基于运动特征的H.264压缩域对象分割

基于从H.264压缩域提取的运动场,提出了一种新的运动对象分割方法.首先采用矢量中值滤波方法滤除运动场中的噪声矢量;再运用后向估计的方法重建预测运动场并进行运动场的累积;然后对存在背景运动的累积运动场进行全局运动补偿;最后基于幅度、散度和旋度3个运动特征,采用改进的统计区域合并方法将运动对象分割出来.实验结果表明.本文方法适用于背景静止或背景运动的H.264压缩视频,且分割质量较好.     

Wavelet transforms for detecting microcalcifications in mammograms

Clusters of fine, granular microcalcifications in mammograms may be an early sign of disease. Individual grains are difficult to detect and segment due to size and shape variability and because the background mammogram texture is typically inhomogeneous. The authors develop a 2-stage method based on wavelet transforms for detecting and segmenting calcifications. The first stage is based on an undecimated wavelet transform, which is simply the conventional filter bank implementation without downsampling, so that the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands remain at full size. Detection takes place in HH and the combination LH+HL. Four octaves are computed with 2 inter-octave voices for finer scale resolution. By appropriate selection of the wavelet basis the detection of microcalcifications in the relevant size range can be nearly optimized. In fact, the filters which transform the input image into HH and LH+HL are closely related to prewhitening matched filters for detecting Gaussian objects (idealized microcalcifications) in 2 common forms of Markov (background) noise. The second stage is designed to overcome the limitations of the simplistic Gaussian assumption and provides an accurate segmentation of calcification boundaries. Detected pixel sites in HH and LH+HL are dilated then weighted before computing the inverse wavelet transform. Individual microcalcifications are greatly enhanced in the output image, to the point where straightforward thresholding can be applied to segment them. FROG curves are computed from tests using a freely distributed database of digitized mammograms.     

一种高效的基于H.264/AVC压缩域信息的全局运动估计方法

本文提出了一种基于H.264/AVC压缩域的高效全局运动估计算法.由于H.264采用了多种新的视频压缩编码技术,使得其压缩码流的运动矢量(MV)场中包含大量噪声运动矢量,可参与全局运动估计的运动矢量相对较少.噪声运动矢量这里指的是与全局运动不相符的运动矢量.为了降低噪声运动矢量的影响、提高全局运动估计的精度和效率,在全...     

一种基于提升小波的快速图像融合算法

提出了一种基于提升小波变换的快速多聚焦图像融合方法。首先利用提升小波算法将原始图像分解为四个子带:LLLHHLHH后将代表三个方向高频细节子带LHHLHH采用提升小波反变换,以获得各方向子带的高频细节图像,采用高斯核权重算法计算所得到的高频细节图像的非均匀加权区域能量,再根据基于能量的图像融合规则得到最终融合图像。对比了几种多聚焦图像融合方案的性能,实验结果表明,在融合效果相当的情况下,文中方法比现有方法在处理速度上有明显的优势。     

Wavelet-based moving object segmentation

A moving object segmentation algorithm in the wavelet domain is proposed. The change detection method with different thresholds in four wavelet sub-bands is used. After merging the different edge masks of four sub-bands with the inverse wavelet or the union operation, further object shape information and more accurate extraction of the moving object is obtained.     

Fast moving object extraction in H.264/AVC compressed domain

This paper presents a novel approach for moving object extraction in the H.264/AVC compressed domain, which based on Ant Colony clustering Algorithm (ACA) and threshold method in macro block layer. Firstly, the Motion Vector (MV) field and the macro block types are extracted from the H.264/AVC compressed video, and then merge MVs with the same characteristic. Secondly, an improved ACA is used to classify the MV field into different motion homogenous regions. At the same time, use macro block types to determine the location of objects. Finally, using the complementarities of macro block template and MVs clustering template to obtain final objects. Experimental results for several video sequences demonstrate that in the case of ensuring accuracy, the proposed approach can extract moving object faster.     

A blind scene-based watermarking for video copyright protection

Due to the replicable nature of video many illegal copies of the original video can be made. So it demands to deliver methods for preventing illegal copying. This paper identifies a new digital watermarking approach for copyright protection of video based on wavelet transformation. First, the motion part of color video is detected by scene change analysis, and then by applying 3D wavelet transformation over detected motion part, 10 sub-bands of wavelet coefficients are obtained. In order to insert the watermark, 3D coefficients of HL, LH and HH with their third level are selected. After all, by using a spread spectrum technique, the watermark is embedded into the selected wavelet coefficients. In extraction step, the original video is not needed, namely, blind detection. So, the resultant watermarking scheme can be used for public watermarking applications, where the original video is not available for watermark extraction. The experimental results show a good performance of the proposed method for transparency and robustness. Furthermore, the robustness of the proposed method against various kinds of attacks such as median filtering, Gaussian noise, frame dropping, frame averaging, frame swapping and lots of lossy compression including MPEG-4, MPEG-2, and H.264 shows the fidelity of our claim.     

