Automatic image segmentation by integrating color-edge extractionand seeded region growing

We propose a new automatic image segmentation method. Color edges in an image are first obtained automatically by combining an improved isotropic edge detector and a fast entropic thresholding technique. After the obtained color edges have provided the major geometric structures in an image, the centroids between these adjacent edge regions are taken as the initial seeds for seeded region growing (SRG). These seeds are then replaced by the centroids of the generated homogeneous image regions by incorporating the required additional pixels step by step. Moreover, the results of color-edge extraction and SRG are integrated to provide homogeneous image regions with accurate and closed boundaries. We also discuss the application of our image segmentation method to automatic face detection. Furthermore, semantic human objects are generated by a seeded region aggregation procedure which takes the detected faces as object seeds.     

基于内容的多层次语义视频对象提取方法研究

视频对象的提取在序列图像的分析中起着重要作用.提出一个基于内容的多层次视频的对象提取算法,利用高斯马尔可夫模型对其进行颜色和纹理的混合特征图像分割.利用Normalize-cut准则,对其运动信息进行分析,然后进行区域聚合,即得到具有语义的视频对象.对于背景运动信息较丰富的序列图像可以取得良好的提取效果.     

一种图像中主要对象的提取方法

图像中主要对象的提取对于图像语义的抽取以及图像内容的自动标注具有重要作用。本文提出了一种面向语义的图像主要目标的提取方法。对分割后的图像计算不同区域的显著相关色得到核心目标,然后从中提取训练样本集,通过人工神经网络对图像中的块进行分类训练,获得各个区域的主要对象。实验表明该方法可以较准确地提取出中心区域的对象以及周围区域的对象并能保留图像的主要信息。     

基于肤色分割与Adaboost融合鲁棒人脸检测方法

人脸检测广泛应用到人脸识别、数字视频处理、安全访问控制、视觉监测、基于内容的检索等领域.比较众多人脸检测算法,文章提出了一种改进的基于Adaboost算法的人脸检测算法.该算法的核心是肤色分割结合基于Adaboost算法的人脸检测.首先,对彩色图像进行肤色分割,通过肤色区域的大小和长宽比等规则去除部分类肤色区域,得到可疑的人脸区域.其次,基于Adaboost算法的灰度特征得到最终的人脸.通过大量彩色图像的实验,证实了该方法的准确性和鲁棒性.     

基于内容的海量监控视频的多层次检索系统

设计和实现了一种基于内容的海量监控视频的多层次检索系统。该系统首先从监控视频中提取关键帧图像,其次利用行人检测、人脸识别及车辆检测等算法将关键帧中的行人图像、人脸图像和车辆图像等感兴趣目标提取出来,然后提取这些图像的颜色、纹理等特征,利用改进的LIRe(Lucene Image Retrieval)建立分布式的特征库,最终形成了多层次的信息数据库。实验表明,该系统具有较高的检索准确率和较快的检索速率,并支持海量监控视频的检索。     

视频帧中字幕信息提取的区域检测算法研究

从影视字幕中获取相应的文本,可为影视节目的内容检索提供一种重要的手段.针对从视频数据中截取下来的帧数据,对灰度化后的彩色单帧视频中的字幕分割方法进行了探讨,提出了区域检测算法的处理方案,实验结果表明用该方法获取文本信息是合理有效的.     

Video Object Extraction for Object-Oriented Applications

In this paper, we propose two novel video object (VO) extraction schemes, specifically designed for two different scenarios of content-based video analysis applications. One is a change detection-based VO extraction algorithm appropriate to surveillance type video sequences, where automatic detection of new appearance of objects are important in envisaging on-line object-oriented applications as well as object-based coding. The other is an object tracking-based method, which is especially robust to video sequences with moving background, although human intervention is needed in the process. In both cases, the semantically meaningful video objects are obtained by a final regularization stage realized by means of a cascade of morphological filters. Experimental results obtained on the MPEG-4 test sequences are presented respectively.     

