引入光流法的活动轮廓模型

本文在活动轮廓模型的基础上，引入了“图象统计势能”和光流法，提出了一种新的图象分割方法，该方法改进了活动轮廓模型的一些不足，能准确地检测出物体轮廓边界，且分割结果对初始位置不敏感，同时提高了对运动速度较快的物体轮廓检测的正确性，实验结果证实了该方法的有效性。     

An interactive authoring system for video object segmentation and annotation

An interactive authoring system is proposed for semi-automatic video object (VO) segmentation and annotation. This system features a new contour interpolation algorithm, which enables the user to define the contour of a VO on multiple frames while the computer interpolates the missing contours of this object on every frame automatically. Typical active contour (snake) model is adapted and the contour interpolation problem is decomposed into a two-directional contour tracking problem and a merging problem. In addition, new user interaction models are designed for the user to interact with the computer. Experiments indicate that this system offers a good balance between algorithm complexity and user interaction efficiency.     

Region tracking for non-rigid video objects in a non-parametric MAP framework

This paper presents a non-parametric maximum a posteriori MAP framework for tracking non-rigid video objects. We formulate the region tracking problem as a MAP probability problem and define the probabilistic models in terms of the distances between the intensity distribution of the object and that of its spatial- and temporal-neighborhood. Furthermore, in order to better model the complex intensity changes due to non-rigid movement, we propose to use a non-parametric method to approximate the likelihood and prior terms in the MAP problem. The proposed non-parametric estimation algorithm mostly relies on intensity features and requires no time-consuming motion estimation. Finally, we employ a contour evolution method in the MAP optimization step to iteratively track the object contour. The experimental results demonstrate that the proposed method achieves satisfactory results and outperforms the previous parametric method.     

Fast two-cycle curve evolution with narrow perception of background for object tracking and contour refinement

The problem of object contour tracking in image sequences remains challenging, especially those with cluttered backgrounds. In this paper, the fast two-cycle level set method with narrow perception of background (FTCNB) is proposed to extract the foreground objects, e.g. vehicles from road image sequences. The curve evolution of the level set method is implemented by computing the signs of region competition terms on two linked lists of contour pixels rather than by solving partial differential equations (PDEs). The curve evolution process mainly consists of two cycles: one cycle for contour pixel evolution and a second cycle for contour pixel smoothness. Based on the curve evolution process, we introduce two tracking stages for the FTCNB method. For coarse tracking stage, the speed function is defined by region competition term combining color and texture features. For contour refinement stage which requires higher tracking accuracy, the likelihood models of the Maximum a posterior (MAP) expressions are incorporated for the speed function. Both the tracking and refinement stages utilize the fast two-cycle curve evolution process with the narrow perception of background regions. With these definitions, we conduct extensive experiments and comparisons for the proposed method. The comparisons with other baseline methods well demonstrate the effectiveness of our work.     

Robust contour tracking based on a coupling between geodesic active contours and conditional random fields

This paper presents a new general framework for contour tracking based on the synergy of two powerful segmentation tools, namely, spatial temporal conditional random fields (CRFs) and geodesic active contours (GACs). The contours of targets are modeled using a level set representation. The evolution of the level sets toward the target contours is formulated as one of the joint region-based (CRF) and boundary-based (GAC) segmentations under a unified Bayesian framework. A variational inference technique is used to solve this otherwise intractable inference problem, leading to approximate MAP solutions of both the new 3D spatial temporal CRF and the GAC model. The tracking result of the previous frame is used to initialize the curve in the current frame. Typical contour tracking problems are considered and experimental results are given to illustrate the robustness of the method against noise and its accurate performance in moving objects boundary localization.     

