A novel adaptive blind image watermarking scheme resistant to Rotation, scaling and translation (RST) attacks is proposed in this paper. Based on fuzzy clustering theory and Human visual system (HVS) model, the spread...A novel adaptive blind image watermarking scheme resistant to Rotation, scaling and translation (RST) attacks is proposed in this paper. Based on fuzzy clustering theory and Human visual system (HVS) model, the spread spectrum watermark is adaptively embedded in Discrete wavelet transform (DWT) domain. In order to register RST transform parameters, a hierarchical neural network is utilized to learn image geometric pattern represented by low order Zernike moments. Watermark extraction is carried out after watermarked image has been synchronized without original image. It only needs a trained neural network.Experiments show that it can embed more robust watermark under certain visual distance, effectively resist Joint photographic experts group (JPEG) compression, noise and RST attacks.展开更多
文摘乳腺癌是女性最为常见的一种癌症。虽然随着医疗的发展,乳腺癌的诊断和治疗技术都有所提高,但是由于不能在乳腺癌早期及时做出诊断,导致乳腺癌的死亡率依然很高。针对此现象,对基于非下采样轮廓变换法(nonsubsampled contourlet transform,NSCT)的乳腺X线图像的分类方法进行了研究。该方法首先对乳腺X线图像的感兴趣区域(region of interest,ROI)进行多分辨的NSCT分解,然后用泽尼克矩(Z-Moments)对NSCT分解后的图像进行特征提取;其次对每一个感兴趣区域所提取的特征形成的矩阵进行奇异值分解(singular value decomposition,SVD),以提取重要的可以概括全局的特征。该方法组合了纹理和形状特征,使用支持向量机(support vector machines,SVM)分类算法将乳腺X线图像分类为正常、良性和恶性,实现了乳腺病变的检测和分类。通过实验可以看出,该方法的准确率达到了96.76%,并且训练时间大大减少,与其他目前最先进的方法相比,在准确率和时间效率上都取得了显著的成效。
文摘A novel adaptive blind image watermarking scheme resistant to Rotation, scaling and translation (RST) attacks is proposed in this paper. Based on fuzzy clustering theory and Human visual system (HVS) model, the spread spectrum watermark is adaptively embedded in Discrete wavelet transform (DWT) domain. In order to register RST transform parameters, a hierarchical neural network is utilized to learn image geometric pattern represented by low order Zernike moments. Watermark extraction is carried out after watermarked image has been synchronized without original image. It only needs a trained neural network.Experiments show that it can embed more robust watermark under certain visual distance, effectively resist Joint photographic experts group (JPEG) compression, noise and RST attacks.
