基于平面区域跟踪的目标位姿参数自动测量

提出一种从序列图像中自动跟踪测量目标位置和姿态参数的方法。利用单应性原理和上一帧图像中目标位姿参数的测量结果,将目标上的典型平面区域重建为同时含有几何信息和亮度信息的平面区域模板;然后根据投影方程,将该模板在一定的位置姿态参数下进行投影仿真成像,当模板的仿真成像结果与当前帧图像中的该平面区域达到最佳匹配时,认为此时仿真成像的位置姿态参数即为当前帧图像的测量结果。通过对该匹配问题进行最优化建模和求解,实现了序列图像中目标位姿参数的自动测量。实验结果表明,本文方法能够在序列图像中对含有典型平面区域的目标实现较高精度的自动跟踪测量。     

A Bayesian, exemplar-based approach to hierarchical shape matching

This paper presents a novel probabilistic approach to hierarchical, exemplar-based shape matching. No feature correspondence is needed among exemplars, just a suitable pairwise similarity measure. The approach uses a template tree to efficiently represent and match the variety of shape exemplars. The tree is generated offline by a bottom-up clustering approach using stochastic optimization. Online matching involves a simultaneous coarse-to-fine approach over the template tree and over the transformation parameters. The main contribution of this paper is a Bayesian model to estimate the a posteriori probability of the object class, after a certain match at a node of the tree. This model takes into account object scale and saliency and allows for a principled setting of the matching thresholds such that unpromising paths in the tree traversal process are eliminated early on. The proposed approach was tested in a variety of application domains. Here, results are presented on one of the more challenging domains: real-time pedestrian detection from a moving vehicle. A significant speed-up is obtained when comparing the proposed probabilistic matching approach with a manually tuned nonprobabilistic variant, both utilizing the same template tree structure.     

Hybrid genetic optimization and statistical model based approachfor the classification of shadow shapes in sonar imagery

We present an original statistical classification method using a deformable template model to separate natural objects from man-made objects in an image provided by a high resolution sonar. A prior knowledge of the manufactured object shadow shape is captured by a prototype template, along with a set of admissible linear transformations, to take into account the shape variability. Then, the classification problem is defined as a two-step process: 1) the detection problem of a region of interest in the input image is stated as the minimization of a cost function; and 2) the value of this function at convergence allows one to determine whether the desired object is present or not in the sonar image. The energy minimization problem is tackled using relaxation techniques. In this context, we compare the results obtained with a deterministic relaxation technique and two stochastic relaxation methods: simulated annealing and a hybrid genetic algorithm. This latter method has been successfully tested on real and synthetic sonar images, yielding very promising results     

基于子区域匹配的稀疏表示跟踪算法

经典稀疏表示目标跟踪算法在处理复杂视频时不免出现跟踪不稳定情况且当目标发生遮挡时易发生漂移现象。针对这一问题,提出一种基于子区域匹配的稀疏表示跟踪算法。首先,将初始目标模板划分为若干子区域,利用LK图像配准算法建立观测模型预测下一帧目标运动状态。然后,对预测的目标模型区域进行同等划分,并在匹配过程中寻找最优子区域。最后,在模板更新过程中引入一种新的模板校正机制,能够有效克服漂移现象。将该算法与多种目标跟踪算法在不同视频序列下进行对比,实验结果表明在目标发生遮挡、运动、光照影响及复杂背景等情况下该算法具有较为理想的跟踪效果,并与经典稀疏表示跟踪算法相比具有较好的跟踪性能。     

Robust visual tracking with structured sparse representation appearance model

In this paper, we present a structured sparse representation appearance model for tracking an object in a video system. The mechanism behind our method is to model the appearance of an object as a sparse linear combination of structured union of subspaces in a basis library, which consists of a learned Eigen template set and a partitioned occlusion template set. We address this structured sparse representation framework that preferably matches the practical visual tracking problem by taking the contiguous spatial distribution of occlusion into account. To achieve a sparse solution and reduce the computational cost, Block Orthogonal Matching Pursuit (BOMP) is adopted to solve the structured sparse representation problem. Furthermore, aiming to update the Eigen templates over time, the incremental Principal Component Analysis (PCA) based learning scheme is applied to adapt the varying appearance of the target online. Then we build a probabilistic observation model based on the approximation error between the recovered image and the observed sample. Finally, this observation model is integrated with a stochastic affine motion model to form a particle filter framework for visual tracking. Experiments on some publicly available benchmark video sequences demonstrate the advantages of the proposed algorithm over other state-of-the-art approaches.     

基于平面模板的机器人TCF标定

针对末端夹持激光位移传感器的机器人TCF(tool/terminal control frame)标定,提出一种基于平面模板的标定方法,机器人操作末端执行器使激光位移传感器在不同位形下对平面模板进行测量,再通过非线性最小二乘拟合求解标定参数.为了减小问题的奇异性,对标定时应该采取的参数控制策略进行了定性分析.该标定方法只需要一块表面精度较高的平面模板,而无需其它测量仪器,标定过程简单、易操作,且易于实现自动化.仿真和实验结果表明本文提出的标定方法具有较高的精度.     

