运动背景下的帧间稳像技术

针对运动背景下帧间稳像技术,提出了一种带运动矢量修正的灰度投影运动估计算法。采用该算法分别对当前帧和参考帧的行、列计算灰度投影序列;将当前帧投影序列局部分块,分别将每一分块与参考帧行、列投影曲线进行互相关计算,得到基于局部投影的行、列运动矢量集合;以分块区域的相关置信度为权系数衡量参数,计算每一分块的像素位移权值,从而计算某一方向帧间的加权运动矢量。实验结果表明:该方法可以使运动目标造成的影响只作用于其中若干个局部分块,而其他分块不受此影响,尽可能保证稳像的准确性。采用该方法稳像后的图像与参考帧图像的均方根误差(RMSE)值明显下降,与参考帧图像更加吻合。     

运动背景下的帧间稳像技术

针对运动背景下帧间稳像技术,提出了一种带运动矢量修正的灰度投影运动估计算法。采用该算法分别对当前帧和参考帧的行、列计算灰度投影序列;将当前帧投影序列局部分块,分别将每一分块与参考帧行、列投影曲线进行互相关计算,得到基于局部投影的行、列运动矢量集合;以分块区域的相关置信度为权系数衡量参数,计算每一分块的像素位移权值,从而计算某一方向帧间的加权运动矢量。实验结果表明：该方法可以使运动目标造成的影响只作用于其中若干个局部分块,而其他分块不受此影响,尽可能保证稳像的准确性。采用该方法稳像后的图像与参考帧图像的均方根误差（RMSE）值明显下降,与参考帧图像更加吻合。     

两阶段旋转、平移亚像素数字稳像算法

提出一种稳健的两阶段层次匹配稳像算法, 在频域结合相位相关和谱对消技术对抖动图像序列间的平移和旋转矢量获得亚像素估计精度。算法第一阶段首先在对数极坐标表示下, 利用相位相关法得到图像帧间的旋转角度和整数像素平移参数; 算法第二阶段运用谱对消技术确定亚像素平移参数。在建立参数运动模型的基础上,通过自适应均值滤波确定各帧修正矢量。实验结果表明, 该方法具有亚像素级的稳像精度。     

红外序列图像中运动弱小目标检测

为检测红外序列图像中的运动弱小目标,分析了目标在序列图像中的运动特性和概率分布特性,以及目标和噪声在序列中的能量分布特性。提出连续M帧高阶累积方法来增强运动弱目标能量,用假设检验对目标和背景进行分割,通过搜寻序列运动能量中心来实现目标的多帧关联检测。通过仿真实验证明了算法对红外弱小目标检测有效性。     

移位相加法日间探测低信噪比恒星

为实现白天红外光电测量系统对低信噪比恒星的质心及能量高精度计算,本文给出一种高效的方法。首先,分析了红外光学系统白天恒星的成像特征。其次,先对采集的图像序列进行预处理操作得到预处理图像。接着对预处理图像序列执行叠加求均值和下采样操作得到下采样图像。在下采样图像中以亮度为特征求取恒星的疑似位置后,在预处理图像序列上建立与疑似位置相对应的目标区域,在目标区域内顺序求取质心序列。对目标区域的图像序列以质心偏移为基础进行移位相加后获取目标图像。在目标图像上以信噪比为判据完成恒星提取,以及质心和能量的计算。再次,分析指出此方法能增强目标信噪比的原理,并给出其适用范围以及相关参数的确定方法。最后通过实验表明,采用移位相加法可增强目标的信噪比,并提高提取正确率;且对于SNR不大于4.8的恒星可将其质心和能量计算精度平均提高0.06 pixel和28.5%。移位相加法对低信噪比的恒星可较为精确地计算其质心和能量。     

