A comparative evaluation of interactive segmentation algorithms

In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of user-experiments, in which participants were tasked with extracting 100 objects from a common dataset: 25 with each algorithm, constrained within a time limit of 2 min for each object. To facilitate the experiments, a “scribble-driven” segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented ground truth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the well-known Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms.     

勾画式交互的网格模型分割

提出一种基于区域增长的交互三维网格模型分割方法。在区域增长的基础上,首先由用户利用基于勾画的交互方式选定部分顶点作为目标和背景,其余顶点作为未知区域,利用区域增长的方法自动生成目标的边界,从而完成模型的分割。此方法中边界顶点分割结果的好坏直接影响到了最终的分割结果,因此,在利用区域增长方法形成边界时,将既与目标相邻又与背景相邻的顶点标记为特殊点,在其余未知部分分割完成之后,重新对特殊点进行一次区域增长算法。此时由于大部分顶点的状态已经确定,获得的边界将更为准确。实验表明分割结果有了很大程度的改进。     

一种基于特征线的曲面网格分割方法

各类网格分割法将曲面网格进行分割后,各子网格区域之间的交界线便可以作为曲面网格的封闭特征线。相反,如果根据网格模型的几何、拓扑特征,确定了网格模型的封闭特征线后,网格曲面便被这些特征线分割开来。为此,从曲面网格封闭特征线的角度出发,提出一种基于特征线的曲面网格分割方法。实验验证了该方法的可行性和有效性。     

基于商空间粒度理论的三维网格分割方法

通过研究已有的网格分割和模型简化方法 ,分析三维模型的网格分割中的商空间粒度思想 ,并将商空间粒度计算引入到网格分割中 ,对网格分割过程进行描述 ,提出了基于粒度分层合成技术的网格分割方法。该算法通过分别提取模型中各三角形网格区域的几何特征构成不同的粒度区域 ,再根据粒度合成理论。将这些所形成的粒度组织起来 ,从而实现对三维网格的最终分割 ,为三角网格模型的简化提供了快速有效的方法。实验表明了该算法对于网格分割的有效性和正确性。     

CAD mesh model segmentation by clustering

CAD mesh models have been widely employed in current CAD/CAM systems, where it is quite useful to recognize the features of the CAD mesh models. The first step of feature recognition is to segment the CAD mesh model into meaningful parts. Although there are lots of mesh segmentation methods in literature, the majority of them are not suitable to CAD mesh models. In this paper, we design a mesh segmentation method based on clustering, dedicated to the CAD mesh model. Specifically, by the agglomerative clustering method, the given CAD mesh model is first clustered into the sparse and dense triangle regions. Furthermore, the sparse triangle region is separated into planar regions, cylindrical regions, and conical regions by the Gauss map of the triangular faces and Hough transformation; the dense triangle region is also segmented by the mean shift operation performed on the mean curvature field defined on the mesh faces. Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.     

网格分割的3维网格模型非盲水印算法

目的 信息技术的发展使得面向3维模型版权保护的问题越来越突出,提出一种新的基于网格分割的3维网格模型非盲水印算法。方法 首先使用基于形状直径函数的网格分割算法对3维网格模型进行有意义的网格分割,然后计算每个分块的鲁棒重心并以此为中心将模型由直角坐标系转换到球面坐标系,最后通过调制每个顶点范数的分布来嵌入水印,在水印检测阶段使用非盲检测的方法提取水印。结果 针对目前基于网格分块的水印算法的网格分割不一致以及对分割边界依赖性过强等问题,引入基于形状直径函数的网格分割算法并在重对齐、重采样过程中加入待检测模型与原始模型分块匹配过程以保证网格分割的一致性,并且选取分块的顶点范数的分布作为水印嵌入基元,使得算法能够有效地减弱对分割边界的依赖性。结论 实验结果表明,该算法可以有效抵抗平移、旋转、缩放、噪声、细分、简化、剪切等常见的攻击以及多种攻击的联合攻击。     

