gabor

：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)

提出了一种基于图像边缘连接的线段检测方法。数字图像中的线段是由很多线段图元组成,在同一 条线段中,它们的方向是相同的并且受到真实线段的方向约束。因此可以通过提取小的线段图元, 然后分析它们 的连接特性, 进行线段的检测。首先用ADM 算法对图像进行边缘检测, 得到单像素宽的边缘 然后在4 个方向上 进行线段的检测 对初步得到的结果进行合并和验证, 去掉短的线段, 连接临近的线段。经过实验证明, 该方法能 够快速地提取图像中的线段,并且对噪声具有一定的鲁棒性(A line segment connecting a detection method based on image edges. The segment of the digital image is composed by many segments elements, their orientation is the same and by the real line segment direction constraint in the same segment. By extracting a small segment Element can then analyze their connection characteristics for the detection of the line segment. First with ADM algorithm for edge detection on the image to obtain a single-pixel-wide edge then in the four directions on the detection of the line segments merger and verification of the preliminary results obtained, removing the short line segments, the line segments connected near. The experiments show that this method can quickly extract the line segments in the image, and certain robustness to noise)

说明：  扫描文件夹，通过文件大小和创建时间判断图片文件是否重复，并分类和重命名(Scanning folders to determine whether image files are duplicated, categorized and renamed by file size and creation time)

tiff图像的读取与处理，并且说明清新，有很大的帮助。(The reading and processing of the tiff image, and the description of the fresh, is of great help.)

是用matlab 实现的cox经典水印算法包括水印的嵌入、提取等(using Matlab realized cox classic watermarking algorithm including the watermark embedding and extraction, etc.)

基于核函数的SAR图像目标识别,利用KPCA对SAR图像进行特征提取(SAR image target recognition based on kernel function，Use KPCA SAR image feature extraction)

本代码实现了对高光谱遥感图像进行混沌序列置乱加密，其中包括读取三维立体遥感图像、混沌加密以及灰度直方图绘制(This code implements the hyperspectral remote sensing images chaotic sequence of scrambling encryption, including reading the three-dimensional sensing image, and histogram drawing Chaotic Encryption)

经典主成分分析法，实现高光谱图像的降维处理 (Classical principal component analysis, to achieve high spectral image dimensionality reduction)

基于Maltab的gui图像配准程序。输入图像需为灰度图像。(matlab-based image registration)

亚像素级cvFindCornerSubPix角点检测，可较为精确地对角点进行检测(The subpixel cvFindCornerSubPix corner detection)