透射式能见距离测量仪中光电转换系统盲区设置的计算与分析

本文推得了透射式能见距离测量仪中能见距离、光电转换系统的最小响应盲区以及仪器与合作目标间的距离三者之间的关系，分别讨论了当仪器与合作目标之间的距离确定时光电转换系统的最小盲区与能见距离、当能见距离确定时光电转换系统的最小盲区与仪器至合作目标的距离、以及当光电转换系统的盲区确定时仪器与合作目标间的距离和最小可探测能见距离之间的关系，分析了接收镜头接收口径的大小对光电转换系统的最小盲区及最小可探测能见距离的影响，得到了有意义的结论。     

层次化点云边界快速精确提取方法研究

为了提高依据邻近点最大夹角提取边界点方法的提取效率,提出了一种层次化快速精确提取边界点的方法。先对任意采样点检索其R邻域内点集,依据R邻域内点集重心点坐标与采样点的距离粗提取边界点,然后将粗提取的边界点及其邻域点投影至微切平面,通过各邻近点与采样点的方向向量求取相邻向量间的最大夹角,再依据最大夹角精提取边界点。通过理论分析和点云数据实验验证了该算法的可行性。结果表明,该算法相较于传统的方法,能缩短22.11%的运行时间,但精度降低5.23%;相较于其它层次化提取方法,在缩短10.99%的运行时间的同时精度提高7.17%。该研究为点云3维重建中边界提取提供了参考。     

基于形状上下文的形状匹配

提出了用于形状匹配的自适应边界点选取的形状上下文方法。首先，根据目标形状，自适应地确定边界点个数；然后，利用选取的边界点，按照本文方法计算该目标的形状直方图；最后，利用形状直方图计算不同目标之间的相似度。基于手写体数字的试验表明，形状上下文在有效描述目标的形状特征的基础上，准确地计算了不同目标之间的相似度，达到了满意的匹配结果。     

利用显著边界点的图像自动分割算法

图像分割是图像分析的必须步骤,提出一种利用显著边界点和局部化区域型轮廓模型将显著性目标从自然图像中自动分割的新颖模型。首先通过颜色提升的Harris算子得到显著的边界点,其次提出核心显著图从显著边界点中得到目标种子点,然后目标显著边界点由这些种子点确定,最后目标显著边界点作为凸包的种子点产生初始轮廓。大型的自然图像库的实验结果令人满意。     

航向式能见距离测量仪中光电转换系统盲区设置的计算与分析

本文推得了透射式能见距离测量仪中能见距离、光电转换系统的最小响应盲区以及仪器与合作目标间的距离三者之间的关系,分别讨论了当仪器与合作目标之间的距离确定时光电转换系统的最小盲区与能见距离、当能见距离确定时光电转换系统的最小盲区与仪器至合作目标的距离、以及当光电转换系统的盲区确定时仪器与合作目标间的距离和最小可探测能见距离之间的关系,分析了接收镜头接收口径的大小对光电转换系统的最小盲区及最小可探测能见     

三维激光扫描数据的网格简化

针对三维激光扫描仪获得的彩色人头数据集压缩问题，提出了基于区域分割的顶点合并网格简化算法。该算法分为区域分割和网格简化两个阶段。在对三维彩色人头数据集进行区域分割的基础上，把网格点分为：区域边界点（在顶点合并操作中，它只能与边界点合并）和区域内部点，然后根据区域加权的误差评价函数进行网格简化。实验表明，该算法压缩比高，网格简化质量好。     

特征压缩在线距离度量学习跟踪

为提高在线学习目标跟踪的实时性和准确率,结合压缩感知理论,提出一种将距离度量学习(DML)运用到目标跟踪的算法。首先,根据所选定的目标位置分别提取目标和背景样本集,运用随机投影理论对样本的Harr-like特征进行压缩;然后,用压缩后的低维特征向量集训练度量矩阵;最后,在新的一帧中抽取目标和背景的样本,用训练得到的度量矩阵计算已知目标和样本间的Mahalanobis距离,距离最小的样本的位置就是所要跟踪的目标的位置。对不同视频序列的测试结果表明,用压缩特征表示目标,使特征计算的计算量压缩到原来的1/4,减少了特征计算的时间;用训练后的度量矩阵计算目标位置,即跟踪器能够根据目标的不断变化自适应调整参数,提高了跟踪的准确率。     

