Patents by Inventor Bogdan Calin Mihai Matei
Bogdan Calin Mihai Matei has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20220222824Abstract: 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.Type: ApplicationFiled: September 15, 2021Publication date: July 14, 2022Inventors: Anil Usumezbas, Bogdan Calin Mihai Matei, Rakesh Kumar, Supun Samarasekera
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Patent number: 10769491Abstract: 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.Type: GrantFiled: August 31, 2018Date of Patent: September 8, 2020Assignee: SRI InternationalInventors: Bogdan Calin Mihai Matei, Xiyang Dai, John Benjamin Southall, Nhon Hoc Trinh, Harpreet Sawhney
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Publication number: 20190073560Abstract: 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.Type: ApplicationFiled: August 31, 2018Publication date: March 7, 2019Inventors: Bogdan Calin Mihai Matei, Xiyang Dai, John Benjamin Southall, Nhon Hoc Trinh, Harpreet Sawhney
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Patent number: 9977972Abstract: 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.Type: GrantFiled: October 21, 2014Date of Patent: May 22, 2018Assignee: SRI InternationalInventors: Saad Masood Khan, Hui Cheng, Dennis Lee Matthies, Harpreet Singh Sawhney, Sang-Hack Jung, Chris Broaddus, Bogdan Calin Mihai Matei, Ajay Divakaran
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Publication number: 20160379062Abstract: 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.Type: ApplicationFiled: October 21, 2014Publication date: December 29, 2016Inventors: Saad Masood Khan, Hui Cheng, Dennis Lee Matthies, Harpreet Singh Sawhney, Sang-Hack Jung, Chris Broaddus, Bogdan Calin Mihai Matei, Ajay Divakaran
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Patent number: 8989483Abstract: A method and apparatus for determining a geographic location of a scene in a captured depiction comprising extracting a first set of features from the captured depiction by algorithmically analyzing the captured depiction, matching the extracted features of the captured depiction against a second set of extracted features associated with reference depictions with known geographic locations and when the matching is successful, identifying the geographic location of the scene in the captured depiction based on a known geographic location of a matching reference depiction from the reference depictions.Type: GrantFiled: June 11, 2012Date of Patent: March 24, 2015Assignee: SRI InternationalInventors: Hui Cheng, Harpreet Singh Sawhney, Bogdan Calin Mihai Matei, Zhiwei Zhu, Nicholas John Vander Valk
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Patent number: 8913783Abstract: 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.Type: GrantFiled: October 28, 2010Date of Patent: December 16, 2014Assignee: SRI InternationalInventors: Saad Masood Khan, Hui Cheng, Dennis Lee Matthies, Harpreet Singh Sawhney, Sang-Hack Jung, Chris Broaddus, Bogdan Calin Mihai Matei, Ajay Divakaran
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Patent number: 8861842Abstract: A computer implemented method for detecting the presence of one or more pedestrians in the vicinity of the vehicle is disclosed. Imagery of a scene is received from at least one image capturing device. A depth map is derived from the imagery. A plurality of pedestrian candidate regions of interest (ROIs) is detected from the depth map by matching each of the plurality of ROIs with a 3D human shape model. At least a portion of the candidate ROIs is classified by employing a cascade of classifiers tuned for a plurality of depth bands and trained on a filtered representation of data within the portion of candidate ROIs to determine whether at least one pedestrian is proximal to the vehicle.Type: GrantFiled: February 7, 2011Date of Patent: October 14, 2014Assignee: SRI InternationalInventors: Sang-Hack Jung, Mayank Bansal, Bogdan Calin Mihai Matei, Jayan Eledath, Harpreet Singh Sawhney, Rakesh Kumar, Raia Hadsell
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Patent number: 8345988Abstract: A method and apparatus for recognizing an object, comprising providing a set of scene features from a scene, pruning a set of model features, generating a set of hypotheses associated with the pruned set of model features for the set of scene features, pruning the set of hypotheses, and verifying the set of pruned hypotheses is provided.Type: GrantFiled: June 22, 2005Date of Patent: January 1, 2013Assignee: SRI InternationalInventors: Ying Shan, Bogdan Calin Mihai Matei, Harpreet Singh Sawhney, Rakesh Kumar
