Motion segmentation of multiple translating objects from line correspondences

This paper presents an analytic approach to motion segmentation of multiple translating objects from line correspondences in three perspective views. The basic idea of our algorithm is to view the estimation of multiple translational motions as the estimation of a single, though more complex, multibody motion model that is then factored into the original models by polynomial differentiation. Experimental results on synthetic and real scenes are presented.     

On the use of the overlapping area matrix for image segmentation evaluation: A survey and new performance measures

The development of common and reasonable criteria for evaluating and comparing the performance of segmentation algorithms has always been a concern for researchers in the area. As it is discussed in the paper, some of the measures proposed are not adequate for general images (i.e. images of any sort of scene, without any assumption about the features of the scene objects or the illumination distribution) because they assume a certain distribution of pixel gray-level or colour values for the interior of the regions. This paper reviews performance measures not performing such an assumption and proposes a set of new performance measures in the same line, called the percentage of correctly grouped pixels (CG), the percentage of over-segmentation (OS) and the percentage of under-segmentation (US). Apart from accounting for misclassified pixels, the proposed set of new measures are intended to compute the level of fragmentation of reference regions into output regions and vice versa. A comparison involving similar measures is provided at the end of the paper.     

Color-based video segmentation using interlinked irregular pyramids

This paper presents a new segmentation technique for video sequences. It relies on building irregular pyramids based on its homogeneity over consecutive frames. Pyramids are interlinked to keep a relationship between the regions in the frames. Virtual nodes are considered to improve matching between low resolution levels of the pyramids. Its performance is good in real-world conditions because it does not depend on image constrains.     

An improved generalized Hough transform for the recognition of overlapping objects

The generalized Hough transform (GHT) is a powerful method for recognizing arbitrary shapes as long as the correct match accounts for both much of the model and much of the sensory object. For moderate levels of occlusion, however, the GHT can hypothesize many false solutions. In this paper, we present an improved two-stage GHT procedure for the recognition of overlapping objects. Each boundary point in the image is described by three features including the concavity, radius and normal direction of the curve segment in the neighborhood of the point. The first stage of the voting process determines the rotational angle of the sensory object with respect to the model by matching those points that have the same concavity and radii. The second stage then determines the centroid of the sensory object by matching those points that have the same concavity, radii and rotational angles. The three point features remove the false contribution of votes in the vote generation phase. Experimental results have shown that the proposed algorithm works well for complex objects under severely overlapping conditions.     

基于边界搜索的运动对象快速凸壳分割算法

目前在H.264/AVC压缩域分割领域中常用方法造成局部运动矢量(MV)缺失,而通过全局运动补偿来还原运动矢量导致其时间复杂度提高。为解决此问题,提出一种基于边界聚类的快速凸壳分割(BS-CHSTF)算法。该方法主要利用码流中的运动矢量场信息进行分割,首先,对MV利用时空域滤波(STF)对运动矢量进行预处理,采用八方向自适应搜索算法进行边界搜索确定运动连通域;然后根据每个连通域边界求解凸壳并对其进行连通域填充,之后利用运动矢量与距离信息设定聚类规则,对多个连通域进行聚类;最后,对其进行优化掩膜达到分割运动对象的效果。实验结果表明,与混合高斯模型(GMM)分割算法和压缩域蚁群算法(ACA)比较,在分割准确率上平均提高了近3%,甚至在运动矢量场严重缺失的情况下,提高了近20%;而在分割速度上平均提高了近25%。该方法着重于求得运动对象的完整性与快速性,在运动对象不完整的情况下,能够获得较好分割精准度。     

Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions

This paper presents a method for multi-scale segmentation of surface data using scale-adaptive region growing. The proposed segmentation algorithm is initiated by an unsupervised learning of optimal seed positions through the surface attribute clustering with a two-criterion score function. The seeds are selected as consecutive local maxima of the clustering map, which is computed by an aggregation of the local isotropic contrast and local variance maps. The proposed method avoids typical segmentation errors caused by an inappropriate choice of seed points and thresholds used in the region-growing algorithm. The scale-adaptive threshold estimate is based on the image local statistics in the neighborhoods of seed points. The performance of this method was evaluated on LiDAR surface images.     

