基于Wasserstein距离的局部能量分割模型

 提出了一种基于Wasserstein距离和图像局部区域直方图信息的非参数活动轮廓分割模型.用该距离对图像中不同区域的直方图进行比较,提高了相似性衡量的准确性;引入高斯内核函数来获取图像局部区域直方图信息,并将信息嵌入模型指导轮廓演化,以克服由于亮度不均造成的图像分割困难;通过水平集规范项提高计算精度并避免水平集演化的重新初始化.实验结果表明,本模型能够对亮度不均的无序特征图像进行有效准确的分割.     

Zernike moment and local distribution fitting fuzzy energy-based active contours for image segmentation

This paper presents a new region-based active contour model for extracting the object boundaries in an image, based on techniques of curve evolution. The proposed model introduces an energy functional that involves intensity distributions in local image regions and fuzzy membership functions. The local image intensity distribution information used to guide the motion of the contour, in the paper, is derived by Hueckel operator in the neighborhood of each image point. The parameters of Hueckel operator are estimated by a set of orthogonal Zernike moments before curve evolution. Meanwhile, the fuzzy membership functions are used to measure the association degree of each image pixel to the region outside and inside the contour. To minimize the energy functional, instead of solving the Euler–Lagrange equation of the underlying problem, the paper employs a direct method to compute the energy alterations. As a result, the model can deal with images with intensity inhomogeneity. In addition, the model effectively alleviates the sensitivity to contour initialization. Moreover, the model reduces computational cost, avoids problems associated with choosing time steps as well as allows fast convergence to the segmentation solutions. Experimental results on synthetic, real images and comparisons with other models show the desired performances of the proposed model.     

结合Gabor纹理特征的局域化多通道水平集分割方法

本文提出了一种局域化多通道主动轮廓模型的图像分割算法.针对纹理特征比较明显的图像,通过Gabor滤波提取纹理特征,与图像灰度信息构成多通道.考虑到演化过程中曲线内部和外部特征属性不均匀,引入局域化思想,通过计算各像素在局部区域的最小能量得到图像分割结果.最后算法结合先验形状对有遮挡目标进行分割,并能得到理想结果.大量实...     

Active contours driven by local Gaussian distribution fitting energy

This paper presents a new region-based active contour model in a variational level set formulation for image segmentation. In our model, the local image intensities are described by Gaussian distributions with different means and variances. We define a local Gaussian distribution fitting energy with a level set function and local means and variances as variables. The energy minimization is achieved by an interleaved level set evolution and estimation of local intensity means and variances in an iterative process. The means and variances of local intensities are considered as spatially varying functions to handle intensity inhomogeneities and noise of spatially varying strength (e.g. multiplicative noise). In addition, our model is able to distinguish regions with similar intensity means but different variances. This is demonstrated by applying our method on noisy and texture images in which the texture patterns of different regions can be distinguished from the local intensity variance. Comparative experiments show the advantages of the proposed method.     

Combining learning-based intensity distributions with nonparametric shape priors for image segmentation

Integration of shape prior information into level set formulations has led to great improvements in image segmentation in the presence of missing information, occlusion, and noise. However, most shape-based segmentation techniques incorporate image intensity through simplistic data terms. A common underlying assumption of such data terms is that the foreground and the background regions in the image are homogeneous, i.e., intensities are piecewise constant or piecewise smooth. This situation makes integration of shape priors inefficient in the presence of intensity inhomogeneities. In this paper, we propose a new approach for combining information from shape priors with that from image intensities. More specifically, our approach uses shape priors learned by nonparametric density estimation and incorporates image intensity distributions learned in a supervised manner. Such a combination has not been used in previous work. Sample image patches are used to learn the intensity distributions, and segmented training shapes are used to learn the shape priors. We present an active contour algorithm that takes these learned densities into account for image segmentation. Our experiments on synthetic and real images demonstrate the robustness of the proposed approach to complicated intensity distributions, and occlusions, as well as the improvements it provides over existing methods.     

利用局部离散度活动轮廓模型的强度非均匀图像分割

强度非均匀现象在真实图像中普遍存在,采用常规基于强度的分割算法会导致严重的误分割。针对强度非均匀图像分割,提出了基于局部离散度的活动轮廓模型分割算法。首先定义基于类内类间距离的离散度,然后利用核函数提取局部区域信息,同时加入边缘指示函数加权的轮廓线长度项能量,建立基于局部离散度的活动轮廓模型。最后引入水平集函数惩罚项,避免水平集方法在演化求解时需要不断初始化的问题。合成图像和真实图像实验结果证明本文算法性能稳定,适应于强度非均匀图像的分割。      

Integrating local distribution information with level set for boundary extraction

This paper presents a general object boundary extraction model for piecewise smooth images, which incorporates local intensity distribution information into an edge-based implicit active contour. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different region-based operators: a Gaussian mixture model (GMM)-based intensity distribution estimator and the Hueckel operator. We propose the local distribution fitting model for more accurate segmentation, which incorporates the operator outcomes into the recent local binary fitting (LBF) model. The GMM and the Hueckel model parameters are estimated before contour evolution, which enables the use of the proposed model without the need for initial contour selection, i.e., the level set function is initialized with a random constant instead of a distance map. Thus our model essentially alleviates the initialization sensitivity problem of most active contours. Experiments on synthetic and real images show the improved performance of our approach over the LBF model.     

