基于像素纹理分类的图像预测编码方法

JPEG-LS标准实现对静止图像的无损压缩以及近无损高保真压缩,其预测编码仅采用简单的中值边缘检测法。将充分利用邻域像素纹理的连续性与相关性,研究参考像素的选取、基于纹理信息的非线性分类预测器的构建与预测器参数的设计,增强梯度检测能力,提出新的四阶分类预测器。实验证明,该算法在低运算复杂度的前提下,有效提高了预测编码的性能。     

Color image segmentation using pixel wise support vector machine classification

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.     

Color image segmentation using automatic pixel classification with support vector machine

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.     

基于测地线的超像素谱聚类彩色图像分割

在图像分割中谱聚类算法得到了广泛的应用,但传统谱聚类算法易受到彩色图像大小和相似性测度的影响,导致计算量大和分割精度低的问题。为了解决这两个问题,提出一种新的基于超像素集测地线特征的谱聚类分割算法。该方法通过对彩色图像进行预分割得到超像素集,并以超像素集为基础构造加权图,利用测地线距离特征和颜色特征构造权值矩阵,最后应用NJW（Ng-Jordan-Weiss）算法得到最终的分割结果。对比实验结果表明该算法在分割精度和计算复杂度上都有较大改善。     

Adaptable image segmentation via simple pixel classification

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.     

Automatic texture feature selection for image pixel classification

Pixel-based texture classifiers and segmenters are typically based on the combination of texture feature extraction methods that belong to a single family (e.g., Gabor filters). However, combining texture methods from different families has proven to produce better classification results both quantitatively and qualitatively. Given a set of multiple texture feature extraction methods from different families, this paper presents a new texture feature selection scheme that automatically determines a reduced subset of methods whose integration produces classification results comparable to those obtained when all the available methods are integrated, but with a significantly lower computational cost. Experiments with both Brodatz and real outdoor images show that the proposed selection scheme is more advantageous than well-known general purpose feature selection algorithms applied to the same problem.     

基于像素聚类的超声图像分割

B型心脏超声图像分割是计算心功能参数前重要的一步。针对超声图像的低分辨率影响分割精度及基于模型的分割算法需要大样本训练集的问题,结合B型心脏超声图像的先验知识,提出了一种基于像素聚类进行图像分割的算法。首先,通过各向异性扩散处理图像;然后,使用一维K-均值对像素进行聚类;最后,根据聚类结果和先验知识将像素值修改为最佳类中心像素值。理论分析表明该算法可以使图像的峰值信噪比（PSNR）达到最大值。实验结果表明：所提算法比大津算法等更准确,PSNR较大津算法提高11.5%;即使在单张图像上也可以进行分割,且适应于分割任意形状的超声图像,有利于更准确地计算各种心功能参数。     

基于纹理检测的骨骼影像分割

针对人体x光图像中的骨骼影像在分割过程中局部出现过度分割或欠分割的现象,提出了一种基于统计区域纹理的检测方法。对检测出不符合分割要求的局部影像,在再次分割前,分别采用补偿灰度或收缩分割区域的方式调整。实验结果表明,该算法能检测出分割后图像的局部性质,并能有效改善过度分割和欠分割的现象,分割后的影像可为下一步的模式识别提供一定的基础。     

基于多FART神经网络的彩色图像分割

提出了一种适用于彩色图像分割技术的多模糊自适应谐振(FART)神经网络结构.网络的输入为RGB色彩空间的彩色图像,并将其转换为HSV色彩空间的三组彩色分量-色调,亮度和饱和度,而后利用多FART神经网络的分类能力,将三组分量进行分类的图像输入到决策层,经过融合和分割处理后,最终得到正确的彩色分割图像.与彩色分水岭算法相比,采用上述图像分割算法得到了较好的分割效果.     

Image segmentation by pixel classification

A fast boundary finding algorithm is presented which works without threshold operation and without any interactive control. The procedure can be described as a hierarchical two-step algorithm. In the first step the image is divided into two disjunct regions, one of them including the whole object of interest.In the second step the problem of boundary finding is suggested as a classification problem, which means that for any pixel a four-dimensional feature vector is computed which allows classification of pixels into contour elements and any other pixels.The algorithm was tested on several thousand cell images and can be easily adapted to other problems by modification of a set of parameters.     

