texture-classification-with-lbp

直线检测 Visual C++编写 用于图像处理(line segment detection)

数字图像的渐进式传输，采用掩膜矩阵进行基本层和增强层的划分(Progressive transmission of the digital image, the base layer and enhancement layer is divided by using the mask matrix)

说明：  输出带连接点的匹配影像，基于opencv(Output matched image with connection points, based on OpenCV)

常见的图像处理matlab源代码，包括梯度锐化、图像镜像、图像平移、图像缩放、图像细化、图像旋转、维纳滤波处理、用Canny算子提取边缘、阈值变换、直方图均衡、中值滤波。(Matlab Source Code about Image Processing)

C-W节约算法是最经典的求解车辆路径问题的启发式算法,()

利用高斯混合模型进行视频前景运动目标的提取！请使用自己的AVI文件。(extract the foreground moving object using the Gaussian mixture model)

基于最大流最小割定理的图像分割,里面有一个例子，(Image segmentation based on maximum flow minimum cut theorem)

vc6.0 控制摄像头源代码，采用directxshow 效率很高，小弟参考了很多的资料才搞明白，和大家共享，希望对大家有帮助(camera control vc6.0 source code, the use of highly efficient directxshow,小弟reference a lot of information in order to engage in to understand, and share, in the hope that we have to help)

通过边缘检测寻找图像上圆点的圆心坐标，可用于大量的图像处理程序(Image through edge detection to find the center coordinates of the dots)

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)