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公开(公告)号：US09652672B2
公开(公告)日：2017-05-16
申请号：US15186901
申请日：2016-06-20
Applicant: Gracenote, Inc.
Inventor： Mihailo M. Stojancic , Prashant Ramanathan , Peter Wendt , Jose Pio Pereira
CPC classification number: G06K9/00503 , G06F17/30799 , G06F17/30805 , G06F17/30814 , G06K9/0053 , G06K9/00744 , G06K9/4676 , H04N5/14
Abstract: Video sequence processing is described with various filtering rules applied to extract dominant features for content based video sequence identification. Active regions are determined in video frames of a video sequence. Video frames are selected in response to temporal statistical characteristics of the determined active regions. A two pass analysis is used to detect a set of initial interest points and interest regions in the selected video frames to reduce the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence. Extracted features and descriptors are robust with respect to image scaling, aspect ratio change, rotation, camera viewpoint change, illumination and contrast change, video compression/decompression artifacts and noise. Compact, representative signatures are generated for video sequences to provide effective query video matching and retrieval in a large video database.
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公开(公告)号：US10007863B1
公开(公告)日：2018-06-26
申请号：US15172826
申请日：2016-06-03
Applicant: Gracenote, Inc.
Inventor： Jose Pio Pereira , Kyle Brocklehurst , Sunil Suresh Kulkarni , Peter Wendt
CPC classification number: G06T7/337 , G06K9/4642 , G06K9/4671 , G06K9/6267 , G06K2009/4666 , G06K2209/25 , G06T7/60 , G06T2207/20052
Abstract: Accurately detection of logos in media content on media presentation devices is addressed. Logos and products are detected in media content produced in retail deployments using a camera. Logo recognition uses saliency analysis, segmentation techniques, and stroke analysis to segment likely logo regions. Logo recognition may suitably employ feature extraction, signature representation, and logo matching. These three approaches make use of neural network based classification and optical character recognition (OCR). One method for OCR recognizes individual characters then performs string matching. Another OCR method uses segment level character recognition with N-gram matching. Synthetic image generation for training of a neural net classifier and utilizing transfer learning features of neural networks are employed to support fast addition of new logos for recognition.
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公开(公告)号：US09798513B1
公开(公告)日：2017-10-24
申请号：US15050123
申请日：2016-02-22
Applicant: Gracenote, Inc.
Inventor： