图象目标识别图形的自适应步级检测

建立图象目标识别模型，用形状、灰度和运动特征描述图象目标。基于目标建模，把目标识别、门限及目标图形区域步级检测、虚漏警调节和目标空域条件有机地结合起来，给出牵引式跟踪系统中图象目标识别图形的自适应步级检测算法。该算法用于检测具有识别特征的图象目标图形，并成功地应用到实际的实时跟踪系统中的图象目标识别和跟踪。对实际图象的处理结果和在实时跟踪系统中的实验说明本文研究的技术的有效性和适用性。     

图象跟踪算法仿真环境

图象跟踪系统具有重要的军事价值，所选用的跟踪算法的俦到图象跟踪系统的性能，为了分析各种跟踪算法在不同应用环境下的跟踪能力，开发研究新的跟踪算法，对各种算法某种评价其跟踪状况的准则，建立了一个图象跟踪算法仿真环境。     

一种基于红外图像的小目标跟踪置前检测算法

为在低信噪比、强杂波的环境下提取目标，人们提出了跟踪置前检测(TbD)的小目标检测方法。讨论了红外图像杂波剔除和用最优非线性滤波器跟踪置前检测的算法，给出了算法模型和基本步骤，利用这种算法可有效地进行杂波抑制和对小目标跟踪置前检测。     

基于跟踪特征的目标模型逼真度评估方法

为了实现红外仿真目标模型逼真度评估的目的，采用了基于跟踪算法提取目标模型关键特征的方法，通过分析跟踪过程的重点，确定以目标为中心的局部视场区域图像来评估目标模型。在现有红外仿真目标模型评估现状的基础上，采用典型的跟踪算法来设计完成试验，并进行了理论分析和试验验证，取得了较好的试验结果。结果表明，基于关键特征提取的方法中相似目标模型是不相似目标模型的2倍以上，有些高达10倍差距，可以基于目标图像关键特征来验证目标模型的有效性。此研究算法为后续红外仿真目标模型逼真度的评估具有实际指导和扩展的意义。     

运动目标去除阴影的跟踪

运动目标的影子会降低视频监控的性能,本文提出了一个提高目标跟踪精确率的去影算法。该算法由四部分组成:建立一个实时性很强自动更新的背景模型,提取运动目标区域,消除运动目标的影子,最后对已去影的运动目标进行检测和跟踪。实验结果表明我们的算法在处理目标去影跟踪上有着良好的性能,能在一定的程度上解决运动目标的遮挡问题,并去除目标自的阴影,提高了运动目标跟踪的鲁棒性。     

目标离开侦测算法的研究

研究目标检测、背景提取、目标像素的处理、跟踪框生成算法,详细分析目标离开侦测算法的思路和原理,实现目标离开算法在不同环境下的识别、跟踪。通过测试结果表明,该算法模型在目标离开过程的侦测、跟踪效果都表现的不错,算法的效果良好。     

遮挡情况下多尺度压缩感知跟踪

针对现有在线学习跟踪算法中目标在线模型更新错误导致跟踪漂移的问题,提出一种在线模型自适应更新的目标跟踪算法:首先利用压缩感知技术的高效性,对多尺度图像特征进行降维,并提取多尺度样本来实现目标尺度自适应更新,再由提取的正负样本低维图像特征训练朴素贝叶斯分类器,利用分类器输出置信度最大处目标样本完成目标跟踪,并依据当前目标置信度来自适应在线模型更新速率,减少了遮挡带来的目标错误更新。实验表明:该方法在尺度变化、局部和全局遮挡、光照变化等情况下均能完成鲁棒跟踪,平均跟踪成功率较原始压缩感知跟踪算法提高了20.3%。     

基于相关滤波扩展记忆系统模型的智能目标跟踪

将目标跟踪过程看作一个多重记忆系统模型,提 出了基于相关滤波的扩展记忆系统模型,实现了基 于记忆系统模型的智能目标跟踪。首先,通过提取跟踪目标特征学习目标信息,生成短时相 关滤波器,产 生短时记忆;然后利用每一帧短期记忆的不断重复与更新,产生长时记忆,生成长时相关滤 波器。短时与 长时记忆构成相关滤波记忆系统模型,完成目标跟踪。在此模型基础上,分析与挖掘模型中 的相关滤波数 据,加入四种智能化控制信息,构建扩展记忆系统模型,实现智能化的目标跟踪。基于相关 滤波的扩展记 忆系统模型利用生物记忆的原理使目标跟踪更加自动化、智能化,增强目标跟踪的准确性。 实验结果表明, 与当前流行的相关滤波跟踪算法相比,本文算法提高了目标跟踪的抗干扰性、抗遮挡性与抗 形变能力,同时保证了在尺度跟踪的有效性。     

