CAJ | 학술논문

提出了一种基于修正交叉视觉皮质模型(MICM)的图像自适应分割新方法. 根据待分割图像的自身特性,自适应地设定参数,并以互信息量为目标函数选取最佳分割结果. 该方法解决了针对不同的图像需要人工设定交叉皮质模型(ICM)参数和需要人工选取最佳分割结果的2个问题. 实验结果表明,与通过大量实验获得模型参数的脉冲耦合神经网络(PCNN)基本模型和ICM基本模型相比,MICM与其综合评价函数值相近;与模糊聚类分割算法和最大类间方差(OTSU)算法相比,MICM算法有较明显的视觉优势,并且其综合评价函数值也分别提高了约15%和13%.
제출료일충기우수정교차시각피질모형(MICM)적도상자괄응분할신방법. 근거대분할도상적자신특성,자괄응지설정삼수,병이호신식량위목표함수선취최가분할결과. 해방법해결료침대불동적도상수요인공설정교차피질모형(ICM)삼수화수요인공선취최가분할결과적2개문제. 실험결과표명,여통과대량실험획득모형삼수적맥충우합신경망락(PCNN)기본모형화ICM기본모형상비,MICM여기종합평개함수치상근;여모호취류분할산법화최대류간방차(OTSU)산법상비,MICM산법유교명현적시각우세,병차기종합평개함수치야분별제고료약15%화13%.
An image segmentation method based on the modified intersecting cortical model (MICM) is proposed to set the MICM parameters adaptively according to the different characteristics of images and choose the optimal segmentation results automatically, which are two main obstacles for the basic intersecting cortical model(ICM) to be used in practice. Experiments show that the comprehensive evaluation value of MICM is close to those of basic pulse coupled neural network(PCNN) and basic intersecting cortical model. Compared with the fuzzy C-means algorithm and OTSU algorithm, MICM is of visually better segmentation and the comprehensive evaluation value of MICM increases by approximately 15% and 13% respectively.