基于对偶树复小波和MRF模型的纹理图像分割

基于对偶树复小波（DT-CWT）和马尔可夫随机场（MRF）模型提出了一种监督纹理图像分割算法，算法包括两个步骤，首先对复小波变换系数进行较为精确的建模，提取其一阶统计信息作为纹理特征，综合多个尺度的信息，基于极大似然标准进行初始分割；其次，将初始分割结果用MRF模型表示，基于贝叶斯最大后验（MAP）融合初始分割结果，得到最终的分割结果。算法应用于合成纹理图像和实际图像得到了良好的结果，对比实验表明算法所采用的纹理特征的提取方法、小波变换方式、用MRF模型来建模标号等是算法简洁有效的基础。     

基于马尔科夫随机场的图像分割算法

该文整合纹理方面的信息,实现了基于马尔科夫场(MRF)的图像快速分割。通过图像纹理特征利用Gabor滤波器数据特征分量,通过马尔科夫算法将像素分类获得分割结果。     

基于非下采样Contourlet变换的视网膜分割

基于非下采样Contourlet变换(NSCT)良好的多尺度、多方向和平移不变性等优点,提出了一种基于NSCT的视网膜图像分割方法.该方法首先通过分析NSCT变换对血管的系数响应,提出基于NSCT的线状特征提取算法.随后对所提取的特征向量利用高斯混合模型(GMM)进行建模,并采用EM算法估计其参数.最后采用贝叶斯规则对...     

一种新的SAR图像无监督分割方法

马尔可夫随机场(MRF)在SAR图像分割中有着广泛的应用。由于合成孔径雷达(SAR)图像本身所固有的相干斑噪声的影响,传统方法很难获得准确的分割,因此提出了一种新的基于MRF(Markov Random Field)融合Gaussian-Hermite矩(GHM)的SAR图像无监督分割算法。利用Gaussian-Hermite矩的不同阶矩作为SAR图像特征得到初始分割;将得到的初始分割结果作为MRF随机场的先验模型,通过引入一个基于两成分权重参数的能量函数,利用最大后验概率(MAP)得到最终的分割结果。通过对合成图像及SAR图像分割实验结果的比较,表明了该方法在误分率、抗噪性以及视觉效果上具有更好的效果。     

基于MRF模型的NSCT域SAR图像分割

针对复杂背景下的合成孔径雷达（SAR）图像的分割问题,提出一种基于非降采样Contourlet变换（NSCT）域马尔可夫（MRF）模型的算法。该算法综合利用了MRF模型在影像分割中的优势和图像的多分辨率描述的信息,采用高斯混合模型建模各个尺度的特征场,Potts模型建模各个尺度的标记场,大尺度的分割结果直接投影到小尺度上,作为分割的初始结果。实验部分与经典的阈值分割算法和马尔可夫分割算法进行比较、分析,结果表明该算法可准确地分割目标,同时保留目标的细节信息。     

结合多特征和SVM的SAR图像分割

为实现灰度共生矩阵(GLCM)多尺度、多方向的纹理特征提取, 提出了一种结合非下采样轮廓变换(NSCT)和GLCM的纹理特征提取方法。先用NSCT对合成孔径雷达(SAR)图像进行多尺度、多方向分解; 再对得到的子带图像使用GLCM提取灰度共生量; 然后对提取的灰度共生量进行相关性分析, 去除冗余特征量, 并将其与灰度特征构成多特征矢量; 最后, 充分利用支持向量机(SVM)在小样本数据库和泛化能力方面的优势, 由SVM完成多特征矢量的划分, 实现SAR图像分割。实验结果表明, 基于NSCT域的GLCM纹理提取方法和多特征融合用于SAR图像分割, 可以提高分割准确率, 获得较好的边缘保持效果。     

分辨率LBP的掌纹特征提取

为了研究NSCT变换(nonsubsampled contourlet transform,NSCT)和局部二值模式(local binary pattern,LBP)在掌纹识别应用方面的可行性和性能,提出了一种基于NSCT变换与局部二值模式相结合的掌纹特征提取算法.该方法能够较好地提取皱褶、乳突纹等掌纹的细节特征,并且能够有效减少掌纹识别中由于图像的平移、旋转和光照对识别结果造成的影响.使用NSCT变换可以稀疏地表示二维奇异曲线并且变换本身具有平移不变性;而LBP算子是一种有效的纹理描述算子并且该算子具有很好的灰度和旋转不变性.首先对掌纹图像进行NSCT变换,然后对变换后的掌纹子图提取局部多分辨率和多尺度的LBP特征.实验结果表明,该算法能够更好地表达掌纹纹线的细节和结构特征,对掌纹图像有更高的鉴别性.     

