Multi-scale 2D tracking of articulated objects using hierarchical spring systems

This paper presents a flexible framework to build a target-specific, part-based representation for arbitrary articulated or rigid objects. The aim is to successfully track the target object in 2D, through multiple scales and occlusions. This is realized by employing a hierarchical, iterative optimization process on the proposed representation of structure and appearance. Therefore, each rigid part of an object is described by a hierarchical spring system represented by an attributed graph pyramid. Hierarchical spring systems encode the spatial relationships of the features (attributes of the graph pyramid) describing the parts and enforce them by spring-like behavior during tracking. Articulation points connecting the parts of the object allow to transfer position information from reliable to ambiguous parts. Tracking is done in an iterative process by combining the hypotheses of simple trackers with the hypotheses extracted from the hierarchical spring systems.     

Generating compact representations of static scenes by means of 3D object hierarchies

This paper presents a new heuristic algorithm for computing a compact hierarchical representation of the objects contained in a 3D static scene. The algorithm first builds a fully-connected adjacency graph that keeps the costs of grouping the different pairs of objects of the scene. Afterward, the graph’s minimum spanning tree is computed and its edges sorted in ascending order according to their cost. Next, from that sorted list, a cost-based clustering technique is applied, thus generating new objects at a higher level in the hierarchy. A new object can be defined after merging two or more objects according to their corresponding linking costs. The algorithm starts over by generating a new adjacency graph from those new objects, along with the objects that could not be merged before. The iterative process is applied until an adjacency graph with a single object is obtained. The latter is the root of the hierarchical representation. Balance and coherence of the hierarchy, in which spatially close objects are also structurally close, is achieved by defining an appropriate cost function. The proposed technique is evaluated upon several 3D scenes and compared to a previous technique. In addition, the benefits of the proposed technique with respect to techniques based on octrees and kd-trees are analyzed in terms of a practical application.     

深度图像的分层图割算法研究

以深度图像为分割对象,在迭代图割算法的基础上,通过引入分层机制加快图割执行速度,并通过引入平衡因子来平衡颜色纹理和深度之间的重要程度,从而有效地对深度图像进行分割。利用平衡因子可以在深度信息能够明显区分前背景的情况下,重点利用深度信息来分割图像,反之则重点利用颜色和纹理信息。而在迭代图割算法中,分层机制的引入能够在不降低分割精度的情况下有效地减少图割算法的执行时间。     

Computing the generalized aspect graph for objects with movingparts

Algorithms for computing the aspect graph representation are generalized to include a larger, more realistic domain of objects known as articulated assemblies those objects composed of rigid parts with articulated connections allowed between parts. The generalization suggests two slightly different representations: one that directly summarizes the possible general views of the object and another (hierarchical) form summarizing the possible general configurations and their respective views. Algorithms are outlined for computing both representations. The generalized aspect graphs of assemblies formed using translational connections are examined     

Symmetry Hierarchy of Man‐Made Objects

We introduce symmetry hierarchy of man‐made objects, a high‐level structural representation of a 3D model providing a symmetry‐induced, hierarchical organization of the model's constituent parts. Given an input mesh, we segment it into primitive parts and build an initial graph which encodes inter‐part symmetries and connectivity relations, as well as self‐symmetries in individual parts. The symmetry hierarchy is constructed from the initial graph via recursive graph contraction which either groups parts by symmetry or assembles connected sets of parts. The order of graph contraction is dictated by a set of precedence rules designed primarily to respect the law of symmetry in perceptual grouping and the principle of compactness of representation. We show that symmetry hierarchy naturally implies a hierarchical segmentation that is more meaningful than those produced by local geometric considerations. We also develop an application of symmetry hierarchies for structural shape editing.     

基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法

刚性目标轮廓具有明显几何特性且不易受光照、纹理和颜色等因素影响.结合上述特性和图像稀疏表示原理,提出一种适用于刚性目标的分级检测算法.在基于部件模型(Part-based model, PBM)的框架下,采用匹配追踪算法将目标轮廓自适应地稀疏表示为几何部件的组合,根据部件与目标轮廓的匹配度,构建描述部件空间关系的有序链式结构.利用该链式结构的有序特性逐级缩小待检测范围,以匹配度为权值对各级部件显著图进行加权融合生成目标显著图. PASCAL图像库上的检测结果表明,该检测方法对具有显著轮廓特征的刚性目标有较好的检测结果,检测时耗较现有算法减少约60%~90%.     

A new pyramid-based color image representation for visual localization

This paper presents a new algorithm to extract a color graph from a real image so that it can be efficiently represented in a compact way. The proposal presents two main novelties. First, a new PCA-based color feature vector has been defined. This vector is resistant against illumination changes, noise and microtextures. Second, the image is segmented by means of a fast, hierarchical split and merge algorithm based on a pyramid structure that implicitly provides a color graph. In order to test the suitability of the proposed representation technique, simple localization experiments have been conducted in different real environments.     