利用边界运动显著性的红外运动目标分割方法

针对传统算法无法对短暂静止的红外运动目标进行准确有效地分割,该文提出了一种利用边界运动特征的红外运动目标分割方法。首先,定义了一种新指标边界运动显著性,该指标利用边界点时空域特性,可以准确反映图像中边界点的运动特征,显著性越高,则该边界点属于运动目标的可能性越大。然后,通过Otsu阈值法提取出显著性高的边界点,并利用历史数据对其进行修正,修正之后的运动边界点作为运动目标种子。最后,通过一种“逐层生长”的区域生长方法,在运动目标种子上分割出完整的运动目标掩膜。该方法在多组红外图像序列中进行测试与对比,结果证明该方法运动目标分割效果良好,目标背景的错分率低,可以准确检测并分割出短暂静止的运动目标。     

分组陪集码的误码特性分析

本文讨论了分组陪集码在最大似然译码与最近陪集译码下的误码特性,这一分析是分别在归一化重量分布和真实距离下做出的,并对其差异进行了比较。本文同时讨论了采用最小距离和最小距离系数进行误码率估计的方法,并指出这些参数可由子码的相应汉明参数得到。     

分段线性变换增强的自适应方法

在对分段灰度变换方法进行分析的基础上,采用最小误差法实现了区间边界点的自适应计算,对不同灰度区间采用拉伸﹑保持和抑制等不同的处理,并对目标区间的拉伸系数进行了讨论.实验结果表明,算法简单,图像视觉效果得到较大改善.     

A hybrid algorithm for automatic segmentation of slowly moving objects

Segmentation of moving objects in video sequences is a basic task in many applications. However, it is still challenging due to the semantic gap between the low-level visual features and the high-level human interpretation of video semantics. Compared with segmentation of fast moving objects, accurate and perceptually consistent segmentation of slowly moving objects is more difficult. In this paper, a novel hybrid algorithm is proposed for segmentation of slowly moving objects in video sequence aiming to acquire perceptually consistent results. Firstly, the temporal information of the differences among multiple frames is employed to detect initial moving regions. Then, the Gaussian mixture model (GMM) is employed and an improved expectation maximization (EM) algorithm is introduced to segment a spatial image into homogeneous regions. Finally, the results of motion detection and spatial segmentation are fused to extract final moving objects. Experiments are conducted and provide convincing results.     

一种基于帧内图像分区的视频对象自动分割算法

视频运动对象的自动分割是实现新一代对象基视频编码标准MPEG-4的重要技术,本文提出了一种基于帧内图像分区的运动对象自动分割算法.首先以时域运动信息为依据利用高斯检验方法得到二值运动掩模图像,并建立MRF随机场模型进一步检验,然后提出了一种结合非线性变换的改进分水岭算法进行帧内图像分区,以划定对象区域与背景区域的界线,最后对时域分割和帧内分区结果进行比重运算,得到最终运动对象.针对MPEG-4标准测试序列和自采集手指视频序列的实验结果说明了算法的有效性.     

基于多帧边缘差异的视频运动对象的分割与跟踪算法

从视频场景中分割和跟踪感兴趣的视频对象对于MPEG-4等基于对象的视频编码来说是关键性的技术之一。针对目前大部分视频对象分割和追踪算法相当复杂但仍不能有效地去除背景噪声的问题,该文提出用于分割和跟踪视频运动对象的一种基于多帧边缘差异的算法。该算法利用一组帧的边缘差异来提取运动对象区域,通过聚类方法去除背景像素点,利用形态学算子得到对象分割模板,同时通过建立前帧感兴趣对象与当前帧运动对象的帧间向量跟踪当前帧的感兴趣视频对象。不同标准视频测试序列的测试结果表明,该算法能够实现对感兴趣的视频运动对象更为精确、快速和有效地分割和跟踪。     

MPEG-4视频对象分割技术

随着MPEG-4,MPEG-7的研究发展,其基于内容的编码和面向对象的存取和操纵技术日益得到人们的重视。基于对象的视频图像分割是实现MPEG-4基于内容的编码和交互功能的关键。视频图像分割方法分为自动分割法和半自动分割法两种。结合视频分割的发展趋势,深入介绍了基于对象的视频分割的主要技术及国内外的最新研究算法,包括数学形态学算法以及活动轮廓模型(蛇模型)在该领域的应用,并分析了当前视频分割技术尚存在的问题和研究前景。     

