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公开(公告)号：US20180039864A1
公开(公告)日：2018-02-08
申请号：US15554208
申请日：2015-04-15
Applicant: Intel Corporation
Inventor： Anbang YAO , Lin XU , Yurong CHEN
CPC classification number: G06K9/6268 , G06K9/00268 , G06K9/38 , G06K9/4642 , G06K9/4652 , G06K9/6202
Abstract: Techniques related to performing skin detection in an image are discussed. Such techniques may include generating skin and non-skin models based on a skin dominant region and another region, respectively, of the image and classifying individual pixels of the image via a discriminative skin likelihood function based on the skin model and the non-skin model.
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公开(公告)号：US20210133911A1
公开(公告)日：2021-05-06
申请号：US16474540
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Anbang YAO , Dongqi CAI , Libin WANG , Lin XU , Ping HU , Shandong WANG , Wehnua CHENG , Yiwen GUO , Liu YANG , Yuqing HOU , Zhou SU
Abstract: Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.
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公开(公告)号：US20220230268A1
公开(公告)日：2022-07-21
申请号：US17517316
申请日：2021-11-02
Applicant: Intel Corporation
Inventor： Anbang YAO , Dongqi CAI , Libin WANG , Lin XU , Ping HU , Shandong WANG , Wenhua CHENG , Yiwen GUO , Liu YANG , Yuqing HOU , Zhou SU
Abstract: Described herein are advanced artificial intelligence agents for modeling physical interactions. In one embodiment, an apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.
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4.发明申请公开(公告)号：US20200234411A1
公开(公告)日：2020-07-23
申请号：US16474848
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Lin XU , Liu YANG , Anbang YAO , dongqi CAI , Libin WANG , Ping HU , Shaodong WANG , Wenhua CHENG , Yiwen GUO , Yurong CHEN
Abstract: Methods and systems are disclosed using camera devices for deep channel and Convolutional Neural Network (CNN) images and formats. In one example, image values are captured by a color sensor array in an image capturing device or camera. The image values provide color channel data. The captured image values by the color sensor array are input to a CNN having at least one CNN layer. The CNN provides CNN channel data for each layer. The color channel data and CNN channel data is to form a deep channel image that stored in a memory. In another example, image values are captured by sensor array. The captured image values by the sensor array are input a CNN having a first CNN layer. An output is generated at the first CNN layer using the captured image values by the color sensor array. The output of the first CNN layer is stored as a feature map of the captured image.
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Patent Agency Ranking
公开(公告)号：US20190057491A1
公开(公告)日：2019-02-21
申请号：US16079308
申请日：2016-03-24
Applicant: INTEL CORPORATION
Inventor： Liu YANG , Weike CHEN , Lin XU
Abstract: Skin smoothing is applied to images using a bilateral filter and aided by a skin map. In one example a method includes receiving an image having pixels at an original resolution. The image is buffered. The image is downscaled from the original resolution to a lower resolution. A bilateral filter is applied to pixels of the downscaled image. The filtered pixels of the downscaled image are blended with pixels of the image having the original resolution, and the blended image is produced.
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公开(公告)号：US20220028042A1
公开(公告)日：2022-01-27
申请号：US17497555
申请日：2021-10-08
Applicant: INTEL CORPORATION
Inventor： Liu YANG , Weike CHEN , Lin XU
Abstract: Skin smoothing is applied to images using a bilateral filter and aided by a skin map. In one example a method includes receiving an image having pixels at an original resolution. The image is buffered. The image is downscaled from the original resolution to a lower resolution. A bilateral filter is applied to pixels of the downscaled image. The filtered pixels of the downscaled image are blended with pixels of the image having the original resolution, and the blended image is produced.
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7.发明申请公开(公告)号：US20200242734A1
公开(公告)日：2020-07-30
申请号：US16474927
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Shandong WANG , Yiwen GUO , Anbang YAO , Dongqi CAI , Libin WANG , Lin XU , Ping HU , Wenhua CHENG , Yurong CHEN
Abstract: Methods and systems are disclosed using improved Convolutional Neural Networks (CNN) for image processing. In one example, an input image is down-sampled into smaller images with a smaller resolution than the input image. The down-sampled smaller images are processed by a CNN having a last layer with a reduced number of nodes than a last layer of a full CNN used to process the input image at a full resolution. A result is outputted based on the processed down-sampled smaller images by the CNN having a last layer with a reduced number of nodes. In another example, shallow CNN networks are built randomly. The randomly built shallow CNN networks are combined to imitate a trained deep neural network (DNN).
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公开(公告)号：US20200027015A1
公开(公告)日：2020-01-23
申请号：US16474515
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Angang YAO , Dongqi CAI , Libin WANG , Lin XU , Ping HU , Shandong WANG , Wenhua CHENG , Yiwen GUO , Liu YANG , Yurong CHEN , Yuqing HOU , Zhou SU
Abstract: Described herein are systems and methods for providing deeply stacked automated program synthesis. In one embodiment, an apparatus to perform automated program synthesis includes a memory to store instructions for automated program synthesis and a compute cluster coupled to the memory. The compute cluster supports the instructions for performing the automated program synthesis including partitioning sketched data into partitions, training diverse sets of individual program synthesis units each having different capabilities with partitioned sketched data and for each partition applying respective transformations, and generating sketched baseline data for each individual program synthesis unit.
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9.发明申请公开(公告)号：US20190197407A1
公开(公告)日：2019-06-27
申请号：US16328182
申请日：2016-09-26
Applicant: INTEL CORPORATION
Inventor： Anbang YAO , Yiwen GUO , Lin XU , Yan LIN , Yurong CHEN
CPC classification number: G06N3/082 , G06F17/16 , G06N3/02 , G06N3/04 , G06N3/0445 , G06N3/0454 , G06N3/084
Abstract: An apparatus and method are described for reducing the parameter density of a deep neural network (DNN). A layer-wise pruning module to prune a specified set of parameters from each layer of a reference dense neural network model to generate a second neural network model having a relatively higher sparsity rate than the reference neural network model; a retraining module to retrain the second neural network model in accordance with a set of training data to generate a retrained second neural network model; and the retraining module to output the retrained second neural network model as a fmal neural network model if a target sparsity rate has been reached or to provide the retrained second neural network model to the layer-wise pruning model for additional pruning if the target sparsity rate has not been reached.
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