搜索资源列表
xingzhuangbubianju
- 利用HU的七个不变矩作为形状特征向量,对输入图像数据的七维形状特征行向量进行提取。 - The seven invariant moments of HU are used as the shape feature vectors to extract the feature vectors of the seven dimensional shape of the input image data.
colorju
- 对图像进行颜色空间转换，提取出图像的3个颜色矩特征。-hu features
feature extraction
- 自己根据公式编写的颜色矩特征，纹理空间自相关函数特征(The color moment feature and texture space auto correlation function feature written by the formula.)
GLCM
- 批量处理图片，提取主要的四个纹理特征（能量、熵、惯性矩、相关性），并存入向量内(Batch processing pictures, extract the main four texture features (energy, entropy, moment of inertia, correlation), and put them into the vector.)
image recognition
- 分别采用有边缘和无边缘两类图像作为样本库，使用用Hu不变矩对图像进行特征提取，并使用K近邻分类法进行分类和识别。(Two types of images with edges and no edges are used as the sample library, the images are extracted using Hu invariant moments, and the images are classified and identified by K nearest neighb
1.2图像的矩特征
- 1.4图像的斑点检测，本节详细介绍斑点的检测技术(1.4 spot detection of the image. This section details spot detection technology)
Feature_extraction
- 用于运动人体的图像特征提取，提取的特征包括，高宽比，速度，紧密度，灰度共生矩阵，hu矩，主成分分析，离散余弦变化，图像的预处理等。(Image feature extraction for moving human body includes features such as aspect ratio, speed, tightness, gray level co-occurrence matrix, Hu moments, principal component analysis, disc
GLDM(2)
- 利用gldm提取图像纹理特征源代码，八维，含能量、熵、惯性矩、相关的均值和标准差(Image texture feature extraction using GLDM source code, eight dimensional, including energy, entropy, moment of inertia, the mean and standard deviation)
Texture feature extraction
- 灰度共生矩 阵即为灰度级的空间相关矩阵，以其为基础的统计方法通过对矩阵统计量的求取较好地提取到了纹理特征，通过选取关键参数编程并进行仿真实现，分别求取了四个方向的灰度共生矩阵及其特征量来分析图像 的纹理特征。(The gray level co-occurrence matrix is the gray level spatial correlation matrix. Based on the gray level co-occurrence matrix, the texture featu
植物虫害检测（GUI，注释，svm算法）
- 植物虫害检测（GUI，注释，svm算法） 该课题为基于MATLAB SVM方法的植物病害检测系统，带GUI界面，可以识别多种被虫害侵蚀的植物叶子，输出结果。带论文和详细注释。 train 对黄瓜子文件夹所有图片提取 颜色矩特征和gabor纹理特征，然后svm训练 test 对测试图像灰度化，滤波，提取 颜色矩特征和gabor纹理特征，然后svm模型测试，输出类别 colorMom.m 颜色矩特征提取 Gabor_palm.m gabor纹理特征提取(Plant pe
garchsk
- Jondeau 、Leon 等提出自回归条件方差—偏度—峰度模型(GARCHSK)，用于同时描述收益率二阶矩、三阶矩和四阶矩的时变特征。此文件为该模型代码。(Jondeau, Leon et al. Proposed an autoregressive conditional variance-skewness-kurtosis model (GARCHSK), which was used to describe the time-varying characteristics of the
全新的子载波调制方式盲识别算法
- 提出一种全新的子载波调制方式盲识别算法,该算法利用OFDM子载波组的统计特性,然后通过推导得到新的基于混合高阶矩的特征量,使得到新的特征量不受信噪比、载波频偏与相位偏移的影响。