lenaentropy

基于纹理的图像检索源码，用matlab实现的，用于学习研究(Texture-based image retrieval source code, using matlab to achieve, for study and research)

说明：  实现直接序列扩频水印的嵌入与提取。基于待嵌入消息生成二进制水印序列,生成扩频码，用消息序列调制生成水印序列。依次处理灰度图像的每一个载体图像像素，根据相应位置 水印序列完成嵌入修改。(Realize the embedding and extraction of direct sequence spread spectrum watermark. The binary watermark sequence is generated based on the message to be embedded, the spread spectrum code is generated, and the watermark sequence is generated by message sequence modulation. Each carrier image pixel of gray image is processed in turn, and the embedding modification is completed according to the corresponding position watermark sequence.)

根据CT成像原理得到Sinogram图像，再经过Randon变换以及反向投影算法对物体图线进行复原。(It is a Matlab ToolBox which can solve CT problems or image reconstruction easily and convincely.)

将Radarsat2极化SAR图像数据转换为散射矩阵及相干矩阵T3的matlab代码,附上的图像文件是用Enviview软件截取转存的各通道实部虚部文件,以及lutsigma.xml查找表文件.作者辛苦所做,希望对SAR初学者有所帮助.(Will Radarsat2 polarization SAR image data is converted to T3 scattering matrix and the coherent matrix of matlab code, the attached image file is transferred Enviview software maintained by the interception of the real part imaginary part of the channel files, and lutsigma.xml lookup table file. Of the hard done, in the hope SAR to help beginners.)

基于matlab做的数字水印，文件包括源代码及原图像和加入水印后的图像，经过测试，能有效抗攻击(Matlab-based digital watermarking so that the document includes the source code and the original image and adding a watermark image, tested and effective anti-attack)

基于图像纹理的特征提取，使用灰度共生矩阵方法。(Texture feature extraction based on the use of gray level co-occurrence matrix method.)

MCA图像修复,包含MCA和EM图像修复算法(MCA inpainting)

改进的sift算法，称为surf，来实现图像的匹配，并显示。 (Improved sift algorithm, known as the surf, to achieve the image matching, and displayed.)

CVPR2007，论文“Saliency detection: A spectral residual approach”的源码(a code for "Saliency detection: A spectral residual approach,"CVPR,2007.)

Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.