共查询到20条相似文献,搜索用时 31 毫秒
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Crowd segmentation is an important issue in video surveillance. With the decrease in their cost, stereo cameras can be used to help develop new algorithms to achieve better accuracy in crowd segmentation. This paper aims to develop a method to explore the depth cues for crowd segmentation in video surveillance. The contributions of this paper are twofold. First, a novel crowd segmentation method closely coupling appearance and stereo information has been developed. Instead of performing disparity calculation as a preprocessing step, stereo information is obtained concurrently with appearance-based crowd segmentation. Second, an object-level disparity algorithm is proposed for object segmentation in surveillance scenarios. Only one disparity value for each hypothetical object greatly reduces the computational complexity and simplifies the segmentation method. Experimental results and quantitative evaluations based on two surveillance scenarios are presented in this paper. The results consistently show the effectiveness of the algorithm in exploring depth cues for crowd segmentation. 相似文献
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The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications. 相似文献
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This paper presents a novel coarse to fine moving object segmentation framework for H.264/AVC compressed videos. The proposed framework integrates the global motion estimation and global motion compensation steps in the segmentation pipeline unlike previous techniques which did not consider such an integration. The integration is based on testing for presence of global motion by classifying the interframe motion vectors into moving camera class and still camera class. The decision boundary separating these two classes is learnt from the training video data. The integration automates the moving object segmentation to be applicable for static, moving and combination of static/moving camera cases which to the best of our knowledge has not been carried out earlier. Further, a novel coarse segmentation technique is proposed by decomposing the inter-frame motion vectors into wavelet sub-bands and utilizing logical operations on LH, HL and HH sub-band wavelet coefficients. The premise is based on the fact that since the LH, HL and HH sub-bands contain the detail information pertaining to horizontal, vertical and diagonal moving blocks respectively, they can be exploited to identify the coarse moving boundaries. The coarse segmentation is fast in comparison to state-of-the-art coarse segmentation methods as demonstrated by our experiments. Finally, these coarse boundaries are modeled in an energy minimization framework and shown that by minimizing the energy using graph cut optimization the segmentation is refined to obtain the fine segmentation. The proposed framework is tested on a number of standard video sequences encoded with H.264/AVC JM encoder and comparison is carried out with state-of-the-art compressed domain moving object segmentation methods as well as with an existing state-of-the-art pixel domain method to establish and validate the proposed moving object segmentation framework. 相似文献
