基于纹理相似度的颜色迁移

提出一种以纹理相似度为依据的颜色迁移算法。通过提取图像的多维纹理特征进行主成分分析和线性判别分析,构建纹理特征空间,以度量像素点邻域的纹理相似度,并以纹理相似度为依据,对图像进行分割,在分割后的局部区域,建立纹理相似度与色度信息的映射关系,实现颜色迁移。实验结果表明,基于纹理相似度的颜色迁移,可解决颜色在边界处的误扩散问题,颜色迁移效果较好。     

复杂海地背景下的舰船目标检测

提出一种新的基于纹理特征相似度的图像区域分割方法--迭代合并-扩张法.通过对各区域直方图的相似性比较,来确定不同区域纹理特征的相似性,把纹理特征相似性权值作为区域一致性度量,迭代合并相似区域完成图像的初始分割,再通过区域扩张来解决小区域纹理特征的不稳定性问题,完成图像中水域与陆地的精确分割.最后利用形态学对比度法在水域中进行目标检测.试验结果表明,此方法能很好地实现复杂海地背景下的舰船目标检测.     

基于纹理特征的钢丝绳图像分割方法

针对复杂背景下钢丝绳图像难以准确分割的问题,提出一种新的基于纹理特征的图像分割方法.首先,采用局部二进制模式(Local Binary Pattern,LBP)特征直方图的一阶熵、二阶熵作为LBP特征的统计测度,降低LBP特征的维数.同时选用边缘密度作为纹理描述的特征之一,弥补LBP算子提取纹理特征不足,抗干扰能力差的缺点.然后以上述纹理特征构成特征矢量,采用模糊C-均值(Fuzzy C-Mean,FCM聚类算法进行聚类分割.在实验中,对比了该算法与灰度共生矩阵、传统LBP算子在钢丝绳图像分割中的效果.结果表明,该算法可以有效地对钢丝绳图像进行纹理分割,并能取得良好的边界定位效果,性能优于另外两种算法.     

岩心颗粒彩色图像的多维特征KFCM聚类分割算法

岩心颗粒的彩色图像包含的信息具有复杂性和多样性,除了人眼视觉系统容易感知的颜色与空间形状特征之外,还隐含着更深层次的纹理特征信息。提出一种多维特征核模糊C均值(Kernel Fuzzy C-Means,KFCM)聚类分割算法:首先使用Gabor滤波器组在频域的不同尺度和方向上对岩心颗粒彩色图像进行卷积滤波处理,并将Gabor滤波结果作为频谱的局部纹理特征;然后将纹理特征、颜色特征以及图像像素点空间位置信息合并到核模糊C均值聚类算法中,从而实现岩心颗粒彩色图像的分割。结果表明:与其他算法的分割结果相比,多维特征KFCM聚类分割算法能更准确地识别不同类型的岩心颗粒的彩色图像,获得了良好的分割结果。     

矿岩爆破图像二值化分割技术的研究与选择

爆破块度图像分析法是当前爆破块度统计的重要方法,图像分割技术是该分析方法的关键与难点。针对爆堆及块石图像的特殊性,研究了基于二值化的矿岩爆破图像分割技术,分析了阈值分割、区域生长分割、标记分水岭分割等当前主要的图像分割方法。通过研究图像上同一截面灰度值切片的变换与矿块实际边缘的吻合程度,发现数学形态学分割方法得到的二值图像边界清晰、断点少,相对于门限法更适用于爆破块度图像,其生成的二值化图像层次清晰,便于像素特征的提取。图像分割先进技术的应用,对于实现矿岩爆破图像块度分析的智能化提供了广阔的空间。     

一种基于同质映射的视频图像中文本检测方法

提出了一种基于同质映射域的纹理特征的文本检测方法,并通过实验验证了该方法的性能.该方法与传统的文本检测方法的不同之处在于,首先将图像映射到同质性空间域中,在此空间域中计算纹理特征,然后通过支持向量机(SVM)分类器确定文本区域.与直接在图像空间域中提取纹理特征的方法相比,该方法对复杂背景下的文本检测更为有效,能有效地解决场景纹理特征与文本区域相近似造成的文本检测错误.     