基于区域分割的三角网格模型相似性比较

为了在工程应用中检索已有的三角网格模型,以便重用相应零件的设计信息,节省设计和加工成本,提出一种基于区域分割技术的三角网格模型相似性比较算法。依据三角网格模型的球面图像将模型分割为若干个区域;对每个分割得到的区域用一个10维向量表达其形状的几何特征和拓扑特征,一个三角网格模型的特征即可通过各分割区域所对应的10维向量组成的向量组表达;将该向量组作为三角网格模型的形状描述子,两个三角网格模型的相似性可通过相对应的形状描述子间的相似性表达。将描述子中的每个向量看成是一个带有属性的节点,通过两组节点组成的完全二分图的最优匹配进行两个形状描述子之间的相似性比较,实现两个三角网格模型之间的相似性比较。实验结果表明,该算法有效可行。     

基于区域离散曲率的三维网格分水岭分割

针对离散曲率估计对噪声敏感且特征值计算量大的特点提出了基于区域离散曲率的三维网格分水岭分割算法。寻找三维模型显著特征点;对三维模型进行预分割,确定分割带;在分割带区域上计算离散曲度极值点,利用测地距离和曲度极值点对三维模型进行分水岭分割。算法在分割前无需进行网格去噪,实验结果证明,对主体分支明显的模型具有较高的分割边缘准确度和较快的分割速度。     

基于Laplace谱嵌入和Mean Shift的 三角网格一致性分割

针对现有网格分割算法对模型姿态及噪声敏感的不足,提出一种基于Laplace谱嵌入和Mean Shift聚类的网格一致性分割算法。采用Laplace-Beltrami算子,将3维空域中的网格模型转化成高维Laplace谱域中的标准型,降低了姿态变化和噪声对分割算法的影响,并增强了网格的结构可分性;在高维谱域中,采用非参数核聚类MeanShift算法,获取模型有视觉意义的语义区域。实验结果表明:该算法可以快速有效地实现具有分支结构三角网格模型的有意义分割且对模型姿态和噪声具有较好的鲁棒性。     

Object count/area graphs for the evaluation of object detection and segmentation algorithms

Evaluation of object detection algorithms is a non-trivial task: a detection result is usually evaluated by comparing the bounding box of the detected object with the bounding box of the ground truth object. The commonly used precision and recall measures are computed from the overlap area of these two rectangles. However, these measures have several drawbacks: they don't give intuitive information about the proportion of the correctly detected objects and the number of false alarms, and they cannot be accumulated across multiple images without creating ambiguity in their interpretation. Furthermore, quantitative and qualitative evaluation is often mixed resulting in ambiguous measures.In this paper we propose a new approach which tackles these problems. The performance of a detection algorithm is illustrated intuitively by performance graphs which present object level precision and recall depending on constraints on detection quality. In order to compare different detection algorithms, a representative single performance value is computed from the graphs. The influence of the test database on the detection performance is illustrated by performance/generality graphs. The evaluation method can be applied to different types of object detection algorithms. It has been tested on different text detection algorithms, among which are the participants of the ICDAR 2003 text detection competition.The work presented in this article has been conceived in the framework of two industrial contracts with France Télécom in the framework of the projects ECAV I and ECAV II with respective numbers 001B575 and 0011BA66.     

三角网格模型的基本群分割

提出一种有效的三角网格模型分割方法。用Dijkstra算法求出三角网格模型上任意给定一个基点到其余顶点的最短路径树;求出该模型对偶图的最大生成树,且对偶图的边与该最短路径树的边不相交;找出该模型上所有既不属于最短路径树也不和最大生成树相交的边,这些边分别与最短路径树组成的最短环集合就是给定基点处的基本群,沿着这些最短环就可以把网格分割成一个拓扑同胚于圆盘的区域。实验结果表明,该分割方法可以快速、有效地实现网格的分割。     

改进的二阶总广义变分图像前后景分割模型

针对总变分TV图像前后景分割模型易导致阶梯效应的缺陷,提出了二阶总广义变分TGV图像前后景分割模型。为进一步提升图像分割质量,在TGV前后景分割模型的正则项中引入边缘指示函数,使其在图像边缘区域减弱扩散,较好地保护边缘;在图像平滑区域增强扩散,有效地消除噪声。为突出前景信息,用矩形框标出图像的前景信息,对框内部、外部和边缘的像素做距离映射,并根据能量最小化原则,在二阶TGV模型的数据项中引入此距离映射函数,使模型总能量更小。最后,提出了一种有效的原始对偶分割算法来求解模型。实验表明,新模型不但能够去除阶梯效应现象,保持图像的边缘信息,还使得模型总能量更小,分割得到的图像视觉效果更好。     

