Motion-based region growing segmentation of image sequences

This paper proposes a motion-based region growing segmentation scheme for the object-based video coding, which segments an image into homogeneous regions characterized by a coherent motion. It adopts a block matching algorithm to estimate motion vectors and uses morphological tools such as open-close by reconstruction and the region-growing version of the watershed algorithm for spatial segmentation to improve the temporal segmentation. In order to determine the reliable motion vectors, this paper also proposes a change detection algorithm and a multi-candidate pro- screening motion estimation method. Preliminary simulation results demonstrate that the proposed scheme is feasible. The main advantage of the scheme is its low computational load.     

基于区域生长的前视红外图像分割方法

红外成像制导技术以其优越的性能成为当今精确制导技术发展的主流。红外图像的精确分割是实现目标识别的基础。针对地面目标前视红外图像的特点和成像制导技术中图像分割的目的,提出了一种基于区域生长的前视红外图像分割算法,它首先在全局阈值分割的基础上选择出种子点所在区域,并在区域中定义局部灰度信息统计准则和策略选取出种子点;然后以目标模板面积作为参考,通过对分割效果的判断自动调整生长阈值进行区域生长以得到分割图像。实验结果表明,本方法分割出的目标完整准确,分割结果对基于边缘特征的目标匹配识别非常有利。     

Automatic blur region segmentation approach using image matting

For images with partial blur such as local defocus or local motion, deconvolution with just a single point spread function surely could not restore the images correctly. Thus, restoration relying on blur region segmentation is developed widely. In this paper, we propose an automatic approach for blur region extraction. Firstly, the image is divided into patches. Then, the patches are marked by three blur features: gradient histogram span, local mean square error map, and maximum saturation. The combination of three measures is employed as the initialization of iterative image matting algorithm. At last, we separate the blurred and non-blurred region through the binarization of alpha matting map. Experiments with a set of natural images prove the advantage of our algorithm.     

Interactive video object segmentation: fast seeded region merging approach

An interactive video object segmentation tool is presented. The user interaction combines marker drawing and region selection. A fast seeded region merging approach is proposed to extract the video object from the regions of watershed segmentation. Experimental results show the effectiveness and convenience of the approach.     

结合梯度重建和非线性处理的语义图像分割

语义图像分割是进一步图像分析与理解的重要基础,本文面向语义分割,针对传统分水岭分割算法对噪声敏感、易于产生过分割等问题,研究了一种结合梯度重建和非线性处理的图像分割方案.首先提出了基于形态学的开闭二次重建获得形态梯度图像的方法,然后对其进行阈值变换,以抑制噪声引起的明暗细节,之后进行给定尺度等级的非线性划分,以降低目标和背景内部细密纹理的影响,而后,采用了一种改进的分水岭浸没标记算法进行分割.实验结果表明,与传统算法和Matlab工具箱中算法相比,本文算法获得了更为理想的分割结果.     

三维医疗影像数据自动分割算法研究

对传统的边缘提取及区域分割方法进行了一定的改进,将其扩张到了三维体数据中,并针对hessian矩阵边缘效应特别强的特点,提取其最大特征值进行图像分割,取得了一定的效果。     

Refining image segmentation by integration of edge and region data

A basic requirement for understanding the dynamics of the Earth's major ecosystems is accurate quantitative information about the distribution and areal extent of the Earth's vegetation formations. Some of this required information can be obtained through the analysis of remotely sensed data. Image segmentation is often one of the first steps of this analysis. This paper focuses on two particular types of segmentation: region-based and edge-based segmentations. Each approach is affected differently by various factors, and both types of segmentations may be improved by taking advantage of their complementary nature. Included among region-based segmentation approaches are region growing methods, which produce hierarchical segmentations of images from finer to coarser resolution. In this hierarchy, an ideal segmentation (ideal for a given application) does not always correspond to one single iteration, but map correspond to several different iterations. This, among other factors, makes it somewhat difficult to choose a stopping criterion for region growing methods. To find the ideal segmentation, the authors develop a stopping criterion for their Iterative Parallel Region Growing (IPRG) algorithm using additional information from edge features, and the Hausdorff distance metric. They integrate information from regions and edges at the symbol level, taking advantage of the hierarchical structure of the region segmentation results. Also, to demonstrate the feasibility of this approach in processing the massive amount of data that will be generated by future Earth remote sensing missions, such as the Earth Observing System (EOS), all the different steps of this algorithm have been implemented on a massively parallel processor     

