demo

通过对DXF文件解析，将解析后的数据以列表的形式展示在winform窗体中

经过测试的基于sift程序的图像拼接。以lowe的程序为sift提取核心程序，此外还包括ransac算法，单应矩阵提取，图像融合（包括加权融合以及平均融合）算法，融合效果在testnew中可见。(Image mosaic based on sift program tested. The lowe program sift extract the core program, in addition to including ransac algorithm homography extract image fusion (including fusion weighted average fusion) algorithm, fusion effect is visible in testnew.)

对测井曲线进行重构的程序，可以充分利用测井特征曲线所反应的岩性特征信息，完成多信息的融合(Log curve reconstruction procedures)

图像格式转换例程源码，程序主要结合易语言图像格式转换支持库，可以将某些图象文件的格式转换到其它图象文件的格式(Image format conversion routines source, the program mainly with easy language support library image format conversion , you can convert some of the image file format to other image file format)

说明：  全变分模型主要是依靠梯度下降流岁图像进行平滑处理的模型,希望在图像的内部对图像进行平滑,使得相邻像素的差值较小(The total variation model is mainly based on the gradient descent flow year image smoothing model, hoping to smooth the image inside the image, so that the difference between adjacent pixels is small)

多尺度LBP，代码根据论文“Learning Multi-scale Block Local Binary Patterns for Face Recognition”编写的(this code is obtained from the paper named "Learning Multi-scale Block Local Binary Patterns for Face Recognition")

视频格式转换 这里主要是mpeg格式 转化为avi，yuv等多种格式(Video format conversion here is mainly converted to avi mpeg format)

点云读取，旋转，平移，缩放，统统是包含全视图的各种视图的显示(Point in put)

运用Perona-Malik各项异性扩散模型对二维图像进行去噪处理。该方法在有效的抑制噪声的同时能够保护图像的边缘。(The use of Perona-Malik Anisotropic Diffusion Model for the treatment of two-dimensional image denoising. The method in the effective suppression of noise at the same time to protect the edge of the image.)

说明：  有数字1和2的图片各四十张，利用mat lab读取图片并将其二值化，得到只由0和1表示的矩阵，根据数字的不同特征，选择两个特征值，用来对分类器进行训练，最后用剩下的5个1和5个2来进行测试，看分类器是否可以正确的对数据进行分类。(Figures 1 and 2 pictures of the 40, using mat lab picture and secondly, the value of reading, and received only by 0 and 1, said matrix, according to the number of different features, select the two characteristic values, used to classifier training, and finally with the remaining 5 1 and 5 2 to carry out tests to see whether the classifier can correctly classify the data.)