保持细节特性的局部误差渐进网格简化算法

针对三维模型简化过程中生成渐进网格时存在局部区域精度与效率平衡优化的问题,提出一种基于局部区域环间法矢夹角变化的半边折叠渐进网格简化算法。首先,获取三维数据点的一环邻近点构成的邻域局部区域受重心度量距离约束的法矢,再获取与一环邻域三角形集合点有交集的三角形集合作为二环邻域区域;然后,以这两个局部区域法矢点乘的值为边折叠的折叠代价,该值越小表示该区域越趋向于平面,应优先简化,否则予以保留;最后,采用三角形内角判断方法来保证简化后网格中三角形的正则度,以减小变形引起的误差。实验结果表明,所提算法在三维模型渐进网格简化中局部细节特性保持和效率上得到较好的平衡,能够满足实际应用的需要。     

图像和视频的低多边形渲染

低多边形是近来艺术设计界的热门风格.为了提高图像和视频低多边形风格化的质量,提出一种基于边缘特征和超像素分割的图像和视频低多边形渲染方法.首先提取相邻超像素的交点以及对特征边和超像素边界的差集的均匀采样点作为三角网格顶点,并执行Delaunay三角剖分来生成初始三角网格;然后采用带约束的二次误差度量方法对生成的网格进行...     

Efficient edge-based object tracking

Object tracking has been widely applied to video surveillance, robot localization and human-computer interaction. In this paper, an edge-based tracking algorithm is proposed. We extract the feature points by efficiently utilizing the image edges in the object region. Then the parameter vector of the object's motion model is estimated based on minimizing the sum-of-squared differences between the reference feature points in the reference frame and the observed feature points in the tracking sequence frame. The experiments show that the edge-based tracking algorithm proposed by us can track object efficiently under uniform and varying illumination conditions.     

基于三角网格的交互式数据分割研究

为了有效地提高三角网格模型数据分割的效率和准确性,设计了一种交互式的数据分割算法--基于夹角追踪的区域边界生成方法.该方法在自动提取三角网格模型特征点的基础上,交互地选取区域边界的起点和终点,由起点和终点建立一个方向向量.沿着方向向量,以夹角追踪的方式查找其它的边界点,直到起点和终点在同一三角形中.根据三角网格所具有的特征,设计了新的区域边界光顺算法和域内顶点的查找方法.部分典型算例表明了该设计算法的正确性和有效性.     

基于时间一致性局部颜色特征的无纹理3D物体实时跟踪

针对无纹理3D物体跟踪算法在复杂背景和运动模糊的情况下容易跟踪失败、跟踪速度难以达到强实时等问题,提出一种基于时间一致性局部颜色特征的3D物体实时跟踪算法.首先在物体3D模型投影轮廓法向搜索线上计算像素颜色的加权均值作为局部颜色特征,增强颜色特征在复杂环境中的表征能力,并对局部颜色特征进行时间一致性更新,剔除前景背景颜色相似的局部颜色特征,以避免相似前景背景颜色导致的跟踪失败;然后定义基于局部颜色特征的能量函数,并推导该能量函数的解析导函数;最后改进了优化物体姿态的高斯牛顿法,通过添加阻尼参数防止姿态优化陷入局部极值,提高姿态估计精度和跟踪速度.实验使用7组测试视频验证文中算法,结果表明,该算法能更有效地克服复杂背景和运动模糊的干扰,在未使用并行计算的前提下可实现强实时跟踪.     

