Patents by Inventor Suat Gedikli
Suat Gedikli 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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Patent number: 11568639Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: GrantFiled: September 15, 2021Date of Patent: January 31, 2023Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20220076055Abstract: Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.Type: ApplicationFiled: November 15, 2021Publication date: March 10, 2022Inventors: Ingo KOSSYK, Suat GEDIKLI
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Publication number: 20220004762Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: ApplicationFiled: September 15, 2021Publication date: January 6, 2022Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Patent number: 11210552Abstract: Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.Type: GrantFiled: November 13, 2019Date of Patent: December 28, 2021Assignee: Cape Analytics, Inc.Inventors: Ingo Kossyk, Suat Gedikli
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Patent number: 11151378Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: GrantFiled: March 27, 2020Date of Patent: October 19, 2021Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20200226373Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: ApplicationFiled: March 27, 2020Publication date: July 16, 2020Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20200151500Abstract: Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.Type: ApplicationFiled: November 13, 2019Publication date: May 14, 2020Inventors: Ingo KOSSYK, Suat GEDIKLI
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Patent number: 10643072Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: GrantFiled: March 14, 2019Date of Patent: May 5, 2020Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Patent number: 10366288Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: GrantFiled: March 14, 2019Date of Patent: July 30, 2019Assignee: CAPE ANALYTICS, INC.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20190213413Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: ApplicationFiled: March 14, 2019Publication date: July 11, 2019Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
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Publication number: 20190213412Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: ApplicationFiled: March 14, 2019Publication date: July 11, 2019Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
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Patent number: 10311302Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: GrantFiled: August 31, 2016Date of Patent: June 4, 2019Assignee: Cape Analytics, Inc.Inventors: Ryan Kottenstette, Peter Lorenzen, Suat Gedikli
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Publication number: 20180218437Abstract: One embodiment is directed to system for analyzing the feet of a subject, wherein the subject may position and orient his feet in a capture configuration relative to a 3-dimensional camera, and the 3-dimensional camera may be utilized to capture a plurality of images about the subject's feet from a plurality of perspectives. A point cloud may be created based upon the captured images, and extraction procedures may be conducted to create individual, or discrete, point clouds for each of the feet from the overall superset point cloud created using the 3-dimensional imaging device. The discrete point clouds may be utilized to conduct various measurements of the feet, which may be utilized in various configurations, such as for shoe fitment or manufacturing.Type: ApplicationFiled: March 27, 2018Publication date: August 2, 2018Inventors: Radu B. Rusu, Suat Gedikli
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Publication number: 20170076438Abstract: Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.Type: ApplicationFiled: August 31, 2016Publication date: March 16, 2017Inventors: Ryan KOTTENSTETTE, Peter LORENZEN, Suat GEDIKLI
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Publication number: 20150235298Abstract: One embodiment is directed to system for analyzing the feet of a subject, wherein the subject may position and orient his feet in a capture configuration relative to a 3-dimensional camera, and the 3-dimensional camera may be utilized to capture a plurality of images about the subject's feet from a plurality of perspectives. A point cloud may be created based upon the captured images, and extraction procedures may be conducted to create individual, or discrete, point clouds for each of the feet from the overall superset point cloud created using the 3-dimensional imaging device. The discrete point clouds may be utilized to conduct various measurements of the feet, which may be utilized in various configurations, such as for shoe fitment or manufacturing.Type: ApplicationFiled: April 30, 2015Publication date: August 20, 2015Applicant: Willow Garage, Inc.Inventors: Radu B. Rusu, Suat Gedikli