图像序列的显著性目标区域检测方法

针对传统视觉显著性模型在自顶向下的任务指导和动态信息处理方面的不足,设计并实现了融入运动特征的视觉显著性模型。利用该模型提取了图像的静态特征和动态特征,静态特征的提取在图像的亮度、颜色和方向通道进行,运动特征的提取采用基于多尺度差分的特征提取方法实现,然后各通道分别通过滤波、差分得到显著图,在生成全局显著图时,提出多通道参数估计方法,计算图像感兴趣区域与眼动感兴趣区域的相似度,从而可在图像上准确定位目标位置。针对20组视频图像序列(每组50帧)进行了实验,结果表明:本文算法提取注意焦点即目标区域的平均相似度为0.87,使用本文算法能够根据不同任务情境,选择各特征通道的权重参数,从而可有效提高目标搜索的效率。     

基于改进动态规划的主动声纳序列图像检测方法

动态规划是序列图像小目标检测的常用方法,该方法假设各帧图像间目标转移过程为马尔可夫过程,将能量累积计算量由原本的按图像帧数幂指数增长变为成倍增长,大大减小了计算量,使计算目标多帧累积能量变为可能。为了提高算法检测能力,该文根据动态规划算法原理及惩罚函数特点对算法进行了改进,使原本的二阶规划过程变为三阶,使指标函数更加有效地累积,有效提高了相同虚警概率下的检测概率。还利用该算法设计了主动声纳序列图像检测过程,并进行了仿真,验证了算法的有效性。     

凝视观测图像空间弱小目标检测方法

针对凝视观测模式下光测图像中弱小目标的检测问题,提出一种多帧频域特征累积的目标检测方法。利用恒星匹配、临帧差分等方法消除背景变化的影响,通过图像时频域特征分析,推导出递归形式频域特征累积方程,并由此检测图像中的弱小目标。实验结果表明采用多帧频域特征累积方法能将图像信噪比提高3倍以上,有利于检测出弱小目标。     

连续帧间差分与背景差分相融合的运动目标检测方法

为了克服背景差分法和帧间差分法的不足,有效提高运动目标检测的准确性、实时性和检测效率,提出了一种将连续帧间差分法与背景差分法相结合的运动目标检测方法.首先通过连续帧间差分法获得连续帧差图像,然后分别通过线性的自适应滤波、非线性的中值滤波获得背景图像进行差分,之后再利用阈值分割技术实现运动目标的增强,从而有效解决背景差分法和帧间差分法中都可能出现的无法检测目标的现象.实验表明,该算法可以有效避免漏检、误检等情况,提高运动目标检测的效率和准确性.     

一种基于PMP的圆型流水线上工件的三维在线检测方法

提出了一种基于PMP原理的对圆形流水线上工件面形进行在线检测的方法。利用DLP将一环形正弦光栅图投射在圆型流水线上某一区域，使用相移器在物体运动方向的垂直方向上进行相移，实现了圆周运动物体各物点相移一致性，同时由于相移方向与物体移动方向垂直，工件移动不会影响相移量，相移量可以较精确地控制。通过采用参考标记和图像旋转恢复可实现N帧变形条纹图像的像素匹配，从而提取三维物体的截断相位，经过相位展开，得到连续相位，并由相位最后解调出物体的高度信息。通过计算机仿真验证了方法的可行性。该方法具有在线、快速，非接触性等特点。     

基于生物视觉特征和视觉心理学的视频显著性检测算法

提出了一种空域和时域相结合的视频显著性检测算法.对单帧图像,受视觉皮层层次化感知特性和Gestalt视觉心理学的启发,提出了一种层次化的静态显著图检测方法.在底层,通过符合生物视觉特性的特征图像（双对立颜色特征及亮度特征图像）的非线性简化模型来合成特征图像,形成多个候选显著区域;在中层,根据矩阵的最小Frobenius-范数（F-范数）性质选取竞争力最强的候选显著区域作为局部显著区域;在高层,利用Gestalt视觉心理学的核心理论,对在中层得到的局部显著区域进行整合,得到具有整体感知的空域显著图.对序列帧图像,基于运动目标在位置、运动幅度和运动方向一致性的假设,对Lucas-Kanade算法检测出的光流点进行二分类,排除噪声点的干扰,并利用光流点的运动幅度来衡量运动目标运动显著性.最后,基于人类视觉对动态信息与静态信息敏感度的差异提出了一种空域和时域显著图融合的通用模型.实验结果表明,该方法能够抑制视频背景中的噪声并且解决了运动目标稀疏等问题,能够较好地从复杂场景中检测出视频中的显著区域.     