基于灰色马尔可夫模型的均值偏移算法

针对Mean shift算法中存在的不足,本文提出了结合均值偏移和灰色马尔可夫预测模型的目标跟踪算法。该方法利用灰色马尔可夫模型预测目标在当前时刻的中心位置,以此点作为均值偏移算法进行目标搜索的起始位置。同时,提取目标的几何特征,根据目标的面积来改善跟踪窗口的大小,利用“目标模型”和“候选模型”之间的目标特征的变化产生模型更新策略。经实验得,该方法能实时稳健的进行跟踪。     

融合关注度和空间投影的核跟踪算法

目标描述是目标跟踪算法中最重要的步骤之一,也是建立鲁棒视觉跟踪系统的关键.为了更好地描述目标,引入了基于关注度的显著性特征提取算法.关注度模型通过模仿人类的视觉机理,能自动从视野中突出最能吸引人类注意的部分.因此,基于关注度的目标描述可以较好地提取目标的最显著特征,从而有利于建立一个与背景具有更好区分性的目标模型.另外,为了融入空间信息,对目标的显著性图像进行了水平投影和垂直投影,并且把均值位移(Mean Shift)迭代过程直接应用到空间投影上,最终得到目标所在坐标位置.实验证明:这种基于关注度和空间投影的核跟踪算法比经典Mean Shift目标跟踪算法更加鲁棒、精确地跟踪复杂场景下的动态目标.同时,算法速度可以提高约50%.     

基于图像目标识别印章的可视数字跟踪系统

图像目标识别印章; 牵引式可视数字跟踪系统; 智能识别跟踪; 图像代数     

基于边缘强度的红外图像阈值分割方法研究

提出一种针对复杂场景的基于图像边缘强度的阈值分割算法。首先进行边缘检测,然后根据边缘强度进行像素灰度值加权平均计算出图像的分割阈值。该算法简单、实用,可对海上及空中目标进行准确分割,并已在实际的跟踪系统中进行了应用,结果表明目标分割正确,系统实时性强、稳定可靠。     

Spatio-temporal segmentation of image sequences for object-oriented low bit-rate image coding

This paper describes a new method of segmentation of time-varying image sequences whose goal is object-oriented image coding. The segmentation represents a partition of each frame of the sequence into a set of regions which are homogeneous with regard to motion criterion. The region borders correspond to spatial contours of objects in the frame. Each spatio-temporal region is characterized by its temporal component, which is a model-dependent vector of motion parameters, and a structural component representing the polygonal approximation of the spatial contour of the region.

The construction of spatio-temporal segmentation includes two phases: the initialization step and temporal tracking. The initialization step is based on the spatial segmentation of the first frame of the sequence. Then homogeneous spatial regions are merged through motion estimation in accordance with a motion-based criterion. The temporal tracking consists of the projection of the segmentation along the time axis, and its adjustment. Special attention is paid to the processing of occlusions.

A predictive coding scheme is proposed which is based on the temporal coherence of the segmentation. This scheme is promising for a low bit-rate image compression.

The results for teleconference and TV sequences show the high visual quality of reconstructed only by prediction images. Moreover, the bit-rates for motion coding are very low: from 0.002 to 0.007 bit/pixel for teleconference sequence and from 0.004 to 0.021 bit/pixel for complex TV sequence. A scheme for encoding of the structural information is proposed which requires 0.083 – 0.17 bit per pixel depending on the content of the sequence.     

基于Kohonen神经网络算法的医学图像分割

长期以来,在医学图像处理方面,图像分割一直是该项工作中的重点,同时也是其中的难点。所谓的图像分割实质上就是遵照相关的原则对图像进行分割,使其被分为几个部分的过程。它是以图像测量、配准、融合和三维重建作为基础,所以在临床医学研究中起着举足轻重的作用。文中研究的Kohonen聚类神经网络算法是以VC++为基础,并在此基础上进行了一定的优化,为图像分割的具体应用提供基础条件,提高效果,这对医生临床诊断具有十分重要的意义。     

