dtlvbo

HOG adaboost pedestrian-detect

这个代码可以获取一种颜色可以生成10进制的数，和一个16进制的数(the code can get a color can generate 10 to the 229 number, and a few of the 229 16)

边缘检测 Edge Detection Edge Detection(Edge Detection)

内置虑镜功能，包括获取图像的属性，锐化，模糊，浮雕等功能． 可以缩放，优化图片．打开图片带预览功能,使用了gdiplus,能处理gif,bmp,jpg,png图片,处理速度也不错，呵呵,一直找图形格式转换的朋友就不需要东找西找了，它完全可以实现你的需求！ (Built-in lens into account features, including access to the properties of the image, sharpen, blur, emboss and other functions. Can be scaled to optimize picture. Open the image with the preview function, use the gdiplus, can handle gif, bmp, jpg, png images, the processing speed is not bad, huh, huh, graphic format conversion has been looking for friends do not need to look east and west, and it can achieve your needs!)

摄像头的标定，作为一般图像处理的第一步，显得尤为重要(Calibration of the camera, as a first step in the general image processing, it is particularly important)

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)

说明：  bitMAP文件格式的图像的读取、与保存的算法，可以作为图像处理的系统的前道，非常实用。(bitMAP file format of the image read, and preservation of the algorithm, as image processing system of the former Road, very practical.)

这是典型的图像分割Livewire算法的源码 结合OPENCV，配置一下就可以用了(This is a typical source image segmentation algorithm Livewire combination OPENCV, can be used to configure what)

改进经典的otsu算法，使其适应目标所占图像比例较小的情况，使图像分割方法更具有自适应性 (Improve classic otsu algorithm to achieve image segmentation)

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.)