On shadow elimination after moving region segmentation based on different threshold selection strategies

A popular approach for detecting moving object regions in video sequences is the application of the background subtraction technique. According to this technique the background (reference) image is subtracted from the current image frame and the moving parts are detected by the selection of a suitable threshold. In this paper we present our work to discriminate the moving pixels of the generated difference images from the relatively stationary pixels through the use of three different threshold selection strategies, namely, (i) ‘3σ edit rule’, (ii) rule utilizing the Hampel identifier, and (iii) rule based on an ad hoc selection of threshold. Further, after segmentation a method of classification, based on a moving shadow search technique, previously developed by the authors, has been applied to segregate the moving shadow region from the actual moving object. The speed-up achieved through the use of the three aforementioned techniques on the core moving shadow search process, compared to that where no such process has been applied, has been documented. The final outcomes of applying the shadow detection technique after segmenting using each of the threshold selection strategies, one at a time, on some indoor video sequences have been demonstrated and comparison of the methods made.     

一种基于时域滤波的红外序列图像去噪算法

红外图像是现代光学设备常用的图像源,图像显示效果直接影响设备的用户体验,而红外序列图像中的噪声会导致显示效果的下降。为了减轻噪声对红外序列图像显示效果的影响,通过历史多帧灰度值的加权和当前帧无噪声图像的估计,提出了一种基于时域高斯滤波的去噪方法。参考空域双边滤波的权值分配方法,引入了灰度值的影响对时域高斯滤波的权值进行修正,解决时域滤波导致的序列图像中运动目标拖尾和模糊。实验结果表明,时域滤波方法能够有效平滑帧间噪声,减轻噪声导致的红外序列图像显示效果的恶化,引入灰度值的影响进行滤波权值修正之后,能够解决时域滤波导致的运动目标拖尾和模糊问题。     

A novel frame shift and integral technique for processing low-light-level moving image sequence

A new method for processing low-light-level moving image sequence is proposed, in which (1) a novel algorithm based on difference processing has been firstly developed to determine the motion parameters of moving image sequence, (2) the spatial relativity of the frames within moving image sequence can be then well established after frame shifting according to motion parameters obtained above, (3) finally, frame integration method can be applied to each processed frame, thereby increasing the signal-to-noise ratio of low-light-level moving image sequence. Experiments have been carried out to verify the validity of the proposed method, which show that the parameters obtained by the developed algorithm coincide well with the actual values, and the SNRs of the moving image sequences were effectively increased with the proposed method, indicating that the method offers significant advantages in the enhancement of low-light-level moving image sequence.     

基于数学形态学的弱点状运动目标的检测

提出了一种新的基于数学形态学的红外图像序列中弱点状运动目标的非参数检测算法。采用数学形态学抑制背景杂波干扰和增强目标,用沿时间轴投影和二维空域搜索代替复杂的时空三维搜索形成组合帧,然后在每条可能的轨迹上将进行目标能量累加,实现了一种快速检测前跟踪(TBD)检测算法。仿真实验表明:在恒虚警概率条件下,该检测算法能高效地检测信噪比约为2的弱点状运动目标,检测性能对噪声分布不敏感,能精确地得到目标的即时位置和速度信息,适合于实时图像处理和目标探测,具有很高的实用价值。     

Vehicle speed measurement based on gray constraint optical flow algorithm

Vehicle speed measurement (VSM) based on video images represents the development direction of speed measurement in the intelligent transportation systems (ITS). This paper presents a novel vehicle speed measurement method, which contains the improved three-frame difference algorithm and the proposed gray constraint optical flow algorithm. By the improved three-frame difference algorithm, the contour of moving vehicles can be detected exactly. Through the proposed gray constraint optical flow algorithm, the vehicle contour's optical flow value, which is the speed (pixels/s) of the vehicle in the image, can be computed accurately. Then, the velocity (km/h) of the vehicles is calculated by the optical flow value of the vehicle's contour and the corresponding ratio of the image pixels to the width of the road. The method can yield a better optical flow field by reducing the influence of changing lighting and shadow. Besides, it can reduce computation obviously, since it only calculates the moving target contour's optical flow value. Experimental comparisons between the method and other VSM methods show that the proposed approach has a satisfactory estimate of vehicle speed.     

