基于马尔可夫随机场与活动轮廓的运动目标分割

针对视频序列图像中的运动目标分割，提出了将马尔可夫随机场模型和活动轮廓模型相结合的运动目标分割算法。该算法首先利用马尔可夫随机场模型的运动检测算法，得到运动目标的初始模板。在此基础上提取出活动轮廓模型的初始轮廓点，然后构造活动轮廓模型的能量函数。用改进的贪婪算法求得能量函数最小值，提取出运动目标的精确轮廓，从而得到具有精确边缘的运动目标。实验结果表明该算法能有效地分割和提取出视频序列中的运动目标。     

An Occlusion-aware Edge-Based Method for Monocular 3D Object Tracking using Edge Confidence

We propose an edge-based method for 6DOF pose tracking of rigid objects using a monocular RGB camera. One of the critical problem for edge-based methods is to search the object contour points in the image corresponding to the known 3D model points. However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions. In this paper, we propose a novel edge-based 3D objects tracking method to tackle this problem. To search the object contour points, foreground and background clutter points are first filtered out using edge color cue, then object contour points are searched by maximizing their edge confidence which combines edge color and distance cues. Furthermore, the edge confidence is integrated into the edge-based energy function to reduce the influence of false contour points caused by cluttered backgrounds and partial occlusions. We also extend our method to multi-object tracking which can handle mutual occlusions. We compare our method with the recent state-of-art methods on challenging public datasets. Experiments demonstrate that our method improves robustness and accuracy against cluttered backgrounds and partial occlusions.     

基于动态轮廓模型的羽毛分割改进算法

从羽毛图像中分割毛杆适合采用动态轮廓模型,而原始原模型易受局部强边缘干扰产生偏差,且计算规模偏大。根据毛杆的特性,提出用毛杆中心线和毛杆宽度来代替毛杆轮廓,把模型中二维轮廓曲线变化成两个相互独立的一维函数,并据此修改能量方程。改进算法利用对称性避免强边缘干扰,减少了计算规模,能实现全自动分割。实验表明该算法具有较强的抗噪性,使分割毛杆效果良好,能满足工业需要。     

一种改进的活动区域轮廓模型 ——无需水平集重新初始化

基于区域的活动区域模型已经成功应用在图像分割、目标跟踪等领域,较之基于梯度的活动轮廓模型具有很多优点。但是,这些水平集模型在演化过程中,为了保持为符号距离函数,必须对其重新初始化,降低了曲线演化速度,增加了实现复杂度。为了解决重新初始化问题,在测地活动区域模型的能量函数中,加入惩罚项来约束水平集保持为符号距离函数,无需再重新初始化,极大地提高了演化速度。将其运用在纹理图像、脑MR图像分割以及视频跟踪中,实验证明该模型是有效的。     

一种基于主动轮廓模型的MRI医学图像序列边缘提取算法

为了提取核共振成像图像序列的边缘，提出了一种改进主动轮廓模型的边缘提取算法。通过调整原始公式的一些参数使得该模型不但能精确地提取图像中的凸形物体的边缘，而且能够接近边缘的凹陷处；引入自适应改变大小的外部约束能量来增大外能的吸引范围，使控制点能够不依赖于初始轮廓而快速地收敛到目标的真实轮廓；结合匹配技术，提高边缘提取结果层间传递的精度。实验结果表明该算法仅需少量用户交互就能快速准确地从医学图像序列中提取出感兴趣的物体边缘。     

基于区域能量最小和主动轮廓模型的医学目标提取

针对传统主动轮廓模型在目标强边缘处容易产生振荡和弱边缘处容易泄露的缺点,提出了一种基于区域能量最小和主动轮廓模型的医学目标提取模型。这一基于目标灰度统计概率和水平集的主动轮廓分割模型,把能量函数表示为在目标区域内对像素点属于目标概率的积分,并在水平集框架下对能量函数最小化,得到分割的迭代方程;同时,通过附加的速度约束项,使得主动轮廓在越过目标边缘时降低速度,大大提高了分割的收敛性和准确度。通过大量冠状动脉和二尖瓣的分割实验以及与几种传统主动轮廓模型和手工提取的比较,表明该模型在医学图像分割方面的健壮性、准确性和有效性。     

基于自适应活动轮廓模型的实时手势跟踪

在基于视觉的手势分析与识别中，一个关键环节是手势跟踪。本文提出了基于颜色信息的自适应活动轮廓模型，并与均值漂移算法相互融合，实现图像序列的实时手势跟踪。跟踪算法分为两步进行，首先应用均值漂移算法实现手部区域的定位，然后基于自适应活动轮廓模型提取手部轮廓。在跟踪过程中，轮廓提取为下一帧的区域定位更新搜索窗口，提高了搜索效率，使目标跟踪达到实时性要求。同时，本文根据跟踪区域模板与目标模板的相似性度量Bhattacaryya系数给出了在跟踪目标被遮挡时的处理方法，有效地解决了这一难题。实验结果证明了在无遮挡和遮挡两种情况下算法均能实现准确、实时的手势跟踪。     