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广播新闻语料识别中的自动分段和分类算法 总被引:1,自引:0,他引:1
该介绍了中文广播新闻语料识别任务中的自动分段和自动分类算法。提出了3阶段自动分段系统。该方法通过粗分段、精细分段和平滑3个阶段,将音频流分割为易于识别的音频段。在精细分段阶段,文中提出两种算法:动态噪声跟踪分段算法和基于单音素解码的分段算法。仿效说话人鉴别中的方法,文中提出了基于混合高斯模型的分类算法。该算法较好地解决了音频段的多类判决问题。在新闻联播测试数据中的实验结果表明,该文提出的自动分段和分类算法性能与手工分段分类性能几乎相当。 相似文献
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《IEEE transactions on medical imaging》2009,28(7):1000-1010
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激光成像受到环境、设备自身等干扰,使得激光图像含有噪声,当前图像分割方法对噪声干扰鲁棒性差,误分割现象出现概率高,重要信息丢失严重,为了克服当前激光图像分割的弊端,提出了基于人工智能深度学习的激光图像分割方法.首先采用小波变换对激光图像进行特征提取,并对噪声干扰进行抑制处理,然后引入人工智能学习算法对激光图像特征向量进... 相似文献
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视频对象分割中基于Gibbs随机场模型的空分割结合方法 总被引:4,自引:0,他引:4
本文提出了一种基于Gibbs随机场模型的时空分割结合方法,用于视频对象的分割.该方法为每一帧图像的分割模板建立Gibbs随机场模型,将时间域分割结果作为初始标记场,空间域的分割结果作为一个图像观察场,然后利用Gibbs模型的约束条件将二者结合起来,得到该帧最后的分割标记场.实验结果表明,这种时空结合方法可以较好地避免以往的比重法过分依赖于空间域分割精度的问题. 相似文献
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视频对象分割中基于Gibbs随机场模型的时空分割结合方法 总被引:5,自引:0,他引:5
本文提出了一种基于Gibbs随机场模型的时空分割结合方法 ,用于视频对象的分割 .该方法为每一帧图像的分割模板建立Gibbs随机场模型 ,将时间域分割结果作为初始标记场 ,空间域的分割结果作为一个图像观察场 ,然后利用Gibbs模型的约束条件将二者结合起来 ,得到该帧最后的分割标记场 .实验结果表明 ,这种时空结合方法可以较好地避免以往的比重法过分依赖于空间域分割精度的问题 . 相似文献
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Frdric Dufaux Iole Moccagatta Fabrice Moscheni Henri Nicolas 《Journal of Visual Communication and Image Representation》1994,5(4)
This paper proposes a segmentation of block-based motion fields under the constraint of the entropy criterion. The segmentation is performed by a vector quantization technique which associates the segmentation pattern to an element of a codebook. Optimization of the bit rate as a trade-off between motion and segmentation on the one hand and prediction error on the other hand is addressed. An estimation of the amount of information to code the displaced frame difference is derived, making it possible to control the segmentation process. Simulation results show the efficiency of the segmentation method combined with the entropy criterion in a video coding scheme. 相似文献
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This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects--shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. Our method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered. 相似文献
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针对目前激光雷达数据分割算法不能适应环境特征确定连续准确阈值的问题,提出一种环境特征自适应激光雷达数据分割算法。依据二维激光雷达的数据特点以及室内环境的几何特征,以激光雷达数据的邻近点拟合虚拟环境线,以虚拟环境线和邻近激光扫描射线的交点作为参考点,确定自适应阈值,完成激光雷达数据的预分割。针对用上述方法完成的数据预分割结果中存在的缺陷,提出数据预分割后伪断点的判断方法,对算法进行了优化。并将此算法与分段阈值分割算法、线性方程阈值分割算法进行比较和分析。环境特征自适应激光雷达数据分割算法对实验数据的分割成功率达到98%,具有更强的环境适应能力和更高的分割准确度。 相似文献
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目前卷积神经网络已成为腹部动脉血管分割领域的研究热点,但经典的卷积网络存在分割精度低和分割血管不连续的问题。为此,文中提出了基于改进3D全卷积网络的腹部动脉血管分割算法。该方法在网络的编码路径上构造不同尺度的侧输入,并将侧输入卷积后的图像与下采样卷积后的图像进行融合,提取更多的特征信息。同时,网络中嵌入了新的多尺度特征提取模块,该模块将通道注意力与密集扩张卷积进行了融合,有效地捕获了更高层次的特征信息。对腹部动脉血管进行分割的结果表明,与其他分割方法相比,所提方法在直观性和定量性上均有提高,证明了该方法能够提升血管分割精度。 相似文献
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针对目前单帧图像阈值分割中分割易受突变影响、目
标背景分割不明显以及分割效果较差等问题,提出了一种基于红外图像帧关
联的自动阈值分割方法。该方法利用自动阈值分割法简单分割单帧图像,然
后根据图像帧关联信息对图像进行分组处理,再对每帧图像进行权重分配,最
终确定每帧图像的分割阈值,以提高分割的抗干扰性,改善分割效果。通过理
论分析和实验仿真验证了该算法的有效性和可行性,并将其与其他算法进行了
对比实验。实验结果表明,本文提出的分割算法的抗干扰性较强,能够将目标
图像从背景中清晰地分割出来,具有更好的分割效果和更强的应用性。 相似文献