基于时空域暗示的运动对象检测和分割*

通过对视频帧间的时域信息和帧内的空域信息的充分开发,提出一种任意视频对象检测与分割算法。初始的运动区域评估利用时间加权的时域帧窗,采用基于点的分割;而近似同质颜色亮度纹理区域利用区域之间的差异和区域内的相似,采用改进的分水岭分割和基于区域特征相似度的合并。时域和空域分割结果的合并基于多数原则。最后,分割结果的完善和修正基于时域的持续性和空域上的一致性标准。通过测试,提出的分割算法获得可靠的对象边界,而且通过调整少量参数,可以适应于室内和室外场景以及高速和低速运动物体。     

Automatic segmentation of granular objects in images: Combining local density clustering and gradient-barrier watershed

Blob or granular object recognition is an image processing task with a rich application background, ranging from cell/nuclei segmentation in biology to nanoparticle recognition in physics. In this study, we establish a new and comprehensive framework for granular object recognition. Local density clustering and connected component analysis constitute the first stage. To separate overlapping objects, we further propose a modified watershed approach called the gradient-barrier watershed, which better incorporates intensity gradient information into the geometrical watershed framework. We also revise the marker-finding procedure to incorporate a clustering step on all the markers initially found, potentially grouping multiple markers within the same object. The gradient-barrier watershed is then conducted based on those markers, and the intensity gradient in the image directly guides the water flow during the flooding process. We also propose an important scheme for edge detection and fore/background separation called the intensity moment approach. Experimental results for a wide variety of objects in different disciplines – including cell/nuclei images, biological colony images, and nanoparticle images – demonstrate the effectiveness of the proposed framework.     

Trajectory-based human action segmentation

This paper proposes a sliding window approach, whose length and time shift are dynamically adaptable in order to improve model confidence, speed and segmentation accuracy in human action sequences. Activity recognition is the process of inferring an action class from a set of observations acquired by sensors. We address the temporal segmentation problem of body part trajectories in Cartesian Space in which features are generated using Discrete Fast Fourier Transform (DFFT) and Power Spectrum (PS). We pose this as an entropy minimization problem. Using entropy from the classifier output as a feedback parameter, we continuously adjust the two key parameters in a sliding window approach, to maximize the model confidence at every step. The proposed classifier is a Dynamic Bayesian Network (DBN) model where classes are estimated using Bayesian inference. We compare our approach with our previously developed fixed window method. Experiments show that our method accurately recognizes and segments activities, with improved model confidence and faster convergence times, exhibiting anticipatory capabilities. Our work demonstrates that entropy feedback mitigates variability problems, and our method is applicable in research areas where action segmentation and classification is used. A working demo source code is provided online for academical dissemination purposes, by requesting the authors.     

Adaptive texture and color segmentation for tracking moving objects

Color segmentation is a very popular technique for real-time object tracking. However, even with adaptive color segmentation schemes, under varying environmental conditions in video sequences, the tracking tends to be unreliable. To overcome this problem, many multiple cue fusion techniques have been suggested. One of the cues that complements color nicely is texture. However, texture segmentation has not been used for object tracking mainly because of the computational complexity of texture segmentation. This paper presents a formulation for fusing texture and color in a manner that makes the segmentation reliable while keeping the computational cost low, with the goal of real-time target tracking. An autobinomial Gibbs Markov random field is used for modeling the texture and a 2D Gaussian distribution is used for modeling the color. This allows a probabilistic fusion of the texture and color cues and for adapting both the texture and color over time for target tracking. Experiments with both static images and dynamic image sequences establish the feasibility of the proposed approach.     

Decentralized sliding mode control design using overlapping decompositions

This paper discusses overlapping decentralized sliding mode controller design for large-scale continuous-time systems. Design issues, like connective reachability of the sliding manifold and the stability of the sliding mode equations in the expanded and original state spaces are examined. Application of the results to automatic generation control is also discussed briefly.     

