专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

11. 发明申请

US20100328452A1 METHOD FOR POSE INVARIANT VESSEL FINGERPRINTING 有权
标题翻译：用于不定式船舶指纹的方法
公开(公告)号：US20100328452A1
公开(公告)日：2010-12-30
申请号：US12758507
申请日：2010-04-12
申请人： Sang-Hack Jung , Ajay Divakaran , Harpreet Singh Sawhney
发明人： Sang-Hack Jung , Ajay Divakaran , Harpreet Singh Sawhney
IPC分类号： H04N7/18 , G06K9/46
CPC分类号： G06K9/00771 , G06K9/6206 , G06K9/6211
摘要： A computer-implemented method for matching objects is disclosed. At least two images where one of the at least two images has a first target object and a second of the at least two images has a second target object are received. At least one first patch from the first target object and at least one second patch from the second target object are extracted. A distance-based part encoding between each of the at least one first patch and the at least one second patch based upon a corresponding codebook of image parts including at least one of part type and pose is constructed. A viewpoint of one of the at least one first patch is warped to a viewpoint of the at least one second patch. A parts level similarity measure based on the view-invariant distance measure for each of the at least one first patch and the at least one second patch is applied to determine whether the first target object and the second target object are the same or different objects.
摘要翻译：公开了一种用于匹配对象的计算机实现的方法。接收至少两个图像，其中至少两个图像中的一个具有第一目标对象，并且至少两个图像中的第二图像具有第二目标对象。提取来自第一目标对象的至少一个第一补丁和来自第二目标对象的至少一个第二补丁。构建基于包括部件类型和姿态中的至少一个的图像部件的对应码本的至少一个第一贴片和至少一个第二贴片中的每一个之间的基于距离的部件编码。所述至少一个第一贴片中的一个的视点弯曲到所述至少一个第二贴片的观点。应用基于对于至少一个第一贴片和至少一个第二贴片中的每一个的视图不变距离度量的零件级相似性度量来确定第一目标对象和第二目标对象是相同还是不同的对象。

12. 发明授权

US07409407B2 Multimedia event detection and summarization 失效
标题翻译：多媒体事件检测与总结
公开(公告)号：US07409407B2
公开(公告)日：2008-08-05
申请号：US10840824
申请日：2004-05-07
申请人： Regunathan Radhakrishnan , Ajay Divakaran
发明人： Regunathan Radhakrishnan , Ajay Divakaran
IPC分类号： G06F17/30 , G06F17/00
CPC分类号： G06F17/30787 , G06F17/30802 , G06F17/30808 , G06F17/30811 , G06F17/30843 , G06K9/00711 , Y10S707/99943 , Y10S707/99945
摘要： A method detects events in multimedia. Features are extracted from the multimedia. The features are sampled using a sliding window to obtain samples. A context model is constructed for each sample. An affinity matrix is determined from the models and a commutative distance metric between each pair of context models. A second generation eigenvector is determined for the affinity matrix, and the samples are then clustered into events according to the second generation eigenvector.
摘要翻译：一种方法来检测多媒体中的事件。功能从多媒体提取。使用滑动窗口对特征进行采样以获得样品。为每个样本构建上下文模型。从模型和每对上下文模型之间的交换距离度量确定亲和度矩阵。针对亲和度矩阵确定第二代特征向量，然后根据第二代特征向量将样本聚类成事件。

13. 发明申请

US20080124042A1 Method and system for video segmentation 有权
标题翻译：视频分割方法和系统
公开(公告)号：US20080124042A1
公开(公告)日：2008-05-29
申请号：US11593897
申请日：2006-11-07
申请人： Ajay Divakaran , Feng Niu , Naveen Goela
发明人： Ajay Divakaran , Feng Niu , Naveen Goela
IPC分类号： H04N5/93
CPC分类号： G06K9/00711 , G06F17/30787 , G10L25/48
摘要： A method segments a video. Audio frames of the video are classified with labels. Dominant labels are assigned to successive time intervals of consecutive labels. A semantic description is constructed for sliding time windows of the successive time intervals, in which the sliding time windows overlap in time, and the semantic description for each time window is a transition matrix determined from the dominant labels of the time intervals. A marker is determined from the transition matrices, in which a frequency of occurrence of the marker is between a low frequency threshold and a high frequency threshold. Then, the video is segmented at the locations of the markers.
摘要翻译：一种方法分割视频。视频的音频帧被分类为标签。主导标签分配给连续标签的连续时间间隔。对于连续时间间隔的滑动时间窗口构成语义描述，其中滑动时间窗口在时间上重叠，并且每个时间窗口的语义描述是从时间间隔的主要标签确定的转换矩阵。从标记的出现频率在低频阈值和高频阈值之间的转移矩阵确定标记。然后，视频在标记的位置被分割。

