一种基于动态规划法的雷达微弱多目标检测方法

在雷达微弱信号检测领域中,动态规划法是一种有效的能量积累方法.本文首先分析了应用动态规划法对多个微弱目标信号积累后数据图像的特点,在此基础上通过求极值实现了对多目标的检测.然后对算法的虚警概率和检测概率做了理论分析,同时进行了仿真实验.仿真结果表明,与传统的只作相参积累的检测方法相比,基于动态规划的极值法带来了4~5dB的非相参积累增益.     

基于蛇模型图像分割研究综述

蛇(Snake)模型,也称活动轮廓模型(Active Contour Model),能利用图像的高层信息能量泛函最小化来解决图像分割问题,多数学者因这点认真研究并改进了Snake,参数活动轮廓最先被研究,从改进力场的角度入手,以GVF-Snake最为出色,该类模型非常适合医学图像的分割,但其本身基于拉格朗日框架,分割结果依赖于初始轮廓的设置,学者借助几何活动轮廓模型,解决参数蛇难于处理拓扑变化问题,使分割以自适应方式进行,极大弱化了初始化要求,提高分割鲁棒性,能分割遥感、纹理、彩色图片。     

彩色图像序列中运动目标外轮廓的自动提取

该文提出了一种新的多运动目标精确外轮廓自动提取算法。算法的主要创新点是:采用指数滤波计算彩色图像的梯度强度,改善了彩色图像梯度局部极值点的定位精度,所得到的梯度强度和运动边缘位置信息供随后的轮廓迭代中使用,成功地避免了动态轮廓的过收缩。此外,充分利用轮廓的方向信息,又有效地克服了动态轮廓迭代中的外扩展。从而,算法自动地提取出一个真正紧贴的目标外轮廓。分析和实验表明,该算法抗干扰能力强,对复杂背景及不重叠物体,可以精确定位并提取出各个运动目标的外轮廓。     

蛇模型综述

活动轮廓模型即蛇模型，由Kass等人在1987年首次提出，在图像分析和计算机视觉等领域得到越来越广泛的应用。蛇模型是一条能量曲线，由内部能量和外部能量共同决定，在能量最小的原则下移动并最终停止到所要寻找的物体边缘附近。现从蛇模型的基本原理、各种改进方法及应用情况来分别进行阐述。     

基于视差信息的立体图像主动闭合轮廓提取算法研究

针对立体图像中目标对象的闭合轮廓提取任务,该文提出一种基于视差信息的轮廓提取算法。该算法在传统贪婪蛇模型的基础上,利用各控制点和中心的视差关系为模型设计收缩和膨胀力,能够有效指导初始轮廓曲线向目标边缘的收敛。同时算法采用重复利用经处理的控制点作为模型输入的循环迭代方式,能够获得分布均匀且较为密集的边缘曲线。实验结果表明,该轮廓提取算法减少了传统的贪婪蛇模型算法对初始值的依赖,准确度和可靠性均得到了很大的提升。     

距离保持水平集在汽车门锁图像分割中的应用研究

针对基于机器视觉的汽车门锁装配尺寸检测系统中摇臂距离的检测问题,研究了距离保持水平集的图像分割算法。该算法是将轮廓表示为一个符号函数,符号函数的零水平面与实际轮廓相对应,并在能量泛函中加入了内部能量泛函,表示两个函数之间的差异。实验表明,与一般的基于边缘检测的图像分割算法相比较,该算法能够很好的解决汽车门锁图像中摇臂外弧线分割问题,提高了汽车门锁检测系统的精度。同时,该算法也可应用到其他类似工业装配件局部图像分割中。     

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.     

结合局部能量与改进的符号距离正则项的图像目标分割算法

针对传统C-V模型对颜色不均匀图像分割失败并且对初始轮廓和位置敏感问题,以及现有符号距离正则项存在周期性振荡和局部极值问题。该文提出结合局部能量信息和改进的符号距离正则项的图像目标分割算法。首先,将全局图像信息扩展到HSV空间,并使用局部能量项信息分析每个像素及其领域内的统计特性,从而在较少的迭代次数内有效分割颜色分布不均匀图像。其次,改进现有符号距离正则项,改进后的符号距离正则项在避免水平集函数的重新初始化的同时,提高了计算效率,保证了水平集函数演化过程的稳定性。然后,定义阈值判断法的水平集函数演化的终止准则,使曲线准确演化到目标轮廓。该算法与同类模型的对比实验表明该模型具有较高的分割精度和对初始轮廓的鲁棒性。     

基于步态能量图的隐马尔可夫模型身份识别

提出一种基于步态能量图的隐马尔可夫模型身份识别算法.首先预处理提取出运动人体的侧面轮廓,根据步态下肢的摆动距离计算出步态周期,得到平均步态能量图.对能量图用K-均值聚类的方法生成观察向量,进行一维离散隐马尔可夫模型训练,用训练好的模型参数进行身份识别.最后在CASIA步态数据库上对所提出的算法进行实验.实验表明该方法具有较好的识别性能.     

一种具有向心力的新型Snake算法

动态轮廓模型（Snake算法）是一种较好的目标轮廓检测方法。但是传统的Snake算法在对其能量函数进行优化时,不能检测多目标、血管内轮廓和凹形轮廓。文章首先对图像进行自动Snake初始化,然后在Snake 的成长过程中加入向心力因子。实验结果表明,新的算法能够检测多目标、血管内轮廓和凹形轮廓,优于传统的Snake算法。     

A novel fast moving object contour tracking algorithm

If a somewhat fast moving object exists in a complicated tracking environment, snake’s nodes may fall into the inaccurate local minima. We propose a mean shift snake algorithm to solve this problem. However, if the object goes beyond the limits of mean shift snake module operation in suc- cessive sequences, mean shift snake’s nodes may also fall into the local minima in their moving to the new object position. This paper presents a motion compensation strategy by using particle filter; therefore a new Parti...     

