Diliver20120920yy

运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。(Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training and test vehicle picture. Due to time reasons, the use of haar feature is not optimized, high dimension, resulting in sliding sash figure is too slow, the effect of the code only output statistics for your reference learning sparse representation in the vehicle detection.)

说明：  能将256色图像转换为黑白图像，程序简单易行，适合初学VC者练笔。(Able to convert 256-color image black and white images, the program is simple, suitable for beginners who Lianbi VC.)

说明：  这是一本关于电子情报雷达信号分析的电子版，主要内容包括电子情报的特征和基础、模拟和数字系统的信噪比考虑、信号功率、极化、波数分析、天线扫描分析、脉内分析、脉冲重复间隔分析、载频分析、脉冲串去交错、确定elint参数的极限以及elint数据文件 (This is an electronic intelligence on the radar signal analysis of the electronic version, the main contents include the characteristics of electronic intelligence and infrastructure, both analog and digital system signal to noise ratio considerations, signal power, polarization, wave-number analysis, antenna scanning analysis, pulse analysis , pulse repetition interval analysis, analysis of carrier frequency, pulse staggered string to determine the limits of the parameters, as well as ELINT ELINT Data Files)

图像数字水印中所涉及到的代码以及具体的文字的详细说明(The code involved in the image digital watermarking)

说明：  使用python中的opensilde库对tiff格式图片进行分割。(TIFF format image segmentation.)

介绍Opencv这个图像处理库环境下的GMM建模与EM算法，从数学的角度深入分析高斯建模和EM算法(Introduce the the Opencv this image processing GMM modeling the EM algorithm in the library environment, in-depth analysis from a mathematical point of the Gaussian model and the EM algorithm)

一个局部直方图均衡化算法，输入为图像和窗口的长宽，并且长宽只能为奇数(A local histogram equalization algorithm, input images and the length and width of the window, and the length and width of only odd-numbered)

改进型自适应陷波谐波检测法的研究，提高陷波器滤波功能(Improved Adaptive Notch harmonic detection method)

说明：  用水平集方法进行细胞图像的分割大家可以看看写的有点简单(level set method using cell image segmentation we can see a little bit simpler to write)

根据D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis 以及 C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision.这两篇文献提供的方法编写的图像分割代码，作者是 Chris M. Christoudias, Bogdan Georgescu，代码经我看了后加了丰富的中文注释，希望可以给各位带来阅读上的方便。 基于meanshift的聚类分割方法包括滤波、区域融合等操作，通过调整sigma和sigmar来调整分割效果。(According to D. Comaniciu, P. Meer: Mean Shift: A robust approach toward feature space analysis, and C. Christoudias, B. Georgescu, P. Meer: Synergism in low level vision. These two documents prepared by the methods provided by image segmentation code , the author is Chris M. Christoudias, Bogdan Georgescu, after I read the code, add a rich Chinese notes, hoping to bring you the convenience of reading. Segmentation method based on clustering meanshift including filtering, regional integration and other operations, and by adjusting the sigma sigmar to adjust segmentation results.)