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Description: 灰度共生矩阵,半调图像
首先算出图像的增强的一维相关性,并用生经网络进行分类,可以分为6的大类
然后针对第三大类Cluster4和Diffuse8,可以利用图像的纹理特征参数(逆差矩)进行分类-Gray Level Co-occurrence matrix, first calculated image halftone images enhanced the relevance of one-dimensional, and the classification of Health through the network can be divided into 6 major categories and then for the third largest category of Cluster4 and Diffuse8, can make use of image texture characteristic parameters (deficit moment) to classify
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Size: 10240 |
Author: 郭世雄 |
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Description: Hu的七个不变矩在图像匹配检索中适用广泛。本代码仅计算图像间Hu的七个矩的D2大小，直观的了解Hu矩的应用-seven moment invariants in image retrieval in the application of broad match. Only the calculation of the code between the image of the seven Hu moments D2 size, intuitive understanding of the application of Hu Moment
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Size: 356352 |
Author: oyq |
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Description: IT Return the Co-ordinates of the two pixels
moment of binary image is calculated
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Size: 2048 |
Author: Tamilarasan |
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Description: ：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
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Size: 573440 |
Author: hahah |
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