基于改进Canny边缘检测算子的电子稳像算法研究*

针对特征点匹配电子稳像算法中提取的特征点容易受到噪声干扰的问题,在研究传统的特征点匹配电子稳像算法的基础上,提出了一种将改进的Canny边缘检测技术应用于电子稳像中的方法。利用改进的Canny算子对图像进行边缘检测,并通过对边缘检测的图像进行合理分区,在子区域内选择灰度最大边缘点作为特征点,并采用距离不变准则对特征点的有效性进行验证,建立二维运动模型进行全局运动估计,以此确定仿射模型的运动参数;最后利用卡尔曼滤波的方法来进行运动补偿,从而达到输出稳定视频图像的目的。实验结果表明,用这种方法提取的特征点具有     

Combinatorial manifold mesh reconstruction and optimization from unorganized points with arbitrary topology

In this paper, a novel approach is proposed to reliably reconstruct the geometric shape of a physically existing object based on unorganized point cloud sampled from its boundary surface. The proposed approach is composed of two steps. In the first step, triangle mesh structure is reconstructed as a continuous manifold surface by imposing explicit relationship among the discrete data points. For efficient reconstruction, a growing procedure is employed to build the 2-manifold directly without intermediate 3D representation. Local and global topological operations with ensured completeness and soundness are defined to incrementally construct the 2-manifold with arbitrary topology. In addition, a novel criterion is proposed to control the growing process for ensured geometric integrity and automatic boundary detection with a non-metric threshold. The reconstructed manifold surface captures the object topology with the built-in combinatorial structure and approximates the object geometry to the first order. In the second step, new methods are proposed to efficiently obtain reliable curvature estimation for both the object surface and the reconstructed mesh surface. The combinatorial structure of the triangle mesh is then optimized by changing its local topology to minimize the curvature difference between the two surfaces. The optimized triangle mesh achieves second order approximation to the object geometry and can serve as a basis for many applications including virtual reality, computer vision, and reverse engineering.     

全局搜索和多实例判别特征的长时跟踪方法

长时目标跟踪相对于短时目标跟踪仍然是一个巨大的挑战. 然而现有的长时跟踪算法通常在面对目标频繁出现消失、目标外观发生剧变等挑战中表现不佳. 本文提出了一种基于局部搜索模块和全局搜索跟踪模块的全新、鲁棒且实时的长时跟踪框架. 局部搜索模块利用TransT短时跟踪器生成一系列候选框, 并通过置信度评分确定最佳候选框. 针对全局重新检测开发了一个新颖的全局搜索跟踪模块, 以Faster R-CNN为基础模型, 在RPN阶段与R-CNN阶段引入非局部操作和多级实例特征融合模块, 以充分挖掘目标实例级特征. 为了改进全局搜索跟踪模块的性能, 设计了双模板更新策略来提升跟踪器的鲁棒能力. 通过使用不同时间点上更新的模板能够更好地适应目标的变化. 根据局部或全局置信度分数判断目标是否存在, 并在下一帧中选择局部或全局搜索跟踪策略. 同时能够为局部搜索模块估计目标的位置和大小. 此外还为全局搜索跟踪器引入了排名损失函数, 隐式学习了区域提议与原始查询目标的相似度. 通过在多个跟踪数据集上进行大量实验对提出的跟踪框架进行了广泛评估. 结果一致表明, 本文提出的跟踪框架实现了令人满意的性能.     

Visual object tracking based on discriminant DCT features

In this paper, a visual object tracking method is proposed based on sparse 2-dimensional discrete cosine transform (2D DCT) coefficients as discriminative features. To select the discriminative DCT coefficients, we give two propositions. The propositions select the features based on estimated mean of feature distributions in each frame. Some intermediate tracking instances are obtained by (a) computing feature similarity using kernel, (b) finding the maximum classifier score computed using ratio classifier, and (c) combinations of both. Another intermediate tracking instance is obtained using incremental subspace learning method. The final tracked instance amongst the intermediate instances are selected by using a discriminative linear classifier learned in each frame. The linear classifier is updated in each frame using some of the intermediate tracked instances. The proposed method has a better tracking performance as compared to state-of-the-art video trackers in a dataset of 50 challenging video sequences.     

