一种两阶段的关键帧提取方法

基于联合直方图的帧间相似性在关键帧提取中存在漏检问题,而基于边缘特征的方法,虽然效果较好,但在计算复杂度方面比较高。本文结合这两种方法的优点进行两阶段的提取方法,即先用基于联合直方图的方法提取视频的候选关键帧,再用基于边缘特征的方法进行二次提取。实验结果表明,该方法提取出来的关键帧有较好的代表性,冗余度底。     

采用多特征融合的镜头边界检测方法

提出了一种特征融合的镜头边界检测方法。HSV色彩直方图进行镜头边界检测是一种常用、有效的方法,该文融合了视频帧的HSV直方图特征、边缘特征和纹理特征,计算出不连续帧。最后,采用Kohonen网络自组织网络对不连续帧值进行聚类得到镜头边界。实验结果表明该方法不仅是可行和有效的,也解决了需要阈值的问题。     

一种二级级联分类的镜头边界检测算法

在分析镜头边界类型、检测方法的基础上,根据镜头的连续性特征,将一个二级级联分类器应用于镜头边界检测．第一级分类器根据视频帧灰度方差特征,将无明显变化的视频序列从原始视频序列中分离出去,得到一个新的视频序列;第二级分类器在新视频序列的基础上,提取视频图像的像素对差值、HSV空间颜色直方图的各分量差值以及边缘直方图X,Y分量差值等视频特征,并采用支持向量机多分类策略进行镜头边界类型的检测．实验结果表明,与积聚算法及SVM—TMRA算法相比,文中算法的综合性能更高且具有较高的实时性．     

切割刀具图像边缘破损检测方法仿真

采用目前方法对切割刀具边缘进行破损检测时,没有对刀具图像进行增强处理,导致方法存在图像对比度低、适用性能差和检测精度低的问题.提出基于直方图均衡化的切割刀具边缘破损检测方法,利用天然橡胶刀具图像的灰度中值,在分割直方图等面积原则的基础上进行灰度直方图分割处理,均衡化处理分割处理后的各子灰度直方图,完成天然橡胶刀具图像的...     

一种新型煤矿视频监控图像滤波算法

针对煤矿视频监控图像存在噪声强度高且对比度低等问题,提出了一种新型煤矿视频监控图像滤波算法。该算法首先采用自适应Canny算子对图像进行边缘检测,实现边缘图像和非边缘图像的有效分离;然后对边缘图像引入直方图均衡化算法进行处理,以突出图像边缘信息,提高图像对比度;从滤波器的构建、结构元素的设计方面对经典数学形态学滤波算法进行改进,将其应用于非边缘图像的滤波;最后对处理后的边缘图像和非边缘图像引入图像融合机制进行加权融合。实验结果表明,与小波阈值法、经典数学形态学滤波算法相比,该算法具有较好的滤波效果。     

基于证据理论的视频语义概念检测

视频语义概念检测是跨越语义鸿沟问题,实现基于语义的视频检索的前提。本文提出了一种基于证据理论的视频语义概念检测方法。首先,分别提取了镜头关键帧的分块颜色矩、小波纹理特征和边缘方向直方图特征;然后,利用支持向量机(Support vector machine,SVM)对3种特征数据分别进行训练,分别建立分类器模型;再次,对各SVM模型泛化误差进行分析,采用折扣系数法对不同SVM模型输出的分类结果进行修正;最后,采用证据融合公式对修正后的输出进行融合,把融合结果作为最终的概念检测结果。实验结果表明,新方法提高了概念检测的准确率,优于传统的线性分类器融合方法。     

复杂光照环境下视频人脸序列的自动检测方法

基于视频序列人脸自动检测是人脸跟踪、识别等研究的基础。提出了一种结合图像增强技术、gabor特征变 换和adaboost算法的视频序列人脸检测方法,其主要思想是使用图像增强技术对图像进行光照补偿,减轻不同的光 照条件(如局部的阴影和高亮等)对检测结果的影响。该方法首先通过高频增强滤波强化图像的边缘和细节信息,用 基于直方图的技术来调节图像的亮度,然后应用gabor小波变换进行特征抽取,最后采用adaboost方法训练样本,完 成人脸的检测。实验表明,该方法能够在不同的光照条件下准确检测出人脸,显示出较强的鲁棒性。     

