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...     

Automatic image segmentation by integrating color-edge extractionand seeded region growing

We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.     

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.     

Content-based blur image retrieval using quaternion approach and frequency adder LBP

Multidimensional Systems and Signal Processing - The paper presents a content based image retrieval scheme based on feature extraction and weighing. Features are extracted using frequency adder...     

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。     

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 hierarchical approach to color image segmentation using homogeneity

In this paper, a novel hierarchical approach to color image segmentation is studied. We extend the general idea of a histogram to the homogeneity domain. In the first phase of the segmentation, uniform regions are identified via multilevel thresholding on a homogeneity histogram. While we process the homogeneity histogram, both local and global information is taken into consideration. This is particularly helpful in taking care of small objects and local variation of color images. An efficient peak-finding algorithm is employed to identify the most significant peaks of the histogram. In the second phase, we perform histogram analysis on the color feature hue for each uniform region obtained in the first phase. We successfully remove about 99.7% singularity off the original images by redefining the hue values for the unstable points according to the local information. After the hierarchical segmentation is performed, a region merging process is employed to avoid over-segmentation. CIE(L*a*b*) color space is used to measure the color difference. Experimental results have demonstrated the effectiveness and superiority of the proposed method after an extensive set of color images was tested.     

Iterative transductive learning for automatic image segmentation and matting with RGB-D data

In this paper, we propose a fully automatic image segmentation and matting approach with RGB-Depth (RGB-D) data based on iterative transductive learning. The algorithm consists of two key elements: robust hard segmentation for trimap generation, and iterative transductive learning based image matting. The hard segmentation step is formulated as a Maximum A Posterior (MAP) estimation problem, where we iteratively perform depth refinement and bi-layer classification to achieve optimal results. For image matting, we propose a transductive learning algorithm that iteratively adjusts the weights between the objective function and the constraints, overcoming common issues such as over-smoothness in existing methods. In addition, we present a new way to form the Laplacian matrix in transductive learning by ranking similarities of neighboring pixels, which is essential to efficient and accurate matting. Extensive experimental results are reported to demonstrate the state-of-the-art performance of our method both subjectively and quantitatively.     

Retrieval of symmetrical image blur using zero sheets

The analytically continued Fourier transform of a two-dimensional image vanishes to zero on a two-dimensional surface embedded in a four-dimensional space. This surface uniquely characteristics the image and is known as a `zero sheet'. Since the manipulation of a function in four-dimensional space is cumbersome, the projections of zero sheets, known as `zero tracks' are calculated. This knowledge of zero sheets can be extended to a number of practical applications including image processing. Image restoration can be realised without prior knowledge of the point spread function, i.e. blind deconvolution is possible even when only a single blurred image is given. If the blurred image concerned contains a point spread function with diagonal symmetry, the point zeros calculated row-wise and column-wise contain some similarities, which supports retrieval of both the true image and a point spread function. This novel scheme performs the separation effectively in the absence of contamination     

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。     

Blind deconvolution using a variational approach to parameter, image, and blur estimation.

Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods.     

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.     

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。     

Unsupervised and reliable image matting based on modified spectral matting

Spectral matting is the state-of-the-art image matting and also a milestone in theoretic matting research. For spectral matting without user intervention, the accuracy of alpha matte is low and the computational cost is high. Therefore, this paper presents a modified version of spectral matting to greatly increase the accuracy of alpha matte and effectively reduce the computational cost. In the proposed modified spectral matting, palette-based component classification is used to obtain reliable foreground and background components. Next, the corresponding matting components are obtained via a linear transformation of the smallest eigenvectors of the matting Laplacian matrix. Finally, the matting components of the foreground and the unknown regions are combined to from the complete alpha matte based on minimizing the matte cost. Moreover, image composition with consistency of color temperature is used to obtain the realistic image composition. Experimental results show that the proposed method outperforms the state-of-the-art methods based on spectral matting.     

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。     

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 tumor segmentation using knowledge-based techniques

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRIs) of the human brain is presented. The MRIs consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled “ground truth” tumor volumes and supervised K-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time     

Variational image segmentation using boundary functions

A general variational framework for image approximation and segmentation is introduced. By using a continuous "line-process" to represent edge boundaries, it is possible to formulate a variational theory of image segmentation and approximation in which the boundary function has a simple explicit form in terms of the approximation function. At the same time, this variational framework is general enough to include the most commonly used objective functions. Application is made to Mumford-Shah type functionals as well as those considered by Geman and others. Employing arbitrary L(p) norms to measure smoothness and approximation allows the user to alternate between a least squares approach and one based on total variation, depending on the needs of a particular image. Since the optimal boundary function that minimizes the associated objective functional for a given approximation function can be found explicitly, the objective functional can be expressed in a reduced form that depends only on the approximating function. From this a partial differential equation (PDE) descent method, aimed at minimizing the objective functional, is derived. The method is fast and produces excellent results as illustrated by a number of real and synthetic image problems.