gmm

用神经网络方法筛选图片，从而判断某一图片是否存在病变(Neural network)

有关三维模型的泊松算法的诸多应用的实现，包括泊松重建和泊松纹理混合算法，由原作者开发，非常可靠(C++ implementation for algorithms including poisson mesh and poisson texture stitching, it is shared by author with wide utilization)

基于区域的主动轮廓模型算法，是论文《Active Contours Without Edges》的VC算法实现(Region-based active contour model algorithm, for the paper " Active Contours Without Edges" of the VC algorithm)

kmeans算法实现，对图像处理。亲自使用过，可用，供参考。(Kmeans algorithm for image processing.)

说明：  多边形裁剪是逐次多边裁剪法，它的基本思想是以窗口的四条边界线单一逐次对多边形裁剪(polygon clipping is Successive multilateral cutting, its basic idea is a window four successive boundaries of a single polygon clipping)

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)

Compression of face images impact the performance of face recognition (FR) systems. JPEG Region of Interest (JROI) compression maintains high image quality in facial regions while compressing the background more, with minimal impact on FR performance.(Compression of face images impact the perf ormance of face recognition (FR) systems. JPEG Region of Interest (JROI) compression maintai ns high image quality in facial regions while co mpressing the background more, with minimal impact on FR performance.)

高分辨率遥感图像道路提取算法实现，主要思路是利用道路中心点，得到道路中心线。(Road Extraction from High Resolution Images algorithm, the main idea is to use the center of the road, get the road centerline.)

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：  基于Kmeans算法的图像分割，一般Kmeans是数据挖掘中用来聚类的，本试验利用图像中的灰度值作为Kmeans算法的原始点，进行图像分割(Kmeans algorithm based on image segmentation, data mining in general Kmeans is used to clustering, the trial use of the image gray value as the original algorithm Kmeans spots for Image Segmentation)