Automatic segmentation of hippocampal subfields based on multi-atlas image segmentation techniques

The volume of hippocampal subfields is closely related with early diagnosis of Alzheimer＇s disease. Due to the anatomical complexity of hippocampal subfields, automatic segmentation merely on the content of MR images is extremely difficult. We presented a method which combines multi-atlas image segmentation with extreme learning machine based bias detection and correction technique to achieve a fully automatic segmentation of hippocampal subfields. Symmetric diffeomorphic registration driven by symmetric mutual information energy was implemented in atlas registration, which allows multi-modal image registration and accelerates execution time. An exponential function based label fusion strategy was proposed for the normalized similarity measure case in segmentation combination, which yields better combination accuracy. The test results show that this method is effective, especially for the larger subfields with an overlap of more than 80%, which is competitive with the current methods and is of potential clinical significance.     

基才区域显著性的彩色图像分割

提出一种彩色图像分割算法主要利用区域间显著性。先是运用了Kmeans聚类的算法对彩色图像进行分割。然后分析彩色图像中的各个分割区域之间的对比度值以及分割的区域在图像中的位置关系,得到显著性值。根据需要设定显著性阚值．得到目标显著性区域并将其他非显著性区域合并。以此达到目标区域与背景的分离。     

用基于运动的区域生长法分割序列图像

针对基于对象的视频编码应用,提出了一种基于运动的区域生长分割方案,将图像分割成具有一致运动特征的区域。方案中采用块匹配法进行运动估计,利用重构式开－闭及区域生长型水岭算法等形态工具进行空间域分割,以改进时间域分割。此外,为了求取可靠的运动矢量,还提出了一种变化检测算法及预选多个候选运动矢量的运动估计方法。初步的模拟结果表明,该方案是可行的。其主要优点是算法简单、计算量少。     

彩色空间的视频对象自动分割

提出了一种基于RGB空间的视频对象自动分割方法。图像简化阶段，采用连接算子中的区域开启闭合算子来简化图像；图像标识提取阶段利用RGB空间的信息得到准确的视频对象轮廓，根据对比度消除区域中噪声小梯度，并在此基础上提取标识，在分水岭阶段，采用类似区域增长的改进分水岭方法，实验证明此方法的结果准确可靠。     

A semi-automatic seeded region growing algorithm for video object localization and tracking

This paper describes a semi-automatic method for moving object segmentation and tracking. This method is suitable when a few objects have to be tracked, while the camera moves and fixates on them. The user delineates approximately the initial locations in a selected frame and specifies the depth ordering of the objects to be tracked. First, motion-based segmentation is obtained through an initial application of a region growing algorithm. The partition map is sequentially tracked from frame to frame using motion compensation and location prediction. The segmentation map is obtained by the region growing algorithm. Translational motion is assumed for the moving objects, and local intensity or color average may be used as additional features. A post-processing procedure regularizes the object boundaries over time.     

基于区域分割的序列红外图像融合算法

针对传统的基于像素点和窗口策略的融合算法对图像特征表征的失真,提出了一种基于区域分割的序列图像融合算法.首先将序列红外图像分割为3个不同的特征区域,目标区域、背景区域以及灰度区域,并将分割结果映射到可见光图像中.随后,利用多尺度几何分析工具非下采样Contourlet变换(NSCT)有效提取图像特征的特点,根据不同区域的特性在NSCT域设计不同的融合规则.对试验结果进行主观和客观的对比,结果表明:基于区域分割的序列图像融合算法不仅能够为融合图像保留更全面、丰富的背景信息,还能够更加有效、准确地提取图像中的目标特征.该算法优于传统的基于像素点和窗口规则的融合算法,是一种有效可行的图像融合算法.     