Tracking visible boundary of objects using occlusion adaptive motion snake

We propose a novel technique for tracking the visible boundary of a video object in the presence of occlusion. Starting with an initial contour that is interactively specified by the user and may be automatically refined by using intra-energy terms, the proposed technique employs piecewise contour prediction using local motion and color information on both sides of the contour segment, and contour snapping using scale-invariant intra-frame and inter-frame energy terms. The piecewise (segmented) nature of the contour prediction scheme and modeling of the motion on both sides of each contour segment enable accurate determination of whether and where the tracked boundary is occluded by another object. The proposed snake energy terms are associated with contour segments (as opposed to node points) and they are scale/resolution independent to allow multi-resolution contour tracking without the need to retune the weights of the energy terms at each resolution level. This facilitates contour prediction at coarse resolution and snapping at fine resolution with high accuracy. Experimental results are provided to illustrate the performance of the proposed occlusion detection algorithm and the novel snake energy terms that enable visible boundary tracking in the presence of occlusion.     

Automated optic disk boundary detection by modified active contour model

This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) <3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM).     

基于GVF Snake的运动目标跟踪方法

针对Snake不能收敛于凹形边缘和收敛速度慢的缺点，结合帧间差分和梯度矢量流主动轮廓模型（GVF Snake），提出了一种运动目标跟踪方法。首先通过帧间差分得到不同时刻的灰度差异图像，然后用形态方法得到的运动目标粗略轮廓作为Snake的初始轮廓，最后利用GVF Snake的特性，准确地收敛到目标边界。实验证明该方法运算速度快、能够快速地收敛到目标轮廓、准确地跟踪目标，并具有一定的抗噪能力。     

Contouring control of biaxial systems based on polar coordinates

A contouring controller for biaxial systems that integrates the effects of feedback, feedforward, and cross-coupled control is proposed in this study. Conventional approaches to contouring control suffer from the complicated contour-error model and from lack of a systematic way for controller design. The integrated controller is based on polar coordinates under which a relatively simple contour-error model can be obtained. Taking the simple contour error as a state variable, the contouring-control problem is transformed into a stabilization problem. The feedback-linearization technique incorporated with linear feedback or robust control (such as sliding-mode control) can then yield the integrated controller. The proposed method is verified both numerically and experimentally and is compared with the conventional approach. It is found that the proposed controller is better for high speed and/or noncircular contouring. In addition, it can be applied to either linear plants or nonlinear plants (like linear motors).     

基于仿射不变的分段可变形模型的图像分割

提出了一种用于图像分割的分段可变形模型,并在模型的能量函数中引入了仿射不变量信息．将模型轮廊进行层次化的分段变形能够保持标记点移动的相关性,改进的内外部能量函数定义减少了计算量,试验表明与传统方法相比,本模型运算速度快,抗噪声和避免陷入局部极小值的能力较强,对医学图像的分割有较好的效果。     

Local morphology fitting active contour for automatic vascular segmentation

In this paper, we propose an active contour model using local morphology fitting for automatic vascular segmentation on 2-D angiogram. The vessel and background are fitted to fuzzy morphology maximum and minimum opening, separately, using linear structuring element with adaptive scale and orientation. The minimization of the energy associated with the active contour model is implemented within a level set framework. As in the current local model, fitting the image to local region information makes the model robust against the inhomogeneous background. Moreover, selective local estimations for fitting that are precomputed instead of updated in each contour evolution makes the evolution of level set robust again initial location compared to the current local model. The results on synthetic image and real angiogram compared with other methods are presented. It is shown that the proposed method can achieve automatic and accurate segmentation of vascular angiogram.     

A multistage, optimal active contour model

Energy-minimizing active contour models or snakes can be used in many applications such as edge detection, motion tracking, image matching, computer vision, and three-dimensional (3-D) reconstruction. We present a novel snake that is superior both in accuracy and convergence speed over previous snake algorithms. High performance is achieved by using spline representation and dividing the energy-minimization process into multiple stages. The first stage is designed to optimize the convergence speed in order to allow the snake to quickly approach the minimum-energy state. The second stage is devoted to snake refinement and to local minimization of energy, thereby driving the snake to a quasiminimum-energy state. The third stage uses the Bellman (1957) optimality principle to fine-tune the snake to the global minimum-energy state. This three-stage scheme is optimized for both accuracy and speed.     

基于极坐标的CMKF定位算法研究

针对经典的推广卡尔曼滤波算法受初值和测量噪声影响大,算法不稳定等缺点,提出了一种新的基于极坐标的转换测量卡尔曼滤波定位算法,计算机仿真结果验证了这种算法具有较好的稳定性和实用性.     

Dynamic directional gradient vector flow for snakes.

Snakes, or active contour models, have been widely used in image segmentation. However, most present snake models do not discern between positive and negative step edges. In this paper, a new type of dynamic external force for snakes named dynamic directional gradient vector flow (DDGVF) is proposed that uses this information for better performance. It makes use of the gradients in both x and y directions and deals with the external force field for the two directions separately. In snake deformation, the DDGVF field is utilized dynamically according to the orientation of snake in each iteration. Experimental results demonstrate that the DDGVF snake provides a much better segmentation than GVF snake in situations when edges of different directions are present which pose confusion for segmentation.