A hybrid algorithm for automatic segmentation of slowly moving objects

Segmentation of moving objects in video sequences is a basic task in many applications. However, it is still challenging due to the semantic gap between the low-level visual features and the high-level human interpretation of video semantics. Compared with segmentation of fast moving objects, accurate and perceptually consistent segmentation of slowly moving objects is more difficult. In this paper, a novel hybrid algorithm is proposed for segmentation of slowly moving objects in video sequence aiming to acquire perceptually consistent results. Firstly, the temporal information of the differences among multiple frames is employed to detect initial moving regions. Then, the Gaussian mixture model (GMM) is employed and an improved expectation maximization (EM) algorithm is introduced to segment a spatial image into homogeneous regions. Finally, the results of motion detection and spatial segmentation are fused to extract final moving objects. Experiments are conducted and provide convincing results.     

MPEG-4视频对象分割技术

随着MPEG-4,MPEG-7的研究发展,其基于内容的编码和面向对象的存取和操纵技术日益得到人们的重视。基于对象的视频图像分割是实现MPEG-4基于内容的编码和交互功能的关键。视频图像分割方法分为自动分割法和半自动分割法两种。结合视频分割的发展趋势,深入介绍了基于对象的视频分割的主要技术及国内外的最新研究算法,包括数学形态学算法以及活动轮廓模型(蛇模型)在该领域的应用,并分析了当前视频分割技术尚存在的问题和研究前景。     

Robust face detection and analysis

This paper presents a method which utilizes color, local symmetry and geometry information of human face based on various models. The algorithm first detects most likely face regions or ROIs (Region-Of-Interest) from the image using face color model and face outline model, produces a face color similarity map. Then it performs local symmetry detection within these ROIs to obtain a local symmetry similarity map. The two maps and local similarity map are fused to obtain potential facial feature points. Finally similarity matching is performed to identify faces between the fusion map and face geometry model under affine transformation. The output results are the detected faces with confidence values. The experimental results demonstrate its validity and robustness to identify faces under certain variations.     

基于连通性检测的视频监控运动目标提取

针对背景相对静止、主要检测对象为行人的视频监控序列,提出了一种基于连通性检测的目标提取算法,它把形态学滤波与连通性检测相结合,对分割后的二值化图像进行噪声干扰去除,在获得若干连通区域后,利用面积、外界矩形及其特征对连通区域进行识别,通过区域重心标注目标在各帧位置,实现目标提取.实验结果表明,该算法简单可靠,具有实时性,易于硬件实现,可应用于实际系统.     

Multiple instance deep learning for weakly-supervised visual object tracking

Intelligently tracking objects with varied shapes, color, lighting conditions, and backgrounds is an extremely useful application in many HCI applications, such as human body motion capture, hand gesture recognition, and virtual reality (VR) games. However, accurately tracking different objects under uncontrolled environments is a tough challenge due to the possibly dynamic object parts, varied lighting conditions, and sophisticated backgrounds. In this work, we propose a novel semantically-aware object tracking framework, wherein the key is weakly-supervised learning paradigm that optimally transfers the video-level semantic tags into various regions. More specifically, give a set of training video clips, each of which is associated with multiple video-level semantic tags, we first propose a weakly-supervised learning algorithm to transfer the semantic tags into various video regions. The key is a MIL (Zhong et al., 2020) [1]-based manifold embedding algorithm that maps the entire video regions into a semantic space, wherein the video-level semantic tags are well encoded. Afterward, for each video region, we use the semantic feature combined with the appearance feature as its representation. We designed a multi-view learning algorithm to optimally fuse the above two types of features. Based on the fused feature, we learn a probabilistic Gaussian mixture model to predict the target probability of each candidate window, where the window with the maximal probability is output as the tracking result. Comprehensive comparative results on a challenging pedestrian tracking task as well as the human hand gesture recognition have demonstrated the effectiveness of our method. Moreover, visualized tracking results have shown that non-rigid objects with moderate occlusions can be well localized by our method.     

