噪声和纹理图象的自适应(Adaptive)分割

本文提出了一种自适应的噪声和纹理图象分割算法.观察图象被模拟为由区域过程、映射过程和噪声过程三个层次综合作用构成的.整个算法包括两个独立的步骤:第一步是层次图象模型的参数估计算法,可以处理高斯噪声和出格点(Outlier)的混合噪声情况,因此具有鲁棒性.第二步是基于模型参数的图象分割算法,其核心是一个改进的多值模拟退火技术.计算机模拟实验证明了算法的有效性和鲁棒性.     

一种改进的综合纹理和形状特征的图像检索方法

提出一种利用小波进行综合纹理和形状特征的具有旋转、平移和尺度不变性的图像检索算法.使用角向矩加权方向定义图像的主方向来进行坐标轴的旋转矫正,得到图像的旋转不变性表示;采用具有平移和尺度不变性的小波变换对图像进行小波分解,利用各子带的能量作为纹理特征;利用小波分解的逼近子图重构图像并进一步利用Hu不变矩提取其形状特征.最后对纹理和形状特征进行高斯归一化,综合其特征进行检索.实验中对算法的尺度不变性、旋转不变性、平移不变性及对噪声的不敏感性进行了验证,实验结果证明了该算法具有更高的鲁棒性和查准率.     

基于图元旋转不变性和相位统计信息的LBP算法在纹理分类中的研究

传统的LBP算法缺少对图元的相位分析,因此不能较好地区分由图元旋转形成的同类纹理图像.文中提出了一种融合图元旋转不变性和相位统计信息的纹理分析算法.新算法利用图元旋转不变性的等价类约简纹理特征,减小纹理旋转带来的分类误差,然后利用图元的统计相位特征进一步划分纹理图像分类集,进而解决由旋转不变性带来的欠分类问题.该算法选...     

基于数学形态学的肝脏B超图象的纹理识别

基于数学形态学的理论,分析了肝脏Ｂ超图象的纹理特征,提出了一种纹理特征抽取算法,它具有旋转不变性,通过分类实验与已有的基于数学形态学的特征进行了比较,实验结果表明提出的方法分类精度高,计算量小。     

基于数学形态学的肝脏B超图像的纹理识别

基于数学形态学的理论,分析了肝脏Ｂ超图象的纹理特征,提出了一种纹理特征抽取算法,它具有旋转不变性,通过分类实验与已有的基于数学形态学的特征进行了比较,实验结果表明提出的方法分类精度高,计算理小。     

一种新的结合纹理特征的SVM图象分割方法

本文提出了一种新的结合纹理特征的支持向量机图象分割方法,将纹理特征和灰度特征一起组成训练特征向量,利用支持向量机分类方法进行图象分割.该算法结合了纹理特征在图象描述中的重要意义和支持向量机方法在模式识别领域已表现出的优越性能,实验证明其在图象分割中取得了良好的效果.同时,当需要处理一批内容相似,感兴趣区域具有相同纹理、灰度特征的同类图象时,只需对其中一幅代表性的图象进行SVM训练,所产生的分类模型适用于所有该类图象,无需逐幅进行处理,大大简化了运算过程.     

基于小波模糊聚类的均质纹理和非均质纹理图象检索

基于内容的图象检索是近年来的研究热点 ,为此提出了一种自动区分均质纹理和非均质纹理图象 ,并对这两类图象分别进行检索的算法 .算法首先从图象离散小波变换的低频子带提取一定的颜色和纹理特征用于模糊聚类 ,将图象的低频子带分割为一定的区域 ;然后根据分割的结果将图象自动语义分类为均质纹理或者非均质纹理图象 ;最后对均质纹理和非均质纹理图象分别提取不同的特征矢量 ,并按照一定的相似度准则检索图象 .实验结果表明 ,该算法具有良好的均质纹理和非均质纹理图象分类和检索性能 .     

空间灰度相关图象纹理分割方法

本文给出了图象纹理分割的空间灰度相关法(SGLDM)中四个描述性强的纹理特征。定义了纹理特征矢量。在此定义基础上,给出了一种新的图象纹理分割方法。最后以四幅分割难度较大的纹理图象实验,说明利用四种纹理特征的方法可以有效地对纹理子图案非随机旋转的图象进行纹理分割。     

基于改进纹理粗糙度的图象检索研究

对原有纹理粗糙度算法在选取邻域尺寸和计算邻域均值差值这两方面进行了改进，并给出了改进纹理粗糙度算法，实验表明，改进纹理粗糙度具有更强的纹理分辨能力和更好的旋转不变性，基于改进纹理粗糙度的图象检索结果优于基于原有纹理粗糙度的图象检索结果。     

