<Emphasis Type="Italic">Skeleton Search</Emphasis>: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model

We describe a top-down object detection and segmentation approach that uses a skeleton-based shape model and that works directly on real images. The approach is based on three components. First, we propose a fragment-based generative model for shape that is based on the shock graph and has minimal dependency among its shape fragments. The model is capable of generating a wide variation of shapes as instances of a given object category. Second, we develop a progressive selection mechanism to search among the generated shapes for the category instances that are present in the image. The search begins with a large pool of candidates identified by a dynamic programming (DP) algorithm and progressively reduces it in size by applying series of criteria, namely, local minimum criterion, extent of shape overlap, and thresholding of the objective function to select the final object candidates. Third, we propose the Partitioned Chamfer Matching (PCM) measure to capture the support of image edges for a hypothesized shape. This measure overcomes the shortcomings of the Oriented Chamfer Matching and is robust against spurious edges, missing edges, and accidental alignment between the image edges and the shape boundary contour. We have evaluated our approach on the ETHZ dataset and found it to perform well in both object detection and object segmentation tasks.     

基于分割块的图像语义分割方法

针对复杂室外环境下,传统语义分割模型无法准确描述对象轮廓的问题,提出了采用结构森林法生成边缘概率,运用分水岭算法将边缘概率转化成初始割块.为避免过分分割,利用超度量轮廓图算法选取适当阈值生成分割块以获取更准确的轮廓信息,通过随机森林训练分割块,得到语义分割结果.实验结果表明:在处理复杂的语义分割任务时,基于分割块的方法在精度、鲁棒性和速率方面均具有良好表现.     

显著性驱动的局部相似拟合模型分割算法研究

灰度不均匀和噪声图像的分割是计算机视觉中的难点。现有的活动轮廓模型尽管能够取得较好的分割效果,但仍然对噪声图像分割效果不理想,初始轮廓曲线的选取敏感,优化易陷入局部极小导致演化速度慢等问题。针对该问题,首先使用局部区域灰度的均值和方差拟合高斯分布,构建新的能量泛函,均值和方差随着能量的最小化过程而变化,从而增强了灰度不均匀和噪声图像的分割能力。此外,结合视觉显著性检测算法获取待分割目标的先验形状信息,并自适应地创建水平集函数,从而降低了初始轮廓位置敏感性及计算时间复杂度,实现全自动的图像分割。实验结果证明,提出的算法可以用于灰度不均匀和噪声图像分割,并取得了较好的分割性能,消除了算法对初始轮廓位置敏感性,减少了迭代次数。     

结合谱聚类与改进RSF模型的医学图像分割

现有的可变区域拟合能量（RSF）模型基于初始轮廓内外灰度值的近似,较好地处理了图像分割中存在的图像灰度不均匀的问题。但当选择不恰当的初始轮廓时,由于RSF模型能量函数的非凸性质,极易陷入局部最小值。为了保证初始化的鲁棒性,提出了一种拟合函数优化的RSF模型。在曲线演化过程中,在演化方向相反的区域增加一个函数来交换曲线内外拟合值,使整条曲线沿物体的同侧边界演化。又将谱图理论引入该模型,使其能对大数据样本聚类且快速收敛至全局最优解。将改进模型应用于医学图像分割,实验结果表明该模型较RSF模型获得了更鲁棒的分割结果和较高的分割效率。     

一种基于模糊主动轮廓的鲁棒局部分割方法

针对局部分割方法对初始轮廓敏感的问题,本文提出一种基于模糊主动轮廓的鲁棒局部分割方法.该方法利用图像的局部信息,定义一种新的平均模糊能量函数.通过对演化曲线进行形态学膨胀和腐蚀运算构建窄带,并在窄带范围内求解模糊能量函数的最小值来实现局部分割.为防止演化曲线陷入局部极小值,在迭代过程中加入对比度约束判断条件,进一步提高了分割方法对初始轮廓的鲁棒性.对合成图像和医学图像的分割实验结果表明,与已有的几种局部分割方法相比,本文方法在分割精度和鲁棒性等方面都有较大提高.     

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data.     

基于多尺度局部特征的图像分割模型

为了解决图像分割中灰度不均匀和初始轮廓敏感的问题,提出一种基于多尺度局部特征的图像分割模型.与传统局部邻域定义在方形区域不同,该模型采用圆形区域来获取更多的局部信息;考虑到局部区域灰度的变化程度不一,提出利用多尺度结构与均值滤波器相结合的方法获得多尺度局部灰度信息;通过转换灰度不均匀模型得到一个逼近真实信息的图像,并将其融合进局部高斯分布拟合(LGDF)模型,构造出基于多尺度局部特征的能量泛函.从理论分析和实验结果表明:由于多尺度结构弱化了灰度不均匀的影响,该模型既能快速、准确地分割灰度不均匀图像,又表现出对初始轮廓具有较强的鲁棒性.     

