multiobject_context

：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)

用AAM算法进行人脸特征点的标定，即对人脸的眼睛，眉毛，嘴巴，鼻子及脸的轮廓进行标记，此代码标记了68个特征点。(AAM algorithm for facial feature points calibration, calibration of this code is the 68 feature points, see the instance document.)

vc++图像开发的附书光盘,包括众多的实例程序,每个程序都可以是功能完备的工具软件.(visual c++ image develop source code and instances)

图像二值化，去离散噪声，彩色图像灰度化，特征提取，灰度化，彩色图像平均灰度(Image binarization, to discrete noise, color gray image feature extraction, gray, color image mean gray)

labview vision 本文主要介绍ision的使用方法(labview vision paper describes ision to use)

三自由度并联机器人工作空间求解matlab程序，用球坐标分割的空间(workspace for solution)

说明：  短时傅里叶变换的一些简单的演示，其中有矩形窗和高斯窗(some demos for the short time fourier transform)

局部二进制模式，LBP，是已被用于纹理特征的一个 分类。在本文中，提出了一种基于使用这些功能的方法 检测缺陷图案的面料。在培训阶段，在第一个步骤LBP算子是 施加到无缺陷织物样品，逐个象素和参考的所有的行（列） 特征矢量的计算。那么这个图像被分为Windows和LBP算子是 应用这些窗口的每一行（列）。根据与参考比较 特征向量一个合适的阈值，无缺陷的窗户被发现。在检测阶段中，一个 测试图像被划分成的窗户，并使用阈值时，有缺陷的窗口可以 检测到。该方法简单，灰度不变。由于其简单性， 线上实现是可能的。(Local Binary Patterns, LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well. )

说明：  matlab图像去模糊算法代码，使用两倍长度的点扩散函数进行维纳滤波，模糊图像加入随机噪声，计算噪声信号功率比，使用噪声能量和图像一维自相关函数进行维纳滤波，模糊后的图像，减小噪声能量进行规则化滤波，增大拉格朗日乘子进行滤波，继续对恢复的图像反卷积，产生模糊图像，最初的PSF，显示最初PSF下恢复的PSF。(MATLAB image deblurring algorithm code, using double-length point spread function for Wiener filtering, adding random noise to blurred image, calculating noise signal power ratio, using noise energy and image one-dimensional autocorrelation function for Wiener filtering, blurred image, reducing noise energy for regular filtering, increasing Lagrange multiplier for filtering, continue to restore the image. Like deconvolution, a blurred image is generated. The original PSF displays the restored PSF under the original PSF.)

说明：  机载合成孔径雷达自聚焦算法，没有实测数据，用的是点目标仿真(Airborne synthetic aperture radar algorithm for self-focusing, there is no measured data, using a point-target simulation)