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Patent Agency Ranking
公开(公告)号：US20230013508A1
公开(公告)日：2023-01-19
申请号：US17378495
申请日：2021-07-16
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu
Abstract: Image-based key points detection using a convolutional neural network (CNN) may be impacted if the key points are occluded in the image. Images obtained from additional imaging modalities such as depth and/or thermal images may be used in conjunction with RGB images to reduce or minimize the impact of the occlusion. The additional images may be used to determine adjustment values that are then applied to the weights of the CNN so that the convolution operations may be performed in a modality aware manner to increase the robustness, accuracy, and efficiency of key point detection.
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公开(公告)号：US11386537B2
公开(公告)日：2022-07-12
申请号：US16802989
申请日：2020-02-27
Inventor： Abhishek Sharma , Meng Zheng , Srikrishna Karanam , Ziyan Wu , Arun Innanje , Terrence Chen
Abstract: Abnormality detection within a defined area includes obtaining a plurality of images of the defined area from image-capture devices. An extent of deviation of one or more types of products from an inference of each of the plurality of images is determined using a trained neural network. A localized dimensional representation is generated in a portion of an input image associated with a first location of the plurality of locations, based on gradients computed from the determined extent of deviation. The generated localized dimensional representation provides a visual indication of an abnormality located in the first location within the defined area. An action associated with the first location is executed based on the generated dimensional representation for proactive control or prevention of occurrence of undesired event in the defined area.
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公开(公告)号：US20210272258A1
公开(公告)日：2021-09-02
申请号：US16802989
申请日：2020-02-27
Inventor： Abhishek Sharma , Meng Zheng , Srikrishna Karanam , Ziyan Wu , Arun Innanje , Terrence Chen
Abstract: Abnormality detection within a defined area includes obtaining a plurality of images of the defined area from image-capture devices. An extent of deviation of one or more types of products from an inference of each of the plurality of images is determined using a trained neural network. A localized dimensional representation is generated in a portion of an input image associated with a first location of the plurality of locations, based on gradients computed from the determined extent of deviation. The generated localized dimensional representation provides a visual indication of an abnormality located in the first location within the defined area. An action associated with the first location is executed based on the generated dimensional representation for proactive control or prevention of occurrence of undesired event in the defined area.
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公开(公告)号：US20210272014A1
公开(公告)日：2021-09-02
申请号：US16804907
申请日：2020-02-28
Inventor： Srikrishna Karanam , Ziyan Wu , Abhishek Sharma , Arun Innanje , Terrence Chen
Abstract: Data samples are transmitted from a central server to at least one local server apparatus. The central server receives a set of predictions from the at least one local server apparatus that are based on the transmitted set of data samples. The central server trains a central model based on the received set of predictions. The central model, or a portion of the central model corresponding to a task of interest, can then be sent to the at least one local server apparatus. Neither local data from local sites nor trained models from the local sites are transmitted to the central server. This ensures protection and security of data at the local sites.
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公开(公告)号：US20210090736A1
公开(公告)日：2021-03-25
申请号：US16580053
申请日：2019-09-24
Inventor： Arun Innanje , Ziyan Wu , Abhishek Sharma , Srikrishna Karanam
Abstract: The present disclosure relates to systems and methods for anomaly detection for a medical procedure. The method may include obtaining image data collected by one or more visual sensors via monitoring a medical procedure and a trained machine learning model for anomaly detection. The method may include determining a detection result for the medical procedure based on the image data using the trained machine learning model. The detection result may include whether an anomaly regarding the medical procedure exists. In response to the detection result that the anomaly exists, the method may further include providing feedback relating to the anomaly.
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