suanshubianma

说明：  SVM支持向量机在图像分割中的应用，包含Matlab源代码和SVM所用函数。(The application of SVM support vector machine in image segmentation, including Matlab source code and functions used by SVM.)

winform 仿QQ截图，可将截图直接保存到内存中 附完整源码下载

最小二乘影像匹配，自己2012年8月写的程序，经过测试，内有详细注释，希望对大家有用。(Least Squares Matching write the program in August 2012, has been tested, with detailed notes, and hope to be useful.)

spiht 图形程序压缩代码 代码可用 请仔细研读 并适当修改为适合自己的 (SPIHT graphics program to compress code code available, please carefully read and amended as appropriate for their own)

同态滤波器 作用：消除不均匀照度的影响, 增强图象细节。(Filter function with the state: to eliminate the impact of uneven illumination, enhanced image detail.)

改变图像灰度，增强图像中需要的信息，可利用与分割前处理(Change the image gray level, increase the information)

一种自适应chirplet分解的快速算法 针对信号自适应chirplet分解未知参数多、实现起来比较困难的特点，文献[1]提出了一种新的chirplet分解快速算法。该算法利用计算信号的二次相位函数，得到其能量分布集中于信号的调频率曲线上的结论，此时通过谱峰检测可同时获得chirplet调频率、时间中心和幅度的估计；然后通过解线性调频技术获得其初始频率和宽度的估计，仿真结果验证了本文算法的有效性。 (Chirplet an adaptive decomposition algorithm for rapid adaptive signal decomposition Chirplet many unknown parameters, is more difficult to achieve the characteristics of the literature [1] proposed a new fast algorithm Chirplet decomposition. The algorithm calculating the signal function of the second phase, its energy distribution to be concentrated in the signal modulation frequency curve on the conclusions, this time through the spectral peak detection can be obtained simultaneously Chirplet tune the frequency, timing and magnitude of the estimated center then through the solution line technology of FM frequency and the width of the initial estimates, the simulation results verify the effectiveness of the algorithm.)

计算局部形状描述符梯度方向直方图（HOG），直接输入图像即可用。(Calculate the local shape descriptors gradient orientation histogram (HOG), the input image can be used directly.)

对变形的条纹图像进行傅立叶变换得到二维频谱图并提取基频(Fourier transform in deformation stripes image two-dimensional spectrum and extract the fundamental frequency )

说明：  使用PCA将数据集降维，降维后将特征脸还原(Use PCA to reduce the dimension of the data set, reduce the feature face after dimension reduction)