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Publication number: 20120314935Abstract: A method and apparatus for determining a geographic location of a scene in a captured depiction comprising extracting a first set of features from the captured depiction by algorithmically analyzing the captured depiction, matching the extracted features of the captured depiction against a second set of extracted features associated with reference depictions with known geographic locations and when the matching is successful, identifying the geographic location of the scene in the captured depiction based on a known geographic location of a matching reference depiction from the reference depictions.Type: ApplicationFiled: June 11, 2012Publication date: December 13, 2012Applicant: SRI INTERNATIONALInventors: HUI CHENG, HARPREET SINGH SAWHNEY, BOGDAN CALIN MIHAI MATEI, ZHIWEI ZHU, NICHOLAS JOHN VANDER VALK
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Patent number: 8224097Abstract: A method for extracting a 3D terrain model for identifying at least buildings and terrain from LIDAR data is disclosed, comprising the steps of generating a point cloud representing terrain and buildings mapped by LIDAR; classifying points in the point cloud, the point cloud having ground and non-ground points, the non-ground points representing buildings and clutter; segmenting the non-ground points into buildings and clutter; and calculating a fit between at least one building segment and at least one rectilinear structure, wherein the fit yields the rectilinear structure with the fewest number of vertices.Type: GrantFiled: June 12, 2008Date of Patent: July 17, 2012Assignee: SRI InternationalInventors: Bogdan Calin Mihai Matei, Supun Samarasekera, Janet Yonga Kim, Charles Fielding Finch Karney, Harpreet Singh Sawhney, Rakesh Kumar
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Publication number: 20120106800Abstract: 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.Type: ApplicationFiled: October 28, 2010Publication date: May 3, 2012Inventors: Saad Masood Khan, Hui Cheng, Dennis Lee Matthies, Harpreet Singh Sawhney, Sang-Hack Jung, Chris Broaddus, Bogdan Calin Mihai Matei, Ajay Divakaran
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Publication number: 20110255741Abstract: A computer implemented method for detecting the presence of one or more pedestrians in the vicinity of the vehicle is disclosed. Imagery of a scene is received from at least one image capturing device. A depth map is derived from the imagery. A plurality of pedestrian candidate regions of interest (ROIs) is detected from the depth map by matching each of the plurality of ROIs with a 3D human shape model. At least a portion of the candidate ROIs is classified by employing a cascade of classifiers tuned for a plurality of depth bands and trained on a filtered representation of data within the portion of candidate ROIs to determine whether at least one pedestrian is proximal to the vehicle.Type: ApplicationFiled: February 7, 2011Publication date: October 20, 2011Inventors: Sang-Hack Jung, Mayank Bansal, Bogdan Calin Mihai Matei, Jayan Eledath, Harpreet Singh Sawhney, Rakesh Kumar, Raia Hadsell
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Publication number: 20090310867Abstract: A method for extracting a 3D terrain model for identifying at least buildings and terrain from LIDAR data is disclosed, comprising the steps of generating a point cloud representing terrain and buildings mapped by LIDAR; classifying points in the point cloud, the point cloud having ground and non-ground points, the non-ground points representing buildings and clutter; segmenting the non-ground points into buildings and clutter; and calculating a fit between at least one building segment and at least one rectilinear structure, wherein the fit yields the rectilinear structure with the fewest number of vertices.Type: ApplicationFiled: June 12, 2008Publication date: December 17, 2009Inventors: Bogdan Calin Mihai Matei, Supun Samarasekera, Janet Yonga Kim, Charles Fielding Finch Karney, Harpreet Singh Sawhney, Rakesh Kumar
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Patent number: 7313252Abstract: A method and system for improving the accuracy and timeliness of video metadata by incorporating information related to the motion of the camera as derived from the video imagery itself. Frame-to-frame correspondences are used to accurately estimate changes in camera pose. While the method and system do not require geo-registration, geo-registration results, if available, may be considered in processing the video images and generating improved camera pose estimates.Type: GrantFiled: March 27, 2006Date of Patent: December 25, 2007Assignee: Sarnoff CorporationInventors: Bogdan Calin Mihai Matei, Clay Douglas Spence, Arthur Robert Pope, Barbara Viviane Hanna, Michael Wade Hansen