Description: 灰度共生矩阵是图像处理中特征提取的纹理特性的一个重要参量，该程序实现2-D灰度图像的共生矩阵的计算-Gray-level co-occurrence matrix is the image processing feature extraction of texture characteristics of an important parameter, the program realize 2-D gray-scale image of co-occurrence matrix calculation Platform: |
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Author:杨文浩 |
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Description: 基于灰度共生矩阵的纹理特征提取，输出为图像四个方向共十六个特征值-Gray-level co-occurrence matrix based texture feature extraction, the output image in four directions for a total of 16 eigenvalues Platform: |
Size: 1024 |
Author:chichen |
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Description: LTP算子提取图像的纹理特征，这种方法对于光照变化十分有效。-LTP operator image texture feature extraction, this method is very effective for the illumination changes. Platform: |
Size: 44032 |
Author:灵芝 |
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Description: 图像纹理特征提取matlab，计算图像的粗糙度、方向度等特征。-Image texture feature extraction matlab, calculated image roughness, and other characteristics of the direction of degrees. Platform: |
Size: 6615040 |
Author:moon |
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Description: 摘要图像特征的提取是视觉图像识别的重要方法之一，采用细胞神经网络（&’’）并行处理器进行图像特征的提取
具有实时快速的优点。该文将介绍&’’ 并行处理器的基本工作原理及其实现图像特征处理的逻辑组合通用方法，并以
图像的纹理分割与识别为例来说明&’’ 并行处理器应用于视觉图像识别的通用编程方法。-Abstract image feature extraction is a visual image to identify one of the important ways, the use of cellular neural networks for parallel processors (& ' ' ) image feature extraction with the advantages of rapid real-time. This paper will introduce & ' ' parallel processors to achieve the basic working principle and characteristics of image processing method of the logic combination of GM and image texture segmentation and recognition as an example to illustrate the application of parallel processors & ' ' visual image recognition General programming method. Platform: |
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Author:martin |
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Description: Feature extraction is a key issue in contentbased
image retrieval (CBIR). In the past, a number of
texture features have been proposed in literature,
including statistic methods and spectral methods.
However, most of them are not able to accurately capture
the edge information which is the most important texture
feature in an image. Recent researches on multi-scale
analysis, especially the curvelet research, provide good
opportunity to extract more accurate texture feature for
image retrieval. Curvelet was originally proposed for
image denoising and has shown promising performance.
In this paper, a new image feature based on curvelet
transform has been proposed. We apply discrete curvelet
transform on texture images and compute the low order
statistics from the transformed images. Images are then
represented using the extracted texture features. Retrieval
results show, it significantly outperforms the widely used
Gabor texture feature. Platform: |
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Author:Swati |
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Description: The indexing of an image database is often referred as feature extraction. Mathematically,
a feature is an n-dimensional vector, with its components computed by some image analysis.