Object tracking using deformable templates

We propose a method for object tracking using prototype-based deformable template models. To track an object in an image sequence, we use a criterion which combines two terms: the frame-to-frame deviations of the object shape and the fidelity of the modeled shape to the input image. The deformable template model utilizes the prior shape information which is extracted from the previous frames along with a systematic shape deformation scheme to model the object shape in a new frame. The following image information is used in the tracking process: 1) edge and gradient information: the object boundary consists of pixels with large image gradient, 2) region consistency: the same object region possesses consistent color and texture throughout the sequence, and 3) interframe motion: the boundary of a moving object is characterized by large interframe motion. The tracking proceeds by optimizing an objective function which combines both the shape deformation and the fidelity of the modeled shape to the current image (in terms of gradient, texture, and interframe motion). The inherent structure in the deformable template, together with region, motion, and image gradient cues, makes the proposed algorithm relatively insensitive to the adverse effects of weak image features and moderate amounts of occlusion     

一种基于随机段的固定音频检索方法

在固定音频检索的整体检索方法中,当检索目标较长时,检索时间会变得很长。为了减小检索时间,提出了一种基于随机段的音频检索方法。把整个检索过程分成随机段检索和整体匹配两个阶段：随机段检索是从参考模板中随机选择一段（随机段）作为检索目标进行检索;整体匹配是在随机段检索出的基础上,判断潜在目标信号是否为参考模板。把这种随机检索的方法应用到计算特征距离和直方图交集方法中,结果证明该检索方法的准确率可以达到90%以上,而且平均检索时间可以降低到随机段与参考模板的比值和整体检索时间的积。     

A Linear-Time Algorithm for Star-Shaped Drawings of Planar Graphs with the Minimum Number of Concave Corners

A star-shaped drawing of a graph is a straight-line drawing such that each inner facial cycle is drawn as a star-shaped polygon, and the outer facial cycle is drawn as a convex polygon. In this paper, we consider the problem of finding a star-shaped drawing of a biconnected planar graph with the minimum number of concave corners. We first show new structural properties of planar graphs to derive a lower bound on the number of concave corners. Based on the lower bound, we prove that the problem can be solved in linear time by presenting a linear-time algorithm for finding a best plane embedding of a biconnected planar graph with the minimum number of concave corners. This is in spite of the fact that a biconnected planar graph may have an exponential number of different plane embeddings.     

基于人类记忆模型的粒子滤波鲁棒目标跟踪算法

当目标被场景中的物体或其它运动目标遮挡,或者目标姿态发生很大改变时,粒子滤波器就会失效。为解决这类问题,受人类记忆机制的启发,文中将人类记忆模型引入到粒子滤波器模板更新过程,提出一种基于记忆的粒子滤波器。每个模板都要经过瞬时记忆、短时记忆和长时记忆3个空间的传输和处理。该粒子滤波器能记住曾经出现的目标模板,从而能更快地适应目标姿态的变化。实验结果验证了该算法的有效性。     

级联特征融合孪生网络目标跟踪算法研究

在光照变化、遮挡、背景相似、变形等复杂情况下,目标跟踪过程中难以精确地提取丰富的特征信息,容易导致目标跟踪出现漂移或者跟踪丢失.由于多层神经网络的浅层特征具有高分辨率,适合于目标定位;深层特征具有丰富的语义信息,适合于目标分类.充分利用这一优势,提出了一种级联特征融合的孪生网络目标跟踪算法.对ResNet-50网络进行...     

基于多任务混合噪声分布模型表示的视频跟踪

视觉跟踪中,目标信息是不确定的非线性变化过程。随时间和空间而变化的复杂动态数据中学习出较为精确的目标模板并用它来线性表示候选样本外观模型,从而使跟踪器较好地适应跟踪作业中内在或外在因素所引起的目标外观变化是视觉目标跟踪研究的重点。提出一种新颖的多任务混合噪声分布模型表示的视频跟踪算法,将候选样本外观模型假设为由一组目标模板和最小重构误差组成的多任务线性回归问题。利用经典的增量主成分分析法从高维数据中学习出一组低维子空间基向量（模板正样本）,并在线实时采样一些特殊的负样本加以扩充目标模板,再利用扩充后的新模板和独立同分布的高斯-拉普拉斯混合噪声来线性拟合当前时刻的候选目标外观模型,最后计算候选样本和真实目标之间的最大似然度,从而准确捕捉当前时刻的真实目标。在一些公认测试视频上的实验结果表明,该算法将能够在线学习较为精准的目标模板并定期更新目标在不同状态时的特殊信息,使得跟踪器始终保持最佳的状态,从而良好地适应不断发生变化的视觉信息（姿态、光照、遮挡、尺度、背景扰乱及运动模糊等）,表现出更好的鲁棒性能。     