基于H.264压缩域的运动目标检测算法

基于H264压缩域的运动目标检测算法。首先,从压缩码流中提取原始运动矢量场,采用中值滤波法滤除运动场中由噪声引起的运动矢量;再利用从压缩码流中提取的宏块分割类型初步排除一些分块较大的伪运动块;然后采用自适应阈值选取法定位出运动区域;最后,通过已定位的运动区域的特征进一步精确识别出运动目标。算法充分利用了H264码流中的运动矢量信息,简单易于实现,适用于智能监控,视频分析等领域。试验结果表明,该算法检测效果较好,且不需要对压缩码流进行全部解码。     

基于块特征的H.264压缩域对象分割

针对H.264压缩域,提出一种改进的运动对象分割方法。首先提取运动矢量场,对该矢量场进行矢量中值滤波,然后基于块进行前向矢量累积获得更加显著的矢量场,最后采用改进的K均值和EM的混合分层聚类方法,基于宏块层、块层两次聚类将运动对象分割出来。实验表明,该方法通过分层聚类,进一步提高了分割效率,能实现满足实时要求的较精确分割。     

基于MPEG压缩域的快速、鲁棒的全局运动信息提取方法

全局运动信息在视频分析中起着重要的作用.本文根据MPEG编码特点,提出了一种从MPEG压缩域中快速有效地进行全局运动参数估计的算法.该算法充分利用了MPEG压缩码流中的信息,通过提取预测残差DC图像的运动背景区域,估计全局运动参数,从而保证了参数估计的准确性,有效地克服了已有文献中仅仅采用运动矢量进行全局运动估计的局限性.根据不同的MPEG测试序列的对比分析,结果表明,本算法可快速准确地对MPEG视频序列进行全局运动信息估计,同时具有很高的鲁棒性.     

基于运动矢量场的运动目标区域检测

基于稀疏运动矢量场,提出一种动态背景下的运动 目标区域检测方法。根据运动矢量场特性分析进行全局运动 参数估计和全局运动补偿,实现动态场景中的背景校正;利用最大树数据结构, 基于运动矢量补偿误差分级表示视频帧中 运动基本一致的连通区域,进行运动区域初始分类;根据运动目标在空间上的连通性和运动 一致性的特点,选择区域相似性 度量准则,进行区域合并和滤波,将具有相似运动的连通区域合并,实现运动目标区域检测 。将检测出的运动目标区域作为 运动矢量外点反过来又应用于全局运动参数估计过程中,全局运动估计和运动目标区域检测 交替地进行,不仅加快了它们的 计算速度,同时也提高了它们计算和检测的准确性。实验结果表明,本文算法能较好地补偿 序列的全局运动,有效地检测出 局部目标运动区域。     

一种新的基于H.264压缩域的运动对象分割算法

提出了一种在H.264压缩域下进行运动对象分割的新算法。算法主要利用H.264码流中的运动矢量信息来进行对象分割,为了提高运动矢量信息的鲁棒性,首先利用I帧中的帧内预测模式和预测残差能量进行区域划分;在P帧中,利用帧间预测残差能量来更新区域划分结果,对部分区域的运动矢量进行归零化处理。再根据P帧中的分块模式,采用不同的滤波器对运动矢量进行滤波;最后利用滤波后的运动矢量信息建立对应的Gibbs势能函数,采用迭代条件模式方法求解最大后验概率,得到可靠的运动对象标记。实验结果表明,该运动对象分割算法可以获得有效并可靠的分割结果。     

基于混沌与整数小波变换的脆弱盲水印

提出了一种新的脆弱盲水印生成算法用于图像完整性证明和内容篡改证明。首先将原始图像分块,对各分块进行一次整数小波变换;计算4个子带(LL,HL,LH,HH)小波系数绝对值的均值,分别映射为混沌初值,经过迭代、量化生成二值水印信息;最后将水印信息随机嵌入到4个子带中。实验结果表明,所提出的算法提高了篡改检测率,对图像的篡改具有高度敏感性和准确的篡改定位能力。水印提取为盲提取,篡改检测只用到了篡改后的水印图像。     

结合空时域下采样与重建的H.264/AVC压缩性能优化

为提高H.264/AVC标准在带宽资源严重受限时的压缩效率,采用空时域相结合的编码思路,提出了一种基于运动检测的自适应抽帧方法,并结合空域下采样与重建研究了一种改进的H.264/AVC压缩性能优化框架。在编码端,原视频先空域下采样以减少空间分辨率,然后根据视频运动特征,采用不同抽帧模式自适应地降低帧率,再经H.264/AVC编码,有效降低了编码码率。在解码端,解码视频则采用与抽帧模式相对应的运动估计与补偿插帧方法重建出抽取帧,再利用超分辨率重建技术将视频恢复到原空间分辨率。实验结果表明,所提方法在低码率段的视频压缩性能优于H.264/AVC标准编解码及相关文献方法。     

