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公开(公告)号：US09977972B2
公开(公告)日：2018-05-22
申请号：US14519927
申请日：2014-10-21
Applicant: SRI International
Inventor： Saad Masood Khan , Hui Cheng , Dennis Lee Matthies , Harpreet Singh Sawhney , Sang-Hack Jung , Chris Broaddus , Bogdan Calin Mihai Matei , Ajay Divakaran
CPC classification number: G06K9/00785 , B64C39/024 , B64C2201/123 , B64D47/08 , G06K9/00651 , G06K9/4671 , G06K9/6269 , G06K9/6282 , G06T7/11 , G06T7/20 , G06T2207/10012
Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models based at least in part on the set of p-scores and the set of n-scores.
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Patent Agency Ranking
公开(公告)号：US12062174B2
公开(公告)日：2024-08-13
申请号：US17476377
申请日：2021-09-15
Applicant: SRI International
Inventor： Anil Usumezbas , Bogdan Calin Mihai Matei , Rakesh Kumar , Supun Samarasekera
IPC: G06T7/10 , G06T3/40 , G06T17/20 , G06V10/776 , G06V20/70
CPC classification number: G06T7/10 , G06T3/40 , G06T17/205 , G06V10/776 , G06V20/70 , G06T2207/10028 , G06T2207/20072 , G06T2207/20081 , G06T2207/20221 , G06T2207/30181 , G06T2210/56
Abstract: A method, machine readable medium and system for semantic segmentation of 3D point cloud data includes determining ground data points of the 3D point cloud data, categorizing non-ground data points relative to a ground surface determined from the ground data points to determine legitimate non-ground data points, segmenting the determined legitimate non-ground and ground data points based on a set of common features, applying logical rules to a data structure of the features built on the segmented determined non-ground and ground data points based on their spatial relationships and incorporated within a machine learning system, and constructing a 3D semantics model from the application of the logical rules to the data structure.
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公开(公告)号：US20220222824A1
公开(公告)日：2022-07-14
申请号：US17476377
申请日：2021-09-15
Applicant: SRI International
Inventor： Anil Usumezbas , Bogdan Calin Mihai Matei , Rakesh Kumar , Supun Samarasekera
IPC: G06T7/10 , G06T17/20 , G06T3/40 , G06V20/70 , G06V10/776
Abstract: A method, machine readable medium and system for semantic segmentation of 3D point cloud data includes determining ground data points of the 3D point cloud data, categorizing non-ground data points relative to a ground surface determined from the ground data points to determine legitimate non-ground data points, segmenting the determined legitimate non-ground and ground data points based on a set of common features, applying logical rules to a data structure of the features built on the segmented determined non-ground and ground data points based on their spatial relationships and incorporated within a machine learning system, and constructing a 3D semantics model from the application of the logical rules to the data structure.
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公开(公告)号：US10769491B2
公开(公告)日：2020-09-08
申请号：US16120023
申请日：2018-08-31
Applicant: SRI International
Inventor： Bogdan Calin Mihai Matei , Xiyang Dai , John Benjamin Southall , Nhon Hoc Trinh , Harpreet Sawhney
Abstract: Techniques are disclosed for identifying discriminative, fine-grained features of an object in an image. In one example, an input device receives an image. A machine learning system includes a model comprising a first set, a second set, and a third set of filters. The machine learning system applies the first set of filters to the received image to generate an intermediate representation of the received image. The machine learning system applies the second set of filters to the intermediate representation to generate part localization data identifying sub-parts of an object and one or more regions of the image in which the sub-parts are located. The machine learning system applies the third set of filters to the intermediate representation to generate classification data identifying a subordinate category to which the object belongs. The system uses the part localization and classification data to perform fine-grained classification of the object.
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5.发明申请公开(公告)号：US20190073560A1
公开(公告)日：2019-03-07
申请号：US16120023
申请日：2018-08-31
Applicant: SRI International
Inventor： Bogdan Calin Mihai Matei , Xiyang Dai , John Benjamin Southall , Nhon Hoc Trinh , Harpreet Sawhney
CPC classification number: G06K9/623 , G06K9/00362 , G06K9/00664 , G06K9/4628 , G06K9/627 , G06K9/6271 , G06N3/0454 , G06N3/08 , G06N20/00
Abstract: Techniques are disclosed for identifying discriminative, fine-grained features of an object in an image. In one example, an input device receives an image. A machine learning system includes a model comprising a first set, a second set, and a third set of filters. The machine learning system applies the first set of filters to the received image to generate an intermediate representation of the received image. The machine learning system applies the second set of filters to the intermediate representation to generate part localization data identifying sub-parts of an object and one or more regions of the image in which the sub-parts are located. The machine learning system applies the third set of filters to the intermediate representation to generate classification data identifying a subordinate category to which the object belongs. The system uses the part localization and classification data to perform fine-grained classification of the object.
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6.发明申请3-D MODEL BASED METHOD FOR DETECTING AND CLASSIFYING VEHICLES IN AERIAL IMAGERY 有权用于在航空影像中检测和分类车辆的基于3-D模型的方法公开(公告)号：US20160379062A1
公开(公告)日：2016-12-29
申请号：US14519927
申请日：2014-10-21
Applicant: SRI International
Inventor： Saad Masood Khan , Hui Cheng , Dennis Lee Matthies , Harpreet Singh Sawhney , Sang-Hack Jung , Chris Broaddus , Bogdan Calin Mihai Matei , Ajay Divakaran
CPC classification number: G06K9/00785 , B64C39/024 , B64C2201/123 , B64D47/08 , G06K9/00651 , G06K9/4671 , G06K9/6269 , G06K9/6282 , G06T7/11 , G06T7/20 , G06T2207/10012
Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.
Abstract translation: 公开了一种用于确定在图像中检测到的车辆的车辆类型的计算机实现的方法。 接收具有检测到的车辆的图像。 在检测到的车辆上投影出具有显着特征点的多个车辆模型。 将来自车辆模型的每个突出特征位置的第一组特征与从所检测到的车辆的相应突出特征位置导出的第二组特征进行比较，以形成一组正的匹配得分（p分数）和 一组负面比赛得分（n分）。 检测到的车辆至少部分地基于p分数集合和n分数集合被分类为车辆模型模型之一。
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