复杂图象轮廓提取与识别技术研究

本文以X线指关节骨图象轮廓提取和识别为典型,研究了具有乘性和加性噪声干扰的复杂图象轮廓提取与识别技术。文中系统地研究了典型的微机图象识别系统设计问题.这类问题通常首先作图象特征与识别参数描述;如果识别特征信息在轮廓线上,则要研究图象轮廓跟踪器设计与实现,然后,研究识别参数提取与识别器设计与实现.文中还深入地研究了轮廓线的视觉模型,根据视觉模型,提出了抗干扰的轮廓跟踪技术:先作梯度变换,作粗轮廓跟踪,最后作精细轮廓跟踪.在精细轮廓跟踪时,使用多个初始点解决了因轮廓多处断裂防碍轮廓线提取的困难.最后对所研究的技术进行了上机实测。测试结果十分好,并已成功地用于实际系统中.     

A novel approach to motion segmentation based on gamma distribution

We provide a new motion segmentation method in image sequences based on gamma distribution. Motion segmentation is very important because it can be employed for video surveillance, object tracking, and action recognition. The Gaussian mixture model (GMM) has been widely used as a distribution model for motion segmentation. However, we found that the gamma distribution model is more suitable than the GMM for the optical flow based motion segmentation. Experimental results show that the proposed method is very effective in producing accurate motion segmentation results in image sequences.     

A survey of perceptual image processing methods

Perceptual approaches have been widely used in many areas of visual information processing. This paper presents an overview of perceptual based approaches for image enhancement, segmentation and coding. The paper also provides a brief review of image quality assessment (IQA) methods, which are used to evaluate the performance of visual information processing techniques. The intent of this paper is not to review all the relevant works that have appeared in the literature, but rather to focus on few topics that have been extensively researched and developed over the past few decades. The goal is to present a perspective as broad as possible on this actively evolving domain due to relevant advances in vision research and signal processing. Therefore, for each topic, we identify the main contributions of perceptual approaches and their limitations, and outline how perceptual vision has influenced current state-of-the-art techniques in image enhancement, segmentation, coding and visual information quality assessment.     

Representation of image content based on RoI-BoW

Representation of image content is an important part of image annotation and retrieval, and it has become a hot issue in computer vision. As an efficient and accurate image content representation model, bag-of-words (BoW) has attracted more attention in recent years. After segmentation, BoW treats all of the image regions equally. In fact, some regions of image are more important than others in image retrieval, such as salient object or region of interest. In this paper, a novel region of interest based bag-of-words model (RoI-BoW) for image representation is proposed. At first, the difference of Gaussian (DoG) is adopted to find key points in an image and generates different size grid as RoI to construct visual words by the BoW model. Furthermore, we analyze the influence of different size segmentation on image content representation by content based image retrieval. Experiments on Corel 5K verify the effectiveness of RoI-BoW on image content representation, and prove that RoI-BoW outperforms the BoW model significantly. Moreover, amounts of experiments illustrate the influence of different size segmentation on image representation based on the Bow model and RoI-BoW model respectively. This work is helpful to choose appropriate grid size in different situations when representing image content, and meaningful to image classification and retrieval.     

Variational level set method for image segmentation with simplex constraint of landmarks

Landmarks are prior image features for a variety of computer vision tasks. In the image processing domain, research on image segmentation methods has always been a significant topic. Due to the image characteristics of heterogeneous nature, lack of clear boundaries, noise and so on, accurate segmentation of the image is still a challenge. In this paper, utilizing a level set framework and the simplex constraint, preferred image point landmarks are combined into a variational segmentation model to enforce the contour evolve with prior points. Then the alternating minimization algorithm of the proposed model is designed, meanwhile the landmarks constraints are doubled ensured with simplex projection. Finally, experiments on many synthetic and real-world images were implemented. Comparing with other state-of-the-art segmentation variational models, the most striking result to emerge from the data is that the proposed method has higher segmentation performance. Benefiting from appropriate point landmarks, the proposed segmentation method can tackle noisy, weak edges and corrupted area images effectively and robustly.     

A human visual system-driven image segmentation algorithm

This paper presents a novel image segmentation algorithm driven by human visual system (HVS) properties. Segmentation quality metrics, based on perceptual properties of HVS with respect to segmentation, are integrated into an energy function. The energy function encodes the HVS properties from both region-based and boundary-based perspectives, where the just-noticeable difference (JND) model is employed when calculating the difference between the image contents. Extensive experiments are carried out to compare the performances of three variations of the presented algorithm and several representative segmentation and clustering algorithms available in the literature. The results show superior performance of our approach.