribution fitting (LGDF), local self-similarity (LSS) and a new numerical scheme on the evolving contour. The proposed method first introduces a texture energy term based on the local self-similarity texture descriptor to the LGDF model, and then the evolving contour could effectively snap to the textures boundary. Secondly, a lattice Boltzmann method (LBM) is deployed as a new numerical scheme to solve the level set equation, which can break the restriction of the Courant-Friedrichs-Lewy (CFL) condition that limits the time step of iterations in former numerical schemes. Moreover, GPU acceleration further improves the efficiency of the contour evolution. Experimental results show that our model can effectively handle the segmentation of synthetic and natural texture images with heavy noises, intensity inhomogeneity and messy background. At the same time, the proposed model has a relatively low complexity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 266, 29 November 2017, Pages 150-164
نویسندگان
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