复杂光照环境下视频人脸序列的自动检测方法

基于视频序列人脸自动检测是人脸跟踪、识别等研究的基础.提出了一种结合图像增强技术、gabor特征变换和adaboost算法的视频序列人脸检测方法,其主要思想是使用图像增强技术对图像进行光照补偿,减轻不同的光照条件(如局部的阴影和高亮等)对检测结果的影响.该方法首先通过高频增强滤波强化图像的边缘和细节信息,用基于直方图的技术采调节图像的亮度,然后应用gabor小波变换进行特征抽取,最后采用adaboost方法训练样本,完成人脸的检测.实验表明,该方法能够在不同的光照条件下准确检测出人脸,显示出较强的鲁棒性.     

基于内容的视频检索中视频镜头检测技术的研究

对基于内容的视频检索中视频镜头检测技术进行研究.首先,特征的选取,使用符合人类感知的HSV空间,并以该空间上的非均匀分块加权颜色直方图作为帧间差特征;其次,以双阈值比较法为检测方法,对其存在的问题进行改进;最后,采用基于局部统计特性的阈值选取方法.实验表明:本方法能取得较好效果,在一定程度上提高检测的查全率和准确率.     

时域多分辨率的非压缩域视频分割算法

由于非压缩视频序列具有明显的直观性视觉特征,并在实际应用中仍占据相当大的比重,针对在非压缩视频的处理中仍然存在对镜头变化的类型、镜头渐变和突变的位置分析不够准确的难点,提出了一种时域分辨率的非压缩域视频分割算法,在提取序列的基于强边缘块的直方图,比较不同序列之间的差异的基础上,采用多分辨率的算法进行镜头检测。实验证明,这种方法实现了自动视频分割并较好地克服了上述的缺点。     

Fast video shot boundary detection framework employing pre-processing techniques

Video shot boundary detection is the initial and fundamental step towards video indexing, browsing and retrieval. Great efforts have been paid on developing accurate shot boundary detection algorithms. However, the high computational cost in shot detection becomes a bottleneck for real-time applications. The problem of making a balance between detection accuracy and speed is addressed in this paper, and a novel fast detection framework is presented. The general framework that employs pre-processing techniques can improve both detection speed and precision. In the pre-processing stage, adaptive local thresholding is adopted to classify non-boundary segments and candidate segments that may contain shot boundaries. The candidate segments are refined using bisection-based comparisons to eliminate non-boundary frames. Only refined candidate segments are preserved for further detections; hence, the speed of shot detection is improved by reducing detection scope. Moreover, prior knowledge about each possible shot boundary such as its type and duration can be obtained in the pre-processing stage, which can accelerate the consequent hard cut and gradual transition detections. Experimental results indicate that the proposed framework is effective in accelerating the shot detection process, and it can also achieve excellent detection accuracies.     

基于基色调的镜头边界检测方法

视频结构化组织是建立视频检索和浏览系统的基础,而镜头边界检测是视频结构化的第一步. 在本文中,我们提出了基于基色调的镜头边界检测方法. 该方法首先利用 I 帧的比特数信息,减少参与检测的 I 帧;然后在考虑基色调的基础上得到局部的自适应阈值;最后用双阈值方法检测镜头边界. 实验证明,该方法可以对长视频序列进行较好的镜头突变和渐变检测,并且减少了计算量,同时能够排除大物体运动和摇镜头对镜头检测的影响.     

A Supervised Learning Approach to Flashlight Detection

Shot boundary detection is a fundamental step of video indexing. One crucial issue of this step is the discrimination of abrupt shot change from flashlight, because flashlight often induces a false shot boundary. Support vector machine (SVM) is a supervised learning technique for data classification. In this paper, we propose a SVM-based technique to detect flashlights in video. Our approach to flashlight detection is based on the facts that the duration of flashlight is short and the video contents before and after a flashlight should be similar. Therefore, we design a sliding window in temporal domain to monitor the instantaneous video variation and extract color and edge features to compare the visual contents between two video segments. Then, a SVM is employed to classify the luminance variation into flashlight or shot cut. Experimental results indicate that the proposed approach is effective and outperforms some existing techniques.     

