DebugDemo

图片处理 去噪音，去模糊为了实现较好保留图像边缘特征的有效去噪,提出了一种基于边缘检测的Contourlet 变换去噪方法(Picture Deal)

这里的图像增强包括灰度修正、平滑、滤波、锐化等，是图像处理的基本操作，本代码调试通过(Here, including gray-level correction image enhancement, smoothing, filtering, sharpening, etc., is the basic image processing operations, the code debugging by)

说明：  用c写的jpg编解码，包括jpeg2bmp和bmp2jpeg，可在vc6下调试。(With c written jpg codec, including jpeg2bmp and bmp2jpeg, may be under vc6 debugger.)

：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)

use Codeblocks MinGW in openCV

利用opencv工具包的图像缩小程序利用opencv工具包的图像缩小程序利用opencv工具包的图像缩小程序(image shrink with opencv image shrink with opencv image shrink with opencv)

多光谱图像出理，离散傅立叶变换，低通滤波，TM条带影像去除(multispectral image integration of remote sensing)

VC编写的位图图像处理的源代码，包括了增强、变换等内容(VC prepared bitmap images deal with the source code, including the enhanced transformation, etc.)

一种简单高效地基于压缩感知的跟踪算法。首先利用符合压缩感知RIP条件的随机感知矩对多尺度图像特征进行降维，然后在降维后的特征上采用简单的朴素贝叶斯分类器进行分类。该跟踪算法非常简单，但是实验结果很鲁棒，速度大概能到达40帧/秒(A simple and efficient tracking algorithm based on compressed sensing. Firstly, with the random sensing matrix compressed sensing RIP conditions for multi-scale image feature dimension reduction, and then use the naive Bias classifier simple classification in the feature reduction after the. The tracking algorithm is very simple, but the results are robust, speed can reach 40 frames per second)

使用C++和CUDA编写的双边滤波器。在平滑图像的同时可以保持边界(Using bilateral filter the C++ and CUDA written. Boundary can be kept in the smooth image while)