hsvfeature2

这是我自己写的关于 图像处理中retinex、SSR、自适应的对比度增强算法的模板，有很好的效果 希望对你们有帮助( this is for retinex module, I wish it can help for you )

说明：  位图分析转换软件 可以分析8，16，24，32位BMP图 输出可选16位，18位，24位RGB格式 同时支持串行下载(Bitmap analysis of 8,16,24,32-bit conversion software can map out the optional 16-bit BMP, 18 bit, 24-bit RGB format also supports serial download)

基于遗传算法的图像分割算法。实现图像的分割，显示原图及处理结果图像。(Based on genetic algorithm image segmentation algorithm. Realize image segmentation, and processing results show the image. )

几个大牛对集成了国内外特征提取最前沿的算法，特征提取，DOG,HARRIS,HARRIS-LAPLACE,HARRIS-AFFINE,MSER.描述符，SIFT,AFFINE-SIFT,GLOH.经测试，速度快，描述符准确。(Integration of feature extraction algorithms from several expert of domestic and international, feature extraction： DOG, HARRIS, HARRIS-LAPLACE, HARRIS-AFFINE, MSER； Descriptors： SIFT, AFFINE-SIFT, GLOH. The test reveal the speed and accurate descriptor.)

说明：  matlab实现帧间差分提取运动目标，包含测试视频和运行效果。在背景固定的视频中效果较好。附赠lena图片以供CV学习。(Matlab achieves inter-frame differential extraction of moving objects, including testing video and running effects. It works well in video with fixed background. Enclosed Lena pictures for CV learning.)

介绍了不同非线性模型在随机共振中的应用，像饱和非线性，阈值非线性等(Describes the different nonlinear model of stochastic resonance in the application, such as nonlinear saturation threshold nonlinearity. . . .)

图像去噪的评价指标ENL, SSIM, PSNR,SNR,EPI(The evaluation indexes of image denoising are ENL, SSIM, PSNR,SNR and EPI)

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.)

Computer Vision:Algorithms and Applications，Richard Szeliski，适合图像处理人群(Computer Vision: Algorithms and Applications, Richard Szeliski, suitable for image processing crowd)

指纹图像的增强，效果很好,计算指纹的频率和方向，采用Gabor的方式增强(Fingerprint image enhancement, the effect is very good, calculate the frequency and direction of fingerprints, using Gabor way to enhance)