一种改进的遥感图像变化检测算法

原有基于简单马尔可夫随机场(MRF)模型的变化检测算法基于全局一致性假设,这一假设往往与实际情况不符,影响到结果准确性。本文提出基于观察场与标号场互相关的改进MRF模型及相应的变化检测算法。以迭代条件模型解决后验概率最大化问题,为像素分类;根据当前分类,利用邻域中同类像素调整观察场中的像素特征值;以新的像素特征进一步优化分类。本文采用两段迭代算法,以多时相遥感图像的差值图像做为观察场。实验证明该算法能有效提高检测结果精度。     

A fully unsupervised color textured image segmentation algorithm using weighted mean histograms features

A new integrated feature distribution-based color textured image segmentation algorithm has been proposed. Two novel histogram-based inherent color texture feature extraction methods have been presented. From the histogram features, mean color texture histogram is calculated. Instead of concatenating the feature channels, a multichannel nonparametric Bayesean clustering is employed for primary segmentation. A region homogeneity-based merging algorithm is used for final segmentation. The proposed feature extraction techniques inherently combine color texture features rather then explicitly extracting it. Use of nonparametric Bayesean clustering makes the segmentation framework fully unsupervised where no a priori knowledge about the number of color texture regions is required. The feasibility and effectiveness of the proposed method have been demonstrated by various experiments using color textured and natural images. The experimental results reveal that superior segmentation results can be obtained through the proposed unsupervised segmentation framework.     

Integrated active contours for texture segmentation.

We address the issue of textured image segmentation in the context of the Gabor feature space of images. Gabor filters tuned to a set of orientations, scales and frequencies are applied to the images to create the Gabor feature space. A two-dimensional Riemannian manifold of local features is extracted via the Beltrami framework. The metric of this surface provides a good indicator of texture changes and is used, therefore, in a Beltrami-based diffusion mechanism and in a geodesic active contours algorithm for texture segmentation. The performance of the proposed algorithm is compared with that of the edgeless active contours algorithm applied for texture segmentation. Moreover, an integrated approach, extending the geodesic and edgeless active contours approaches to texture segmentation, is presented. We show that combining boundary and region information yields more robust and accurate texture segmentation results.     

Image Segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor

In this paper, we propose an interactive color natural image segmentation method. The method integrates color feature with multiscale nonlinear structure tensor texture (MSNST) feature and then uses GrabCut method to obtain the segmentations. The MSNST feature is used to describe the texture feature of an image and integrated into GrabCut framework to overcome the problem of the scale difference of textured images. In addition, we extend the Gaussian Mixture Model (GMM) to MSNST feature and GMM based on MSNST is constructed to describe the energy function so that the texture feature can be suitably integrated into GrabCut framework and fused with the color feature to achieve the more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the symmetric KL divergence is chosen to produce the estimates of the tensor statistics instead of the Riemannian structure of the space of tensor. The Conjugate norm was employed using Locality Preserving Projections (LPP) technique as the distance measure in the color space for more discriminating power. An adaptive fusing strategy is presented to effectively adjust the mixing factor so that the color and MSNST texture features are efficiently integrated to achieve more robust segmentation performance. Last, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. Experiments using synthesis texture images and real natural scene images demonstrate the superior performance of our proposed method.     

A Markov random field model-based approach to unsupervised texturesegmentation using local and global spatial statistics

Many studies have proven that statistical model-based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this correspondence, we present an unsupervised texture segmentation method that does not require knowledge about the different texture regions, their parameters, or the number of available texture classes. The proposed algorithm relies on the analysis of local and global second and higher order spatial statistics of the original images. The segmentation map is modeled using an augmented-state Markov random field, including an outlier class that enables dynamic creation of new regions during the optimization process. A Bayesian estimate of this map is computed using a deterministic relaxation algorithm. Results on real-world textured images are presented.     