A bivariate autoregressive technique for analysis andclassification of planar shapes

A bivariate autoregressive model is introduced for the analysis and classification of closed planar shapes. The boundary coordinate sequence of a digitized binary image is sampled to produce a polygonal approximation to an object's shape. This circular sample sequence is then represented by a vector autoregressive difference equation which models the individual Cartesian coordinate sequences as well as coordinate interdependencies. Several classification features which are functions or transformations of the estimated coefficient matrices and the associated residual error covariance matrices are developed. These features are shown to be invariant to object transformations such as translation, rotation, and scaling. Laboratory experiments involving object sets representative of industrial shapes are presented. Superior classification results are demonstrated     

Multiview camera-calibration framework for nonparametric distortions removal

This paper proposes a technique that uses a planar calibration object and projective constraints to calibrate parametric and nonparametric distortions. An iterative surface fitting is first used to constrain a B-spline distortion model by fusing the corrective distortion maps obtained from multiple views. The model is then refined within the whole camera-calibration process.     

多线程下的视频微动目标检测与提取算法研究

在多线程并发情况下提取实时视频图像中的微动人物目标,基于多线程的思想,在多路并发请求的情况下,运用肤色检测和聚类思想对图像中的人物进行提取分割,并对处理过程中的多路访问资源的问题进行访问冲突避免。首先采用边缘检测的思想对运动目标边缘进行范围定位,然后采用聚类算法和肤色检测等方法进行目标模板的提取和完整性补充,根据得到的目标模板结合原始图片对应像素点坐标,将原始像素点颜色着色到新的背景模型的对应位置。仿真结果表明,在保证处理质量的情况下,提出的基于多线程思想的算法能满足实际工程应用实时处理的要求。     

Representation and shape matching of 3-d objects

A three-dimensional scene analysis system for the shape matching of real world 3-D objects is presented. Various issues related to representation and modeling of 3-D objects are addressed. A new method for the approximation of 3-D objects by a set of planar faces is discussed. The major advantage of this method is that it is applicable to a complete object and not restricted to single range view which was the limitation of the previous work in 3-D scene analysis. The method is a sequential region growing algorithm. It is not applied to range images, but rather to a set of 3-D points. The 3-D model of an object is obtained by combining the object points from a sequence of range data images corresponding to various views of the object, applying the necessary transformations and then approximating the surface by polygons. A stochastic labeling technique is used to do the shape matching of 3-D objects. The technique matches the faces of an unknown view against the faces of the model. It explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. It is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. The objective here is to match the individual views of the object taken from any vantage point. Details of the algorithm are presented and the results are shown on several unknown views of a complicated automobile casting.     

Model construction and shape recognition from occluding contours

A technique is presented for recognizing a 3D object (a model in an image library) from a single 2D silhouette using information such as corners (points with high positive curvatures) and occluding contours, rather than straight line segments. The silhouette is assumed to be a parallel projection of the object. Each model is stored as a set of the principal quadtrees, from which the volume/surface octree of the model is generated. Feature points (i.e. corners) are extracted to guide the recognition process. Four-point correspondences between the 2D feature points of the observed object and 3D feature points of each model are hypothesized, and then verified by applying a variety of constraints to their associated viewing parameters. The result of the hypothesis and verification process is further validated by 2D contour matching. This approach allows for a method of handling both planar and curved objects in a uniform manner, and provides a solution to the recognition of multiple objects with occlusion as demonstrated by the experimental results     

A Stochastic Deterministic Model Predictive Control: Its Accuracy

The loss in control quality due to the use of a suboptimal deterministic model predictive control for a stochastic object is evaluated. Special attention is paid to the estimation of prediction bias in stochastic deterministic-predictive control. The results are illustrated by an example of a stochastic deterministic model predictive control for missing data.     

一种基于视觉知识加工模型的目标识别方法

提出了一种基于视觉知识加工模型的目标识别方法. 该加工模型结合目标定位、模板筛选和MFF-HMAX (Hierarchical model and X based on multi-feature fusion)方法对图像进行学习, 形成相应的视觉知识库, 并用于指导目标的识别. 首先, 利用Itti模型获取图像的显著区, 结合视觉通路中What和Where通道的位置、大小等特征以及视觉知识库中的定位知识确定初期候选目标区域; 然后, 采用二步去噪处理获取候选目标区域, 利用MFF-HMAX模型提取目标区域的颜色、亮度、纹理、轮廓、大小等知识特征, 并采用特征融合思想将各项特征融合供目标识别; 最后, 与单一特征以及目前的流行方法进行对比实验, 结果表明本文方法不仅具备较高的识别效果, 同时能够模仿人脑学习视觉知识的过程形成视觉知识库.