基于骨架的垂测电离图F层自动判读算法*

为了实现F层参数的自动度量,为电离层短期监测提供实时可靠的数据,以MATLAB7.6.0为仿真平台,首先对F层描迹图像进行预处理去除离散噪声点;针对F层描迹常见的寻常波和非寻常波重叠的现象,根据描迹形态特征,采用基于形态学算子的骨架提取方法以及骨架分解算法提取出寻常波;利用最小二乘分离F1层和F2层;结合形态学重建、Radon变换以及图像投影方法读取参数.该方法可以实时自动读取F层主要参数并获得较好的识别率.在不考虑Es层的多次反射遮蔽情况下,实验验证了方法的可行性同时具有一定的自抗干扰能力.     

基于高7位平面嵌入的灰度图像可逆数据隐藏算法

现有基于直方图平移的可逆数据隐藏算法都致力于提高嵌入率,而忽略了算法所产生边界图的大小;数量较大的边界图将不能够有效存储进而影响算法的实施。本文对现有典型算法存在的这一问题进行分析,并给出了解决方法。最后本文提出了基于高7位平面嵌入的灰度图像可逆数据隐藏算法,与传统可逆数据隐藏算法使用全部位平面进行数据嵌入不同,本文所提出的算法使用高7位平面当做数据载体。由高7位平面构成的载体图像具有像素值分布更加集中的特点,因而使用该载体图像进行可逆数据嵌入能够有效的控制边界图大小并且明显的提高嵌入率。实验结果表明,本文所提出的算法在提高嵌入率的同时,还能使嵌入附加数据的图像具有高的峰值信噪比值。     

基于显著封闭边界的图像检索算法

在分析了基于边缘的图像检索方法的缺点后,提出了一种基于显著封闭边界的图像检索新算法。它使用基于图论方法的边缘比算法从噪声图像中获取图像的显著封闭边界,然后以显著封闭边界来代表图像,每条显著封闭边界由两个特征：边缘幅度直方图和边缘角度直方图来刻画,并综合这两个特征进行图像间的相似性度量。实验结果表明,本文算法具有较高的检索准确率。     

一种基于模糊连接度和维诺图的混合分割方法

介绍一种可用于医学图像处理的、集成了模糊连接度和维诺图分类算法的混合分割方法。首先采用模糊连接度算法对指定图像区域进行过滤处理形成组织样本数据,这些输出数据将作为维诺图分类算法的输入数据和分类标准,然后通过维诺图分类算法对其进行迭代处理直至形成近似的图像区域边界。最终的输出值为一组分割后的三维图像数据,可以采用体绘制方法形成三维图像分割结果,也可用于进一步的图像处理。和其他医学图像分割方法相比,这种混合分割方法集成了基于区域和基于边界两种不同的分割方法,兼具两者的优点,通过两种分割方法的协同工作,提高了图像分割的精度,适用于复杂图像的分割处理。在医学图像计算机辅助诊断系统中集成了这一方法并取得了良好的实际应用效果。     

边界显著性与模式挖掘

针对目标在图像边界上带来的检测误差,提出了边界显著性算法。首先在多尺度下对图像进行超像素分割,计算边界差异,估计其边界显著性。而后对所有超像素进行模式挖掘,得到显著性种子,并与边界显著性相结合。最后通过显著性传播得到最终显著图。在三个公开的测试数据集上将本文提出算法与其他18种主流的现有算法进行对比。大量实验结果表明,所提出的算法在不同数据集上都优于目前主流算法。     

改进的LLMMSE滤波器对扩散加权图像的降噪

扩散加权图像具有多边界的特点,在扩散加权图像中,准确的边界信号对扩散张量图像的计算尤其重要。通过对局部线性最小均方误差滤波器（Local Linear Minimum Mean Square Error filter,LLMMSE filter）在图像边界处降噪特点进行分析,提出基于最小方差数据集的改进的LLMMSE滤波算法。通过将所提算法应用于模拟数据及真实数据,以及与LLMMSE算法进行比较,验证了本算法具有更好的边界信号降噪能力。     

基于梯度融合规则的医学图像融合方法*

为了更好地满足临床诊断和治疗的需要,本文提出了一种在图像融合阶段对测量值进行自适应梯度加权和图像重建时采用CoSaMP重建算法相结合的方法。该算法首先对两幅源图像分块并进行稀疏表示,同时利用观测矩阵进行测量。在测量数据融合阶段引入图像梯度来反应图像本身的边界信息,先计算每幅分块子图像的梯度;然后利用自适应梯度加权的融合规则得到融合的测量数据,并对融合测量数据进行随机压缩采样;最后通过CoSaMP算法对采样数据进行信息重构实现测量数据的恢复。该方法克服了图像融合时信息畸变的缺陷,并且可以根据不同融合区域自动调整融合规则的权重系数,有效地避免了设置固定权重系数造成的融合误差。实验结果和评价指标验证了本文算法的有效性和先进性。     