基于主动轮廓模型联合Camshift算法的目标跟踪方法

针对城市道路交通环境中传统Camshift算法跟踪窗不能描述运动目标轮廓以及易受近似颜色干扰跟踪丢失的问题,提出基于Snake主动轮廓模型联合Chamshift区域模型的目标跟踪方法,算法将原Camshift算法中使用的HSV颜色特征和LTP纹理特征融合,增加了抗干扰能力,同时利用多尺度小波改进的Snake主动轮廓模型进一步对物体轮廓检测,去除阴影区域,更为直观的描述了物体的轮廓。实验证明,新方法在城市道路交通监控中有良好的应用前景。     

Tracking nonparameterized object contours in video

We propose a new method for contour tracking in video. The inverted distance transform of the edge map is used as an edge indicator function for contour detection. Using the concept of topographical distance, the watershed segmentation can be formulated as a minimization. This new viewpoint gives a way to combine the results of the watershed algorithm on different surfaces. In particular, our algorithm determines the contour as a combination of the current edge map and the contour, predicted from the tracking result in the previous frame. We also show that the problem of background clutter can be relaxed by taking the object motion into account. The compensation with object motion allows to detect and remove spurious edges in background. The experimental results confirm the expected advantages of the proposed method over the existing approaches.     

Segmentation and tracking in echocardiographic sequences: activecontours guided by optical flow estimates

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability     

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.     

A hand tracking algorithm with particle filter and improved GVF snake model

To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion. This work has been supported by the National Natural Sciencal Foundation of China (No.61403274), and the Tianjin Technology Project of Intelligent Manufacturing (No.15ZXZNGX00160). E-mail:agwu@tju.edu.cn      

Automatic bootstrapping and tracking of object contours

A new fully automatic object tracking and segmentation framework is proposed. The framework consists of a motion-based bootstrapping algorithm concurrent to a shape-based active contour. The shape-based active contour uses finite shape memory that is automatically and continuously built from both the bootstrap process and the active-contour object tracker. A scheme is proposed to ensure that the finite shape memory is continuously updated but forgets unnecessary information. Two new ways of automatically extracting shape information from image data given a region of interest are also proposed. Results demonstrate that the bootstrapping stage provides important motion and shape information to the object tracker. This information is found to be essential for good (fully automatic) initialization of the active contour. Further results also demonstrate convergence properties of the content of the finite shape memory and similar object tracking performance in comparison with an object tracker with unlimited shape memory. Tests with an active contour using a fixed-shape prior also demonstrate superior performance for the proposed bootstrapped finite-shape-memory framework and similar performance when compared with a recently proposed active contour that uses an alternative online learning model.     

Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours

Recording rolling leukocyte velocities from intravital microscopic video imagery is a critical task in inflammation research and drug validation. Since manual tracking is excessively time consuming, an automated method is desired. This paper illustrates an active contour based automated tracking method, where we propose a novel external force to guide the active contour that takes the hemodynamic flow direction into account. The construction of the proposed force field, referred to as motion gradient vector flow (MGVF), is accomplished by minimizing an energy functional involving the motion direction, and the image gradient magnitude. The tracking experiments demonstrate that MGVF can be used to track both slow- and fast-rolling leukocytes, thus extending the capture range of previously designed cell tracking techniques.     

基于Hausdorff距离的非刚体目标自适应轮廓跟踪

本文提出了一种基于Ｈａｕｓｄｏｒｆ距离的非刚体目标的轮廓跟踪算法。它的特点在于从二维序列图像中提取非刚体目标的二维可变模板，从而实现对非刚体运动目标的跟踪。此算法的主要思想在于将空间运动的非刚体目标的二维图像变化分解成二维运动变化和二维形状变化，从而加大了模板适应力，辅以过零点检测及金字塔快速搜索算法，可以实现非刚体目标的快速跟踪。最后，本文给出了对人体轮廓进行跟踪的实验结果。     

基于部分可观马氏决策过程的多平台主被动传感器调度

为了使有限时域内的跟踪精度和辐射风险达到最佳平衡,本文研究了多传感器平台在协同跟踪目标时的主被动传感器调度问题.将该问题建立成基于部分可观马氏决策过程的数学模型以同步实现目标跟踪和辐射控制.在先见优化思想的基础上,借助由无迹采样近似得到的精度收益及由隐马氏模型滤波器推导出的辐射代价将调度问题转化成决策树问题,并采用分枝定界方法求解.仿真结果表明了该方法的有效性.     