Circulant dissimilarity-based shape registration for object segmentation

ABSTRACT

A shape prior-based object segmentation is developed in this paper by using a shape transformation distance to constrain object contour evolution. In the proposed algorithm, the transformation distance measures the dissimilarity between two unaligned shapes by cyclic shift, which is called ‘circulant dissimilarity’. This dissimilarity with respect to transformation of the object shape is represented by circular convolution, which could be efficiently computed by using fast Fourier transform. Given a set of training shapes, the kernel density estimation is adopted to model shape prior. By integrating low-level image feature, high-level shape prior and transformation distance, a variational segmentation model is proposed to solve the transformation invariance of shape prior. Numerical experiments demonstrate that circulant dissimilarity-based shape registration outperforms the iterative optimization on explicit pose parameters, and show promising results and highlight the potential of the method for object registration and segmentation.     

基于区域混合活动轮廓模型的医学图像分割

针对变分水平集算法在图像分割过程中计算量较大且收敛速度慢的现象, 在一些基于区域的活动轮廓模型基础上提出了一种新的基于区域混合模型的非凸正则化活动轮廓模型。该模型构造了一个新的能量泛函,该能量泛函结合了考虑图像局部聚类性质的LBF模型和测地线模型,增加了非凸正则化项,加快了轮廓曲线的收敛速度,可以很好地保持区域形状并能防止边缘过平滑,然后通过经典有限差分法求得能量泛函的极小值。最后,在合成图像和医学图像上做了仿真实验,结果表明,该算法具有较快的收敛速度 和很好的鲁棒性,分割结果也较准确。     

分水岭优化的Snake模型肝脏图像分割

Snake算法是主动轮廓模型的经典算法,是近年来图像分割和视频领域研究的热点。针对Snake模型中存在的初始轮廓敏感和能量函数中曲率约束不足等问题,提出将分水岭变换和主动轮廓模型相结合的主动轮廓分割算法。首先通过引入标记函数和强制最小值技术解决传统分水岭变换可能导致的过分割问题,然后利用改进的强制标记分水岭算法优化Snake模型的初始轮廓曲线,最后通过在Snake模型中增加一项与曲线形状相关的外部力弥补能量约束函数中曲率约束的不足,从而实现更精确的图像分割。改进后的Snake模型应用于腹部MR图像中,对肝脏图像的识别和分割取得了良好效果。     

Elastic shapes models for improving segmentation of object boundaries in synthetic aperture sonar images

We present a variational framework for naturally incorporating prior shape knowledge in guidance of active contours for boundary extraction in images. This framework is especially suitable for images collected outside the visible spectrum, where boundary estimation is difficult due to low contrast, low resolution, and presence of noise and clutter. Accordingly, we illustrate this approach using the segmentation of various objects in synthetic aperture sonar (SAS) images of underwater terrains. We use elastic shape analysis of planar curves in which the shapes are considered as elements of a quotient space of an infinite dimensional, non-linear Riemannian manifold. Using geodesic paths under the elastic Riemannian metric, one computes sample mean and covariances of training shapes in each classes and derives statistical models for capturing class-specific shape variability. These models are then used as shape priors in a variational setting to solve for Bayesian estimation of desired contours as follows. In traditional active contour models curves are driven towards minimum of an energy composed of image and smoothing terms. We introduce an additional shape term based on shape models of relevant shape classes. The minimization of this total energy, using iterated gradient-based updates of curves, leads to an improved segmentation of object boundaries. This is demonstrated using a number of shape classes in two large SAS image datasets.     

Information-Theoretic Active Polygons for Unsupervised Texture Segmentation

Curve evolution models used in image segmentation and based on image region information usually utilize simple statistics such as means and variances, hence can not account for higher order nature of the textural characteristics of image regions. In addition, the object delineation by active contour methods, results in a contour representation which still requires a substantial amount of data to be stored for subsequent multimedia applications such as visual information retrieval from databases. Polygonal approximations of the extracted continuous curves are required to reduce the amount of data since polygons are powerful approximators of shapes for use in later recognition stages such as shape matching and coding. The key contribution of this paper is the development of a new active contour model which nicely ties the desirable polygonal representation of an object directly to the image segmentation process. This model can robustly capture texture boundaries by way of higher-order statistics of the data and using an information-theoretic measure and with its nature of the ordinary differential equations. This new variational texture segmentation model, is unsupervised since no prior knowledge on the textural properties of image regions is used. Another contribution in this sequel is a new polygon regularizer algorithm which uses electrostatics principles. This is a global regularizer and is more consistent than a local polygon regularization in preserving local features such as corners.Supported by NSF grant CCR-0133736.Partially supported by AFOSR grant F49620-98-1-0190 and NSF grant CCR-9984067.     

A local region-based Chan–Vese model for image segmentation

In this paper, a new region-based active contour model, namely local region-based Chan–Vese (LRCV) model, is proposed for image segmentation. By considering the image local characteristics, the proposed model can effectively and efficiently segment images with intensity inhomogeneity. To reduce the dependency on manual initialization in many active contour models and for an automatic segmentation, a degraded CV model is proposed, whose segmentation result can be taken as the initial contour of the LRCV model. In addition, we regularize the level set function by using Gaussian filtering to keep it smooth in the evolution process. Experimental results on synthetic and real images show the advantages of our method in terms of both effectiveness and robustness. Compared with the well-know local binary fitting (LBF) model, our method is much more computationally efficient and much less sensitive to the initial contour.     