基于绝对最小Lipschitz延拓的图像编码算法

提出一种基于图像水平集绝对最小Lipschitz延拓(AMLE)的图像编码方法。该算法在原有AMLE算法的基础上,保留图像水平集的边界数据,采用逐层处理方法实现图像重构。实验结果表明,其峰值信噪比高于原有AMLE算法,取得了更好的视觉效果。     

基于PC的医学图像体绘制方法研究与仿真

体绘制技术是一种能够准确反映体数据内部信息的可视化技术。本文主要介绍了一种改进的体绘制算法，即首先对三维医学图像施加模糊增强，然后对增强后的图像进行模糊阈值分割，从而可以清晰地对三维数据场进行分类，即边界区与非边界区。对边界区与非边界区分别进行绘制。在PC机上进行仿真的结果表明，此方法既能提高绘制的速度，又能保证绘制质量。     

基于主导边界Radon变换的SAR目标方位角估计方法

针对仅利用主导边界估计带来的目标垂直与水平方位的模糊问题,提出一种基于主导边界Radon变换的合成孔径雷达(SAR)图像目标方位角估计方法。该方法基于分割图像中目标主导边界长度的判别准则进行解模糊,同时引入目标主导边界Radon变换的估计算法,解决了传统主导边界算法中长、短主导边界不易分离的问题。MSTAR实测数据的实验结果表明提出的算法具有良好的精确度和适应性。     

Efficient planar object tracking and parameter estimation usingcompactly represented cubic B-spline curves

In this paper, we consider the problem of matching 2D planar object curves from a database, and tracking moving object curves through an image sequence. The first part of the paper describes a curve data compression method using B-spline curve approximation. We present a new constrained active B-spline curve model based on the minimum mean square error (MMSE) criterion, and an iterative algorithm for selecting the “best” segment border points for each B-spline curve. The second part of the paper describes a method for simultaneous object tracking and affine parameter estimation using the approximate curves and profiles. We propose a novel B-spline point assignment algorithm which incorporates the significant corners for interpolating corresponding points on the two curves to be compared. A gradient-based algorithm is presented for simultaneously tracking object curves, and estimating the associated translation, rotation and scaling parameters. The performance of each proposed method is evaluated using still images and image sequences containing simple objects     

Face posture estimation using eigen analysis on an IBR (image based rendered) database

In this paper, we present a novel representation of the human face for estimating the orientation of the human head in a two dimensional intensity image. The method combines the use of the much familiar eigenvalue based dissimilarity measure with image based rendering. There are two main components of the algorithm described here: the offline hierarchical image database generation and organization, and the online pose estimation stage. The synthetic images of the subject's face are automatically generated offline, for a large set of pose parameter values, using an affine coordinate based image reprojection technique. The resulting database is formally called as the IBR (or image based rendered) database. This is followed by the hierarchical organization of the database, which is driven by the eigenvalue based dissimilarity measure between any two synthetic image pair. This hierarchically organized database is a detailed, yet structured, representation of the subject's face. During the pose estimation of a subject in an image, the eigenvalue based measure is invoked again to search the synthetic (IBR) image closest to the real image. This approach provides a relatively easy first step to narrow down the search space for complex feature detection and tracking algorithms in potential applications like virtual reality and video-teleconferencing applications.     

Tracking a person with pre-recorded image database and a pan, tilt, and zoom camera

Abstract. This paper proposes a novel tracking strategy that can robustly track a person or other object within a fixed environment using a pan, tilt, and zoom camera with the help of a pre-recorded image database. We define a set of camera states which is sufficient to survey the environment for the target. Background images for these camera states are stored as an image database. During tracking, camera movements are restricted to these states. Tracking and segmentation are simplified, as each tracking image can be compared with the corresponding pre-recorded background image. Received: 26 August 1999 / Accepted: 22 February 2000     

Real-time view-based pose recognition and interpolation for tracking initialization

In this paper we propose a new approach to real-time view-based pose recognition and interpolation. Pose recognition is particularly useful for identifying camera views in databases, video sequences, video streams, and live recordings. All of these applications require a fast pose recognition process, in many cases video real-time. It should further be possible to extend the database with new material, i.e., to update the recognition system online. The method that we propose is based on P-channels, a special kind of information representation which combines advantages of histograms and local linear models. Our approach is motivated by its similarity to information representation in biological systems but its main advantage is its robustness against common distortions such as clutter and occlusion. The recognition algorithm consists of three steps: (1) low-level image features for color and local orientation are extracted in each point of the image; (2) these features are encoded into P-channels by combining similar features within local image regions; (3) the query P-channels are compared to a set of prototype P-channels in a database using a least-squares approach. The algorithm is applied in two scene registration experiments with fisheye camera data, one for pose interpolation from synthetic images and one for finding the nearest view in a set of real images. The method compares favorable to SIFT-based methods, in particular concerning interpolation. The method can be used for initializing pose-tracking systems, either when starting the tracking or when the tracking has failed and the system needs to re-initialize. Due to its real-time performance, the method can also be embedded directly into the tracking system, allowing a sensor fusion unit choosing dynamically between the frame-by-frame tracking and the pose recognition.     