Extraction of Video Objects via Surface Optimization and Voronoi Order

We implement a video object segmentation system that integrates the novel concept of Voronoi Order with existing surface optimization techniques to support the MPEG-4 functionality of object-addressable video content in the form of video objects. The major enabling technology for the MPEG-4 standard are systems that compute video object segmentation, i.e., the extraction of video objects from a given video sequence. Our surface optimization formulation describes the video object segmentation problem in the form of an energy function that integrates many visual processing techniques. By optimizing this surface, we balance visual information against predictions of models with a priori information and extract video objects from a video sequence. Since the global optimization of such an energy function is still an open problem, we use Voronoi Order to decompose our formulation into a tractable optimization via dynamic programming within an iterative framework. In conclusion, we show the results of the system on the MPEG-4 test sequences, introduce a novel objective measure, and compare results against those that are hand-segmented by the MPEG-4 committee.     

Unsupervised video object segmentation with distractor-aware online adaptation

Unsupervised video object segmentation is a crucial application in video analysis when there is no prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a video clip. In this paper, a novel unsupervised video object segmentation approach via distractor-aware online adaptation (DOA) is proposed. DOA models spatiotemporal consistency in video sequences by capturing background dependencies from adjacent frames. Instance proposals are generated by the instance segmentation network for each frame and they are grouped by motion information as positives or hard negatives. To adopt high-quality hard negatives, the block matching algorithm is then applied to preceding frames to track the associated hard negatives. General negatives are also introduced when there are no hard negatives in the sequence. The experimental results demonstrate these two kinds of negatives are complementary. Finally, we conduct DOA using positive, negative, and hard negative masks to update the foreground and background segmentation. The proposed approach achieves state-of-the-art results on two benchmark datasets, the DAVIS 2016 and the Freiburg-Berkeley motion segmentation (FBMS)-59.     

Approach to automatic video motion segmentation

A novel, fast automatic motion segmentation approach is presented. It differs from conventional pixel or edge based motion segmentation approaches in that the proposed method uses labelled regions (facets) to segment various video objects from the background. Facets are clustered into objects based on their motion and proximity details using Bayesian logic. Because the number of facets is usually much lower than the number of edges and points, using facets can greatly reduce the computational complexity of motion segmentation. The proposed method can tackle efficiently the complexity of video object motion tracking, and offers potential for real-time content-based video annotation.     

Segmentation of moving objects in image sequence: A review

Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In second-generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation, and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized.     

视频序列客体分布阵集成检测分层技术

视频图像序列按客体分层在很多领域都具有重要的应用，通常，视频图像可分为背景客体和其它目标客体。本文根据场景视频图像序列中不同客体的特性，针对背景客体研究了分布阵集成检测分层技术，采用两级模式识别及多帧加权估计。实验表明该技术具有很好的分层效果，提高了客体分层的可靠性，极大地抑制了噪声。     

Video representation with three-dimensional entities

Very low bit-rate coding requires new paradigms that go well beyond pixel- and frame-based video representations. We introduce a novel content-based video representation using tridimensional entities: textured object models and pose estimates. The multiproperty object models carry stochastic information about the shape and texture of each object present in the scene. The pose estimates define the position and orientation of the objects for each frame. This representation is compact. It provides alternative means for handling video by manipulating and compositing three-dimensional (3-D) entities. We call this representation tridimensional video compositing, or 3DVC for short. We present the 3DVC framework and describe the methods used to construct incrementally the object models and the pose estimates from unregistered noisy depth and texture measurements. We also describe a method for video frame reconstruction based on 3-D scene assembly, and discuss potential applications of 3DVC to video coding and content-based handling. 3DVC assumes that the objects in the scene are rigid and segmented. By assuming segmentation, we do not address the difficult questions of nonrigid segmentation and multiple object segmentation. In our experiments, segmentation is obtained via depth thresholding. It is important to notice that 3DVC is independent of the segmentation technique adopted. Experimental results with synthetic and real video sequences where compression ratios in the range of 1:150-1:2700 are achieved demonstrate the applicability of the proposed representation to very low bit-rate coding