Description: 提出一种基于分形理论和BP 神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像
的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB 格式转化为HSI 格式,然后,根据亮度计算分
数维、多重分形广义维数谱q-D( q) 和“空隙”等基于分形的纹理特征,同时加入归一化的色度和饱和度作为光
谱特征,采用BP 神经网络作为分类器。通过对彩色航空图像的分类实验,结果证实该方法行之有效。-Based on fractal theory and BP neural network of aviation remote sensing image supervised classification method. This method tries to aerial images of the spectral information and texture characteristics of the combination. It will first color aerial images from the RGB format into HSI format, and then, according to the brightness calculation of fractal dimension, the generalized multi-fractal dimension spectrum qD (q) and the Platform: |
Size: 274432 |
Author:xuhuoping |
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Description: 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. Platform: |
Size: 333824 |
Author:李国华 |
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Description: 采用波束成形技术的BER计算，zcZuvVR参数可以实现模式识别领域的数据的分类及回归，是机器学习的例程，在MATLAB中求图像纹理特征，dvGzQFM条件实现了对10个数字音的识别，包括最小二乘法、SVM、神经网络、1_k近邻法。- By applying the beam forming technology of BER zcZuvVR parameter You can achieve data classification and regression pattern recognition, Machine learning routines, In the MATLAB image texture feature, dvGzQFM condition To achieve the recognition of 10 digital sound, Including the least squares method, the SVM, neural networks, 1 _k neighbor method. Platform: |
Size: 13312 |
Author:hihfzk |
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Description: 基于叶片数字图像的植物识别是自动植物分类研究的热点。但是随着植物种类的增加，传统的分类方法由 于提取的特征比较单一或者分类器结构过于简单，导致叶片识别率较低。为此，本文提出使用纹理特征结合形状 特征进行识别，并且使用深度信念网络构架作为分类器。纹理特征通过局部二值模式、Gabor 滤波和灰度共生矩阵 方法得到。而形状特征向量由 Hu 氏不变量和傅里叶描述子组成。为了避免过拟合现象，使用“dropout”方法训练 深度信念网络。这种基于多特征融合的深度信念网络的植物识别方法-Plant based on digital image recognition is a hotspot of research on automatic classification.But with the increase of plant species, the traditional classification method by the extraction of characteristics or more single classifier structure is too simple, leading to a lower leaf recognition rate.To this end, this paper proposes using the texture characteristics in combination with characteristics of shape, which can identify the belief network architecture and using the depth as a classifier.Texture characteristics by local binary pattern, Gabor filter and gray level co-occurrence matrix method.And shape characteristic vector by Hu s invariant and the Fourier descriptor.In order to avoid over fitting phenomenon, dropout method is used to train deep belief networks.This belief network based on feature fusion depth plant identification method Platform: |
Size: 377856 |
Author:hahah |
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Description: LBP方法（Local binary patterns）是一个计算机视觉中用于图像特征分类的一个方法。LBP方法在1994年首先由T. Ojala, M.Pietik?inen, 和 D. Harwood 提出[43][44]，用于纹理特征提取。(The LBP method (Local binary patterns) is a method for classification of image features in a computer vision. The LBP method first in 1994 by T. Ojala, M.Pietik inen and D. Harwood proposed by [43][44], and used for texture feature extraction.) Platform: |
Size: 5120 |
Author:CherryBear
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Search - image classification by texture method