Patents by Inventor OFIR LEVY
OFIR LEVY 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: 11727725Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from youtube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.Type: GrantFiled: February 11, 2021Date of Patent: August 15, 2023Inventors: Lior Wolf, Ofir Levy
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Publication number: 20230186584Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.Type: ApplicationFiled: February 6, 2023Publication date: June 15, 2023Applicant: Tahoe Research, Ltd.Inventors: Amit BLEIWEISS, Chen PAZ, Ofir LEVY, Itamar BEN-ARI, Yaron YANAI
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Patent number: 11574453Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.Type: GrantFiled: September 4, 2020Date of Patent: February 7, 2023Assignee: Tahoe Research, Ltd.Inventors: Amit Bleiweiss, Chen Paz, Ofir Levy, Itamar Ben-Ari, Yaron Yanai
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Publication number: 20210166055Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from youtube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.Type: ApplicationFiled: February 11, 2021Publication date: June 3, 2021Inventors: Lior WOLF, Ofir Levy
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Publication number: 20210056768Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.Type: ApplicationFiled: September 4, 2020Publication date: February 25, 2021Applicant: INTEL CORPORATIONInventors: Amit Bleiweiss, Chen Paz, Ofir Levy, Itamar Ben-Ari, Yaron Yanai
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Patent number: 10922577Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from youtube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.Type: GrantFiled: October 28, 2019Date of Patent: February 16, 2021Inventors: Lior Wolf, Ofir Levy
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Patent number: 10769862Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.Type: GrantFiled: August 2, 2018Date of Patent: September 8, 2020Assignee: Intel CorporationInventors: Amit Bleiweiss, Chen Paz, Ofir Levy, Itamar Ben-Ari, Yaron Yanai
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Publication number: 20200065608Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from youtube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.Type: ApplicationFiled: October 28, 2019Publication date: February 27, 2020Inventors: Lior Wolf, Ofir Levy
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Patent number: 10460194Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from YouTube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.Type: GrantFiled: March 6, 2015Date of Patent: October 29, 2019Inventors: Lior Wolf, Ofir Levy
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Patent number: 10248839Abstract: In accordance with some embodiments, connected-component labeling is performed in both the screen dimensions (which may be referred to as the x and y dimensions) and a depth dimension to label objects in a depth image. Then the contour of labeled blobs may be used to identify an object in the depth image. Using contours may be advantageous in some embodiments because it reduces the amount of data that must be handled and the extent of computations, compared to conventional techniques which use bit map based operations.Type: GrantFiled: November 30, 2015Date of Patent: April 2, 2019Assignee: Intel CorporationInventors: Ofir Levy, Maoz Madmony, Orly Weisel
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Publication number: 20180357834Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.Type: ApplicationFiled: August 2, 2018Publication date: December 13, 2018Applicant: INTEL CORPORATIONInventors: Amit Bleiweiss, Chen Paz, Ofir Levy, Itamar Ben-Ari, Yaron Yanai
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Patent number: 10068385Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.Type: GrantFiled: December 15, 2015Date of Patent: September 4, 2018Assignee: Intel CorporationInventors: Amit Bleiweiss, Chen Paz, Ofir Levy, Itamar Ben-Ari, Yaron Yanai
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Publication number: 20170169620Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.Type: ApplicationFiled: December 15, 2015Publication date: June 15, 2017Applicant: INTEL CORPORATIONInventors: Amit Bleiweiss, Chen Paz, Ofir Levy, Itamar Ben-Ari, Yaron Yanai
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Publication number: 20170154432Abstract: In accordance with some embodiments, connected-component labeling is performed in both the screen dimensions (which may be referred to as the x and y dimensions) and a depth dimension to label objects in a depth image. Then the contour of labeled blobs may be used to identify an object in the depth image. Using contours may be advantageous in some embodiments because it reduces the amount of data that must be handled and the extent of computations, compared to conventional techniques which use bit map based operations.Type: ApplicationFiled: November 30, 2015Publication date: June 1, 2017Inventors: Ofir Levy, Maoz Madmony, Orly Weisel
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Publication number: 20170017857Abstract: A technique and system for counting the number of repetitions of approximately the same action in an input video sequence using 3D convolutional neural networks is disclosed. The proposed system runs online and not on the complete video. It analyzes sequentially blocks of 20 non-consecutive frames. The cycle length within each block is evaluated using a deep network architecture and the information is then integrated over time. A unique property of the disclosed method is that it is shown to successfully train on entirely synthetic data, created by synthesizing moving random patches. It therefore effectively exploits the high generalization capability of deep neural networks. Coupled with a region of interest detection mechanism and a suitable mechanism to identify the time scale of the video, the system is robust enough to handle real world videos collected from YouTube and elsewhere, as well as non-video signals such as sensor data revealing repetitious physical movement.Type: ApplicationFiled: March 6, 2015Publication date: January 19, 2017Inventors: Lior Wolf, Ofir Levy
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Publication number: 20150095776Abstract: A device may comprise a display interface and a processor coupled to the display interface. The processor may be configured to couple to a remote network-connected device over a computer network; generate a graphic representation of the network-connected device on the display and send the generated graphic representation to the display interface. A status of the network-connected device may then be received over the computer network and cause, responsive to receiving the status of the network-connected device, the graphic representation of the network-connected device to change appearance depending upon the received state the network-connected device.Type: ApplicationFiled: December 6, 2013Publication date: April 2, 2015Applicant: Western Digital Technologies, Inc.Inventors: MICHAEL F. EGAN, OFIR LEVY