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Patent Agency Ranking
公开(公告)号：US10380741B2
公开(公告)日：2019-08-13
申请号：US15478947
申请日：2017-04-04
Applicant: Samsung Electronics Co., Ltd.
Inventor： Arvind Yedla , Marcel Nassar , Mostafa El-Khamy , Jungwon Lee
Abstract: Apparatuses and methods of manufacturing same, systems, and methods for object detection using a region-based deep learning model are described. In one aspect, a method is provided, in which a region proposal network (RPN) is used to identify regions of interest (RoI) in an image by assigning a confidence levels, the assigned confidence levels of the RoIs are used to boost the background score assigned by the downstream classifier to each RoI, and the background scores are used in a softmax function to calculate the final class probabilities for each object class.
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公开(公告)号：US10075195B2
公开(公告)日：2018-09-11
申请号：US14706388
申请日：2015-05-07
Applicant: Samsung Electronics Co., Ltd.
Inventor： Marcel Nassar , Mostafa El-Khamy , Inyup Kang , Jungwon Lee
CPC classification number: H03M13/413 , H03M13/395 , H03M13/4107 , H03M13/4115
Abstract: A electronic system includes: a support chip configured to receive an input code stream; a circular Viterbi mechanism, coupled to the support chip, configured to: generate a final path metric for the input code stream, store intermediate path metrics at the repetition depth, generate a repetition path metric for the input code stream, and calculate a soft correlation metric based on the final path metric, the repetition path metric, and the intermediate path metrics.
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3.发明申请ELECTRONIC SYSTEM WITH VITERBI DECODER MECHANISM AND METHOD OF OPERATION THEREOF 审中-公开具有VITERBI解码器机构的电子系统及其操作方法公开(公告)号：US20160065245A1
公开(公告)日：2016-03-03
申请号：US14706388
申请日：2015-05-07
Applicant: Samsung Electronics Co., Ltd.
Inventor： Marcel Nassar , Mostafa El-Khamy , Inyup Kang , Jungwon Lee
CPC classification number: H03M13/413 , H03M13/395 , H03M13/4107 , H03M13/4115
Abstract: A electronic system includes: a support chip configured to receive an input code stream; a circular Viterbi mechanism, coupled to the support chip, configured to: generate a final path metric for the input code stream, store intermediate path metrics at the repetition depth, generate a repetition path metric for the input code stream, and calculate a soft correlation metric based on the final path metric, the repetition path metric, and the intermediate path metrics.
Abstract translation: 电子系统包括：被配置为接收输入码流的支持芯片; 耦合到所述支持芯片的循环维特比机构，被配置为：为所述输入代码流生成最终路径度量，在所述重复深度处存储中间路径量度，生成所述输入代码流的重复路径度量，并计算软相关 基于最终路径度量，重复路径度量和中间路径度量的度量。
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4.发明授权公开(公告)号：US11645869B2
公开(公告)日：2023-05-09
申请号：US16808357
申请日：2020-03-03
Applicant: Samsung Electronics Co., Ltd.
Inventor： Mostafa El-Khamy , Arvind Yedla , Marcel Nassar , Jungwon Lee
CPC classification number: G06V40/168 , G06T7/70 , G06V40/161 , G06T2207/20081 , G06T2207/20084 , G06T2207/30201
Abstract: A system to recognize objects in an image includes an object detection network outputs a first hierarchical-calculated feature for a detected object. A face alignment regression network determines a regression loss for alignment parameters based on the first hierarchical-calculated feature. A detection box regression network determines a regression loss for detected boxes based on the first hierarchical-calculated feature. The object detection network further includes a weighted loss generator to generate a weighted loss for the first hierarchical-calculated feature, the regression loss for the alignment parameters and the regression loss of the detected boxes. A backpropagator backpropagates the generated weighted loss. A grouping network forms, based on the first hierarchical-calculated feature, the regression loss for the alignment parameters and the bounding box regression loss, at least one of a box grouping, an alignment parameter grouping, and a non-maximum suppression of the alignment parameters and the detected boxes.
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5.发明授权公开(公告)号：US10032067B2
公开(公告)日：2018-07-24
申请号：US15224487
申请日：2016-07-29
Applicant: Samsung Electronics Co., Ltd.
Inventor： Mostafa El-Khamy , Arvind Yedla , Marcel Nassar , Jungwon Lee
Abstract: A system to recognize objects in an image includes an object detection network outputs a first hierarchical-calculated feature for a detected object. A face alignment regression network determines a regression loss for alignment parameters based on the first hierarchical-calculated feature. A detection box regression network determines a regression loss for detected boxes based on the first hierarchical-calculated feature. The object detection network further includes a weighted loss generator to generate a weighted loss for the first hierarchical-calculated feature, the regression loss for the alignment parameters and the regression loss of the detected boxes. A backpropagator backpropagates the generated weighted loss. A grouping network forms, based on the first hierarchical-calculated feature, the regression loss for the alignment parameters and the bounding box regression loss, at least one of a box grouping, an alignment parameter grouping, and a non-maximum suppression of the alignment parameters and the detected boxes.
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