基于流形特征与形状先验的红外直升机图像分割

为有效分割复杂天空背景下的直升机目标,提出了基于流形特征与形状先验的变分分割模型.根据图像数据的灰度分布构造区域能量项,推动变形曲线向目标边界演化;引入对称正定（Symmetric Positive Definite,SPD）矩阵流形上的区域协方差描述子构造流形特征能量项以提高分割算法的鲁棒性.在区域项、边界项和流形特征项的共同作用下获取红外直升机目标的第一阶段分割结果.在第二阶段分割过程中,基于主成分分析（Principal Component Analysis,PCA）方法获取直升机目标的先验形状变化模式,以变形曲线在PCA空间重构的形状作为先验知识约束曲线的演化,最终实现红外直升机图像的分割.实验结果表明,本文方法能够有效获取直升机目标的完整轮廓.     

Minimization of region-scalable fitting energy for image segmentation

Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.     

High speed VLSI architecture for improved region based active contour segmentation technique

Active contour segmentation is an important stage in image analysis applications. In this article, an improved region based active contour segmentation is proposed. The proposed active contour model speeds up the contour convergence by up to 40% while maintaining the advantages of a local region based active contour model by reducing the number of iterations. Moreover, we propose a low-complexity pipelined VLSI architecture for improved region based active contour model targeting FPGA and 90 nm ASIC platforms. The proposed pipelined design offers an increased speed of operation. Its complexity is independent of the size of image.     

基于SVM能量模型的改进主动轮廓图像分割算法研究

为克服经典主动轮廓模型曲线内外区域能量定义在复杂目标与背景分布情况下的不足,本文将高效的支持向量机有监督学习分类器引入基于Mumford-shah模型的主动轮廓图像分割算法中,提出了基于SVM能量模型的改进主动轮廓图像分割方法.该方法首先利用支持向量机的分类结果对于封闭曲线的内外区域分别构造了一种新的图像能量表示方法,因为分割过程充分利用了有监督学习策略,使得本文提出的算法具有更高的稳定性和更加广泛的适用范围,特别是对目标灰度分布不均或存在多纹理的目标也可以得到较好的分割结果.分割时,首先利用SVM实现粗分割得到目标初始轮廓,然后利用改进的Mumford-shah主动轮廓模型进行精确分割,采用粗分割策略一方面可以大大提高分割速度,另一方面也可以提高了算法的自动化程度.对比实验结果表明本文提出的算法具有更大灵活性和更好的分割性能.     

基于局部图划分的多相活动轮廓图像分割模型

几何活动轮廓模型是图像分割领域的强有力工具。最近,一种基于成对相似性的图划分活动轮廓(GPAC)模型被提出,并有效应用于均质图像分割。但是,该模型的连接权函数仅与图像光谱相关,使得模型在低对比度模糊图像的应用存在较大局限,同时,成对相似性的计算量大,模型的数值实现效率不甚理想。针对这些问题,该文引入测地核函数定义连接权函数,结合多相水平集,提出了基于局部图划分的多相活动轮廓图像分割模型。自然图像的实验结果证明了该模型的有效性。     

Multi-manifold metric learning for face recognition based on image sets

In this paper, we propose a new multi-manifold metric learning (MMML) method for the task of face recognition based on image sets. Different from most existing metric learning algorithms that learn the distance metric for measuring single images, our method aims to learn distance metrics to measure the similarity between manifold pairs. In our method, each image set is modeled as a manifold and then multiple distance metrics among different manifolds are learned. With these distance metrics, the intra-class manifold variations are minimized and inter-class manifold variations are maximized simultaneously. For each person, we learn a distance metric by using such a criterion that all the learned distance metrics are person-specific and thus more discriminative. Our method is extensively evaluated on three widely studied face databases, i.e., Honda/UCSD database, CMU MoBo database and YouTube Celebrities database, and compared to the state-of-the-arts. Experimental results are presented to show the effectiveness of the proposed method.     

