figure4_41

C#的图像处理，用于验证码的识别功能，可以识别简单的有背景干扰的验证码(C# image processing, is used to identify functional verification code, you can recognize simple background interference verification code)

softcast的2d dct实现，先对图像转灰度图，得到的亮度信号矩阵做DCT 变换(softcast of 2d dct implementation, the first image to grayscale, luminance signal obtained by DCT transformation matrix do)

可逆数字水印算法 Article (2009-Sachnev-IEEE_J_CASVT-989) Sachnev, V. Kim, H. J. Nam, J. Suresh, S. & Shi, Y. Q. Reversible Watermarking Algorithm Using Sorting and Prediction #IEEE_J_CASVT#, (Reversible watermarking Article (2009-Sachnev-IEEE_J_CASVT-989) Sachnev, V. Kim, H. J. Nam, J. Suresh, S. & Shi, Y. Q. Reversible Watermarking Algorithm Using Sorting and Prediction#IEEE_J_CASVT#, 2009, 19, 989-999 )

相机的单目标定，使用张正友标定法，可靠使用(The monocular camera calibration, using the zhangzhengyou calibration method, reliable use)

粒子滤波与PHD多目标跟踪比较，三目标分别进行粒子滤波和marginalPHD滤波跟踪比较结果。(PHD particle filter multi-target tracking compared with the three goals were to filter tracking particle filter and marginalPHD comparison.)

本程序对同一图像增加噪声后，进行均值滤波、高斯平滑滤波和中值滤波三种方法的去噪和结果比较， 并进行了评价指标PSNR和SSIM的计算以及算法耗时的统计。 ( The program on the same image after the noise increases, mean filter, de-noising and results of the Gaussian smoothing filter and median filter three methods of comparison, and the index PSNR and SSIM calculation algorithm and time-consuming statistics.)

矩不变量，适用于图像匹配，数字水印，图像重建(Moment invariants, applicable to the image matching, digital watermarking, image reconstruction)

本程序利用SIFT算法实现影像匹配的基础上，实现全景图的拼接，实用性很强，大家可以把拍摄的相邻的几张有重叠度的照片拼接成一张全景图。(The program uses SIFT image matching algorithm based on the realization of panorama stitching, very practical, we can put a few have taken the overlap of adjacent photos into a panoramic mosaic map.)

传统的图像分割方法可以分为基于阈值、边缘、区域的方法等，而传统的分割方法在复杂图像分割中效果不好。为了提高图像分割的性能，我们提出了一种基于局部自适应窗口的Otsu方法。首先，分析了传统阈值法在复杂图像分割中不能很好地形成的原因，比较了全局阈值和局部阈值对图像分割的影响。其次，提出了根据局部信息自适应地改变局部窗口大小的方法，分析了该方法的特点。最后，证明了所提出的新方法优于其他方法。实验结果表明，与其他传统方法相比，该方法能保持更多的细节，获得更满意的结果。(The traditional image segmentation methods can be divided into thresholding, edge and region methods. The traditional segmentation methods do not perform well in the segmentation of complicated images. In order to improve the performance of image segmentation, we propose an adaptive windowed range-constrained Otsu method based on local information.First, the reason why traditional thresholding methods do not perform well in the segmentation of complicated images is analyzed. Therein, the influences of global and local thresholdings on the image segmentation are compared. Secondly, a method of adaptively changing the local window size based on local information is proposed, and the characteristics of the proposed method are analyzed. Finally, the superiority of the proposed method over other methods, It is validated by the experiments that the proposed method can keep more details and acquire much more satisfying results as compared with the other conventional methods.)

混合高斯背景建模，可以把连续帧图片前景与背景区分开来(Mixed gaussian background modeling, can continuous frame picture foreground and background to distinguish )