curves-movement-by-level-set-method

PCNN实现降噪的不错 PCNN是很好的自动分割工具 PCNN是很好的自动分割工具(PCNN是很好的自动分割工具，)

一种简单的代数重建算法，用于CT图像二维重建，供初学者学习使用。(A simple algebraic reconstruction algorithm, used in CT image reconstruction, for beginners learning to use.)

Image Database 2.5.0 (w/ thumbnails)

图像去噪的源程序，有英文论文，效果很不错(Denoising of the source, an English paper)

c#联合halcon自定义直线卡尺

：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)

基于小波多尺度的灰度图像纹理特征检测源码，小波可以自定义(Based on wavelet multi-scale texture features of the gray image source detection, wavelet can customize)

说明：  matlab图像融合，对一幅图像的RGB三通道分别做不同的处理，然后重新融合(Matlab image fusion, an image of RGB three channels to do different processing, and then re-fusion)

可以进行图象处理,一些简单变化,使用范围比较广泛(it can change image)

一个很好类子来说明类模板的使用，可以比较明了类模板的使用方法，最好进行单步调试(use of class template )