14. 发明申请

US20060075346A1 Video presentation using compositional structures 审中-公开
标题翻译：使用组合结构的视频演示
公开(公告)号：US20060075346A1
公开(公告)日：2006-04-06
申请号：US10951192
申请日：2004-09-27
申请人： Tom Lanning , Ajay Divakaran , Kadir Peker , Regunathan Radhakrishnan , Ziyou Xiong , Clifton Forlines
发明人： Tom Lanning , Ajay Divakaran , Kadir Peker , Regunathan Radhakrishnan , Ziyou Xiong , Clifton Forlines
IPC分类号： G11B27/00
CPC分类号： G11B19/025 , G11B27/105 , G11B27/107 , G11B27/11 , G11B27/28 , G11B27/34 , G11B2220/20 , G11B2220/65 , G11B2220/90 , H04N5/85 , H04N9/8042 , H04N9/8233 , H04N21/42646 , H04N21/4312 , H04N21/4314 , H04N21/4325 , H04N21/812 , H04N21/84 , H04N21/8456
摘要： A method presents a video according to compositional structures associated with the video. Each compositional structure has a label, and multiple segments that can be organized temporally or hierarchically. A particular compositional structure is selected with a remote controller, and the video is presented by a playback controller on a display device according to the compositional structure.
摘要翻译：一种方法根据与视频相关联的组合结构呈现视频。每个组成结构都有一个标签，并且可以在时间上或分层上组织的多个片段。利用遥控器选择特定的组成结构，根据组成结构，视频由显示设备上的播放控制器呈现。

15. 发明申请

US20050154987A1 System and method for recording and reproducing multimedia 审中-公开
标题翻译：用于录制和再现多媒体的系统和方法
公开(公告)号：US20050154987A1
公开(公告)日：2005-07-14
申请号：US10757138
申请日：2004-01-14
申请人： Isao Otsuka , Ajay Divakaran , Masaharu Ogawa , Kazuhiko Nakane
发明人： Isao Otsuka , Ajay Divakaran , Masaharu Ogawa , Kazuhiko Nakane
IPC分类号： G06F15/00 , G06F17/30 , G10L19/00 , G11B27/00 , G11B27/28 , H04N5/91 , H04N9/804 , H04N9/82
CPC分类号： H04N9/8205 , G06F16/71 , G06F16/739 , G06F16/7834 , G11B27/28 , H04N9/8042 , H04N21/42646 , H04N21/4325 , H04N21/4394 , H04N21/44008 , H04N21/4508 , H04N21/4542 , H04N21/84
摘要： A system and method summarizes multimedia stored in a compressed multimedia file partitioned into a sequence of segments, where the content of the multimedia is, for example, video signals, audio signals, text, and binary data. An associated metadata file includes index information and an importance level for each segment. The importance information is continuous over as closed interval. An importance level threshold is selected in the closed interval, and only segments of the multimedia having a particular importance level greater than the importance level threshold are reproduced.
摘要翻译：系统和方法总结了存储在被分割成段序列的压缩多媒体文件中的多媒体，其中多媒体的内容是例如视频信号，音频信号，文本和二进制数据。关联的元数据文件包括索引信息和每个段的重要性级别。重要性信息作为闭合间隔连续。在闭合间隔中选择重要性级别阈值，并且仅再现具有大于重要性级别阈值的特定重要性级别的多媒体段。