基于分形特征的图像边缘检测方法

运用分形理论描述图像纹理特征,通过分析不同纹理图像及图像边缘处的分形参数,得到一种新的边缘检测分形特征,从而提出一种基于分形特征的图像边缘检测方法。自适应阈值的引入,能够实现不同图像的边缘检测。该算法简单迅速,并具有良好的抗噪性能。     

基于K均值聚类和HSV颜色模型的烟雾图像分割

烟雾作为火灾发生前的最大特征之一,所以对火灾的检测可依靠于烟雾的检测,而煅雾图像分割是烟雾图像检测分析中最为困难且不可缺少的步骤,为有效排除非烟雾图像的干扰,降低周围环境对真实烟雾的误检,提出一种基于K均值聚类和HSV颜色模型的烟雾图像分割,该方法首先从RGB空间转换到HSV色彩空间,采用K均值聚类的烟雾图像分割,之后把分割的图像进行shen滤波去噪和区域标记去噪。实验结果表明,对烟雾图像的分割,该建议方法能够将烟雾从背景中分割出来。     

融合双特征的玻璃缺陷图像分割算法

目的 针对玻璃的材料透明性以及条带噪声等固有属性使得传统玻璃缺陷分割算法准确率较低等问题,提出一种基于双特征高斯混合模型的玻璃缺陷分割方法.方法 首先,利用分数阶运算对玻璃缺陷增强,用灰度共生矩阵获取纹理特征,从而构建玻璃缺陷的双特征向量;将双特征向量引入高斯混合模型,并利用马尔科夫随机场的相邻像素空间信息对玻璃缺陷分割高斯混合模型进行改进,通过交替进行玻璃缺陷像素点与标号场之间映射关系的估计和基于高斯核函数空间约束更新,完成玻璃缺陷分割;最后,应用模糊熵对缺陷图像分割结果进行后续处理.结果 对疖瘤、污点、气泡以及夹杂等4种典型缺陷样本图像进行性能测试和不同算法对比分析实验,实验结果表明,所提算法的Dice指标达到98.59％,crM指标达到7.03％,衡量指标优于其他算法.结论 将灰度特征和纹理特征引入玻璃缺陷分割的马尔科夫随机场,能够抑制非缺陷目标,并保留低对比度玻璃缺陷,提高玻璃缺陷分割算法的鲁棒性和准确性.     

基于频谱滤波的多纹理提取算法

研究了一种在有多种纹理叠加的复杂图像中进行单一纹理提取的算法.首先采用极坐标方法分析纹理频谱的环型和楔型特征并求出纹理分布的周期和方向特征;然后根据这些特征在频域中构建环型和楔型Gauss带通滤波器对纹理频谱进行滤波;再将滤波后的频谱图像转换到时域中就得到了只保留相应纹理成分的图像;最后经过纹理区域矫正处理后就可以提取出真实的纹理.实验结果表明,该方法可以准确提取出图像中叠加的多种纹理,并能完整保留每种纹理的基本特征,在纹理分布不均匀的区域也能提取出纹理的骨架.     

Double regularization control based on level set evolution for tablet packaging image segmentation

This paper proposes a novel double regularization control(DRC) method which is used for tablet packaging image segmentation.Since the intensities of tablet packaging images are inhomogenous,it is difficult to make image segmentation.Compared to methods based on level set,the proposed DRC method has some advantages for tablet packaging image segmentation.The local regional control term and the rectangle initialization contour are first employed in this method to quickly segment uneven grayscale images and accelerate the curve evolution rate.Gaussian filter operator and the convolution calculation are then adopted to remove the effects of texture noises in image segmentation.The developed penalty energy function,as regularization term,increases the constrained conditions based on the gradient flow conditions.Since the potential function is embedded into the level set of evolution equations and the image contour evolutions are bilaterally extended,the proposed method further improves the accuracy of image contours.Experimental studies show that the DRC method greatly improves the computational efficiency and numerical accuracy,and achieves better results for image contour segmentation compared to other level set methods.     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Object detetion and image segmentation using texture pressure energy in parametric active contour models

Abstract

In recent years, Active Contour Models (ACMs) have become powerful tools for object detection and image segmentation in computer vision and image processing applications. This paper presents a new energy function in parametric active contour models for object detection and image segmentation. In the proposed method, a new pressure energy called “texture pressure energy” is added to the energy function of the parametric active contour model to detect and segment a textured object against a textured background. In this scheme, the texture features of the contour are calculated by a moment based method. Then by comparing these features with texture features of the object, the contour curve is expanded or contracted in order to be adapted to the object boundaries. Experimental results show that the proposed method has more efficient and accurate segmenting functionality than the traditional method when both object and background have texture properties.     

