2-D-window-for-a-sample-image

钢板表面质量检测中，图片是否有拉伸的监测和分析程序(Steel plate surface quality detection, image whether the stretching of the monitoring and analysis program )

调用 envi5.3自带的FMask云检测功能，并输出掩膜文件(Call the FMash cloud detection function of envi5.3 and output the mask file)

马尔科夫随机场 EM算法 做图像变化检测 请多指教(MRF EM image change detection algorithms do please advise)

BMP转位图 6410 logo bmp-C(BMP bitmap transfer)

用C写的图像融合代码，包括了医学图像融合、多焦点图像融合和多传感器图像融和等多种类型的融和算法，运行速度快。(Image fusion with the C written code, including the medical image fusion, multi-focus image fusion and multi-sensor image fusion and many other types of fusion algorithms, to run fast.)

说明：  进行图像的特征提取，为图像拼接做准备，matlab(Feature extraction of image)

图像细化注意，本程序的细化算法要应用于于含线条的黑白二值图像才可以看出细化效果。 (The thinning Note refinement algorithm of this program is to be applied to black and white binary image containing lines can be seen that the refinement effect.)

说明：  HOG LBP 在python matlab C++环境下的实现(Implementation of HOG LBP in Python matlab C++ environment)

局部二进制模式，LBP，是已被用于纹理特征的一个 分类。在本文中，提出了一种基于使用这些功能的方法 检测缺陷图案的面料。在培训阶段，在第一个步骤LBP算子是 施加到无缺陷织物样品，逐个象素和参考的所有的行（列） 特征矢量的计算。那么这个图像被分为Windows和LBP算子是 应用这些窗口的每一行（列）。根据与参考比较 特征向量一个合适的阈值，无缺陷的窗户被发现。在检测阶段中，一个 测试图像被划分成的窗户，并使用阈值时，有缺陷的窗口可以 检测到。该方法简单，灰度不变。由于其简单性， 线上实现是可能的。(Local Binary Patterns, LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well. )

社会力模型，基于社会力的行人流模型，包括行人间作用力，行人与墙体等障碍物的作用力等。(Social force model)