公开(公告)号：US08958645B2

公开(公告)日：2015-02-17

申请号：US13451436

申请日：2012-04-19

申请人： Juwei Lu ,  Bradley Denney

发明人： Juwei Lu ,  Bradley Denney

IPC分类号： G06K9/34 ,  G06T7/00

CPC分类号： G06T7/0081 ,  G06F17/30849 ,  G06K9/00751 ,  G06K9/00765 ,  G11B27/031 ,  G11B27/105 ,  H04N5/91 ,  H04N21/2668 ,  H04N21/8549

摘要： Systems and methods for summarizing a video assign frames in a video to at least one of two or more groups based on a topic, generate a respective first similitude measurement for the frames in a group relative to the other frames in the group based on a feature, rank the frames in a group relative to one or more other frames in the group based on the respective first similitude measurement of the respective frames, and select a frame from each group as a most-representative frame based on the respective rank of the frames in a group relative to the other frames in the group.

摘要翻译： 用于总结视频的视频的系统和方法基于主题将视频中的帧分配给两个或更多个组中的至少一个，基于特征生成组中相对于组中的其他帧的帧的相应的第一相似度测量 基于相应帧的相应的第一相似度测量对组中的一个或多个其他帧进行排序，并且基于帧的相应等级从每个组中选择一个帧作为最具代表性的帧 在组中相对于组中的其他帧。

公开(公告)号：US07945075B2

公开(公告)日：2011-05-17

申请号：US11948026

申请日：2007-11-30

申请人： Juwei Lu ,  Mohanaraj Thiyagarajah ,  Hui Zhou

发明人： Juwei Lu ,  Mohanaraj Thiyagarajah ,  Hui Zhou

IPC分类号： G06K9/00

CPC分类号： G06K9/00228

摘要： Converting a digital image from color to gray-scale. In one example embodiment, a method for converting a digital image from color to gray-scale is disclosed. First, an unconverted pixel having red, green, and blue color channels is selected from the color digital image. Next, the red color channel of the pixel is multiplied by α. Then, the green color channel of the pixel is multiplied by β. Next, the blue color channel of the pixel is multiplied by γ. Then, the results of the three multiplication operations are added together to arrive at a gray-scale value for the pixel. Finally, these acts are repeated for each remaining unconverted pixel of the color digital image to arrive at a gray-scale digital image. In this example method, α+β+≈1 and α>β.

摘要翻译： 将数字图像从颜色转换为灰度。 在一个示例实施例中，公开了一种将数字图像从彩色转换成灰度级的方法。 首先，从彩色数字图像中选择具有红色，绿色和蓝色通道的未转换像素。 接下来，像素的红色通道乘以α。 然后，将像素的绿色通道乘以＆bgr。 接下来，将像素的蓝色通道乘以γ。 然后，将三个乘法运算的结果相加在一起，得到像素的灰度值。 最后，对于彩色数字图像的每个剩余的未转换像素重复这些动作，以得到灰度数字图像。 在此示例中，α+＆bgr; +≈1和α>＆bgr。

公开(公告)号：US20080304714A1

公开(公告)日：2008-12-11

申请号：US11759460

申请日：2007-06-07

申请人： Juwei Lu ,  Hui Zhou

发明人： Juwei Lu ,  Hui Zhou

IPC分类号： G06K9/00

CPC分类号： G06K9/00228 ,  G06K9/6257

摘要： Systems and methods for training an AdaBoost based classifier for detecting symmetric objects, such as human faces, in a digital image. In one example embodiment, such a method includes first selecting a sub-window of a digital image. Next, the AdaBoost based classifier extracts multiple sets of two symmetric scalar features from the sub-window, one being in the right half side and one being in the left half side of the sub-window. Then, the AdaBoost based classifier minimizes the joint error of the two symmetric features for each set of two symmetric scalar features. Next, the AdaBoost based classifier selects one of the features from the set of two symmetric scalar features for each set of two symmetric scalar features. Finally, the AdaBoost based classifier linearly combines multiple weak classifiers, each of which corresponds to one of the selected features, into a stronger classifier.