用无需选取参数的Unit-linking PCNN进行自动图像分割

脉冲耦合神经网络（PCNN-Pulse Coupled Neural Network）是一种有生物学依据的人工神经网络，它可有效地用于图像分割。基于PCNN的图像分割效果取决于PCNN中各参数的选择。然而，图像分割时，各种不同的图像对应的PCNN参数是不同的，而PCNN参数的选择是困难的。本文提出了一种基于Unit-linking PCNN的图像分割新方法，解决了PCNN图像分割参数选择的难题。用本文提出的新方法可有效地自动分割各种图像，而无需考虑PCNN参数的选择，这对于PCNN的理论研究和实际应用有重要的意义。     

Image segmentation via image decomposition and fuzzy region competition

Taking into account the morphological diversity of images, this paper presents a novel multiphase image segmentation method that combines image decomposition and fuzzy region competition into a unified model. To efficiently solve the minimization of the energy functional, we design an optimal iteration algorithm which integrates a modified cartoon-texture dictionary learning algorithm and wavelet shrinkage. Compared with the classical fuzzy region competition method, the proposed method not only improves the overall segmentation results, but also has more strong robustness. A series of experimental results demonstrate the applicability and effectiveness of the proposed method.     

Hybrid image segmentation using watersheds and fast region merging

A hybrid multidimensional image segmentation algorithm is proposed, which combines edge and region-based techniques through the morphological algorithm of watersheds. An edge-preserving statistical noise reduction approach is used as a preprocessing stage in order to compute an accurate estimate of the image gradient. Then, an initial partitioning of the image into primitive regions is produced by applying the watershed transform on the image gradient magnitude. This initial segmentation is the input to a computationally efficient hierarchical (bottom-up) region merging process that produces the final segmentation. The latter process uses the region adjacency graph (RAG) representation of the image regions. At each step, the most similar pair of regions is determined (minimum cost RAG edge), the regions are merged and the RAG is updated. Traditionally, the above is implemented by storing all RAG edges in a priority queue. We propose a significantly faster algorithm, which additionally maintains the so-called nearest neighbor graph, due to which the priority queue size and processing time are drastically reduced. The final segmentation provides, due to the RAG, one-pixel wide, closed, and accurately localized contours/surfaces. Experimental results obtained with two-dimensional/three-dimensional (2-D/3-D) magnetic resonance images are presented.     

A hybrid defocused region segmentation approach using image matting

Multidimensional Systems and Signal Processing - In this paper, a hybrid defocused region segmentation using image matting is proposed. The technique incorporates three sharpness metrics which are...     

Hierarchical color image region segmentation for content-basedimage retrieval system

In this work, we propose a model of a content-based image retrieval system by using the new idea of combining a color segmentation with relationship trees and a corresponding tree-matching method. We retain the hierarchical relationship of the regions in an image during segmentation. Using the information of the relationships and features of the regions, we can represent the desired objects in images more accurately. In retrieval, we compare not only region features but also region relationships.     

Automatic segmentation of thalamus from brain MRI integrating fuzzy clustering and dynamic contours

Thalamus is an important neuro-anatomic structure in the brain. In this paper, an automated method is presented to segment thalamus from magnetic resonance images (MRI). The method is based on a discrete dynamic contour model that consists of vertices and edges connecting adjacent vertices. The model starts from an initial contour and deforms by external and internal forces. Internal forces are calculated from local geometry of the model and external forces are estimated from desired image features such as edges. However, thalamus has low contrast and discontinues edges on MRI, making external force estimation a challenge. The problem is solved using a new algorithm based on fuzzy C-means (FCM) unsupervised clustering, Prewitt edge-finding filter, and morphological operators. In addition, manual definition of the initial contour for the model makes the final segmentation operator-dependent. To eliminate this dependency, new methods are developed for generating the initial contour automatically. The proposed approaches are evaluated and validated by comparing automatic and radiologist's segmentation results and illustrating their agreement.