A multi-formalism approach for model-based dynamic distribution of user interfaces of critical interactive systems

Evolution in the context of use requires evolutions in the user interfaces even when they are currently used by operators. User Centered Development promotes reactive answers to this kind of evolutions either by software evolutions through iterative development approaches or at runtime by providing additional information to the operators such as contextual help for instance. This paper proposes a model-based approach to support proactive management of context of use evolutions. By proactive management we mean mechanisms in place to plan and implement evolutions and adaptations of the entire user interface (including behaviour) in a generic way. The approach proposed handles both concentration and distribution of user interfaces requiring both fusion of information into a single UI or fission of information into several ones. This generic model-based approach is exemplified on a safety critical system from space domain. It presents how the new user interfaces can be generated at runtime to provide a new user interface gathering in a single place all the information required to perform the task. These user interfaces have to be generated at runtime as new procedures (i.e. sequences of operations to be executed in a semi-autonomous way) can be defined by operators at any time in order to react to adverse events and to keep the space system in operation. Such contextual, activity-related user interfaces complement the original user interfaces designed for operating the command and control system. The resulting user interface thus corresponds to a distribution of user interfaces in a focus+context way improving usability by increasing both efficiency and effectiveness.     

A probabilistic approach for foreground and shadow segmentation in monocular image sequences

This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori—Markov random field estimation is used to boost the spatial connectivity of segmented regions.     

A robust framework for joint background/foreground segmentation of complex video scenes filmed with freely moving camera

This paper explores a robust region-based general framework for discriminating between background and foreground objects within a complex video sequence. The proposed framework works under difficult conditions such as dynamic background and nominally moving camera. The originality of this work lies essentially in our use of the semantic information provided by the regions while simultaneously identifying novel objects (foreground) and non-novel ones (background). The information of background regions is exploited to make moving objects detection more efficient, and vice-versa. In fact, an initial panoramic background is modeled using region-based mosaicing in order to be sufficiently robust to noise from lighting effects and shadowing by foreground objects. After the elimination of the camera movement using motion compensation, the resulting panoramic image should essentially contain the background and the ghost-like traces of the moving objects. Then, while comparing the panoramic image of the background with the individual frames, a simple median-based background subtraction permits a rough identification of foreground objects. Joint background-foreground validation, based on region segmentation, is then used for a further examination of individual foreground pixels intended to eliminate false positives and to localize shadow effects. Thus, we first obtain a foreground mask from a slow-adapting algorithm, and then validate foreground pixels (moving visual objects + shadows) by a simple moving object model built by using both background and foreground regions. The tests realized on various well-known challenging real videos (across a variety of domains) show clearly the robustness of the suggested solution. This solution, which is relatively computationally inexpensive, can be used under difficult conditions such as dynamic background, nominally moving camera and shadows. In addition to the visual evaluation, spatial-based evaluation statistics, given hand-labeled ground truth, has been used as a performance measure of moving visual objects detection.     

Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset

Background/foreground separation is the first step in video surveillance system to detect moving objects. Recent research on problem formulations based on decomposition into low-rank plus sparse matrices shows a suitable framework to separate moving objects from the background. The most representative problem formulation is the Robust Principal Component Analysis (RPCA) solved via Principal Component Pursuit (PCP) which decomposes a data matrix into a low-rank matrix and a sparse matrix. However, similar robust implicit or explicit decompositions can be made in the following problem formulations: Robust Non-negative Matrix Factorization (RNMF), Robust Matrix Completion (RMC), Robust Subspace Recovery (RSR), Robust Subspace Tracking (RST) and Robust Low-Rank Minimization (RLRM). The main goal of these similar problem formulations is to obtain explicitly or implicitly a decomposition into low-rank matrix plus additive matrices. These formulation problems differ from the implicit or explicit decomposition, the loss function, the optimization problem and the solvers. As the problem formulation can be NP-hard in its original formulation, and it can be convex or not following the constraints and the loss functions used, the key challenges concern the design of efficient relaxed models and solvers which have to be with iterations as few as possible, and as efficient as possible. In the application of background/foreground separation, constraints inherent to the specificities of the background and the foreground as the temporal and spatial properties need to be taken into account in the design of the problem formulation. Practically, the background sequence is then modeled by a low-rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. Although, many efforts have been made to develop methods for the decomposition into low-rank plus additive matrices that perform visually well in foreground detection with reducing their computational cost, no algorithm today seems to emerge and to be able to simultaneously address all the key challenges that accompany real-world videos. This is due, in part, to the absence of a rigorous quantitative evaluation with synthetic and realistic large-scale dataset with accurate ground truth providing a balanced coverage of the range of challenges present in the real world. In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation. For this, we first provide a preliminary review of the recent developments in the different problem formulations which allows us to define a unified view that we called Decomposition into Low-rank plus Additive Matrices (DLAM). Then, we examine carefully each method in each robust subspace learning/tracking frameworks with their decomposition, their loss functions, their optimization problem and their solvers. Furthermore, we investigate if incremental algorithms and real-time implementations can be achieved for background/foreground separation. Finally, experimental results on a large-scale dataset called Background Models Challenge (BMC 2012) show the comparative performance of 32 different robust subspace learning/tracking methods.     

视频监控系统中的图像分割算法综述*

视频监控系统在智能安防、人机交互、交通、娱乐、军事等领域有着广泛的应用空间,是近来研究热点之一。目标的分割是视频监控系统中的首要任务,其分割的有效性对于后续的目标识别、跟踪、行为理解等处理至关重要。从时间分割法和空间分割法的分类角度出发,对国内外的视频监控研究中的若干目标分割方法进行了归纳总结。     

A hybrid approach to feature segmentation of triangle meshes

Segmentation of a polygonal mesh is a method of breaking the mesh down into ‘meaningful’ connected subsets of meshes called regions or features. Several methods have been proposed in the past and they are either vertex based or edge based. The vertex method used here is based on the watershed segmentation scheme which appears prominently in the image segmentation literature and was later applied to the 3D segmentation problem [9] and [10]. Its main drawback is that it is a vertex based method and no hard boundaries (edges) are created for the features or regions. Edge based methods rely on the dihedral angle between polygon faces to determine if the common edge should be classified as a Feature Edge. However, this method results in many disconnected edges and thereby incomplete feature loops.We propose a hybrid method which takes advantage of both methods mentioned earlier and create regions with complete feature loops. Satisfactory results have been achieved for both CAD parts as well as other laser scanned objects such as bones and ceramic vessels.     

The iterative development of a tangible pin-board to symmetrically link physical and digital documents

There is an asymmetry in many tangible interfaces: while physical objects can be used to manipulate digital information, the reverse is often not possible—the digital world cannot push back. We introduce a new push-back tangible technology, a pin-board that physically ejects paper documents. This is realized by extending the Pin&Play technology to support ‘pouts’, addressable pin-like devices that can remove themselves from a board using muscle wire actuators. We describe how this technology has been developed through two iterations of prototyping, application and formative study. An initial study revealed how potential mismatches between the physical and digital characteristics of pouts caused difficulties with users predicting pop-out events and reasoning about the state of pouts. This led us to extend pouts to reveal more of their internal state, an approach verified through a second study. It also raises more general issues for the design of pushback tangible technologies and ubiquitous interfaces.     

A novel 3D mesh compression using mesh segmentation with multiple principal plane analysis

This paper proposes a novel scheme for 3D model compression based on mesh segmentation using multiple principal plane analysis. This algorithm first performs a mesh segmentation scheme, based on fusion of the well-known k-means clustering and the proposed principal plane analysis to separate the input 3D mesh into a set of disjointed polygonal regions. The boundary indexing scheme for the whole object is created by assembling local regions. Finally, the current work proposes a triangle traversal scheme to encode the connectivity and geometry information simultaneously for every patch under the guidance of the boundary indexing scheme. Simulation results demonstrate that the proposed algorithm obtains good performance in terms of compression rate and reconstruction quality.