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公开(公告)号：US11810329B2
公开(公告)日：2023-11-07
申请号：US16953029
申请日：2020-11-19
申请人： Yuanhao Yu , Shuhao Li , Juwei Lu , Jin Tang
发明人： Yuanhao Yu , Shuhao Li , Juwei Lu , Jin Tang
摘要： Methods and systems for determining a surface color of a target surface under an environment with an environmental light source. A plurality of images of the target surface are captured as the target surface is illuminated with a variable intensity, constant color light source and a constant intensity, constant color environmental light source, wherein the intensity of the light source on the target surface is varied by a known amount between the capturing of the images. A color feature tensor, independent of the environmental light source, is extracted from the image data, and used to infer a surface color of the target surface.
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公开(公告)号：US11669743B2
公开(公告)日：2023-06-06
申请号：US16874478
申请日：2020-05-14
申请人： Niamul Quader , Juwei Lu , Peng Dai , Wei Li
发明人： Niamul Quader , Juwei Lu , Peng Dai , Wei Li
IPC分类号： H04N21/462 , G06K9/62 , H04N21/466 , G06N3/08 , G06V20/40 , G06N3/04 , H04N21/4402 , G06F9/50
CPC分类号： H04N21/4621 , G06F9/5055 , G06K9/6277 , G06N3/0454 , G06N3/08 , G06V20/41 , H04N21/440227 , H04N21/4666 , G06V20/44
摘要： An adaptive action recognizer for video that performs multiscale spatiotemporal decomposition of video to generate lower complexity video. The adaptive action recognizer has a number of processing pathways, one for each level of video complexity with each processing pathway having a different computational cost. The adaptive action recognizer applies a decision making scheme that encourages using low average computational costs while retaining high accuracy.
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公开(公告)号：US20230057261A1
公开(公告)日：2023-02-23
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： H04N19/51 , 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.
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公开(公告)号：US20220156979A1
公开(公告)日：2022-05-19
申请号：US16953029
申请日：2020-11-19
申请人： Yuanhao YU , Shuhao LI , Juwei LU , Jin TANG
发明人： Yuanhao YU , Shuhao LI , Juwei LU , Jin TANG
摘要： Methods and systems for determining a surface color of a target surface under an environment with an environmental light source. A plurality of images of the target surface are captured as the target surface is illuminated with a variable intensity, constant color light source and a constant intensity, constant color environmental light source, wherein the intensity of the light source on the target surface is varied by a known amount between the capturing of the images. A color feature tensor, independent of the environmental light source, is extracted from the image data, and used to infer a surface color of the target surface.
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5.发明申请公开(公告)号：US20210294423A1
公开(公告)日：2021-09-23
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.
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公开(公告)号：US11023710B2
公开(公告)日：2021-06-01
申请号：US16280760
申请日：2019-02-20
申请人： Peng Dai , Juwei Lu , Bharath Sekar , Wei Li , Jianpeng Xu , Ruiwen Li
发明人： Peng Dai , Juwei Lu , Bharath Sekar , Wei Li , Jianpeng Xu , Ruiwen Li
摘要： System and method for classifying data objects occurring in an unstructured dataset, comprising: extracting feature vectors from the unstructured dataset, each feature vector representing an occurrence of a data object in the unstructured dataset; classifying the feature vectors into feature vector sets that each correspond to a respective object class from a plurality of object classes; for each feature vector set: performing multiple iterations of a clustering operation, each iteration including clustering feature vectors from the feature vector set into clusters of similar feature vectors and identifying outlier feature vectors, wherein for at least one iteration after a first iteration of the clustering operation, outlier feature vectors identified in a previous iteration are excluded from the clustering operation; and outputting a key cluster for the feature vector set from a final iteration of the multiple iterations, the key cluster including a greater number of similar feature vectors than any of the other clusters of the final iteration; and assembling a dataset that includes at least the feature vectors from the key clusters of the feature vector sets.
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公开(公告)号：US20200265218A1
公开(公告)日：2020-08-20
申请号：US16280760
申请日：2019-02-20
申请人： Peng Dai , Juwei Lu , Bharath Sekar , Wei Li , Jianpeng Xu , Ruiwen Li
发明人： Peng Dai , Juwei Lu , Bharath Sekar , Wei Li , Jianpeng Xu , Ruiwen Li
摘要： System and method for classifying data objects occurring in an unstructured dataset, comprising: extracting feature vectors from the unstructured dataset, each feature vector representing an occurrence of a data object in the unstructured dataset; classifying the feature vectors into feature vector sets that each correspond to a respective object class from a plurality of object classes; for each feature vector set: performing multiple iterations of a clustering operation, each iteration including clustering feature vectors from the feature vector set into clusters of similar feature vectors and identifying outlier feature vectors, wherein for at least one iteration after a first iteration of the clustering operation, outlier feature vectors identified in a previous iteration are excluded from the clustering operation; and outputting a key cluster for the feature vector set from a final iteration of the multiple iterations, the key cluster including a greater number of similar feature vectors than any of the other clusters of the final iteration; and assembling a dataset that includes at least the feature vectors from the key clusters of the feature vector sets.
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公开(公告)号：US07844085B2
公开(公告)日：2010-11-30
申请号：US11759460
申请日：2007-06-07
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。
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9.发明授权公开(公告)号：US07840037B2
公开(公告)日：2010-11-23
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
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。
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公开(公告)号：US12001613B2
公开(公告)日：2024-06-04
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
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.
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