With the nonsubsampled contourlet transform （NSCT）, a novel region-segmentation-based fusion algorithm for infrared （IR） and visible images is presented. The IR image is segmented according to the physical feature...With the nonsubsampled contourlet transform （NSCT）, a novel region-segmentation-based fusion algorithm for infrared （IR） and visible images is presented. The IR image is segmented according to the physical features of the target. The source images are decomposed by the NSCT, and then, different fusion rules for the target regions and the background regions are employed to merge the NSCT coefficients respectively. Finally, the fused image is obtained by applying the inverse NSCT. Experimental results show that the proposed algorithm outperforms the pixel-based methods, including the traditional wavelet-based method and NSCT-based method.展开更多
目的为了更有效地提高中智模糊C-均值聚类对非凸不规则数据的聚类性能和噪声污染图像的分割效果,提出了核空间中智模糊均值聚类算法。方法引入核函数概念。利用满足Mercer条件的非线性问题,用非线性变换把低维空间线性不可分的输入模式...目的为了更有效地提高中智模糊C-均值聚类对非凸不规则数据的聚类性能和噪声污染图像的分割效果,提出了核空间中智模糊均值聚类算法。方法引入核函数概念。利用满足Mercer条件的非线性问题,用非线性变换把低维空间线性不可分的输入模式空间映射到一个先行可分的高维特征空间进行中智模糊聚类分割。结果通过对大量图像添加不同的加性和乘性噪声进行分割测试获得的核空间中智模糊聚类算法提高了现有算法的对含噪声聚类的鲁棒性和分类性能。峰值信噪比至少提高0.8 d B。结论本文算法具有显著的分割效果和良好的鲁棒性,并适应于医学,遥感图像处理需要。展开更多
The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,whic...The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.展开更多
基金the National Natu-ral Science Foundation of China (No.60572152)the National 863 Program of China (No.2006AA01Z127).
文摘With the nonsubsampled contourlet transform （NSCT）, a novel region-segmentation-based fusion algorithm for infrared （IR） and visible images is presented. The IR image is segmented according to the physical features of the target. The source images are decomposed by the NSCT, and then, different fusion rules for the target regions and the background regions are employed to merge the NSCT coefficients respectively. Finally, the fused image is obtained by applying the inverse NSCT. Experimental results show that the proposed algorithm outperforms the pixel-based methods, including the traditional wavelet-based method and NSCT-based method.
文摘目的为了更有效地提高中智模糊C-均值聚类对非凸不规则数据的聚类性能和噪声污染图像的分割效果,提出了核空间中智模糊均值聚类算法。方法引入核函数概念。利用满足Mercer条件的非线性问题,用非线性变换把低维空间线性不可分的输入模式空间映射到一个先行可分的高维特征空间进行中智模糊聚类分割。结果通过对大量图像添加不同的加性和乘性噪声进行分割测试获得的核空间中智模糊聚类算法提高了现有算法的对含噪声聚类的鲁棒性和分类性能。峰值信噪比至少提高0.8 d B。结论本文算法具有显著的分割效果和良好的鲁棒性,并适应于医学,遥感图像处理需要。
文摘The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.