基于引导滤波和Tsallis熵的SAR图像分割算法

针对合成孔径雷达(SAR)图像易受噪声干扰、分割方法精度低的问题,提出了一种基于频域引导滤波和Tsallis熵的SAR图像多阈值分割算法.利用非下采样Contourlet变换(NSCT)对图像多尺度分解,提取图像各方向的高频信息;通过引导滤波增强高频分量的边缘信息,在保持边缘的同时抑制了相干斑噪声;利用改进的二维Tsallis熵多阈值对增强图像精确分割.实验结果表明:分割算法对噪声不敏感,分割精度和适应性明显提高.     

一种基于多尺度马尔可夫随机场的SAR图像分割算法

利用小波变换模大值边缘检测算法得到SAR图像不同尺度下的边缘信息，再利用MRF分割算法对SAR图像进行分割。实验结果表明，该方法改善了SAR图像分割的质量，有效地改善了MRF图像分割算法的方向灵敏性。     

形态小波域多尺度马尔可夫模型在纹理图像分割中的应用

针对图像分割中小波域多尺度马尔可夫模型(MRMRF-W)无法有效描述图像非线性特征,提出了一种在形态小波域下的多尺度MRF模型（MRMRF-MW）,实现纹理图像分割。该模型结合了形态小波和MRF各自的优势,能够对图像进行非线性多尺度分解,并在各尺度上进行空间关系建模。通过对两个纹理图像库（Brodatz纹理库、Prague纹理库）中图像的分割实验,验证了该模型的有效性。     

High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the state-of-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking.

     

基于NSCT-GLCM的CT图像特征提取算法

针对海量CT图像分割中特征提取的难题,提出一种非下采样轮廓变换(NSCT)和灰度共生矩阵(GLCM)相融合的CT图像特征提取算法。首先采用NSCT对CT图像进行多尺度、多方向分解,并采用GLCM提取子带图像的共生特征量,然后对共生特征量进行主成分分析,消除冗余特征量,构成多特征矢量,最后利用支持向量机完成多特征矢量空间的划分,实现CT图像分割。实验结果表明,NSCT-GLCM能够较好地提取CT图像特征,提高了CT图像分割准确率,可以为医生诊断提供辅助信息。     

结合纹理去除的遥感图像分割

针对包含复杂纹理信息的遥感图像难以进行精准图像分割的问题,提出了一种结合纹理去除的遥感图像分割方法。首先,改进了相对全变差纹理去除方法,通过引入新的范数约束使相对全变差纹理去除方法可以在去除纹理信息的同时凸显图像中的主要结构,达到辅助分割的效果;然后,使用均值漂移算法对经过纹理去除的遥感图像进行无监督聚类,达到分割的目的;最后,提出的遥感图像分割算法在不同遥感图像上进行了测试。实验结果表明,在高分辨遥感图像的分割上,所提算法可以分割出遥感图像中的主要目标,和直接分割或者结合其他纹理去除方法相比取得了更好的分割结果。所提出的分割算法可以降低纹理信息对图像分割的影响,提高遥感图像分割的精度。     

