liver_ultr

说明：  该程序实现的功能是：图像反色，线形变换，对于图像处理初学者非常实用。(The realization of the program)

多基线干涉合成孔径雷达干涉条纹的获取，通过两幅单视复图像进行配准，获取干涉相位(Multi-baseline interferometric synthetic aperture radar interferometry fringes of the acquisition, through two single look complex image registration, access to intervention phase)

用hough变换提取直线的C++代码，效果比较好，可以灵活改变可提取直线的条数，适用于提取多条直线。(line detecting using hough tranaformation)

OPENCV做图像变形的经典代码啊，适合比较初级的同学看看(OPENCV do image warping the classic code ah, suitable for elementary students to see more)

说明：  本课题的主要内容是图像预处理,它主要从摄像头中获取人脸图像然后进行处理,以便提高定位和识别的准确率.该模块主要包含光线补偿、图像灰度化、高斯平滑、均衡直方图、图像对比度增强，图像预处理模块在整个系统中起着极其关键的作用，图像处理的好坏直接影响着后面的定位和识别工作,内有源代码和全部论文资料(this issue is the major content of image preprocessing, mainly from the camera to obtain images Face then, in order to improve the recognition and positioning accuracy. The module consists mainly of light compensation, Grayhound, Gaussian smoothing, balanced histogram, image contrast enhancement, image pre- processing module in the system plays a crucial role in image processing will have a direct impact behind the positioning and identification, within Active code and all papers information)

说明：  在地震处理中，非常经典的进行时深转换的程序，通常比较好用(In seismic processing, the very classic procedure for time-depth conversion, usually more useful)

小波算法。机械化的提高我们的效率。让我们减少错误率。大家要对她有性心。(Wavelet algorithm. Mechanization to improve our efficiency. Let us reduce the error rate. We have to have sex to her heart.)

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.)

说明：  实现机器视觉双目标定，三维重建等算法，具有很好地擦考价值( Robot with two cameras to switch recognition system. (Note that not binocular vision) and the reference used opencv1.0 cmv. )

关于脊波变换的图像分割源码，运行良好，测试可行，绝对超值(Ridgelet transform for image segmentation source code, run the test feasible, the absolute value)