SELF-READING-SYSTEM

经典的数字图像处理的形态学处理，包含源代码(Digital image processing morphological processing)

该文档描述了如何使用sketchup制作3D模型，并上传到google的3D仓库。(This document describes how to create 3D models using sketchup and uploaded to google' s 3D warehouse.)

本程序和实现对一幅包含人脸的bmp图像进行光纤补偿，对人脸区域进行检测实现人眼和人嘴的检测与定位，最后勾绘出人脸的特征以实现人脸检测的目的。(This procedure and implementation of a compensate the fiber contains face of BMP image, the face region detection to realize human eyes and mouth detection and positioning, finally drew out the facial features to achieve the purpose of face detection. )

全屏显示窗体。通过快捷键F11实现程序全屏显示的效果。(Full-screen display of the form. Through the shortcut key F11 to achieve the effect of full screen display.)

用VC++视觉检测程序，可以打开PCB板的照片文件，对其进行编辑、点运算、几何变换、正交变换、图像增强、形态变换、边缘与轮 廓检测、图像分析与复原、图像编码等功能。其中点运算包括图像反色、线性变换、阀值变换等功能；边缘检测包括Robert算子、 Sobel算子、Gauss-Laplace算子等；正交变换包括傅里叶变换、离散余弦变换、沃尔什变换；图像编码包括哈弗曼编码表、香农弗 曼编码表、行程编码、LZW编码等。 对于研究电子封装技术，视觉检测及电机精确控制的同学学习参考使用。(visual detecting for PCB.rar)

小波处理，滤波，复原，融合等VC源代码.(Wavelet processing, filtering, recovery, integration VC source code.)

PACS系统的图像浏览软件的分析与设计 硕士学位论文 DICOM通讯(PACS system, image viewing software analysis and design of a master' s degree thesis DICOM communication)

实现运动目标的跟踪，首先用鼠标选取一个区域。之后能自动跟踪这个目标(Tracking of moving objects, first use the mouse to select a region. After automatic tracking of this objective)

：植物种类识别方法主要是根据叶片低维特征进行自动化鉴定。然而，低维特征不能全面描述叶片信息，识别准确率低，本文提 出一种基于多特征降维的植物叶片识别方法。首先通过数字图像处理技术对植物叶片彩色样本图像进行预处理，获得去除颜色、虫洞、 叶柄和背景的叶片二值图像、灰度图像和纹理图像。然后对二值图像提取几何特征和结构特征，对灰度图像提取 Hu不变矩特征、灰 度共生矩阵特征、局部二值模式特征和 Gabor 特征，对纹理图像提取分形维数，共得到 2183 维特征参数。再采用主成分分析与线性 评判分析相结合的方法对叶片多特征进行特征降维，将叶片高维特征数据降到低维空间。使用降维后的训练样本特征数据对支持向量 机分类器进行训练(plant species identification method is mainly based on blade automatic identification of low dimensional characteristics.However, can not fully describe blade low-dimensional feature information, identification accuracy is low, in this paper A kind of plant leaves recognition method based on multiple feature dimension reduction.First by digital image processing technology to the plant leaf color sample image preprocessing, obtain background color removal, wormhole, petioles, and the blades of a binary image, gray image and texture image.Then the binary image to extract the geometric characteristics and characteristics of structure and characteristics of gray image extraction Hu moment invariants, gray co-occurrence matrix feature, local binary pattern features and Gabor, to extract the fractal dimension of texture image, get 2183 d characteristic parameters.By principal component analysis and linear uation analysis method of combining the characteristics of blade more feature dimensi)

这篇文章是一种特征降维的方法，不过跟一般的计较离散度的方法不同，这个特征变换的准则是：尽可能地保留原始样本的熵(Kernel Entropy Component Analysis Jenssen, R Pattern Analysis and Machine Intelligence, IEEE Transactions on Volume: PP , Issue: 99 )