Position domain contour control for multi-DOF robotic system

Contour tracking control is one of the fundamental operations for robotic systems. In this paper, a position domain PD control is developed to control a multi-DOF nonlinear robotic system for improving contour tracking performance. In this new position domain control system, a robotic system is viewed as a master–slave system where the master motion is used as an independent reference through equidistantly sampling, while slave motions are described as functions of the master motion according to contour tracking requirements. A position domain dynamic model of the robotic system based on the master motion is developed through one-to-one mapping of the original dynamic model from time domain to position domain. Stability analysis is conducted for the proposed position domain PD control, the global boundedness of the tracking errors is guaranteed through the Lyapunov method, and the effectiveness is successfully verified through simulation study for linear and nonlinear contour tracking problems. Compared results demonstrate that the position domain PD control is better than its time domain counterpart for contour tracking of multi-DOF robotic systems.     

Optimal edge-based shape detection

We propose an approach to accurately detecting two-dimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-dimensional (1-D) optimal step edge operator, which minimizes both the noise power and the mean squared error between the input and the filter output. This operator is found to be the derivative of the double exponential (DODE) function, originally derived by Ben-Arie and Rao (1994). We define an operator for shape detection by extending the DODE filter along the shape's boundary contour. The responses are accumulated at the centroid of the operator to estimate the likelihood of the presence of the given shape. This method of detecting a shape is in fact a natural extension of the task of edge detection at the pixel level to the problem of global contour detection. This simple filtering scheme also provides a tool for a systematic analysis of edge-based shape detection. We investigate how the error is propagated by the shape geometry. We have found that, under general assumptions, the operator is locally linear at the peak of the response. We compute the expected shape of the response and derive some of its statistical properties. This enables us to predict both its localization and detection performance and adjust its parameters according to imaging conditions and given performance specifications. Applications to the problem of vehicle detection in aerial images, human facial feature detection, and contour tracking in video are presented.     

一种基于局部特征分块的目标跟踪算法

为了解决传统目标跟踪算法在天空背景下面临高能激光反射时图像像素灰度分布发生剧烈变化, 从而导致目标遮挡或丢失的问题, 采用一种基于局部特征分块思想的相关跟踪算法, 根据局部特征对跟踪模板进行了分块处理, 计算并选取其中特征稳定度高的块模板, 在跟踪区域内对每个块做模板匹配, 并进行了理论分析和试验验证。结果表明, 该算法在强光干扰下能够有效地对目标实时稳定跟踪, 且图像处理延迟时间在2ms以内。该研究对基于高能激光发射下的超高精度跟踪系统工作性能的保证是有帮助的。     

Cloud tracking by scale space classification

The problem of cloud tracking within a sequence of geo-stationary satellite images has direct relevance to the analysis of cloud life cycles and to the detection of cloud motion vectors (CMVs). The proposed approach first identifies a homogeneous consistent cloud mass for tracking and then establishes motion correspondence within an image sequence. In contrast to the crosscorrelation based approach as adopted in automatic CMV detection analysis, a scale space classifier is designed to detect cloud mass in the source image taken at time t and the destination image at time t+δt. Boundaries of the extracted cloud segments are matched by computing a correspondence between high curvature points. This shape based method is capable of tracking in the cases of rotation, scaling, and shearing, while the correlation technique is limited to translational motion. The final tracking results provide motion magnitude and direction for each contour point, allowing reliable estimation of meteorological events and wind velocities aloft. With comparable computational expense, the scale space classification technique exceeds the performance of the traditional correlation-based approach in terms of reduced localization error and false matches     

飞机噪声等值线绘制算法的研究

针对机场噪声的特征及分布特点,提出了一种基于路径栅格的机场噪声等值线追踪算法。通过构造有效网格、建立具有顶点和的路径栅格,在等值线追踪过程中唯一确定下一个等值点,能够快速生成等值线图,然后根据自身特点对等值线进行光滑,最后,为了方便查看不同区域的噪声影响情况,需要对等值线用不同的颜色进行填充处理。     

A novel method for automatic face segmentation, facial feature extraction and tracking

The present paper describes a novel method for the segmentation of faces, extraction of facial features and tracking of the face contour and features over time. Robust segmentation of faces out of complex scenes is done based on color and shape information. Additionally, face candidates are verified by searching for facial features in the interior of the face. As interesting facial features we employ eyebrows, eyes, nostrils, mouth and chin. We consider incomplete feature constellations as well. If a face and its features are detected once reliably, we track the face contour and the features over time. Face contour tracking is done by using deformable models like snakes. Facial feature tracking is performed by block matching. The success of our approach was verified by evaluating 38 different color image sequences, containing features as beard, glasses and changing facial expressions.