Using incremental subspace and contour template for object tracking

Object tracking in the presence of appearance variation and occlusion is a hot topic in research, many algorithms were proposed in recent years. Early contour tracking algorithms used particle filter in a high dimensional space. In practice, contour points can move independently, hence contour deformation forms a high dimensional deformation space. As a result, the application of particle filter is calculation expensive. In this paper, we address the problem of tracking contour in complex environments by involving subspace and a contour template. Specifically, our algorithm tracks the global motion and the local contour deformation separately. We track the global motion by weighted distance to subspace, which is adaptive to the complex environment variation by incremental learning, and then use contour model to track local deformation and evolve the contour to the edge points. The experimental results show that our method can track object contour undergoing partially occlusion and shape deforming, which verify the effectiveness of the proposed algorithm.     

基于GVF Snake模型的人脸轮廓提取

活动轮廓模型是计算机视觉领域的重要研究方向。针对传统的活动轮廓模型（Snake模型）对凹形轮廓处理效果差、初始轮廓必须充分接近图像边缘的缺点,通过改进外部能量项,提出了一种基于梯度矢量流活动轮廓模型的人脸轮廓提取算法。该算法把梯度矢量场作为外部能量场,克服了传统Snake模型力场范围小以及不能收敛于凹形边缘的缺点。实验结果表明,该方法能够快速、准确地提取人脸轮廓。     

基于自适应模板更新的改进孪生卷积网络目标跟踪算法

现有的孪生网络目标跟踪算法采用边界框模板进行跟踪,在目标形变、遮挡等干扰下很容易导致跟踪漂移。在轮廓检测网络和孪生卷积网络(Siamese)跟踪网络的基础上,提出一种基于深度轮廓模板更新的改进孪生卷积网络目标跟踪算法。利用轮廓检测网络获取目标边缘轮廓,降低背景杂波干扰;利用改进的Siamese网络获得轮廓模板和搜索区域的深度特征;通过相似性匹配获得最优跟踪目标。仿真实验结果表明,所提出的改进模型能够提高目标形变、遮挡等干扰下目标跟踪性能,具有较高的工程应用价值。     

Decoupled active contour (DAC) for boundary detection

The accurate detection of object boundaries via active contours is an ongoing research topic in computer vision. Most active contours converge toward some desired contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. Such an approach is elegant, but suffers from a slow convergence rate and frequently misconverges in the presence of noise or complex contours. To address these limitations, a decoupled active contour (DAC) is developed which applies the two energy terms separately. Essentially, the DAC consists of a measurement update step, employing a Hidden Markov Model (HMM) and Viterbi search, and then a separate prior step, which modifies the updated curve based on the relative strengths of the measurement uncertainty and the nonstationary prior. By separating the measurement and prior steps, the algorithm is less likely to misconverge; furthermore, the use of a Viterbi optimizer allows the method to converge far more rapidly than energy-based iterative solvers. The results clearly demonstrate that the proposed approach is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to five other published methods and across many image sets, the DAC is found to be faster with better or comparable segmentation accuracy.     

基于阈值判断的CamShift目标跟踪算法

针对CamShift算法只利用目标的颜色信息,在跟踪过程中,易受目标相似物、遮挡以及光照等复杂背景影响导致目标搜索窗口发散,跟踪稳定性能降低,提出了一种基于阈值判断的目标跟踪方法。该方法将OTSU法和Snake模型结合,利用OTSU法以最佳阈值对图像进行分割,分离前景区域和背景区域,初步提取目标轮廓作为Snake模型的初始轮廓,经收敛得到目标的精准轮廓,利用轮廓外接最小矩形框内的像素计算目标质心,判断与CamShift算法中目标搜索窗口质心之间的欧式距离,如果未超出阈值,则直接使用CamShift算法跟踪目标,反之,则将计算出的目标质心作为CamShift算法中当前帧目标搜索窗口的质心跟踪目标。实验结果表明,该算法跟踪目标具有较好的实时性,跟踪性能稳定、可靠。     

基于局部模型匹配的目标轮廓跟踪

在复杂背景下,传统轮廓跟踪方法会发生漂移,甚至丢失目标。针对上述问题,提出一种基于局部模型匹配（LMM）的目标轮廓跟踪算法。利用超像素技术结合EMD相似性度量构建局部特征模型,从而进行局部模型匹配。结合粒子滤波的Snake模型作提取目标轮廓,实现目标轮廓精确跟踪。实验结果表明,该算法在目标形变、部分遮挡、复杂背景等条件下均具有较高的跟踪成功率。与多种目标轮廓跟踪算法进行对比,该算法具有较高的准确性和鲁棒性。     

