A variational model and graph cuts optimization for interactive foreground extraction

In this paper, an interactive segmentation method is proposed, which is based on an improved Chan–Vese model, i.e. multiple piecewise constant model with geodesic active contour. The k-means method is used to learn the models of the foreground and background, which are the optimal piecewise constant approximation of the original image according to the input seeds clue by the user. Based on the piecewise constant models of the foreground and background, the multiple piecewise constant with a geodesic active contour energy function can be minimized by effective graph cuts algorithm, and the minimum cuts can be used to partition the image into the foreground and background. Numerical experiments demonstrate the superior performance of the proposed interactive foreground extraction method based on the improved Chan–Vese model compared to the original Chan–Vese model by simple user interaction.     

基于改进先验形状CV模型的目标分割

由于空间目标姿态变化较大,且其灰度与地球背景差异较小,传统CV（Chan and Vese）模型难以获得理想的分割结果。针对目标被部分遮挡或部分信息丢失情况下CV模型不能正确识别问题,Chan和Zhu在CV模型基础上引入先验能量项,构建的先验形状模型只具有旋转、缩放和平移不变性。本文提出了一种先验形状约束的变分水平集改进模型,用于分割星空及复杂地球背景下的空间目标。在保持先验形状模型具有旋转、缩放和平移不变性的基础上,本文改进的变分水平集模型增加了X、Y方向拉伸以及剪切不变约束能量项,增强了先验形状对目标变化的自适应性。实验结果表明本文方法对复杂背景下姿态变化较大的空间目标,具有更好的分割效果。      

Image segmentation and selective smoothing by using Mumford-Shah model.

Recently, Chan and Vese developed an active contour model for image segmentation and smoothing by using piecewise constant and smooth representation of an image. Tsai et al. also independently developed a segmentation and smoothing method similar to the Chan and Vese piecewise smooth approach. These models are active contours based on the Mumford-Shah variational approach and the level-set method. In this paper, we develop a new hierarchical method which has many advantages compared to the Chan and Vese multiphase active contour models. First, unlike previous works, the curve evolution partial differential equations (PDEs) for different level-set functions are decoupled. Each curve evolution PDE is the equation of motion of just one level-set function, and different level-set equations of motion are solved in a hierarchy. This decoupling of the motion equations of the level-set functions speeds up the segmentation process significantly. Second, because of the coupling of the curve evolution equations associated with different level-set functions, the initialization of the level sets in Chan and Vese's method is difficult to handle. In fact, different initial conditions may produce completely different results. The hierarchical method proposed in this paper can avoid the problem due to the choice of initial conditions. Third, in this paper, we use the diffusion equation for denoising. This method, therefore, can deal with very noisy images. In general, our method is fast, flexible, not sensitive to the choice of initial conditions, and produces very good results.     

A multi-object image segmentation C–V model based on region division and gradient guide

The Chan–Vese (C–V) model is an ineffective method for processing images in which the intensity is inhomogeneous. This is especially true for multi-object segmentation, in which the target may be missed or excessively segmented. In addition, for images with rich texture information, the processing speed of the C–V is slow. To overcome these problems, this paper proposes an effective multi-object C–V segmentation model based on region division and gradient guide. First, a rapid initial contour search is conducted using Otsu’s method. This contour line becomes the initial contour for our multi-object segmentation C–V model based on a gradient guide. To achieve the multi-object segmentation the image is then converted to a single level set whose evolution is controlled using an adaptive gradient. The feasibility of the proposed model is analyzed theoretically, and a number of simulation experiments are conducted to validate its effectiveness.     

Image segmentation using fuzzy energy-based active contour with shape prior

This paper presents a fuzzy energy-based active contour model with shape prior for image segmentation. The paper proposes a fuzzy energy functional including a data term and a shape prior term. The data term, inspired from the region-based active contour approach proposed by Chan and Vese, evolves the contour relied on image information. The shape term inspired from Chan and Zhu’s work, defined as the distance between the evolving shape and a reference one, constrains the evolving contour with respect to the reference shape. To align the shapes, we exploit the shape normalization procedure which takes into account the affine transformation. In addition, to minimize the energy functional, we utilize a direct method to calculate the energy alterations. The proposed model therefore can deal with images with background clutter and object occlusion, improves the computational speed, and avoids difficulties associated with time step selection issue in gradient descent-based approaches.     

基于Chan-Vese模型的树形结构多相水平集图像分割算法

由于Chan-Vese(C-V)模型通过单个水平集的符号将待分割图像划分为目标和背景两个部分,所以当图像的多个目标的轮廓成多连接时,C-V模型将无法表示.为了解决C-V模型在表示目标轮廓上的局限,提出了基于C-V模型的树形结构多相水平集算法.关键策略是通过改变图像背景,使得水平集在新图像上重新收敛;核心技术是依据同时明度对比提出的背景填充技术;算法流程采用多水平集串行收敛方式实现多相分割(n-1次收敛可以实现n相分割,n＞1).实验结果表明,本算法可以表示复杂的区域连接情况(n相分割最多可以表示n连接情况),能够实现多目标分割(n相分割可以实现n-1个目标分割),特别适合于目标中含有子目标的图像.     

Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.     

Comparative study of semi-implicit schemes for nonlinear diffusion in hyperspectral imagery.

Nonlinear diffusion has been successfully employed over the past two decades to enhance images by reducing undesirable intensity variability within the objects in the image, while enhancing the contrast of the boundaries (edges) in scalar and, more recently, in vector-valued images, such as color, multispectral, and hyperspectral imagery. In this paper, we show that nonlinear diffusion can improve the classification accuracy of hyperspectral imagery by reducing the spatial and spectral variability of the image, while preserving the boundaries of the objects. We also show that semi-implicit schemes can speedup significantly the evolution of the nonlinear diffusion equation with respect to traditional explicit schemes.     

基于自适应TV的Chan—Vese图像分割方法

自适应整体变分(Total Variation,TV)图像平滑模型能有效去除噪声,具有较强的图像保征能力.基于多相水平集的Chan-Vese图像分割模型能有效地实现多质图像的分割.将自适应TV图像平滑方法和Chan-Vese图像分割方法有机整合,提出了自适应TV的Chan-Vese图像分割方法.实验表明,该方法能得到较好的分割结果.     

基于Gabor小波的无边缘活动围道纹理分割方法

该文提出了一种基于Gabor小波的活动围道纹理分割新方法。该方法先用Gabor小波提取图像的纹理特征,再用Chan-Vese模型进行分割。与其它基于Chan-Vese模型的纹理分割方法相比,基于Gabor小波的活动围道的纹理分割方法有两个优点:一是同时使用纹理特征和灰度信息演化围道,可分割纹理图像和非纹理图像,分割方法的灵活性好;二是在分割多类目标时,采用多级分层式曲线演化方法解决了初始围道难以选择的问题。对自然界真实图像和遥感图像的分割实验结果说明,该文提出的分割方法精度高。     

基于活动轮廓和Gabor滤波器的细胞分割

将Tony F Chan提出的基于曲线进化、Mumford-shah泛函和level set技术的没有边界泛函的活动轮廓模型与Gabor滤波器相结合,利用Tony F Chan提出的不依赖于边界梯度的活动轮廓能够检测出边界梯度较弱的物体边界与Gabor滤波器能在不同方向和频率增强图像的特点,先对原始细胞图像进行不同角度、不同频率Gabor滤波从而在不同方向增强细胞边界削弱细胞内部信息,然后把不同方向滤波后的图像融合以得到边界增强噪声减少的图像,最后在该图像上应用活动轮廓取得了较好的分割效果.     

一种红外和可见光双通道视频目标跟踪方法

室外场景下由于场景背景条件变化容易导致视频目标跟踪稳定性差。该文提出一种利用红外和可见光传感器的双通道视频目标跟踪方法。该算法利用可见光图像的目标颜色特征和红外图像的目标轮廓特征,结合均值漂移算法与水平集曲线演化实现目标定位,并给出了目标尺度和模板更新方法;对多目标跟踪的互相遮挡问题,通过判断目标合并与分离实现遮挡时多个目标的定位。实验结果表明,该文方法能够有效处理光照变化、阴影、遮挡等情况,实现目标的稳定跟踪。     

一种耦合卡通-纹理分解及边缘检测的方法

咸阳师范学院专项科研基金重点项目(No.07XSYK107,07XSYK214)资助。摘要：在Luminita A.Vese和Stanley J.Osher提出的基于总变差最小化的纹理图像分解模型的基础上,运用半二次规整化方法（half-quadratic regularization）得到联合纹理特征提取和边缘检测的耦合变分模型。数值计算的结果表明,本文方法对图像的卡通-纹理分解以及边缘的提取都有较好的效果。     

Detection and Segmentation of Concealed Objects in Terahertz Images

Terahertz imaging makes it possible to acquire images of objects concealed underneath clothing by measuring the radiometric temperatures of different objects on a human subject. The goal of this work is to automatically detect and segment concealed objects in broadband 0.1–1 THz images. Due to the inherent physical properties of passive terahertz imaging and associated hardware, images have poor contrast and low signal to noise ratio. Standard segmentation algorithms are unable to segment or detect concealed objects. Our approach relies on two stages. First, we remove the noise from the image using the anisotropic diffusion algorithm. We then detect the boundaries of the concealed objects. We use a mixture of Gaussian densities to model the distribution of the temperature inside the image. We then evolve curves along the isocontours of the image to identify the concealed objects. We have compared our approach with two state-of-the-art segmentation methods. Both methods fail to identify the concealed objects, while our method accurately detected the objects. In addition, our approach was more accurate than a state-of-the-art supervised image segmentation algorithm that required that the concealed objects be already identified. Our approach is completely unsupervised and could work in real-time on dedicated hardware.      