16. 发明申请

US20050125223A1 Audio-visual highlights detection using coupled hidden markov models 审中-公开
标题翻译：使用耦合的隐马尔可夫模型的视听亮点检测
公开(公告)号：US20050125223A1
公开(公告)日：2005-06-09
申请号：US10729164
申请日：2003-12-05
申请人： Ajay Divakaran , Ziyou Xiong , Regunathan Radhakrishnan
发明人： Ajay Divakaran , Ziyou Xiong , Regunathan Radhakrishnan
IPC分类号： G06F17/30 , G06K9/00 , G06K9/20 , G06K9/62 , G06T7/00 , G10L11/00 , G10L15/00 , G10L15/04 , G10L15/10 , G10L15/26 , G10L17/00 , G10L19/12 , H04N5/76
CPC分类号： G06K9/00711 , G06F16/739 , G06F16/7834 , G06F16/786 , G06K9/6297
摘要： A method uses probabilistic fusion to detect highlights in videos using both audio and visual information. Specifically, the method uses coupled hidden Markov models (CHMMs). Audio labels are generated using audio classification via Gaussian mixture models (GMMs), and visual labels are generated by quantizing average motion vector magnitudes. Highlights are modeled using discrete-observation CHMMs trained with labeled videos. The CHMMs have better performance than conventional hidden Markov models (HMMs) trained only on audio signals, or only on video frames.
摘要翻译：一种方法使用概率融合来检测使用音频和视觉信息的视频中的高光。具体来说，该方法使用耦合的隐马尔可夫模型（CHMM）。使用高斯混合模型（GMM）的音频分类生成音频标签，并且通过量化平均运动矢量幅度来生成视觉标签。亮点是使用用标记视频训练的离散观察CHMM进行建模。 CHMM具有比仅在音频信号上训练的传统隐马尔可夫模型（HMM）更好的性能，或仅在视频帧上训练。

17. 发明授权

US06618507B1 Methods of feature extraction of video sequences 失效
标题翻译：视频序列特征提取方法
公开(公告)号：US06618507B1
公开(公告)日：2003-09-09
申请号：US09236838
申请日：1999-01-25
申请人： Ajay Divakaran , Huifang Sun , Hiroshi Ito
发明人： Ajay Divakaran , Huifang Sun , Hiroshi Ito
IPC分类号： G06K946
CPC分类号： G06K9/00744 , G06F17/30811
摘要： This invention relates to methods of feature extraction from MPEG-2 and MPEG-4 compressed video sequences. The spatio-temporal compression complexity of video sequences is evaluated for feature extraction by inspecting the compressed bitstream and the complexity is used as a descriptor of the spatio-temporal characteristics of the video sequence. The spatio-temporal compression complexity measure is used as a matching criterion and can also be used for absolute indexing. Feature extraction can be accomplished in conjunction with scene change detection techniques and the combination has reasonable accuracy and the advantage of high simplicity since it is based on entropy decoding of signals in compressed form and does not require computationally expensive inverse Discrete Cosine Transformation (DCT).
摘要翻译：本发明涉及从MPEG-2和MPEG-4压缩视频序列中提取特征的方法。通过检查压缩比特流来评估视频序列的时空压缩复杂度，并将复杂度用作视频序列的时空特征的描述符。时空压缩复杂度测量用作匹配标准，也可用于绝对索引。特征提取可以结合场景变化检测技术来实现，并且组合具有合理的精度和高简单性的优点，因为它是基于压缩形式的信号的熵解码，并且不需要计算上昂贵的反离散余弦变换（DCT）。