The most commonly used visual cues are color, texture, shape, spatial information, and motion
in video. For example, a feature may represent the color information in an image Platform: |
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Author:rach |
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Description: 为了更有效地提取图像的局部特征，提出了一种基于2维偏最小二乘法(two—dimensional partial least
square，2DPLS)的图像局部特征提取方法，并将其应用于面部表情识别中。该方法首先利用局部二元模式(1ocal
binary pattern，LBP)算子提取一幅图像中所有子块的纹理特征，并将其组合成局部纹理特征矩阵。由于样本图像
被转化为局部纹理特征矩阵，因此可将传统PLS方法推广为2DPLS方法，用来提取其中的判别信息。2DPLS方法
通过对类成员关系矩阵的构造进行相应的修改，使其适应样本的矩阵形式，并能体现出人脸局部信息重要性的差
异。同时，对于类成员关系协方差矩阵的奇异性问题，也推导出了其广义逆的解析解。基于JAFFE人脸表情库的
实验结果表明，该方法不但可以有效地提取图像局部特征，并能取得良好的表情识别效果。-To better the image of the local feature extraction, a partial least squares method based on 2D (two-dimensional partial least
square, 2DPLS) image local feature extraction method, and applied to facial expression recognition. In this method, use of local binary pattern (1ocal
binary pattern, LBP) operator extracts an image texture features of all sub-blocks, and their combination into the local texture feature matrix. As the sample image
Be translated into the local texture feature matrix, so the traditional PLS method can be generalized to 2DPLS method used to extract the identification information. 2DPLS method
Through the class membership matrix in the corresponding modifications to adapt the sample matrix, and can reflect the importance of face poor local information
Different. Meanwhile, members of the class covariance matrix of the singular relations issues, also derived the generalized inverse of the analytical solution. Based on the JAFFE facial expression database
Platform: |
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Author:MJ |
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Description: 1．分析研究了基于内容的图像检索系统的工作原理，关键技术如：纹
理、形状等图像底层特征的描述方法， 图像间的相似性度量方法，
图像库索引机制等。
2．研究了基于纹理特征的图像检索方法，并提出了一种基于NSCT 变
换的纹理特征提取方法。通过对SAR 图像及相关图像进行NSCT
分解，计算不同尺度不同方向上的系数幅度序列的均值，标准方差
和三阶中心矩，以此构成特征向量来描述图像的纹理。实验证明本
文提出的采用NSCT 算法有较好的特征提取效果，引入三阶中心矩
作为特征向量优于只使用均值和方差的组合特征，提高了图像检索
的查准率。
3．研究了基于形状特征的图像检索方法，并提出一种基于NSCT变换
的形状特征提取方法。把改进型Canny算子和NSCT变换相结合，先
对SAR图像及相关图像运用改进型Canny算子提取边缘，在此基础
上再进行NSCT变换，把图像的形状信息分解到不同尺度不同方向
上，从而保留各个频率分量，减少了图像形状信息的丢失。-1. First we analyze and study the principle of image retrieval system and
key techniques and algorithms of CBIR, such as the low-level feature
descriptions including texture, shape, the similarity measure between
images, the indexing methods and so on
2. Researching on the texture-based image retrieval algorithm, we
propose an algorithm of texture feature extraction based on the
Nonsubsampled Contourlet transform in this thesis. The image is
decomposed by the Nonsubsampled Contourlet transform. The mean,
standard deviation and third central moment of the magnitude of the
Nonsubsampled Contourlet coefficients at different scales and directions
are computed to extract the texture feature vector.Experiment proves the
third central moment added in NSCT arithmetic is overperformded than
only use the mean and standard deviation, and precision ratio has
improved.
3. Researching on the shape-based image retrieval algorithm, we propose
an algorithm of shape feature extraction bas Platform: |
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Author:周二牛 |
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Description: LBP纹理特征提取算法。首先将检测窗口划分为16×16的小区域（cell），对于每个cell中的一个像素，将其环形邻域内的8个点（也可以是环形邻域多个点，如图 3‑ 4. 应用LBP算法的三个邻域示例所示）进行顺时针或逆时针的比较，如果中心像素值比该邻点大，则将邻点赋值为1，否则赋值为0，这样每个点都会获得一个8位二进制数（通常转换为十进制数）。然后计算每个cell的直方图，即每个数字（假定是十进制数）出现的频率（也就是一个关于每一个像素点是否比邻域内点大的一个二进制序列进行统计），然后对该直方图进行归一化处理。最后将得到的每个cell的统计直方图进行连接，就得到了整幅图的LBP纹理特征-LBP texture feature extraction algorithm. First detection window is divided into 1616 small area (cell), one pixel in each cell, its circular neighborhood of 8 points (You can also ring neighborhood, as shown in Figure 3-4 example of application of the LBP algorithm, three neighborhood shown) clockwise or counterclockwise, the center pixel value than the adjacent points, then adjacent points assigned to 1, otherwise the assignment is 0, so that each point will an 8-bit binary number (usually converted to a decimal number). And then computing the histogram of each cell, each number (assuming that is a decimal number) the frequency (that is, one on each pixel next to the interior point for a binary sequence of statistics), then the histogram normalized. Finally, get the histogram of each cell to connect, to get the whole image of the LBP texture features Platform: |
