gmkl

matlab做的图像去噪程序，使用的是维纳滤波法（winner），内附图像，可直接运行(matlab image denoising procedure, using the Wiener filter method (winner), included images, can be run directly)

对场景分类和语义特征稀疏化的高层图像表示(Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification)

这是新提出来的用于图像去噪的非局部均值滤波方法, matlab程序.非常好,你试试.(this is a new proposed image denoising method--non local means filtering, matlab file. you can try!)

说明：  在地震处理中，非常经典的进行时深转换的程序，通常比较好用(In seismic processing, the very classic procedure for time-depth conversion, usually more useful)

Tamura纹理特征提取及应用的matlab代码，附有介绍绍Tamura纹理的文献和测试用图片。，已通过测试。 (Tamura texture feature extraction and application of the Matlab code, accompanied by Shao Tamura texture literature and test pictures. , Has been tested.)

说明：  一、主要研究内容 1.基本内容 本设计目标在于利用Matlab强大的图像处理能力和实用便捷的编程方法，通过处理包含人脸的视频帧系列图像，灰度积分投影技术的眼睛定位方法，进而利用perclos计数，计算眨眼率，从而得到比较准确的疲劳状况。包括lun文初稿撰写。 2.具体要求 本设计基于灰度积分投影技术的眼睛定位方法，再结合perclos技术。首先通过图像预处理技术得到灰度分配较为均匀的图像,然后分别利用水平和垂直灰度积分投影曲线结合人脸的结构特征找到眼睛的位置坐标,实现了准确的眼睛定位,通过perclos技术技术眨眼率，根据先验值得到是否疲劳。 二、算法原理 视频输入——预处理去除非人脸区域——人脸定位——人眼定位（灰度积分投影）——眼睛张合度——perclos技术统计——输出结果 三、GUI界面设计 [需要有一定编程功底](1. Basic contents The goal of this design is to use the powerful image processing ability of MATLAB and the practical and convenient programming method, through processing the video frame series images including face, the eye location method of gray integral projection technology, and then using PERCLOS count to calculate the blink rate, so as to get a more accurate fatigue condition. Including the first draft of Lun.)

说明：  This book is targeted at R programmers who want to learn the graphing capabilities of R. This book will presume that you have working knowledge of R.

Encapsulate Namespace tracking logic for use by SAX drivers.

adaboostcode，这是matlab环境下的adaboost代码，希望对各位有帮助。(adaboostcode, this is the matlab environment AdaBoost code, you want to be helpful.)

实现了基于关键像素点的FLICM图像分割算法，关键像素由局部最大值方法提取。(A FLICM image segmentation algorithm based on key pixels is implemented. Key pixels are extracted by local maximum method.)