区域生长法

图像处理的空域滤波增强部分程序代码的编写(Spatial filtering of the image processing, enhanced part of the program code is written)

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：  小波变换图像增强，经过matlab6.0测试，放心可用(Wavelet image enhancement, after matlab6.0 testing, confidence can be)

利用matlab，对图像进行高斯滤波，然后通过非极大值抑制，设置双阈值，实现图像边缘检测(Image edge detection)

说明：  Donoho提出的小波阀值去噪的基本思想是将信号通过小波变换（采用Mallat算法）后，信号产生的小波系数含有信号的重要信息，将信号经小波分解后小波系数较大，噪声的小波系数较小，并且噪声的小波系数要小于信号的小波系数，通过选取一个合适的阀值，大于阀值的小波系数被认为是有信号产生的，应予以保留，小于阀值的则认为是噪声产生的，置为零从而达到去噪的目的。尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点。(The basic idea of wavelet threshold de-noising proposed by Donoho is that the wavelet coefficients generated by the signal after passing through the wavelet transform (Mallat algorithm) contain the important information of the signal. After the wavelet decomposition, the wavelet coefficients of the signal are larger, the wavelet coefficients of the noise are smaller, and the wavelet coefficients of the noise are smaller than the wavelet coefficients of the signal. By selecting a suitable threshold, the wavelet coefficients of the noise are larger than the valve The wavelet coefficients of values are considered to be generated by signals, which should be preserved. The ones less than the threshold are considered to be generated by noise, which is set to zero to achieve the purpose of denoising.)

程序包括三种经典的边缘检测方法，均可独立运行，参数可调(Program includes three classical edge detection methods can be run independently, adjustable parameters)

模式识别相关技术，EM算法简介，EM算法是关于半监督学习的一种技术(Pattern recognition technology, EM algorithm About, EM algorithm is on a semi-supervised learning techniques)

拉普拉斯边缘检测与图像增强，Matlab6.5实现的.m文件，附有原图、检测到的边缘图和增强后的图。(Laplacian edge detection and image enhancement, Matlab6.5 of implementation. M file, with image, detected and enhanced edge map after the map.)

单目标定很好用的程序，对于单目摄像机的一个标定。张正友博士的方法，对于摄像机标定，切记标定板不能用手机拍摄，因为手机拍摄是变焦的(Single objective of the program is very good, for a single camera calibration. Dr. Zhang Zhengyou s method for camera calibration, remember that the camera calibration board can not be used to shoot the phone, because the phone is to shoot a zoom )

多波段图像融合的一个实例，matlab编写。(Multi-band image fusion an example, matlab prepared.)