web-images

说明：  实现了图像的打开功能，并能实现图像打开时的扫描，渐进，马赛克等功能(To realize the image of the open function, and can achieve image scans open, progressive, mosaic and other functions)

说明：  光谱数据降维处理，结合了主成分分析和LDA方法，可直接运行(Spectral data dimensionality reduction processing, combined with principal component analysis and LDA method, can be directly operated)

织物疵点检测和识别系统.采用2中方法，有gui界面，支持实际纺织物的检测(Fabric defect detection and identification systems. Using 2 methods, gui interface, support for the detection of the actual textile)

用于Landsat5 TM数据的辐射定标(Calibration for Landsat TM data using IDL)

混合高斯(Mixture of Gaussian, MOG) 背景建模算法和Codebook 背景建模算法被广泛应用于监控视频的运动目标检测问题, 但混合高斯的球体模型通常假设RGB 三个分量是独立的, Codebook 的圆柱体模型假设背景像素值在圆柱体内均匀分布且背景亮度值变化方向指向坐标原点, 这些假设使得模型对背景的描述能力下降. 本文提出了一种椭球体背景模型, 该模型克服了混合高斯球体模型和Codebook 圆柱体模型假设的局限性, 同时利用主成分分析(Principal components analysis, PCA) 方法来刻画椭球体背景模型, 提出了一种基于主成分分析的Codebook 背景建模算法. 实验表明, 本文算法不仅能够更准确地描述背景像素值在RGB 空间中的分布特征, 而且具有良好的鲁棒性.(The background modeling algorithm of mixture of Gaussian (MOG) and codebook is widely used in moving object detection of surveillance video. However, the ball model of MOG usually assumes that the three components of RGB are independent, while the cylinder model of codebook assumes that the value of background pixel is distributed uniformly within the cylinder and the changing direction of brightness points to the origin of the coordinate system. These assumptions reduce the descriptive capability for background modeling. Therefore, the paper proposes an ellipsoid- based background model, which overcomes the MOG and codebook0s limitations. By using principal component analysis to depict the ellipsoid background model, a novel PCA-based codebook background modeling algorithm is proposed. Experiments show that this algorithm can not only give more accurate description of the distribution of background pixels but also have a better robustness.)

图像融合工具包，适合各种图像的融合，希望对你有所帮助(Image Fusion Toolkit for the integration of a variety of images, I hope for your help)

图像处理基本算法其中之一的图像细化(Image processing one of the basic algorithm for image thinning)

在尽可能好的代表原始数据的前提下，PCA通过线性变换将高维空间中的样本数据投影到低维空间中。(In the best possible on behalf of the original data, under the precondition of PCA through linear transformation in the high-dimensional space sample data projection to low dimensional space)

说明：  matlab双目视觉标定程序研究图像配准的可以用这个(matlab study of binocular vision calibration procedure of image registration can use this)

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