一种血管约束的局部活动轮廓模型

活动轮廓作为一种重要的图像分割工具,近几年来在理论和应用方面都有很大的发展。然而,现有轮廓模型在处理灰度均匀性较差的图像时,通常存在较高的分割误差,并且对初始轮廓曲线位置敏感。为此,本文提出一种基于血管特征约束的活动轮廓模型,该模型首先使用局部相位（Local Phase）的血管增强算法对图像进行增强处理以生成一种不同于图像灰度的血管特征信息,然后将血管信息和图像灰度以线性加权的形式引入到局部二值拟合（Local Binary Fitting,LBF）能量泛函中,指导图像血管分割。基于视网膜血管图像数据（Digital Retinal Images for Vessel Extraction,DRIV）的实验显示：该模型能成功地从灰度分布不均匀和弱边界轮廓的视网膜图像中提取血管,分割灵敏度和准确性分别达到74.43%和93.67%,同时对初始轮廓曲线位置的敏感性大为降低。由上述可知,该模型具有高分割准确性和低初始位置敏感性。     

Vascular active contour for vessel tree segmentation

In this paper, a novel active contour model is proposed for vessel tree segmentation. First, we introduce a region competition-based active contour model exploiting the gaussian mixture model, which mainly segments thick vessels. Second, we define a vascular vector field to evolve the active contour along its center line into the thin and weak vessels. The vector field is derived from the eigenanalysis of the Hessian matrix of the image intensity in a multiscale framework. Finally, a dual curvature strategy, which uses a vesselness measure-dependent function selecting between a minimal principal curvature and a mean curvature criterion, is added to smoothen the surface of the vessel without changing its shape. The developed model is used to extract the liver and lung vessel tree as well as the coronary artery from high-resolution volumetric computed tomography images. Comparisons are made with several classical active contour models and manual extraction. The experiments show that our model is more accurate and robust than these classical models and is, therefore, more suited for automatic vessel tree extraction.     

An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery

Active contours and active shape models (ASM) have been widely employed in image segmentation. A major limitation of active contours, however, is in their 1) inability to resolve boundaries of intersecting objects and to 2) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation with automated initialization based on watershed. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term is the prior shape term, modeled on the object of interest, thereby constraining the deformation achievable by the active contour. The second term, a boundary-based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei and lymphocytes reveals that the model easily outperforms two state of the art segmentation schemes (geodesic active contour and Rousson shape-based model) and on average is able to resolve up to 91% of overlapping/occluded structures in the images.     

贴片电阻焊点内部空洞缺陷自适应检测

贴片电阻在回流焊过程中,受工艺影响,焊点内部或多或少会存在空洞缺陷,空洞占比率过高会严重降低器件的可靠性。该文融合局部预拟合(LPF)活动轮廓模型和自适应圆形卷积核,提出一种贴片电阻焊点内部空洞缺陷自适应检测方法。首先,根据贴片电阻图像具有明暗两个明显区域的特点,通过求解区域平均灰度差异最大的优化问题将其自适应地分为较暗和较亮两个区域。然后,针对较暗区域中空洞与背景之间对比度低、空洞分布较稀疏、面积偏大等特点,采用局部预拟合活动轮廓模型进行空洞检测;针对较亮区域中空洞与背景之间差异明显、空洞分布密集、面积偏小等特点,提出一种自适应圆形卷积核检测空洞。最后,采用形状因子和平均灰度策略剔除误检测,实现贴片电阻焊点内部空洞精细检测。实验结果表明,该文算法相较于其他检测算法性能有明显的提升,平均Dice系数高达0.8846。     

基于相位一致性改进的活动轮廓分割模型

为了实现甲状腺超声图像中结节组织的快速准确分割,克服图像灰度分布不均匀和边缘模糊对分割结果造成的影响,采用了基于相位一致性改进的活动轮廓分割模型。首先,利用相位一致性边缘检测原理构造一种新的速度函数,不仅弥补了梯度算子边缘检测中由于滤波处理造成边缘损坏的缺陷,而且可以灵活地控制曲线演化速率;然后,将该速度函数乘入到无边缘主动轮廓模型的能量项中,避免了线性组合中的权重分配问题,同时具有全局分割能力。通过理论分析和实验验证,改进模型的相对差异度均小于1%,运行时间均低于对比模型。结果表明,新模型实现了灰度分布不均匀图像的精确分割,同时分割效率也有所提高。     

一种非参数估计的活动围道图像分割方法

该文采用Parzen窗非参数估计的活动围道方法进行图像分割,不必假定图像区域符合特定的概率分布,大大增加了该算法的应用范围。通过动态调整Parzen窗口参数,更好地利用了图像灰度区域一致的信息,使之更好地适应图像分割的任务。通过合成图像和真实图像的实验证明此方法具有很好的合理性,具有很好的分割精度和抗噪性能。