基于Parzen窗法的贝叶斯参数估计

针对贝叶斯估计中所需的非规则概率密度函数, 提出用Parzen窗算法估计相关概率密度, 从而求解不同损失函数下的贝叶斯参数估计器. 实例分析中, 选择一组电阻测量值作为样本, 利用Parzen窗法计算出相应的概率密度函数, 最后用交叉验证法得出了该样本的最小绝对值误差参数估计器.     

Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.     

一种改进的基于分水岭算法的粘连分割研究

在细胞图像制作过程中,由于设备或人为原因,经常会出现细胞粘连程度分布不均匀的情况,从而影响分割的效果,给后续的统计分析造成了一定的困难。提出自适应距离变换,结合分水岭分割的处理方法,把细胞的粘连程度信息引入到距离变换函数中,根据细胞粘连程度的不同,自适应地变换距离函数的参数,从而有效地防止欠分割和过分割。实验证明,这种改进的方法能成功地分离不同粘连程度的粘连细胞,并具有良好的稳定性。     

Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images

The computer algorithms for the delineation of anatomical structures and other regions of interest on the medical imagery are important component in assisting and automating specific radiological tasks. In addition, the segmentation of region is an important first step for variety image related application and visualization tasks. In this paper, we propose a fast and automated connectivity-based local adaptive thresholding (CLAT) algorithm to segment the carotid artery in sequence medical imagery. This algorithm provides the new feature that is the circumscribed quadrangle on the segmented carotid artery for region-of-interest (ROI) determination. By using the preserved connectivity between consecutive slice images, the size of the ROI is adjusted like a moving window according to the segmentation result of previous slice image. The histogram is prepared for each ROI and then smoothed by local averaging for the threshold selection. The threshold value for carotid artery segmentation is locally selected on each slice image and is adaptively determined through the sequence image. In terms of automated features and computing time, this algorithm is more effective than region growing and deformable model approaches. This algorithm is also applicable to segment the cylinder shape structures and tree-like blood vessels such as renal artery and coronary artery in the medical imagery. Experiments have been conducted on synthesized images, phantom and clinical data sets with various Gaussian noise.     

基于变化检测的视频对象分割算法研究

随着基于对象视频应用的发展,视频对象的分割成为人们研究的热点。提出了一种基于变化检测的视频对象分割算法。该算法首先求出连续两帧图像之间的差分,利用帧间变化信息可以得到视频对象的运动区域,根据差分图像的中值（MED）和中值绝对差（MAD）及原始图像均值确定阈值并滤除噪声,用数学形态学方法进行后处理,最终得到精确的视频运动对象。实验结果表明,该方法能够从背景不变的视频序列中较好地提取出视频运动对象,而且算法简单、高效、准确。     

一种粘连血细胞图像分割新方法

提出一种基于血细胞近圆形特征的粘连血细胞分割方法。用形态学腐蚀得到距离图和图像的边缘,并利用灰度重建得到细胞的中心区域和圆心,在此基础上基于细胞圆形特征对细胞的边缘进行搜索,得到粘连细胞的分离点,从而求得分离线,完成对粘连血细胞的精确分割。实验表明,该分离方法是有效可行的。     

一种有效的基于时空信息的视频运动对象分割算法

为实现视频编码标准MPEG-4中语义对象的自动提取,提出一种基于时空信息的运动对象分割算法。在时域上通过双边加权累积帧差和分块高阶统计算法得到目标的运动区域检测模板,以在充分利用时域信息的同时提高算法的速度;在提取空域信息时,先对视频序列的灰度图进行对比度增强处理,然后利用自适应Canny算子获取准确的空间边缘信息;最后进行时空融合,用空域边缘信息修正过的时域运动模板来提取运动对象。实验结果表明,本算法可以快速准确地分割视频运动对象。     

Graph-based multilevel temporal video segmentation

This paper presents a graph-based multilevel temporal video segmentation method. In each level of the segmentation, a weighted undirected graph structure is implemented. The graph is partitioned into clusters which represent the segments of a video. Three low-level features are used in the calculation of temporal segments’ similarities: visual content, motion content and shot duration. Our strength factor approach contributes to the results by improving the efficiency of the proposed method. Experiments show that the proposed video scene detection method gives promising results in order to organize videos without human intervention.