Target recognition by texture segmentation algorithm

In order to improve the performance of image segmentation, this paper presented a gray level jump segmentation algorithm, which defined the direction of the texture, simultaneously, calculated the width of ridge line, gave the distance characteristics between textures, and established the mathematical model of the texture border, accordingly presented a new texture segmentation algorithm and compared with other texture segmentation algorithms. The simulation results show that the segmentation algorithm has some advantages to texture segmentation, such as has higher segmentation precision, faster segmentation speed, stronger anti-noise capability, less lost information of target, and so on. The segmented regions hardly contain other texture regions and background region. Moreover, this paper extracted the characteristic points and characteristic parameters in various segmented regions for texture image to obtain the characteristic vector, compared the characteristic vector with the standard template vectors, and identified the type of target in a range of threshold value. Experimental results show that the proposed target recognition approach has higher recognition rate and faster recognition speed than the existing target recognition approaches. Advancements in image processing through the study of texture segmentation are not only applicable to image fields, but also are of important theoretical value to target recognition. These researches in this paper will play an important role in a theoretical reference and practical significance to the development of all target recognition departments based on image system such as the aerospace, public security, road traffic, and so on.     

一种融合聚类与区域生长的彩色图像分割方法

论文提出了一种将聚类和区域生长有机融合的彩色图像分割方法。为了捕获图像的纹理特征,首先将图像划分成16×16子块,然后在块中按照视觉一致性准则进行颜色聚类,对于聚类后的子块,提取其颜色与纹理特征,然后采用符合人类视觉特征的生长规则,进行基于子块的区域生长。该方法充分利用了聚类算法和区域生长算法的各自优点,并符合人类视觉特征的分割策略。利用提出的算法对多幅自然图像进行了分割实验,实验结果证明了算法的有效性。     

Rapid,man-made object morphological segmentation for aerial images using a multi-scaled,geometric image analysis

This paper describes a man-made object segmentation method for aerial images based on a modified watershed segmentation algorithm. Our segmentation procedure includes three steps: (1) a multi-scaled geometric image analysis of aerial images by the non-subsampled contourlet transform (NSCT) method, (2) watershed segmentation, and (3) region classification of man-made objects. First, background of multi-scaled geometric image analysis is introduced briefly, and NSCT is used to represent the features for the purpose of man-made object segmentation. Thanks to the properties of NSCT, it not only avoids pseudo-Gibbs phenomena around singularities in image de-noising with regard to shift invariance, but it also enriches the set of basis functions, which makes it possible to extract orientational contour of man-made objects more effectively. In the NSCT decomposition step, the best basis selection is employed for ensuring maximum information content. Second, the “texture gradient” of combined features is calculated based on the first NSCT decomposition step and the resulting best basis selection, afterward the watershed transform is applied. According to their feature values, the aerial images are divided into several homogenous regions. Third, the fractional Brownian motion (fBm) model is used to determine the man-made object regions. Last, the experimental results show that the outcome of man-made object segmentation becomes more continuous and satisfying as a result of the homogenous texture-regions extraction and the modified watershed procedure.     

基于t混合模型和Greedy EM算法的彩色图像分割

为了快速更好地进行彩色图像分割,以图像的颜色、纹理及空间位置作为综合特征,基于t混合模型,提出了一种自适应的图像分割方法,即先采用贪婪的EM(Greedy EM)算法估计混合模型的参数,然后根据贝叶斯最小错误率准则对图像进行分割。由于t混合模型的稳健性和Greedy EM算法对于数据的初始化不敏感,且能收敛到全局最优,因此与其他的方法相比,不仅速度提高,而且能取得更好的分割结果。     

一种图像纹理分割的小波-像素曲线演化方法

提出了图像纹理分割的小波—曲线演化方法，该方法运用二维小波对纹理图像进行分解，由四个小波系数描述纹理特征，并组成四维小波系数特征矢量图，然后采用最大后验概率模型的曲线演化方法，对特征矢量图进行分割和分类，从而获得原图像纹理分割结果．实验结果表明，与小波—FCM聚类纹理分割方法相比，该方法对双纹理图像能取得较好的分割效果，分割出的边界连续，并且具有较强的抗噪能力．     

基于区域一致性和NSCT的SAR图像分割

提出反映合成孔径雷达（sAR）图像区域一致性的G值图,在此基础上运用区域生长方法对图像进行粗分割。在对粗分图像进行区域合并的过程中,对于含有丰富方向和纹理信息的SAR图像,使用非下采样Contourlet变换（NSCT）进行区域特征提取,并采用一种新方法进行区域合并。仿真结果证明了SAR图像分割算法的有效性。