摘要翻译： 用于训练基于AdaBoost的分类器的系统和方法，用于在数字图像中检测对象对象（例如人脸）。 在一个示例实施例中，这种方法包括首先选择数字图像的子窗口。 接下来，基于AdaBoost的分类器从子窗口中提取多组两个对称标量特征，一组位于子窗口的右半边，一个位于子窗口的左半边。 然后，基于AdaBoost的分类器最小化两组对称标量特征的两个对称特征的联合误差。 接下来，基于AdaBoost的分类器从两组对称标量特征中选择两个对称标量特征集合中的一个特征。 最后，基于AdaBoost的分类器将多个弱分类器线性组合，每个弱分类器对应于所选特征之一，成为更强的分类器。

公开(公告)号：US20080219558A1

公开(公告)日：2008-09-11

申请号：US11684478

申请日：2007-03-09

申请人： Juwei Lu ,  Hui Zhou ,  Mohanaraj Thiyagarajah

发明人： Juwei Lu ,  Hui Zhou ,  Mohanaraj Thiyagarajah

IPC分类号： G06K9/46

CPC分类号： G06K9/00234 ,  G06K9/00248

摘要： A method and system for efficiently detecting faces within a digital image. One example method includes identifying a digital image comprised of a plurality of sub-windows and performing a first scan of the digital image using a coarse detection level to eliminate the sub-windows that have a low likelihood of representing a face. The subset of the sub-windows that were not eliminated during the first scan are then scanned a second time using a fine detection level having a higher accuracy level than the coarse detection level used during the first scan to identify sub-windows having a high likelihood of representing a face.

摘要翻译： 一种用于有效检测数字图像内的面部的方法和系统。 一个示例性方法包括识别由多个子窗口组成的数字图像，并使用粗略检测水平执行数字图像的第一次扫描，以消除具有低的表示脸部可能性的子窗口。 然后使用具有比在第一次扫描期间使用的粗略检测级别更高的精度水平的精细检测级别来第二次扫描在第一次扫描期间未被消除的子窗口的子集，以识别具有高似然性的子窗口 代表一个脸。

公开(公告)号：US11966516B2

公开(公告)日：2024-04-23

申请号：US17827939

申请日：2022-05-30

申请人： Juwei Lu ,  Sayem Mohammad Siam ,  Wei Zhou ,  Peng Dai ,  Xiaofei Wu ,  Songcen Xu

发明人： Juwei Lu ,  Sayem Mohammad Siam ,  Wei Zhou ,  Peng Dai ,  Xiaofei Wu ,  Songcen Xu

IPC分类号： G06F3/01 ,  G06V10/25 ,  G06V10/82 ,  G06V40/20

CPC分类号： G06F3/017 ,  G06V10/25 ,  G06V10/82 ,  G06V40/28

摘要： Methods and systems for gesture-based control of a device are described. A virtual gesture-space is determined in a received input frame. The virtual gesture-space is associated with a primary user from a ranked user list of users. The received input frame is processed in only the virtual gesture-space, to detect and track a hand. Using a hand bounding box generated by detecting and tracking the hand, gesture classification is performed to determine a gesture input associated with the hand. A command input associated with the determined gesture input is processed. The device may be a smart television, a smart phone, a tablet, etc.

公开(公告)号：US11778223B2

公开(公告)日：2023-10-03

申请号：US17406845

申请日：2021-08-19

申请人： Wentao Liu ,  Yuanhao Yu ,  Yang Wang ,  Juwei Lu ,  Xiaolin Wu ,  Jin Tang

发明人： Wentao Liu ,  Yuanhao Yu ,  Yang Wang ,  Juwei Lu ,  Xiaolin Wu ,  Jin Tang

IPC分类号： H04N19/59 ,  H04N19/51 ,  H04N19/184 ,  H04N19/136

CPC分类号： H04N19/51 ,  H04N19/136 ,  H04N19/184

摘要： A method, device and computer-readable medium for generating a super-resolution version of a compressed video stream. By leveraging the motion information and residual information in compressed video streams, described examples are able to skip the time-consuming motion-estimation step for most frames and make the most use of the SR results of key frames. A key frame SR module generates SR versions of I-frames and other key frames of a compressed video stream using techniques similar to existing multi-frame approaches to VSR. A non-key frame SR module generates SR version of the non-key inter frames between these key frames by making use of motion information and residual information used to encode the inter frames in the compressed video stream.

