OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ...OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ,该算法使用 AF CM方法对数据进行聚类 ,并获取基函数的平滑参数 ,然后使用 OL S方法从聚类结果中选取网络中心。利用实测的 4类飞机目标数据对其进行性能检验 ,试验结果验证了该训练算法可提高网络的训练速度 ,缩小网络规模 ,提高网络的分类能力。展开更多
Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich textur...Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM) . Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.展开更多
Different modalities in biomedical images, like CT, MRI and PET scanners, provide detailed cross-sectional views of human anatomy. This paper introduces three-dimensional brain reconstruction based on CT slices. It co...Different modalities in biomedical images, like CT, MRI and PET scanners, provide detailed cross-sectional views of human anatomy. This paper introduces three-dimensional brain reconstruction based on CT slices. It contains filtering, fuzzy segmentation, matching method of contours, cell array structure and image animation. Experimental results have shown its validity. The innovation is matching method of contours and fuzzy segmentation algorithm of CT slices.展开更多
文摘OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ,该算法使用 AF CM方法对数据进行聚类 ,并获取基函数的平滑参数 ,然后使用 OL S方法从聚类结果中选取网络中心。利用实测的 4类飞机目标数据对其进行性能检验 ,试验结果验证了该训练算法可提高网络的训练速度 ,缩小网络规模 ,提高网络的分类能力。
基金Under the auspices of National Natural Science Foundation of China (No. 30370267)Key Project of Jilin Provincial Science & Technology Department (No. 20075014)
文摘Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM) . Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.
基金This project was supported by the National Natural Science Foundation of China (69931010).
文摘Different modalities in biomedical images, like CT, MRI and PET scanners, provide detailed cross-sectional views of human anatomy. This paper introduces three-dimensional brain reconstruction based on CT slices. It contains filtering, fuzzy segmentation, matching method of contours, cell array structure and image animation. Experimental results have shown its validity. The innovation is matching method of contours and fuzzy segmentation algorithm of CT slices.