18. 发明授权

US06600784B1 Descriptor for spatial distribution of motion activity in compressed video 有权
标题翻译：压缩视频中运动活动空间分布的描述符
公开(公告)号：US06600784B1
公开(公告)日：2003-07-29
申请号：US09496707
申请日：2000-02-02
申请人： Ajay Divakaran , Kadir A. Peker , Huifang Sun
发明人： Ajay Divakaran , Kadir A. Peker , Huifang Sun
IPC分类号： H04N712
CPC分类号： G06F17/30781 , G06K9/00335 , G06K9/00711 , G06T7/215 , G06T2207/10016 , G06T2207/20021
摘要： A method describes motion activity in a video sequence. A motion activity matrix is determined for the video sequence. A threshold for the motion activity matrix is determined. Connected regions of motion vectors at least equal to the threshold are identified and measured for size. A histogram of the distribution of the sizes of the connected areas is constructed for the entire video sequence. The histogram is normalized to characterize the spatial distribution of the video sequence in a motion activity descriptor.
摘要翻译：一种方法描述视频序列中的运动活动。为视频序列确定运动活动矩阵。确定运动活动矩阵的阈值。至少等于阈值的运动矢量的连接区域被识别并测量尺寸。为整个视频序列构建连接区域的大小分布的直方图。将直方图归一化以表征运动活动描述符中的视频序列的空间分布。

19. 发明授权

US07558809B2 Task specific audio classification for identifying video highlights 有权
标题翻译：用于识别视频亮点的任务特定音频分类
公开(公告)号：US07558809B2
公开(公告)日：2009-07-07
申请号：US11326818
申请日：2006-01-06
申请人： Regunathan Radhakrishnan , Michael Siracusa , Ajay Divakaran
发明人： Regunathan Radhakrishnan , Michael Siracusa , Ajay Divakaran
IPC分类号： G06F17/00
CPC分类号： H04N21/4394 , G06K9/00718 , G10L25/00 , H04N21/235 , H04N21/435 , H04N21/8456 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945 , Y10S707/99948
摘要： A method classifies segments of a video using an audio signal of the video and a set of classes. Selected classes of the set are combined as a subset of important classes, the subset of important classes being important for a specific highlighting task, the remaining classes of the set are combined as a subset of other classes. The subset of important classes and classes are trained with training audio data to form a task specific classifier. Then, the audio signal can be classified using the task specific classifier as either important or other to identify highlights in the video corresponding to the specific highlighting task. The classified audio signal can be used to segment and summarize the video.
摘要翻译：一种方法使用视频的音频信号和一组类来分类视频的片段。集合的所选类被组合为重要类的子集，重要类的子集对于特定高亮任务是重要的，集合的剩余类被组合为其他类的子集。通过训练音频数据来训练重要类和类的子集，以形成任务特定的分类器。然后，可以使用任务专用分类器将音频信号分类为重要的或其他的，以识别与特定突出显示任务相对应的视频中的亮点。分类音频信号可用于分割和总结视频。

20. 发明申请

US20090132245A1 Denoising Acoustic Signals using Constrained Non-Negative Matrix Factorization 有权
标题翻译：使用约束非负矩阵因子分解去噪声信号
公开(公告)号：US20090132245A1
公开(公告)日：2009-05-21
申请号：US11942015
申请日：2007-11-19
申请人： Kevin W. Wilson , Ajay Divakaran , Bhiksha Ramakrishnan , Paris Smaragdis
发明人： Kevin W. Wilson , Ajay Divakaran , Bhiksha Ramakrishnan , Paris Smaragdis
IPC分类号： G10L21/02
CPC分类号： G10L21/0208 , G10L21/02 , G10L21/0232 , G10L21/0272
摘要： A method and system denoises a mixed signal. A constrained non-negative matrix factorization (NMF) is applied to the mixed signal. The NMF is constrained by a denoising model, in which the denoising model includes training basis matrices of a training acoustic signal and a training noise signal and statistics of weights of the training basis matrices. The applying produces weight of a basis matrix of the acoustic signal, of the mixed signal. A product of the weights of the basis matrix of the acoustic signal and the training basis matrices of the training acoustic signal and the training noise signal is taken to reconstruct the acoustic signal. The mixed signal can be speech and noise.
摘要翻译：一种方法和系统去除混合信号。受限的非负矩阵分解（NMF）被应用于混合信号。 NMF由去噪模型约束，其中去噪模型包括训练声信号和训练噪声信号的训练基矩阵以及训练基矩阵的权重统计。该应用产生混合信号的声信号的基矩阵的权重。采用声信号的基矩阵的权重和训练声信号的训练基矩阵和训练噪声信号的乘积来重构声信号。混合信号可以是语音和噪声。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式