一种动态场景下基于时空信息的视频对象提取算法

在实际应用中,许多视频序列具有运动背景,使得从其中提取视频对象变得复杂,为此提出了一种基于运动估计和图形金字塔的动态场景下的视频对象提取算法。该算法首先引入了相位相关法求取运动向量,因避免了视频序列中光照变化的影响,故可提高效率和稳健性;接着再根据参数模型进行全局运动估计来得到最终运动模板;然后利用图形金字塔算法对当前模板内图像区域进行空间分割,最终提取出语义视频对象。与现有算法相比,对于从具有动态场景的视频流中提取运动对象的情况,由于使用该算法能有效地避开精准背景补偿,因而不仅节省了计算量,而且提取出来的语义对象精度较高。实验表明,无论是对动态场景中刚性还是非刚性运动物体的分割,该算法都具有较好的效果。     

基于有向带权图迭代的面向对象系统分解方法

针对如何从现存的系统中提取构件的问题,提出了一种基于有向带权图迭代分析的面向对象系统分解方法.它将面向对象系统抽象为一个有向带权图,使用迭代算法考察不同粒度的子图的独立性,并选择独立性高的作为候选构件.实验结果表明,该方法是一种有效的系统分解方法,在准确性上比现有系统分解方法有所提高.     

一种基于SVG的在线连续手写汉字多步分割方法*

基于可伸缩矢量图SVG的在线手写汉字是以SVG图像作为汉字图像格式、以SVG的path对象作为笔画的基本存储单元来对汉字进行显示和存储的,笔画的轮廓是以手写过程中记录的坐标值作为特征数值加以确定的。基于此种SVG手写汉字存储和表示形式,本文提出一种基于图论的在线连续手写汉字多步分割方法。该方法根据汉字笔画间的坐标位置关系对手写笔画序列构建无向图模型,并利用图的广度优先搜索将原笔画序列分割为互不连通的笔画部件,使偏旁部首分离较远、非粘连汉字得到正确分割;然后利用改进的tarjan算法对部件中的粘连字符进行分割,最后基于笔画部件间距,利用二分类迭代算法对间距进行分类,找出全局最佳分割位置,对过分割的部件进行重组合并。实验结果表明,该方法对于在线手写汉字的分割是有效可行的。     

基于骨架层次分解的目标的图表示

基于骨架的目标表示技术是模式识别和计算机视觉的重要研究内容,近年来人们提出了许多骨架化算法,但是有关利用骨架信息表示并识别目标的研究还非常有限。Ablameyko等1996年提出了通过分解由距离标号的骨架为有意义的结构基元从而获得目标的层次结构图的方法。该图可以准确地刻画基元之间的拓扑关系,但是它对于骨架中的噪声比较敏感。主要表现为噪声基元破坏其它基元的完整性和图的稳定性。该文采用将分支编组为分支链以及构造多尺度结构图的改进策略来克服这些缺点,最终获得了目标的节点数更小、节点显著度更高、节点间连接关系更稳定的多尺度图,从而显著地提高后续利用不精确图匹配技术进行目标识别的效率。这项技术已经被应用于一个基于形状特征的图像数据库检索系统中。     

Automatic sub-category partitioning and parts localization for learning a robust object model

This paper introduces a novel topic model for learning a robust object model. In this hierarchical model, the layout topic is used to capture the local relationships among a limited number of parts when the part topic is used to locate the potential part regions. Naturally, an object model is represented as a probability distribution over a set of parts with certain layouts. Rather than a monolithic model, our object model is composed of multiple sub-category models designed to capture the significant variations in appearance and shape of an object category. Given a set of object instances with a bounding box, an iterative learning process is proposed to divide them into several sub-categories and learn the corresponding sub-category models without any supervision. Through an experiment in object detection, the learned object model is examined and the results highlight the advantages of our present method compared with others.     

Active improvement of hierarchical object features under budget constraints

When we think of an object in a supervised learning setting, we usually perceive it as a collection of fixed attribute values. Although this setting may be suited well for many classification tasks, we propose a new object representation and therewith a new challenge in data mining; an object is no longer described by one set of attributes but is represented in a hierarchy of attribute sets in different levels of quality. Obtaining a more detailed representation of an object comes with a cost. This raises the interesting question of which objects we want to enhance under a given budget and cost model. This new setting is very useful whenever resources like computing power, memory or time are limited. We propose a new active adaptive algorithm (AAA) to improve objects in an iterative fashion. We demonstrate how to create a hierarchical object representation and prove the effectiveness of our new selection algorithm on these datasets.