结合肤色分割的支持向量机人脸检测方法

人脸检测是人脸识别与图像及视频检索的一项重要任务。本文提出了一种结合分割与模板匹配-支持向理机(Support Vector Machines,SVM)的正面人脸检测算法。算法在肤色区域分割基础上，输入图像采用基于Harr小波描述的特征提取方法，然后结合平均脸模板匹配利用 SVM分类器进行分类。实验结果表明该算法十分有效。     

基于背景差分的一种运动目标检测方法

针对运动目标检测中,传统背景差分法在运动目标和背景颜色相近时不易检测的缺点,提出了一种检测完整运动目标的方法。该方法对YUV彩色空间下的3个通道分别选取独立的阈值进行初次检测,最大化地利用了视频中图像的色彩信息。在包含初次检测所获运动目标的最小矩形区域内进行二次检测,有效地提高了检测精度。实验证明,相比于常规方法,该方法的检测结果更加清晰完整。     

Segmentation and tracking of moving objects for content-based videocoding

To enable content-based functionalities in video coding, a decomposition of the scene into physical objects is required. Such objects are normally not characterised by homogeneous colour, intensity, or optical flow. Therefore, conventional techniques based on these low-level features cannot perform the desired segmentation. The authors address segmentation and tracking of moving objects and present a new video object plane (VOP) segmentation algorithm that extracts semantically meaningful objects. A morphological motion filter detects physical objects by identifying areas that are moving differently from the background. A new filter criterion is introduced that measures the deviation of the estimated local motion from the synthesised global motion. A two-dimensional binary model is derived for the object of interest and tracked throughout the sequence by a Hausdorff object tracker. To accommodate for rotations and changes in shape, the model is updated every frame by a two-stage method that accounts for rigid and non-rigid moving parts of the object. The binary model then guides the actual VOP extraction, whereby a novel boundary post-processor ensures high boundary accuracy. Experimental results demonstrate the performance of the proposed algorithm     

一种基于多级梯度能量特征的DCT 压缩域人脸检测算法

压缩域人脸检测在图像/视频信息处理中具有重要意义.本文提出了一种基于多级梯度能量特征的DCT (Discrete Cosine Transform)压缩域人脸检测算法.依据DCT压缩图像色差信号的直流系数进行肤色分割,减小检测范围.在分割为肤色的区域提取多级梯度能量特征,即利用不同大小的检测窗口提取归一化的特征向量,表示不同大小的人脸.特征向量输入到级联分类器中分类,确定是否表示人脸.级联分类器由若干简单分类器和一个神经网络分类器构成.简单分类器利用一些先验知识排除大部分明显不是人脸的特征向量,通过简单分类器的特征由神经网络最终确定是否表示人脸.多级梯度能量特征与DCT域图像缩放相结合实现了对不同大小人脸的快速检测.对多级梯度能量特征的定义,减少了检测算法中压缩域图像缩放的次数,从而大幅度减少了计算复杂度,提高了检测速度.实验结果表明提出的多级梯度能量特征可有效描述DCT域人脸模式,同时也证明了该算法的快速有效性.     

基于肤色分割和改进AdaBoost算法的人脸检测

为解决当被检测图像中具有复杂背景或者含有多人脸时,不能够快速准确的进行人脸检测的问题,本文提出一种基于肤色分割和改进AdaBoost算法的人脸检测方法。首先利用肤色分割方法对样本图像实现图像的预处理,排除样本图像的复杂背景和人体非肤色区域,简化后续的人脸检测工作。然后对AdaBoost算法的弱分类器使用双阈值判决方法,以减少弱分类器个数,提升训练速度;改进权值更新规则,防止训练过程中出现过分配现象。最后对基于肤色分割得到的区域图像利用改进后的Adaboost算法进行最后的精确人脸检测。仿真试验表明,两种算法结合后在训练速度上提升,在检测速度和检测率上有明显提高。     

Efficient Automatic Text Location Method and Content-Based Indexing and Structuring of Video Database

An efficient automatic text detection and location method for video documents is proposed and its application for the content-based retrieval of video is presented and discussed. Target frames are selected at fixed time intervals from shots detected by a scene-change detection method. For each selected frame, segmentation by color clustering is performed around color peaks using a color histogram. For each color plane, text-lines are detected using heuristics, and the temporal and spatial position and the text-image of each text-line are stored in a database. Experimental results for text detection in video images and the performance of the method are reported for various video documents. A user interface for text-image based browsing is designed for direct content-based access to video documents, and other applications are discussed.