Performance analysis of computer aided brain tumor detection system using ANFIS classifier

The abrupt changes in brain cells due to the environmental effects or genetic disorders leads to form the abnormal lesions in brain. These abnormal lesions are combined as mass and known as tumor. The detection of these tumor cells in brain image is a complex task due to the similarities between normal cells and tumor cells. In this paper, an automated brain tumor detection and segmentation methodology is proposed. The proposed method consists of feature extraction, classification and segmentation. In this paper, Grey Level Co‐Occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Law's texture features are used as features. These features are fed to Adaptive Neuro Fuzzy Inference System (ANFIS) classifier as input pattern, which classifies the brain image. Morphological operations are now applied on the classified abnormal brain image to segment the tumor regions. The proposed system achieves 95.07% of sensitivity, 99.84% of specificity and 99.80% of accuracy for tumor segmentation.     

改进的模糊阈值图像分割方法

提出了一种自适应的模糊阈值图像分割方法,通过预分割和直方图信息相结合的方法,解决了传统的模糊闽值图像分割法难以自动获取窗宽的困难;并针对模糊闽值图像分割方法不能适用于直方图呈单峰分布的图像的缺陷,提出了一个新的平滑迭代公式。该平滑迭代公式利用像素点的邻域信息使图像增强,再使用自适应的模糊阈值图像分割方法进行分割,可以拓宽模糊阈值图像分割方法的适用范围。实验结果表明,使用该方法的目标分割正确率达97.3％,显示了较高的分割精度和较强的鲁棒性。     

Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features

The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier based Glioma brain tumor detection and segmentation methodology using feature optimization technique. The texture features are derived from brain MRI image and these derived feature set are now optimized by ant colony optimization algorithm. These optimized set of features are trained and classified using random forest classification method. This classifier classifies the brain MRI image into Glioma or non-Glioma image based on the optimized set of features. Furthermore, energy-based segmentation method is applied on the classified Glioma image for segmenting the tumor regions. The proposed methodology for Glioma brain tumor stated in this paper achieves 97.7% of sensitivity, 96.5% of specificity, and 98.01% of accuracy.     

旋转机械故障诊断的图形识别方法研究

以旋转机械振动三维参数图形为研究对象,提出了基于图形识别技术的旋转机械故障诊断方法。该方法用表征纹理的统计法、结构法及图形纹理方向的梯度法形成描述图形纹理特征的灰度-梯度-基元三维共生矩阵。该矩阵精确地反映了图形纹理的粗糙程度、重复方向和空间复杂度及纹理方向,准确地描述了图形灰度空间分布特性(概率)、空间统计相关性和图形内各像素点梯度的分布规律。描述了灰度统计和空间结构的纹理特征,有效地提取旋转机械状态参数图形中纹理特征信息。最后,利用RBF人工神经网络实现旋转机械故障诊断。在汽轮机转子试验台上进行了6种状态试验研究,诊断结果表明该方法具有较高的诊断准确率,为旋转机械故障诊断探索了一条新路。     

Brain MRI denoizing and segmentation based on improved adaptive nonlocal means

Denoizing of magnetic resonance (MR) brain images has been focus of numerous studies in the past. The performance of subsequent stages of image processing, in automated image analysis, is substantially improved by explicit consideration of noise. Nonlocal means (NLM) is a popular denoizing method which exploits usual redundancy present in an image to restore noise free image. It computes restored value of a pixel as weighted average of candidate pixels in a search window. In this article, we propose an improved version of the NLM algorithm which is modified in two ways. First, a robust threshold criterion is introduced, which helps selecting suitable pixels for participation in the restoration process. Second, the search window size is made adaptive using a window adaptation test based on the proposed threshold criterion. The modified NLM algorithm is named as improved adaptive nonlocal means (IANLM). An alternate implementation of IANLM is also proposed which exploits the image smoothness property to yield better denoizing performance. The computational burden is reduced significantly due to proposed modifications. Experiments are performed on simulated and real brain MR images at various noise levels. Results indicate that the proposed algorithm produces not only better denoizing results (quantitatively and qualitatively), but is also computationally more efficient. Moreover, the proposed technique is incorporated in an already proposed segmentation framework to check its validity in the practical scenario of segmentation. Improved segmentation results (quantitative and qualitative) verify the practical usefulness of the proposed algorithm in real world medical applications. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 235–248, 2013