基于多尺度形态学提取眼前节组织

shp 全国村镇边界数据

两船协同导航，利用EKF滤波，主要用于主从式的协同导航，滤波参数要根据实际需要合理调节(Collaborative navigation of both vessels, the use of the EKF filter, mainly used for the master-slave collaboration navigation filter parameters according to the actual needs of a reasonable adjustment)

：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)

数字图象处理VC++实现,里面有很多图像处理方面的算法，有参考意义。(Digital image processing in VC++)

对指纹进行识别，通过gabor进行指纹增强，然后细化得到结果(Fingerprints were identified and enhanced with gabor, and the results were refined)

主要功能时串口调试，附带的C#自带画图以及MATLAB画图可供参考，根据实际需求更改源码。注意：使用MATLAB画图需要安装运行环境。

SIFT算法，可以有效的找到不同画面中的对应特征点，在跟踪和图形识别方面有很好的用途(SIFT algorithm, can effectively find a different screen the corresponding feature points, in the tracking and pattern recognition as a very good use of)

显著区域检测的一篇文章所带的测试算法，CVPR(Salient Region Detector CVPR 2009)

1.DCT变换，对整个图像的直接DCT变换和分块为8*8做DCT变换，并设定阈值。 2.汉明编码（7，4），译码以及找出错误图样和纠错。并在不同的转移概率的BSC道下进行传输。(DCT:Direct DCT and IDCT,blocking. hamming coding:decoding and picking up errors.)

说明：  opencv基础教程，对于初学者来说是个不错的选择。(opencv basic tutorial for beginners is a good choice.)