排序方式： 共有21条查询结果，搜索用时 0 毫秒
11.
针对纹理合成中基本的 MRF 类模型在大尺度结构表达上的缺陷,提出了在纹理模式空间和素材空间内对纹元及其分布的新的描述框架:使用纹元基本形和特征向量表示纹元,使用特征纹理表示纹元分布状况.在此结构上进行素材合成,可以保留 MRF 类模型在素材合成上的优势.实验结果表明,对于结构性较强的纹理,文中方法可以得到更加接近纹理机制的合成结果和某些纹理合成特效. 相似文献
12.
13.
基于独立纹元矩的纹理图像检索 总被引：1，自引：0，他引：1
基于统计方法和视觉感知机制,提出一种新型的纹理特征--独立纹元矩.首先通过独立成分分析方法从图像集中学习得到独立纹元滤波器,该滤波器表现出与图像集的良好相关性;然后利用独立纹元滤波器对纹理图像进行滤波分解,抽取滤波系数的一阶矩和二阶矩组成图像的纹理特征向量.在Brodatz纹理库中的实验结果表明,独立纹元矩特征的查准率和查全率比Gabor提高了6%,达到79.27%,而且在特征向量维度相同的条件下,独立纹元矩抽取的速度比Gabor快2个数量级. 相似文献
14.
This paper put forward a new method of co-occurrence matrix to describe image features. This method can express the spatial correlation of textons. During the course of feature extracting, we have quantized the original images into 256 colors and computed color gradient from the RGB vector space, and then calculated the statistical information of textons to describe image features. Image retrieval experimental results have shown that our proposed method has the discrimination power of color, texture and shape features, the performances are better than that of GLCM and CCG. 相似文献
15.
针对医学图像检索中相似性表达的自身困难,以及噪声影响的问题,提出一种通过张量积图进行扩散,利用其他数据点的上下信息改进基于纹理元的成对相似性度量的方法。首先,采用纹理元的统计方法进行医学图像特征描述和提取,并通过对纹理元相似性加权,得到图像的成对相似性;然后,利用张量积图沿着数据点的内在流形进行相似性的传播,实现全局的相似性度量。在ImageCLEFmed 2009上的实验结果表明,该算法与基于Gabor的检索算法相比,其类平均精度提高了32%,与基于尺度不变特征转换(SIFT)的检索算法相比,其类平均精度提高了19%,能良好地应用于医学图像检索。 相似文献
16.
Designing realistic noise patterns from scratch is hard. To solve this problem, recent contributions have proposed involved spectral analysis algorithms that enable procedural noise models to faithfully reproduce some class of textures. The aim of this paper is to propose the simplest and most efficient noise model that allows for the reproduction of any Gaussian texture. Texton noise is a simple sparse convolution noise that sums randomly scattered copies of a small bilinear texture called texton. We introduce an automatic algorithm to compute the texton associated with an input texture image that concentrates the input frequency content into the desired texton support. One of the main features of texton noise is that its evaluation only consists to sum 30 texture fetches on average. Consequently, texton noise generates Gaussian textures with an unprecedented evaluation speed for noise by example. A second main feature of texton noise is that it allows for high‐quality on‐the‐fly anisotropic filtering by simply invoking existing GPU hardware solutions for texture fetches. In addition, we demonstrate that texton noise can be applied on any surface using parameterization‐free surface noise and that it allows for noise mixing. 相似文献
17.
18.
Contour and Texture Analysis for Image Segmentation 总被引：29，自引：2，他引：29
Jitendra Malik Serge Belongie Thomas Leung Jianbo Shi 《International Journal of Computer Vision》2001,43(1):7-27
This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown. 相似文献
19.
A. Jayachandran R. Dhanasekaran 《International journal of imaging systems and technology》2014,24(1):72-82
Magnetic resonance image (MRI) segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumor detection techniques are presented in the literature. In this article, we have developed an approach to brain tumor detection and severity analysis is done using the various measures. The proposed approach comprises of preprocessing, segmentation, feature extraction, and classification. In preprocessing steps, we need to perform skull stripping and then, anisotropic filtering is applied to make image suitable for extracting features. In feature extraction, we have modified the multi‐texton histogram (MTH) technique to improve the feature extraction. In the classification stage, the hybrid kernel is designed and applied to training of support vector machine to perform automatic detection of tumor region in MRI images. For comparison analysis, our proposed approach is compared with the existing works using K‐cross fold validation method. From the results, we can conclude that the modified multi‐texton histogram with non‐linear kernels has shown the accuracy of 86% but the MTH with non‐linear kernels shows the accuracy of 83.8%. 相似文献
20.