先验形状力场参数活动轮廓模型及其医学图像分割

先验形状参数活动轮廓模型是一种抗噪声干扰稳定的图像分割方法．它具有对弱边缘、凹区域进行分割的能力,同时有较大的边缘捕捉范围．通过引入一种非距离性的先验形状力场,构建一种新的能反映先验形状的参数活动轮廓模型．新的先验形状活动轮廓模型避免了曲线之间距离的计算,减少了模型的复杂性．新的方法可以较好地解决传统型参数活动轮廓模型的一些本质缺陷.实验对带噪声且为弱边缘的医学CT图像和超声图像进行分割能得到理想的边缘轮廓.     

基于图割优化的能量最小化模型图像分割方法

针对主动轮廓模型中利用梯度下降法求解能量函数容易陷入局部极小的不足,设计了一个离散化最小能量函数模型。该模型以Chan-Vese模型为基础,利用图割方法优化能量泛函,实现能量的全局最优解。新模型首先将图像映射为图,将基于像素的能量泛函转换为可用图表示的离散化能量函数,通过计算节点及其邻域关系权值,迭代求解最小化能量并将其作用于形变轮廓曲线,直至达到稳定状态。新模型改进了主动轮廓模型对弱边界图像初始轮廓敏感的问题,提高了分割精度和运行速度。     

“Brownian strings”: Segmenting images withstochastically deformable contours

This paper describes an image segmentation technique in which an arbitrarily shaped contour was deformed stochastically until it fitted around an object of interest. The evolution of the contour was controlled by a simulated annealing process which caused the contour to settle into the global minimum of an image-derived “energy” function. The nonparametric energy function was derived from the statistical properties of previously segmented images, thereby incorporating prior experience. Since the method was based on a state space search for the contour with the best global properties, it was stable in the presence of image errors which confound segmentation techniques based on local criteria, such as connectivity. Unlike “snakes” and other active contour approaches, the new method could handle arbitrarily irregular contours in which each interpixel crack represented an independent degree of freedom. Furthermore, since the contour evolved toward the global minimum of the energy, the method was more suitable for fully automatic applications than the snake algorithm, which frequently has to be reinitialized when the contour becomes trapped in local energy minima. High computational complexity was avoided by efficiently introducing a random local perturbation in a time independent of contour length, providing control over the size of the perturbation, and assuring that resulting shape changes were unbiased. The method was illustrated by using it to find the brain surface in magnetic resonance head images and to track blood vessels in angiograms     

Moment-based alignment for shape prior with variational B-spline level set

This paper presents a new shape prior-based implicit active contour model for image segmentation. The paper proposes an energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach, evolves the contour based on the region information of the image to segment. The shape prior term, defined as the distance between the evolving shape and a reference shape, constraints the evolution of the contour with respect to the reference shape. Especially, in this paper, we present shapes via geometric moments, and utilize the shape normalization procedure, which takes into account the affine transformation, to align the evolving shape with the reference one. By this way, we could directly calculate the shape transformation, instead of solving a set of coupled partial differential equations as in the gradient descent approach. In addition, we represent the level-set function in the proposed energy functional as a linear combination of continuous basic functions expressed on a B-spline basic. This allows a fast convergence to the segmentation solution. Experiment results on synthetic, real, and medical images show that the proposed model is able to extract object boundaries even in the presence of clutter and occlusion.     

稀疏形状先验的脑肿瘤图像分割

目的 在脑部肿瘤图像的分析过程中,准确分割出肿瘤区域对于计算机辅助脑部肿瘤疾病的诊断及治疗过程具有重要意义。然而,由于脑部图像常存在结构复杂、边界模糊、灰度不均以及肿瘤内部存在明暗区域的问题,使得肿瘤图像分割工作面临严峻挑战。为了克服上述困难,更好地实现脑部肿瘤图像分割,提出一种基于稀疏形状先验的脑肿瘤图像分割算法。方法 首先,研究脑部肿瘤图像的配准与形状描述,并以此为基础构建脑部肿瘤的稀疏形状先验约束模型;继而,将该稀疏形状先验约束模型与区域能量描述方法相结合,构建基于稀疏形状先验的能量函数;最后,对能量函数进行优化及迭代,输出脑部肿瘤区域分割结果。结果 本文使用脑胶质瘤公开数据集BraTS2017进行算法测试,本文算法的分割结果与真实数据之间的平均相似度达到93.97%,灵敏度达到91.3%,阳性预测率达到95.9%。本文算法的实验准确度较高,误判率较低,鲁棒性较强。结论 本文算法能够结合水平集方法在拓扑结构描述和稀疏表达方法在复杂形状表达方面的优势,同时由于加入了形状约束,能够有效削弱肿瘤内部明暗区域对分割结果造成的影响,从而更准确和稳定地实现脑部肿瘤图像分割。