Counting Objects by Diffused Index: Geometry-free and training-free approach

Counting objects is a fundamental but challenging problem. In this paper, we propose diffusion-based, geometry-free, and learning-free methodologies to count the number of objects in images. The main idea is to represent each object by a unique index value regardless of its intensity or size, and to simply count the number of index values. First, we place different vectors, refer to as seed vectors, uniformly throughout the mask image. The mask image has boundary information of the objects to be counted. Secondly, the seeds are diffused using an edge-weighted harmonic variational optimization model within each object. We propose an efficient algorithm based on an operator splitting approach and alternating direction minimization method, and theoretical analysis of this algorithm is given. An optimal solution of the model is obtained when the distributed seeds are completely diffused such that there is a unique intensity within each object, which we refer to as an index. For computational efficiency, we stop the diffusion process before a full convergence, and propose to cluster these diffused index values. We refer to this approach as Counting Objects by Diffused Index (CODI). We explore scalar and multi-dimensional seed vectors. For Scalar seeds, we use Gaussian fitting in histogram to count, while for vector seeds, we exploit a high-dimensional clustering method for the final step of counting via clustering. The proposed method is flexible even if the boundary of the object is not clear nor fully enclosed. We present counting results in various applications such as biological cells, agriculture, concert crowd, and transportation. Some comparisons with existing methods are presented.     

A Robust Algorithm Based on Object Contours and Order Matching for Disparity Map Post-Processing

1　ＩｎｔｒｏｄｕｃｔｉｏｎＤｉｓｐａｒｉｔｙｅｓｔｉｍａｔｉｏｎ ,ｎａｍｅｌｙ ,ｓｔｅｒｅｏｍａｔｃｈｉｎｇ ,ｉｓｔｈｅｋｅｙｔｏｓｔｅｒｅｏｖｉｓｉｏｎａｎｄｓｔｅｒｅｏｉｍａｇｅｃｏｍｐｒｅｓ ｓｉｏｎ .Ｆｏｒｔｈｅｐｒｏｂｌｅｍｏｆｓｃｅｎｅｒｅｃｏｎｓｔｒｕｃｔｉｏｎｕｓｉｎｇｓｔｅｒｅｏｉｍａｇｅｓ,ｔｈｅｐｒｅｃｉｓｉｏｎｏｆｅｓｔｉｍａｔｅｄｄｉｓｐａｒｉｔｙｄｅｔｅｒｍｉｎｅｓｔｈｅｄｅｇｒｅｅｏｆｔｒｕｔｈｆｕｌｎｅｓｓｏｆｒｅｃｏｎｓｔｒｕｃｔ ｅｄｏｂｊｅｃｔｓ.Ｍｏｒｅｏｖ…     

Colorization using edge-preserving smoothing filter

Colorization is the process of replacing a scalar value stored at each pixel of a grayscale image or film by a vector in a multidimensional color space. Mapping between scalar and color is therefore not unique, and colorization is ambiguous in nature and requires some amount of human interaction or external information. This paper presents a new method of interactive colorization by introducing a new concept to the bilateral filter. We adopt the bilateral filter as a range weights filter (bilateral filter without Gaussian spatial domain), and we show that the Nikolaou filter is equivalent to the range weights bilateral filter, but has better behavior near the edges. In our algorithm, the user selects grayscale image regions by directly painting these regions; the user does not need to paint over the whole object. Each selection can be automatically expanded from the user’s paint brush and aligned with the object boundary. Robustness and quality of the results obtained over a collection of several challenging images demonstrate the efficiency of this new method for some difficult cases, such as human faces or images with confusing lighting.     

基于总变分方法去噪的测地线活动轮廓的应用

传统的测地线活动轮廓（geodesic active contour）在目标轮廓的提取中虽然能使一条初始曲线朝着目标边界逼近,但是高斯平滑的各向同性性使得图像的边缘信息模糊甚至丢失,这样,曲线的演化过程会变得不稳定而导致演化速度变慢。据此,提出一种基于总变分方法的测地线活动轮廓模型。由于总变分方法可以在去除噪声的同时,对边缘的信息进行增强,实验证明该模型不但能够使曲线精准地收敛到期望轮廓上,且运算时间短。     

利用多尺度分析和编组的基于目标的注意计算模型

模拟生物视觉感知提出一种基于目标的注意计算模型,主要用到两个关键技术:多尺度分析和编组.用于多尺度分析的微分算子从原始图像中提取重要边缘,随后源于格式塔知觉组织规则的轮廓编组过程将边缘组织成感知目标.注意焦点按照各目标显著程度递减的顺序在目标间转移,目标显著程度由边缘重要性、区域对比度和轮廓闭合性共同决定.该模型考虑了目标的独立性和完整性,因此比基于空间的注意有更高的检测精度.多尺度分析为轮廓编组提供了候选边缘,从而提高了编组的效率.对多类自然图像的实验验证了该模型计算上的高效性和生物学上的合理性.     

Color TV: total variation methods for restoration of vector-valuedimages

We propose a new definition of the total variation (TV) norm for vector-valued functions that can be applied to restore color and other vector-valued images. The new TV norm has the desirable properties of (1) not penalizing discontinuities (edges) in the image, (2) being rotationally invariant in the image space, and (3) reducing to the usual TV norm in the scalar case. Some numerical experiments on denoising simple color images in red-green-blue (RGB) color space are presented.