一种改进的球栅阵列封装焊点射线图像阈值分割算法

球栅阵列封装焊点的射线图像具有信噪比差、背景不均匀等特点,故传统的阈值分割方法无法将目标焊点与背景图像很好的分割.本文通过对球栅阵列封装焊点射线图像直方图的分析,利用了自适应维纳滤波对阈值分割前的图像进行了预处理.根据图像的差异来调整该滤波器的参量,对局部差异大的地方进行小的平滑操作,对局部差异小的地方进行大的平滑操作.在最大类间方差法的基础上,对分割后的图像进行了进一步的分析并提出了改进的二次分割方法.改进的方法为并不直接通过OTSU法进行二值化处理来去除背景,而是在阈值分割得到的两个灰度级内通过计算中值和统计最大灰度像素的方法得到了更优化的阈值,使得去除背景后的焊点图像整体更加清晰和均匀.在背景灰度级内寻找了一个合适的灰度级作为处理后的灰度图像新背景,实验证明该方法明显改进了传统最大类间方差法对球栅阵列封装焊点射线图像的阈值分割效果.     

Real-time extended-field-of-view ultrasound based on a standard PC

Extended field-of-view (EFOV) can acquire a full field of vision, which can help doctors to make more objective and accurate diagnosis. Current EFOV techniques suffer from the low computation speed due to the large amount of ultrasound data to be processed. This paper describes an efficient technique to register 2D multiframe ultrasound images and produce EFOV images with significantly reduced computation time based on a standard PC. For registration of any two adjacent images, we propose to select less image blocks which are regarded as the most valid blocks based on the importance of image content. In registration of a sequence of images, with an assumption that the moving direction and speed of the probe are nearly identical during the data collection, we estimate the moving speed of the probe at the beginning of data collection and ignore redundant image data by processing a smaller number of frames according to a frame interval. The experimental results show that the computation speed of our method is increased by 7–80 times in comparison with two traditional methods, and can accurately produce EFOV images in real-time.     

Multifocus image fusion scheme using focused region detection and multiresolution

To solve the fusion problem of the multifocus images of the same scene, a novel algorithm based on focused region detection and multiresolution is proposed. In order to integrate the advantages of spatial domain-based fusion methods and transformed domain-based fusion methods, we use a technique of focused region detection and a new fusion method of multiscale transform (MST) to guide pixel combination. Firstly, the initial fused image is acquired with a novel multiresolution image fusion method. The pixels of the original images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions can be determined, and the techniques of morphological opening and closing are employed for post-processing. Then the pixels within the focused regions in each source image are selected as the pixels of the fused image; meanwhile, the initial fused image pixels which are located at the focused border regions are retained as the pixels of the final fused image. The fused image is then obtained. The experimental results show that the proposed fusion approach is effective and performs better in fusing multi-focus images than some current methods.     

Moving object detection based on improved VIBE and graph cut optimization

Extracting foreground moving objects from video sequences is an important task and also a hot topic in computer vision and image processing. Segmentation results can be used in many object-based video applications such as object-based video coding, content-based video retrieval, intelligent video surveillance and video-based human–computer interaction. In this paper, we present a novel moving object detection method based on improved VIBE and graph cut method from monocular video sequences. Firstly, perform moving object detection for the current frame based on improved VIBE method to extract the background and foreground information; then obtain the clusters of foreground and background respectively using mean shift clustering on the background and foreground information; Third, initialize the S/T Network with corresponding image pixels as nodes (except S/T node); calculate the data and smoothness term of graph; finally, use max flow/minimum cut to segmentation S/T network to extract the motion objects. Experimental results on indoor and outdoor videos demonstrate the efficiency of our proposed method.