Incremental Clustering-Based Facial Feature Tracking Using Bayesian ART

Person-independent, emotion specific facial feature tracking have been of interest in the machine vision society for decades. Among various methods, the constrained local model (CLM) has shown significant results in person-independent feature tracking. In 63this paper, we propose an automatic, efficient, and robust method for emotion specific facial feature detection and tracking from image sequences. Considering a 17-point feature model on the frontal face region, the proposed tracking framework incorporates CLM with two incremental clustering algorithms to increase robustness and minimize tracking error during feature tracking. The Patch Clustering algorithm is applied to build an appearance model of face frames by organizing previously encountered similar patches into clusters while the shape Clustering algorithm is applied to build a structure model of face shapes by organizing previously encountered similar shapes into clusters followed by Bayesian adaptive resonance theory (ART). Both models are used to explore the similar features/shapes in the successive images. The clusters in each model are built and updated incrementally and online, controlled by amount of facial muscle movement. The overall performance of the proposed incremental clustering-based facial feature tracking (ICFFT) is evaluated using the FGnet database and the extended Cohn-Kanade (CK+) database. ICFFT demonstrates better results than baseline-method CLM and provides robust tracking as well as improved localization accuracy of emotion specific facial features tracking.     

A robust incremental clustering-based facial feature tracking

Emerging significance of person-independent, emotion specific facial feature tracking has been actively tracked in the machine vision society for decades. Among distinct methods, the Constrained Local Model (CLM) has shown significant results in person-independent feature tracking. In this paper, we propose an automatic, efficient, and robust method for emotion specific facial feature detection and tracking from image sequences. A novel tracking system along with 17-point feature model on the frontal face region has also been proposed to facilitate the tracking of human basic facial expressions. The proposed feature tracking system keeps patch images and face shapes till certain number of key frames incorporating CLM-based tracker. After that, incremental patch and shape clustering algorithms is applied to build appearance model and structure model of similar patches and similar shapes respectively. The clusters in each model are built and updated incrementally and online, controlled by amount of facial muscle movement. The overall performance of the proposed Robust Incremental Clustering-based Facial Feature Tracking (RICFFT) is evaluated on the FGnet database and the Extended Cohn-Kanade (CK+) database. RICFFT demonstrates mean tracking accuracy of 97.45% and 96.64% for FGnet and CK+ database respectively. Also, RICFFT is more robust by minimizing average shape distortion error of 0.20% and 1.86% for FGnet and CK+ (apex frame) database, as compared with classic method CLM.     

多特征融合的自适应相关滤波跟踪算法

针对图像目标跟踪问题,为提高跟踪精度,提出了一种多特征融合的自适应相关滤波跟踪算法。算法首先选取HOG和CN两种互补特征,分别训练两个相关滤波跟踪器跟踪图像目标,然后利用提出的响应图置信度计算公式计算两个跟踪器的响应图权重并进行自适应融合做出决策。滤波器更新阶段,算法结合两个特征的响应图置信度与两帧之间的变化率动态调整滤波器学习速率。仿真实验采用跟踪基准数据库（OTB-2013）中的36组彩色视频序列进行实验,对比了流行的相关滤波跟踪算法,结果表明,该算法在平均跟踪精度上优于其他算法,具有一定的应用价值。     

基于互相关的虚拟粒子图像示踪方法

针对复杂流场中示踪粒子跟随性差、追踪困难等问题,提出一种新的虚拟粒子图像示踪方法,用于进行复杂流场分析。通过选定特定区域的图像作为虚拟示踪粒子,利用互相关算法追踪这些粒子的运动,以生动地表征粒子在图像内的运动情况。实验结果表明,采用该方法在流场进行示踪时,2帧图像间的误差大约在10-2像素量级,精度较高,适应性较好。     

基于Level Set方法的人脸轮廓提取与跟踪

提出一种基于level set方法的图像序列中人脸轮廓提取与跟踪算法，首先利用图像帧间差分快速检测出运动区域，并根据人脸图像的投影映射规则确定人脸所在的外接矩形，然后以此矩形作为初始曲线，采用一种改进的1evelset模型精确提取出入脸轮廓。由于图像序列中人脸是一直运动的，该文引入一阶线性Kalman滤波模型对人脸运动进行估计，从而较好地跟踪了运动中的人脸轮廓，实验结果表明该方法是有效的。     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.