用于半自动视频对象提取的自适应网格图像分割

随着MPEG－4标准的发展和基于内容的视频处理研究,视频对象平面（VOP）的有效产生成为一个关键问题。本文提出一种基于区域的自适应网格彩色图像分割方法,可用于获得半自动视频对象跟踪和提取所需的初始VOP。该方法利用CIE L＊a*b＊色彩空间的特征量,对视频序列的第一帧进行三角形网格的分裂与合并。对MPEG－4标准测试序列的分割实验取得了较好的结果。     

基于空间几何结构的特征跟踪算法

针对传统的基于标记的增强现实系统场景受限等缺点,提出一种基于特征空间几何结构的无标记跟踪算法。在传统的金字塔Kanade-Lucas-Tomasi(KLT)跟踪算法基础上,通过建立图像的多尺度空间模型并在多尺度空间模型中对图像进行实时跟踪,同时根据图像特征间特有的空间几何结构信息优选跟踪特征点,解决了在多尺度变化情况下视频图像特征跟踪稳定性问题。实验结果表明,提出的跟踪算法在给定的数据库上性能高效稳定,与同类跟踪算法相比跟踪精度大幅提高,每帧重投影错误率均小于1像素,保持在亚像素级别。     

An algorithm for distorted fingerprint matching based on local triangle feature set

Coping with nonlinear distortions in fingerprint matching is a challenging task. This paper proposes a novel method, a fuzzy feature match (FFM) based on a local triangle feature set to match the deformed fingerprints. The fingerprint is represented by the fuzzy feature set: the local triangle feature set. The similarity between the fuzzy feature set is used to characterize the similarity between fingerprints. A fuzzy similarity measure for two triangles is introduced and extended to construct a similarity vector including the triangle-level similarities for all triangles in two fingerprints. Accordingly, a similarity vector pair is defined to illustrate the similarities between two fingerprints. The FFM method maps the similarity vector pair to a normalized value which quantifies the overall image to image similarity. The proposed algorithm has been evaluated with NIST 24 and FVC2004 fingerprint databases. Experimental results confirm that the proposed FFM based on the local triangle feature set is a reliable and effective algorithm for fingerprint matching with nonlinear distortions.     

Direct Model-Based Tracking of 3D Object Deformations in Depth and Color Video

The tracking of deformable objects using video data is a demanding research topic due to the inherent ambiguity problems, which can only be solved using additional assumptions about the deformation. Image feature points, commonly used to approach the deformation problem, only provide sparse information about the scene at hand. In this paper a tracking approach for deformable objects in color and depth video is introduced that does not rely on feature points or optical flow data but employs all the input image information available to find a suitable deformation for the data at hand. A versatile NURBS based deformation space is defined for arbitrary complex triangle meshes, decoupling the object surface complexity from the complexity of the deformation. An efficient optimization scheme is introduced that is able to calculate results in real-time (25 Hz). Extensive synthetic and real data tests of the algorithm and its features show the reliability of this approach.     

局部特征熵的网格非均匀简化算法

针对三维模型简化后的精度与效率上难以平衡的问题进行研究,提出一种局部特征熵的半边折叠非均匀网格简化算法。采用两次局部区域聚类探测,首先探测三维数据点所在边聚类局部区域,获取该探测区域法向量,其次以三维数据点临近点区域的重心约束来探测二次聚类区域法向量;根据信息熵的定义利,用两次探测的法向量之间夹角信息构建局部区域特征熵值做为半边折叠的代价,局部区域特征熵越大表示该区域越趋于平面,应优先简化,否则当保留;最后采用三角形内角判断方法来保留简化后网格中三角形的正则度,以减小变形引起的误差。实验结果表明,本算法在三维模型分均匀简化中在局部细节特性精度上和时间效率上能达到较优的平衡。     

Similarity-guided streamline placement with error evaluation

Most streamline generation algorithms either provide a particular density of streamlines across the domain or explicitly detect features, such as critical points, and follow customized rules to emphasize those features. However, the former generally includes many redundant streamlines, and the latter requires Boolean decisions on which points are features (and may thus suffer from robustness problems for real-world data). We take a new approach to adaptive streamline placement for steady vector fields in 2D and 3D. We define a metric for local similarity among streamlines and use this metric to grow streamlines from a dense set of candidate seed points. The metric considers not only Euclidean distance, but also a simple statistical measure of shape and directional similarity. Without explicit feature detection, our method produces streamlines that naturally accentuate regions of geometric interest. In conjunction with this method, we also propose a quantitative error metric for evaluating a streamline representation based on how well it preserves the information from the original vector field. This error metric reconstructs a vector field from points on the streamline representation and computes a difference of the reconstruction from the original vector field.     