基于纹理谱的纹理分割方法

纹理分析是图象处理中的一个重要领域。本文提出一种基于纹理谱特征分割纹理图象的方法。它首次将纹理谱特征与区域生长算法结合起来，从而实现了无监督的纹理分割。纹理谱特征具有对方向性敏感等优点，基于纹理谱的纹理图象分割取得了良好效果。     

基于小波包特征的纹理影像分割

利用小波包变换来检测纹理图象上的灰度变化，以得到多分辨率、多方向性的微观统计特征图象，在此基础上用Ｅｎｖｅｌｏｐ算法提取基于边缘信息的纹理特征，为了克服纹理特征之间的相关性，采用了子空间分类器对纹理特征图象进行分类，取得了较好的分割效果。     

Automatic texture segmentation for content-based image retrieval application

In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed. The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm. The proposed technique is tested on a range of textured images including composite texture images, synthetic texture images, real scene images as well as our main source of images, the museum images of various kinds. An extension to the automatic texture segmentation, a texture identifier is also introduced for integration into a retrieval system, providing an excellent approach to content-based image retrieval using texture features.     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

Independent component analysis for texture segmentation

Independent component analysis (ICA) of textured images is presented as a computational technique for creating a new data dependent filter bank for use in texture segmentation. We show that the ICA filters are able to capture the inherent properties of textured images. The new filters are similar to Gabor filters, but seem to be richer in the sense that their frequency responses may be more complex. These properties enable us to use the ICA filter bank to create energy features for effective texture segmentation. Our experiments using multi-textured images show that the ICA filter bank yields similar or better segmentation results than the Gabor filter bank.     

A genetic algorithm for MRF-based segmentation of multi-spectral textured images

A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm; it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. In this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multi-spectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal that the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images.     

Image segmentation and similarity of color-texture objects

We aim for content-based image retrieval of textured objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, region-based texture segmentation is applied on the target images prior to the actual image retrieval process. The retrieval scheme is empirically verified on color images taken from textured objects under different lighting conditions.     

Texture segmentation by frequency-sensitive elliptical competitive learning

In this paper, a new learning algorithm is proposed with the purpose of texture segmentation. The algorithm is a competitive clustering scheme with two specific features: elliptical clustering is accomplished by incorporating the Mahalanobis distance measure into the learning rules and under-utilization of smaller clusters is avoided by incorporating a frequency-sensitive term. In the paper, an efficient learning rule that incorporates these features is elaborated. In the experimental section, several experiments demonstrate the usefulness of the proposed technique for the segmentation of textured images. On the compositions of textured images, Gabor filters were applied to generate texture features. The segmentation performance is compared to k-means clustering with and without the use of the Mahalanobis distance and to the ordinary competitive learning scheme. It is demonstrated that the proposed algorithm outperforms the others. A fuzzy version of the technique is introduced, and experimentally compared with fuzzy versions of the k-means and competitive clustering algorithms. The same conclusions as for the hard clustering case hold.     

Aeroplane detection in very-high-resolution images using deep feature representation and rotation-invariant Hough forests

This letter proposes a processing chain for detecting aeroplanes from very high-resolution (VHR) remotely sensed images with the fusion of deep feature representation and rotation-invariant Hough forests. First, superpixel segmentation is used to generate meaningful and non-redundant patches. Second, deep learning techniques are exploited to construct a multi-layer feature encoder for representing high-order features of patches. Third, a set of multi-scale rotation-invariant Hough forests are trained to detect aeroplanes of varying orientations and sizes. Experiments show that the proposed method is a promising solution for detecting aeroplanes from VHR remotely sensed images, with a completeness, correctness, and F-measure of 0.956, 0.970, and 0.963, respectively. Comparative studies with four existing methods also demonstrate that the proposed method outperforms the other existing methods in accurately detecting aeroplanes of varying appearances, orientations, and sizes.     

基于图像纹理和小波变换的聚类分割方法

针对纹理图像,本文提出了一种基于图像纹理特征的非学习分割方法。采用小波变换和快速k-means聚类分割算法,减少了整个处理过程的运算量。为了保证分类算法的精确性,运用了总体流量变化最小（Total Variation Flow）[1]非线性去噪方法对图像进行预处理,从而将减小图像噪声污染带来的分割误差。在图像特征的提取上,运用Gabor滤波器原理生成滤波空间,并让图像通过滤波空间而生成特征向量空间。通过制定一个快速寻优策略,从而达到分割图像的目的。