Producing-Letters-on-7Segment

在matlab开发环境中，使用模糊C均值聚类的方法对数据进行聚类(In the Matlab development environment, the use of fuzzy C-means clustering methods for data clustering)

Transferring color to greyscale images的原文及源代码，包括详细的测试图像以及相应的论文及源代码，是学习图像色采转移必看的经典内容。(Transferring color to greyscale images of the original text and source code, including detailed test image and the corresponding papers and source code is studying the image to see the classic color adopted transfer content.)

Harris角点检测程序（内附示例图像），利用鼠标在图像上选择提取交点的区域，进行角点提取。程序语言简洁，利于初学者学习。(Harris corner detection procedures (with sample images), using the mouse in the image on the choice of extraction point of intersection of the region, for Corner Detection. Programming language is simple and conducive to learning for beginners.)

matlab代码实现的MSR图像增强，有参考图片(MSR image enhancement matlab code, reference picture)

以SIFT特征为目标输入，LDAHash方法进行降维实现目标识别(SIFT features input LDAHash method to reduce the dimensionality of the target recognition)

matlab实现粒子滤波算法,对图像处理,定位,跟踪研究都有借鉴作用!(Matlab achieve particle filter for image processing, positioning and tracking studies have reference!)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

这写程序是对加水印之后图像的攻击，包括加噪声，各种滤波，剪切，压缩攻击和旋转等。(This writing process is a plus watermark image after the attacks, including the increase in noise, various filtering, shear, compression and rotation attacks.)

VC调用matlab引擎因为类别当中没有此类,所以放到了数值计算(VC calls the Matlab engine because there is no such class, so it is put into numerical calculation.)

说明：  PCA图像降噪新算法-LPG PCA，全名叫做Two-stage Image Denoising by Principal Component Analysis with Local Pixel Grouping。内含算法代码和数据集，均经本人实测，可进行灰度图像和RGB图像的降噪，且PSNR和SSIM指标明显好于小波滤波、K-SVD等经典算法。值得学习图像降噪的看看！(The new algorithm of PCA image denoising LPG PCA, full name is two stage image denoising by principal component analysis with local pixel grouping. The algorithm code and data set can be used to reduce the noise of gray image and RGB image, and the PSNR and SSIM are better than the classical algorithms such as wavelet filtering and K-SVD. It's worth learning about image denoising!)