公共区域监控视频数据目标特征跟踪定位方法

为了提高公共区域监控视频的目标定位检测能力,需要进行目标特征跟踪定位算法设计,提出一种基于图像超分辨率重建的公共区域监控视频数据目标特征跟踪定位方法。构建公共区域监控视频的三维图像重建模型,采用边缘层的高分辨融合方法进行公共区域监控视频图像数据的三维结构重组,提取公共区域监控视频的关键特征点,用图像退化模型进行公共区域监控视频数据目标特征检测,结合线性滤波模型使得监测输出图像满足最优匹配特征解,提高对公共区域监控视频数据目标特征跟踪能力。引入引导滤波方法进行公共区域监控视频数据的图像超分辨重建,实现对目标特征准确跟踪定位。仿真结果表明,采用该方法进行公共区域监控视频数据目标特征跟踪定位的准确性较高,图像重建能力较强,归一化均方根误差较小。     

基于背景配准的矿井危险区域视频目标检测算法

针对矿井危险区域视频监控视场背景复杂,难以实现视频目标精确提取的问题,提出了一种基于背景配准的视频目标检测算法。该算法实现步骤:提取SIFT特征点,计算特征点区域H-S光流矢量;通过区域运动特性分析提取出背景运动区域,对背景运动区域特征点做帧间匹配;计算仿射参数,配准差分后提取出精确的目标区域。实验结果表明,该算法能够去除前景目标特征点对背景配准的影响,可获得较为精确的目标区域。     

面向多目标跟踪系统的专用循环目标检测器

多目标跟踪技术在视频分析、信号处理等领域有着广泛的应用。在现代多目标跟踪系统通常遵循的“按检测跟踪”模式中,目标检测器的性能决定了多目标跟踪任务的跟踪精度和速度。为提高多目标跟踪系统跟踪性能,提出了面向多目标跟踪系统的专用循环目标检测器,它利用视频帧序列间高度相似性的特点,依据先前帧的目标位置信息和当前帧相对于先前帧的变化得分图来选取候选框,解决了传统二阶段目标检测器中使用候选框推荐网络带来的参数量和计算量大的问题,同时融合了目标外观特征提取分支,进一步减少了多目标跟踪系统整体运行时间。实验表明,专用循环目标检测器及其他最先进的检测器分别应用于多目标跟踪系统,采用专用循环目标检测器时能够在保证多目标跟踪系统跟踪精度的情况下提升跟踪速度。     

OPTICS聚类与目标区域概率模型的多运动目标跟踪

目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。     

Online parameter tuning for object tracking algorithms

Object tracking quality usually depends on video scene conditions (e.g. illumination, density of objects, object occlusion level). In order to overcome this limitation, this article presents a new control approach to adapt the object tracking process to the scene condition variations. More precisely, this approach learns how to tune the tracker parameters to cope with the tracking context variations. The tracking context, or context, of a video sequence is defined as a set of six features: density of mobile objects, their occlusion level, their contrast with regard to the surrounding background, their contrast variance, their 2D area and their 2D area variance. In an offline phase, training video sequences are classified by clustering their contextual features. Each context cluster is then associated to satisfactory tracking parameters. In the online control phase, once a context change is detected, the tracking parameters are tuned using the learned values. The approach has been experimented with three different tracking algorithms and on long, complex video datasets. This article brings two significant contributions: (1) a classification method of video sequences to learn offline tracking parameters and (2) a new method to tune online tracking parameters using tracking context.