dianziwenxiang

说明：  双目视觉导航的文章，介绍了CCD标定，图像匹配，三维重建等关键技术(TE PLEASE DOWNLOAD IT )

在自动化生产线VC开发的运动控制机器视觉程序，XYZ三个轴有PCI板卡控制，视频卡采集图像后经算法处理找到图像的点，VC控制步进电机到加胶位置加胶，在机器上运行每日点胶100000次无故障(VC development in automated production lines motion control machine vision program, XYZ three-axis control with a PCI card, video card capture images after processing algorithm to find the image of the point, VC controlled stepper motor to position plus plus glue glue on the machine run the daily dispensing 100,000 times without failure)

介绍混合像元解混里面的端元提取算法N-FINDR(Describes the mixed pixel unmixing inside endmember extraction algorithm N-FINDR)

图像处理的IPP库使用手册，包括每个函数的使用说明，以及参数的意义。(IPP library of image processing using the manual, including instructions for each function, as well as the significance of the parameters.)

过线检测：依据物体运运动轨迹，分析是否过线 (In lane detection: Based on the trajectories of objects transported to analyze whether the line)

说明：  图像形态学处理，图像腐蚀，图像膨胀，图像细化，边缘检测(Morphological image processing, image corrosion, image expansion, image thinning, edge detection)

LSB替换隐写软件，该软件能把ASCII码嵌入到图像中，其载体图像是24位的BMP格式图像(LSB Steganography tool which provides hide ASCII infomation within 24-bit Bitmap images)

说明：  编程实现对bmp图像最大值，最小值，标准差，熵，各灰度值频率的计算(Programming to calculate the maximum value, minimum value, standard deviation, entropy and gray value frequency of BMP image)

以SIFT特征为目标输入，LDAHash方法进行降维实现目标识别(SIFT features input LDAHash method to reduce the dimensionality of the target recognition)

：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)