公开(公告)号：US11430138B2

公开(公告)日：2022-08-30

申请号：US17102114

申请日：2020-11-23

申请人： Zhixiang Chi ,  Rasoul Mohammadi Nasiri ,  Zheng Liu ,  Jin Tang ,  Juwei Lu

发明人： Zhixiang Chi ,  Rasoul Mohammadi Nasiri ,  Zheng Liu ,  Jin Tang ,  Juwei Lu

IPC分类号： G06T7/246 ,  G06N3/08

摘要： Systems and methods for multi-frame video frame interpolation. Higher-order motion modeling, such as cubic motion modeling, achieves predictions of intermediate optical flow between multiple interpolated frames, assisted by relaxation of the constraints imposed by the loss function used in initial optical flow estimation. A temporal pyramidal optical flow refinement module performs coarse-to-fine refinement of the optical flow maps used to generate the intermediate frames, focusing a proportionally greater amount of refinement attention to the optical flow maps for the high-error middle frames. A temporal pyramidal pixel refinement module performs coarse-to-fine refinement of the generated intermediate frames, focusing a proportionally greater amount of refinement attention to the high-error middle frames. A generative adversarial network (GAN) module calculates a loss function for training the neural networks used in the optical flow estimation module, temporal pyramidal optical flow refinement module, and/or temporal pyramidal pixel refinement module.

公开(公告)号：US11249557B2

公开(公告)日：2022-02-15

申请号：US17308408

申请日：2021-05-05

申请人： Wei Zhou ,  Mona Hosseinkhani Loorak ,  Gaganpreet Singh ,  Xiu Yi ,  Juwei Lu ,  Wei Li

发明人： Wei Zhou ,  Mona Hosseinkhani Loorak ,  Gaganpreet Singh ,  Xiu Yi ,  Juwei Lu ,  Wei Li

IPC分类号： G06F3/01

摘要： Methods and apparatus for gesture-based control of a device in a multi-user environment are described. The methods prioritize users or gestures based on a predetermined priority ruleset. A first-user-in-time ruleset prioritizes gestures based on when in time they were begun by a user in the camera FOV. An action-hierarchy ruleset prioritizes gestures based on the actions they correspond to, and the relative positions of those actions within an action hierarchy. A designated-master-user ruleset prioritizes gestures performed by an explicitly designated master user. Methods for designating a new master user and for providing gesture-control-related user feedback in a multi-user environment are also described.

公开(公告)号：US11112875B1

公开(公告)日：2021-09-07

申请号：US16842717

申请日：2020-04-07

申请人： Wei Zhou ,  Mona Hosseinkhani Loorak ,  Gaganpreet Singh ,  Xiu Yi ,  Juwei Lu ,  Wei Li

发明人： Wei Zhou ,  Mona Hosseinkhani Loorak ,  Gaganpreet Singh ,  Xiu Yi ,  Juwei Lu ,  Wei Li

IPC分类号： G06F3/01

摘要： Methods and apparatus for gesture-based control of a device in a multi-user environment are described. The methods prioritize users or gestures based on a predetermined priority ruleset. A first-user-in-time ruleset prioritizes gestures based on when in time they were begun by a user in the camera FOV. An action-hierarchy ruleset prioritizes gestures based on the actions they correspond to, and the relative positions of those actions within an action hierarchy. A designated-master-user ruleset prioritizes gestures performed by an explicitly designated master user. Methods for designating a new master user and for providing gesture-control-related user feedback in a multi-user environment are also described.

公开(公告)号：US10963702B1

公开(公告)日：2021-03-30

申请号：US16566179

申请日：2019-09-10

申请人： Ruiwen Li ,  Peng Dai ,  Varshanth Ravindra Rao ,  Juwei Lu ,  Wei Li ,  Jianpeng Xu

发明人： Ruiwen Li ,  Peng Dai ,  Varshanth Ravindra Rao ,  Juwei Lu ,  Wei Li ,  Jianpeng Xu

IPC分类号： G06K9/00 ,  G11B27/031 ,  G11B27/30 ,  G06F40/205

摘要： Methods and systems for video segmentation and scene recognition are described. A video having a plurality of frames and a subtitle file associated with the video are received. Segmentation is performed on the video to generate a first set video frames comprising one or more video frames based on a frame-by-frame comparison of features in the frames of the video. Each video frame in the first includes a frame indicator which indicates at least a first start frame of the video frame. The subtitle file associated with the video is parsed to generate one or more subtitle segments based on a start and an end time of each dialogue in the subtitle file. A second set of video frames comprising one or more second video frames are generated based on the video frames of the first set of video frames and the e or more subtitle segments.