基于特征的实时镜头切变检测

提出了一种新的基于边缘特征的帧间相似性度量方法,并在此基础上实现了一个实时的镜头切变检测算法。为了降低基于特征的算法的运算复杂度,该算法采用一种快速的边缘模式分类方法从部分解码的码流中提取视频帧的边缘特征,通过考察相邻帧边缘分布的相似性定义了一种反映局部信息的帧间相似性度量。结合反映全局特征的基于彩色直方图的相似性的度量和改进的滑动窗算法,实现了高性能的镜头边缘检测。相对于现有的基于特征的算法,该算法具有更低的运算复杂度,适合实时应用。     

To accelerate shot boundary detection by reducing detection region and scope

Video Shot Boundary Detection (SBD) is the fundamental process towards video summarization and retrieval. A fast and efficient SBD algorithm is necessary for real-time video processing applications. Extensive work has focused on accurate shot boundary detection at the expense of demanding computational costs. In this paper, we propose a fast SBD approach that reduces the computation pixel-wise and frame-wise while still giving satisfactory accuracy. The proposed approach substantially speeds up the computation through reducing both detection region and scope. Color histogram and mutual information are used together to measure the difference between frames. Corner distribution of frames is utilized to exclude most of false boundaries. We conduct extensive experiments to evaluate the proposed approach, and the results show that our approach can not only speed up SBD, but also detect shot boundaries with high accuracy in both Cut (CUT) and Gradual Transition (GT) boundaries.     

视频突变检测的规范化灰度分布帧差方法

研究视频检索中的镜头突变检测问题,分析造成镜头突变检测中出现误检的原因,提出一种鲁棒的、适用于自适应阈值突变镜头检测方法的规范化灰度分布帧差的定义。实验表明,当视频图像中噪声较低时,采用规范化灰度分帧差定义,应用自适应取阈值的检测方法,对视频中镜头突变的检测有较高的准确率。     

Shot Partitioning Based Recognition of TV Commercials

Digital video applications exploit the intrinsic structure of video sequences. In order to obtain and represent this structure for video annotation and indexing tasks, the main initial step is automatic shot partitioning. This paper analyzes the problem of automatic TV commercials recognition, and a new algorithm for scene break detection is then introduced. The structure of each commercial is represented by the set of its key-frames, which are automatically extracted from the video stream. The particular characteristics of commercials make commonly used shot boundary detection techniques obtain worse results than with other video content domains. These techniques are based on individual image features or visual cues, which show significant performance lacks when they are applied to complex video content domains like commercials. We present a new scene break detection algorithm based on the combined analysis of edge and color features. Local motion estimation is applied to each edge in a frame, and the continuity of the color around them is then checked in the following frame. By separately considering both sides of each edge, we rely on the continuous presence of the objects and/or the background of the scene during each shot. Experimental results show that this approach outperforms single feature algorithms in terms of precision and recall.     

基于视觉注意特征和SVM的镜头边界检测算法

镜头边界检测是视频分析的基础。借鉴心理学中有关视觉注意的研究成果,提出了一种采用符合人类视觉注意的特征,并利用支持矢量机进行视频镜头边界检测的算法。通过对TRECVID2007数据库进行实验的结果表明,该算法在查全率和查准率方面都获得了较好的性能。     

EDTriangles: a high-speed triangle detection algorithm with a false detection control

We present a high-speed method for triangular object detection. The proposed method utilizes the recently developed, real-time edge segment detection algorithm, Edge Drawing; hence, the name EDTriangles, which consists of a detection stage and a validation stage. In the detection stage, EDTriangles extracts edge segments from the image using Edge Drawing and converts these edge segments into line segments, which are then converted into line pairs according to the angles between the line segments and the distance between their endpoints. Next, the line pairs are combined together using some heuristics to generate many triangle candidates, some of which are valid detections and some invalid. Finally, in the validation stage the candidate triangles are validated using the Helmholtz principle and number of false alarms computation to eliminate false detections. Experimental results show that EDTriangles runs very fast, detects various types of triangular objects ranging from narrow to wide-angled triangles and offers a higher detection performance compared to some of the well-known triangle detection algorithms found in the literature.