Segmentation of Gabor-filtered textures using deterministicrelaxation

A supervised texture segmentation scheme is proposed in this article. The texture features are extracted by filtering the given image using a filter bank consisting of a number of Gabor filters with different frequencies, resolutions, and orientations. The segmentation model consists of feature formation, partition, and competition processes. In the feature formation process, the texture features from the Gabor filter bank are modeled as a Gaussian distribution. The image partition is represented as a noncausal Markov random field (MRF) by means of the partition process. The competition process constrains the overall system to have a single label for each pixel. Using these three random processes, the a posteriori probability of each pixel label is expressed as a Gibbs distribution. The corresponding Gibbs energy function is implemented as a set of constraints on each pixel by using a neural network model based on Hopfield network. A deterministic relaxation strategy is used to evolve the minimum energy state of the network, corresponding to a maximum a posteriori (MAP) probability. This results in an optimal segmentation of the textured image. The performance of the scheme is demonstrated on a variety of images including images from remote sensing.     

Combined morphological-spectral unsupervised image segmentation.

The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.     

Adaptive perceptual color-texture image segmentation.

We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low-resolution, degraded, and compressed images.     

Boundary localization in texture segmentation

Localizing boundaries between textured image regions without sacrificing the labeling accuracy of interior regions remains a problem in segmentation. Difficulties arise because of the conflicting requirements of localization and labeling. Boundary localization usually demands observing the features over small neighborhoods, whereas labeling accuracy increases with the size of the observation neighborhood. This problem is further exacerbated in texture segmentation by the spatially distributed nature of texture features. In this correspondence, we develop a multiresolution approach that combines localized and distributed features to directly address boundary localization in texture segmentation. Maximum localization is achieved by using the gray-level discontinuities at the boundary between textures to define the boundary. The properties that characterize the gray-level discontinuity at texture boundaries are developed and an algorithm is designed to localize the boundary using these discontinuities. This segmentation algorithm is implemented and successfully tested on a set of Brodatz texture mosaics and AVHRR satellite imagery.     

基于图像分解的稀疏正则化多区域图像分割方法

针对图像具有不同特征的成分,提出一种基于图像分解的多区域图像分割模型和算法.首先将图像分解项引入到图像分割模型中,递减了纹理和噪声对分割的影响;其次使用稀疏正则化方法保持分割区域的边缘几何结构;最后基于增广Lagrange乘子法,给出一种由扩散流引导的小波迭代阈值图像分割算法.一系列实验结果表明,提出的方法抗干扰能力强,对噪声具有更好的鲁棒性.提出的方法不仅能够分割结构图像,并且能够分割较复杂的纹理图像.     

融合纹理特征与螺旋状搜索聚类的超像素分割算法及应用

针对传统SLIC(simple linear iterative clustering)超像素分割算法没有综合考虑图像的纹理信息特征,导致对边缘信息较强和纹理复杂的图像进行超像素分割时,出现边缘检测不灵敏,分割效果不理想的问题.提出了把原图像先经过噪声抑制提取出纹理特征分量,构建以颜色特性、纹理特征和空间位置特征相融合的相似性度量方法.改进后的方法提高了边缘检测的灵敏度,增强了算法在对边缘信息较强和纹理复杂图像进行分割时的鲁棒性.另外,提出利用螺旋线状的搜索方式进行聚类,加速了算法的收敛速度,提高了分割效率.改进后的方法在BSDS500公共数据集上进行了实验,结果显示改进后的方法在边缘召回率、欠分割错误率、可完成的分割精度以及算法运行时间四项指标上优于传统算法.     

基于ADE的ReliefF特征选择在高光谱图像分类中的应用

针对高光谱图像谱段数目较多、近邻谱段相关性过高而导致分类困难的问题,提出了一种自适应差分进化特征选择的高光谱图像分类算法.首先初始化种群向量集,利用自适应差分进化算法搜索特征的自适应性生成特征子集;然后,通过使用ReliefF技术根据特征排序去除重复特征,从而为所有的特征构建一个特征列表;最后,借助于模糊k-近邻分类器计算每个向量的分类精度,利用包裹模型评估特征子集.在印第安纳数据集和KSC数据集上的实验结果验证了算法的有效性及可靠性,实验结果表明,相比其他几种特征选择算法,该算法取得了更高的总分类精度和更好的Kappa系数.     

Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method

This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostate tissues in different zones around prostate boundary. Each G-SVM consists of a Gabor filter bank for extraction of rotation-invariant texture features and a kernel support vector machine for robust differentiation of textures. In the deformable segmentation procedure, these pretrained G-SVMs are used to tentatively label voxels around the surface of deformable model as prostate or nonprostate tissues by a statistical texture matching. Subsequently, the surface of deformable model is driven to the boundary between the tentatively labeled prostate and non-prostate tissues. Since the step of tissue labeling and the step of label-based surface deformation are dependent on each other, these two steps are repeated until they converge. Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.     

Textured image segmentation by context enhanced clustering

An unsupervised textured image segmentation technique based on multidimensional feature vector clustering is described, where the features are the parameters of an autoregressive model, The benefits of incorporating spatial contextual information are demonstrated on both true cluster number estimation and actual image segmentation. A simple within-cluster distance is used for cluster validity analysis, where feature vectors are modified through local spatial dependency. This greatly reduces the dispersion in the raw feature data fed to the clustering process, and improves the true cluster number estimation. At the segmentation stage, three schemes incorporating contextual information at feature vector and label levels are proposed to enhance the segmentation accuracy. One is a development of a technique due to Mardia and Hainsworth (1988). The proposed approaches are tested on a four-class textured image     

Gabor feature based nonlocal means filter for textured image denoising

The nonlocal means (NLM) filter has distinct advantages over traditional image denoising techniques. However, in spite of its simplicity, the pixel value-based self-similarity measure used by the NLM filter is intrinsically less robust when applied to images with non-stationary contents. In this paper, we use Gabor-based texture features to measure the self-similarity, and thus propose the Gabor feature based NLM (GFNLM) filter for textured image denoising. This filter recovers noise-corrupted images by replacing each pixel value with the weighted sum of pixel values in its search window, where each weight is defined based on the Gabor-based texture similarity measure. The GFNLM filter has been compared to the classical NLM filter and four other state-of-the-art image denoising algorithms in textured images degraded by additive Gaussian noise. Our results show that the proposed GFNLM filter can denoise textured images more effectively and robustly while preserving the texture information.     

Combining static and dynamic features using neural networks and edge fusion for video object extraction

Semantic object representation is an important step for digital multimedia applications such as object-based coding, content-based access and manipulations. The authors propose an image sequence segmentation scheme which provides region information for the semantic object representation of those applications. The objective is to develop a hardware-friendly segmentation algorithm by combining static and dynamic features simultaneously in one scheme. In the initial stage, a multiple feature space is transformed to one-dimensional label space by using self-organising feature map (SOFM) neural networks. The next stage is an edge fusion process in which edge information is incorporated into the neural network outputs to generate more precisely located boundaries of segmentation. The proposed algorithm differs from existing methods as follows: it can segment textured images with low-dimensional features; leads to more meaningful segmentation region boundaries; and is easier to map into hardware than existing methods. Experimental results are compared with an existing segmentation method using evaluation metrics to clarify the advantages of the proposed algorithm objectively.     

Image segmentation using a texture gradient based watershed transform

The segmentation of images into meaningful and homogenous regions is a key method for image analysis within applications such as content based retrieval. The watershed transform is a well established tool for the segmentation of images. However, watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In order to segment such regions properly, the concept of the "texture gradient" is introduced. Texture information and its gradient are extracted using a novel nondecimated form of a complex wavelet transform. A novel marker location algorithm is subsequently used to locate significant homogeneous textured or non textured regions. A marker driven watershed transform is then used to segment the identified regions properly. The combined algorithm produces effective texture and intensity based segmentation for application to content based image retrieval.     

自适应的基于Gabor函数的指纹图像增强算法

针对实际应用时采集的指纹库中指纹图像纹线粗细和质量差别很大的情况,提出了一种根据纹线宽度自适应地决定Gabor滤波器变换窗大小的指纹图像增强算法.改进了方向图的算法,在小波变换之后的低频子图上进行方向估算.实验结果表明,基于低频于图计算方向图使得运算速度和结果准确性均有提高,尤其对于低质量的指纹图像因为小波变换滤除噪声的作用,方向图准确性提高更为明显;自适应决定变换窗大小使Gabor滤波器能更好地发挥连接断线和分离粘连的作用,处理过粗或过细指纹时更具优越性.