基于Gabor 变换的GrabCut 纹理图像分割

多数自然图像都包含纹理信息, 它相对颜色特征而言具有描述方向性与尺度差异的特性. 因此, 可以利用半交互式的GrabCut 的图像分割方式对图像前景区域与背景区域进行有效的分割, 通过建立前景和背景所对应的高斯混合模型(GMM), 结合最大流最小割的图像分割方式实现全局优化, 并利用前景和背景的KL 测度, 自适应地终止分割过程. 实验对比分析表明, 所提出的方法对于合成纹理图像与自然纹理图像具有较好的整体分割效果及较高的分割准确率.

     

改进分水岭算法的彩色图像分割技术

针对传统的分水岭变换可能产生严重的过渡分割,提出的分水岭变换算法在梯度图中去除了无相关点,用梯度临界值控制分割函数式。实验结果显示该算法对具有不同特征的图像均可有效地改进分割的精确性。     

基于视觉注意机制的彩色图像分割

提出一种基于视觉注意机制的彩色图像分割方法。受生物学启发,该方法模仿人类自下而上的视觉选择性注意过程,提取图像的底层特征,构造相应的显著图。根据显著图,检测出图像中的显著区域;将显著区域和背景分离,即得到图像分割结果。在多幅自然图像上进行实验,结果表明,该方法能够取得与人类视觉系统一致的分割结果。     

基于IC芯片的彩色图像分割方法研究与实现

以IC芯片彩色图像为研究对象,分析了迭代阈值法,松弛迭代算法,颜色空间聚类算法在此类图像分割中的不足,并改进迭代阈值法,对原始图像进行颜色空间转换,由RGB空间转化到CIE Lab空间;同时利用八叉树算法对图像进行8位量化,对得到的灰度图像进行迭代阈值分割得到最佳阈值,从而提出了专门针对彩色图像背景分割的彩色迭代阙值法.最后基于Visual Studio6.0平台实现上述4种方法,并通过对比实验证明本文所采用的方法的可行性和实用性.     

基于均值偏移的彩色图像分割算法

提出了一种基于均值偏移的彩色图像分割算法。首先阐述了在CIE LUV均匀彩色模型下均值偏移算法的基本原理,然后给出了在图像分割中的具体实现方法：选定一个像素,在适当的空间窗和色彩窗限定的特征空间中寻找模式点,实现窗口中心从选定点到模式点的偏移,重复此过程,直到找到稳定的模式点并用模式点的色彩值代替该像素,遍历所有像素,最终达到对所有像素进行聚类。通过两幅图像对算法进行检验,实验结果证明该算法对彩色图像具有良好的分割效果。     

融合颜色和区域信息的彩色图像分割新方法

结合了均值漂移算法和区域合并算法,取长补短,提出了一种融合颜色和区域信息的彩色图像分割方法。该算法首先利用均值漂移求取各个局部极值(聚类中心),在带宽求取和权重设置上使用了自适应法则,使算法更具有适用性;然后使用一个基于阀值的区域合并算法,解决了均值漂移对纹理和关照变化的过分割。实验证明,该算法是有效的。     

基于纹理分析的指纹图像分割算法

低质量指纹图像处理是近年来自动指纹识别技术的研究重点,对低质量指纹图像的分割是实现后续处理的前提。文中在分析了方差作为分割指标的局限性基础上,从指纹图像的纹理特征出发,研究了指纹图像的灰度分布规律,提出了基于纹理的指纹图像分割算法。实验结果表明,相比于基于灰度方差的指纹图像分割算法,文中算法的分割效果更好,对噪声的抵抗能力更强。     

复杂纹理图像分割方法研究与实现

复杂纹理分割是图像分割领域的难点之一,给出了基于模糊粒度计算与模糊聚类相结合的分割算法,实验表明该算法对复杂纹理图像分割十分有效。     

A new algorithm for remotely sensed image texture classification and segmentation

In this paper, we propose a new algorithm for remotely sensed image texture classification and segmentation. We observe that the traditional method least square error (LSE) is unstable in practical applications. This motivates us to develop a more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation (TV) for reducing instability in texture analysis, and apply it to remotely sensed image texture classification and segmentation. Experimental results on remotely sensed images demonstrate that our new algorithm is superior to LSE and seems promising in applications.     

基于改进区域生长算法的彩色图像分割

本文提出一种改进的区域生长算法.该算法利用颜色分类结果和连续图像的相似性,改进了种子搜索方法,与全局搜索种子的方法相比减少了种子搜索的时间,并且实现简单有效.实验结果表明改进的区域增长算法应用于RoboCup中型组足球机器人的全景彩色图像分割具有良好的时效性.