Accurate object contour tracking based on boundary edge selection

In this paper, a novel method for accurate subject tracking, by selecting only tracked subject boundary edges in a video stream with a changing background and moving camera, is proposed. This boundary edge selection is achieved in two steps: (1) removing background edges using edge motion, and from the output of the previous step, (2) selecting boundary edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant edges, by only selecting boundary edge pixels. In order to remove background edges using edge motion, the tracked subject motion is computed and edge motions and edges having different motion directions from the subjects are removed. In selecting boundary edges using the normal contour direction, the image gradient values on every edge pixel are computed, and edge pixels with large gradient values are selected. Multi-level Canny edge maps are used to obtain proper details of a scene. Multi-level edge maps allow tracking, even though the tracked object boundary has complex edges, since the detail level of an edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.     

A background robust active appearance model using active contour technique

This paper proposes an active contour-based active appearance model (AAM) that is robust to a cluttered background and a large motion. The proposed AAM fitting algorithm consists of two alternating procedures: active contour fitting to find the contour sample that best fits the face image and then the active appearance model fitting over the best selected contour. We also suggest an effective fitness function for fitting the contour samples to the face boundary in the active contour technique; this function defines the quality of fitness in terms of the strength and/or the length of edge features. Experimental results show that the proposed active contour-based AAM provides better accuracy and convergence characteristics in terms of RMS error and convergence rate than the existing robust AAM. The combination of the existing robust AAM and the proposed active contour-based AAM (AC-R-AAM) had the best accuracy and convergence performances.     

不用高斯平滑的边缘活动轮廓模型

在基于边缘的活动轮廓模型中,边缘停止函数的选择是十分重要的。边缘停止函数是一个单调递减的正函数和高斯平滑后的图像梯度模的复合函数。基于这种边缘停止函数的活动轮廓模型存在两个缺点：一是在同质区域演化速度慢;二是图像需要预先进行高斯平滑（滤波）,但平滑噪声的同时,也平滑了目标边缘,可能使分割不够准确。提出一个新的不用高斯平滑的边缘停止函数。实验表明,基于这种边缘停止函数的活动轮廓模型能够减少迭代次数与分割时间约50%。     

基于LBF方法的测地线活动轮廓模型

经典的测地线活动轮廓模型分割含有弱边界的目标时,难以得到真实边界。为解决这一问题,文中将结合局部二元拟合(LBF)方法和测地线活动轮廓模型的优点,提出一种基于LBF方法的测地线活动轮廓模型。首先,将LBF方法的能量泛函进行归一化处理,取代测地线活动轮廓模型的边缘停止函数。其次,构建梯度下降流,促使轮廓曲线运动到目标边界上。最后,对5组含有弱边界的图像进行仿真实验。实验结果表明,文中模型能准确分割含有弱边界的目标,具有抗噪性,同时对初始曲线的位置不敏感,优于其它常见改进的测地线活动轮廓模型。     

Object's contour detection scheme based on image fusion

Three novel object's contour detection schemes based on image fusion are proposed in this paper. In these schemes an active contour model is applied to detect the object's contour edge. Since an object's contour in an infrared (IR) image is usually clearer than that in a visible image, the convergent active contour in a visible image is improved with that in an IR image. The first contour detection scheme is realized by revising the shape-preserving active contour model. The second scheme minimizes the B-spline L ₂ norm's square of the difference of the B-spline control point vectors in two modal images. Contour tracking and extraction experiments indicate that the first scheme outperforms the second one. Moreover, a third scheme based on the active contour and pixel-level image fusion is proposed for images with incomplete but complementary scene information. An example using contour extraction of a partially hidden tank proves its efficacy.     

基于均值漂移和边缘检测的轮廓跟踪算法

实时的轮廓跟踪算法可以为视频监控系统提供物体的轮廓信息以供对物体类别、物体行为等进行识别.提出一种基于均值漂移和边缘检测的轮廓跟踪算法.方法中,首先利用均值漂移算法跟踪得到目标物体的中心位置,同时用高斯统计模型进行背景更新,从前景图像和背景图像中分别得到具有相同位置和大小的前景矩形区域和背景矩形区域,然后用背景分割的方法得到目标物体区域,再对目标物体区域进行边缘检测就得到了目标物体的轮廓,进而实现了对目标物体的轮廓跟踪.实验表明,可以实时、准确、稳定地对目标物体进行轮廓跟踪.