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Author:nana |
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Description: 这是谷歌特征点提取，可以提取到图像的轮廓，特征点及内部的纹理线，共同描述一幅图像-This is Google feature point extraction, can be extracted into the outline of the image, the texture of the feature points and the internal lines, common to describe an image Platform: |
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Author:王玉影 |
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Description: Abstract—Noninvasive ultrasound imaging of carotid plaques
allows for the development of plaque-image analysis methods associated
with the risk of stroke. This paper presents several plaqueimage
analysis methods that have been developed over the past
years. The paper begins with a review of clinical methods for visual
classification that have led to standardized methods for image
acquisition, describes methods for image segmentation and denoizing,
and provides an overview of the several texture-feature
extraction and classification methods that have been applied. We
provide a summary of emerging trends in 3-D imaging methods
and plaque-motion analysis. Finally, we provide a discussion of the
emerging trends and future directions in our concluding remarks. Platform: |
Size: 737280 |
Author:JUHWAN LEE |
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Description: Colourhistogram
II. TEXTURE FEATURE EXTRACTION IN CBIR
An overview of the proposed CBIR system is illustrated
in Fig. 1. The proposed algorithm, Label Wavelet Transform
(LWT), is based on color image segmentation [1], and it is
an extension of DWT-based texture feature extraction method.
The 2-D DWT is computed by applying separable filter banks
to the gray level images. The detail images Dn,1, Dn,2,
and Dn,3 are obtained by band-pass filtering in a specific
direction, and they can be categorized into three frequency
bands: HL, LH, HH band, respectively. Each band contains
different directional information at scale n. The texture feature
is extracted from the variance (ó2
n,i) of the coefficients cn,i of
the detail image Dn,1, Dn,2, and Dn,3 at different scale n.To
represent the texture feature of an image q, the texture feature
vector of DWT is defined as [2]:
TDWT (q) = [ó2
1,1, ó2
1,2, ó2
1,3, ..., ó2N
max,3], (1)
where Nmax denotes the largest scale. In this work, Nmax Platform: |
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Author:lavanya |
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Description: 一篇关于图像纹理提取的经典文章，涉及图像处理领域的方向动态！-An article about the classic image texture extraction, involves the direction in the field of dynamic image processing. Platform: |
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Author:余道喜 |
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Description: LBP（Local Binary Pattern，局部二值模式）是一种用来描述图像局部纹理特征的算子；它具有旋转不变性和灰度不变性等显著的优点。它是首先由T. Ojala, M.Pietikä inen, 和 D. Harwood 在1994年提出，用于纹理特征提取。而且，提取的特征是图像的局部的纹理特征；-LBP (Local Binary Pattern, local binary pattern) is an image used to describe the local texture features of operator it has significant advantages rotation invariance and gray invariance. It was first proposed in 1994 by the T. Ojala, M.Pietikinen, and D. Harwood, for texture feature extraction. Moreover, the extracted features are local texture features of the image Platform: |
Size: 4096 |
Author:木言 |
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Description: 使用改进的LBP算法CLBP实现图像纹理特征的提取，并使用卡方统计方法计算类间距离并实现图像分布。本人已经实验，对15类病毒图像进行分类，不调任何参数的情况下实现67 以上的准确率。-
Using an improved algorithm CLBP LBP texture feature extraction of image, and use the chi-square statistic calculated inter-class distance and achieve image distribution. I have experimented on 15 viroid image classification, and more than 67 accuracy rate without adjusting any parameters. Platform: |
Size: 29696 |
Author:雨哥 |
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Search - texture feature extraction of an image