Motion-based object segmentation using hysteresis and bidirectional inter-frame change detection in sequences with moving camera

We present an unsupervised motion-based object segmentation algorithm for video sequences with moving camera, employing bidirectional inter-frame change detection. For every frame, two error frames are generated using motion compensation. They are combined and a segmentation algorithm based on thresholding is applied. We employ a simple and effective error fusion scheme and consider spatial error localization in the thresholding step. We find the optimal weights for the weighted mean thresholding algorithm that enables unsupervised robust moving object segmentation. Further, a post processing step for improving the temporal consistency of the segmentation masks is incorporated and thus we achieve improved performance compared to the previously proposed methods. The experimental evaluation and comparison with other methods demonstrate the validity of the proposed method.     

Moving object detection and tracking from video captured by moving camera

This paper presents an effective method for the detection and tracking of multiple moving objects from a video sequence captured by a moving camera without additional sensors. Moving object detection is relatively difficult for video captured by a moving camera, since camera motion and object motion are mixed. In the proposed method, the feature points in the frames are found and then classified as belonging to foreground or background features. Next, moving object regions are obtained using an integration scheme based on foreground feature points and foreground regions, which are obtained using an image difference scheme. Then, a compensation scheme based on the motion history of the continuous motion contours obtained from three consecutive frames is applied to increase the regions of moving objects. Moving objects are detected using a refinement scheme and a minimum bounding box. Finally, moving object tracking is achieved using a Kalman filter based on the center of gravity of a moving object region in the minimum bounding box. Experimental results show that the proposed method has good performance.     

Moving object detection in the H.264/AVC compressed domain for video surveillance applications

In this paper a novel method is presented to detect moving objects in H.264/AVC [T. Wiegand, G. Sullivan, G. Bjontegaard, G. Luthra, Overview of the H.264/AVC video coding standard, IEEE Transactions on Circuits and Systems for Video Technology, 13 (7) (2003) 560–576] compressed video surveillance sequences. Related work, within the H.264/AVC compressed domain, analyses the motion vector field to find moving objects. However, motion vectors are created from a coding perspective and additional complexity is needed to clean the noisy field. Hence, an alternative approach is presented here, based on the size (in bits) of the blocks and transform coefficients used within the video stream. The system is restricted to the syntax level and achieves high execution speeds, up to 20 times faster than the related work. To show the good detection results, a detailed comparison with related work is presented for different challenging video sequences. Finally, the influence of different encoder settings is investigated to show the robustness of our system.     

Video inpainting under constrained camera motion.

A framework for inpainting missing parts of a video sequence recorded with a moving or stationary camera is presented in this work. The region to be inpainted is general: it may be still or moving, in the background or in the foreground, it may occlude one object and be occluded by some other object. The algorithm consists of a simple preprocessing stage and two steps of video inpainting. In the preprocessing stage, we roughly segment each frame into foreground and background. We use this segmentation to build three image mosaics that help to produce time consistent results and also improve the performance of the algorithm by reducing the search space. In the first video inpainting step, we reconstruct moving objects in the foreground that are "occluded" by the region to be inpainted. To this end, we fill the gap as much as possible by copying information from the moving foreground in other frames, using a priority-based scheme. In the second step, we inpaint the remaining hole with the background. To accomplish this, we first align the frames and directly copy when possible. The remaining pixels are filled in by extending spatial texture synthesis techniques to the spatiotemporal domain. The proposed framework has several advantages over state-of-the-art algorithms that deal with similar types of data and constraints. It permits some camera motion, is simple to implement, fast, does not require statistical models of background nor foreground, works well in the presence of rich and cluttered backgrounds, and the results show that there is no visible blurring or motion artifacts. A number of real examples taken with a consumer hand-held camera are shown supporting these findings.     

H.264压缩域中mean-shift聚类运动目标分割算法

针对视频监控、检索,提出了一 种在H.264压缩域下运动对象分 割的新算法。根据实际监控应用特点,算法主要利用H.264码流中提 取的运动矢量(MV)、分块尺寸信息对原始的MV场(MVF)进 行可靠性分析以及空间滤波、mean-shift聚类处理,从而得到可信度较高的 MVF场。首先,从H.264码流中获取原 始MV 并进行归一化处理,同时对原始MVF进行两步空间滤波;其次,对不同的块大 小分配相应的权值作为每个样本 的权重系数,将处理之后的MVF作为样本空间,利用mean-shift聚类获取 真实的MVF;最后,根据可靠的MVF 标记运动目标。实验结果表明,本文提出的mean-shift聚类运动目标分割算 法可以获得有效并可靠的分割结果。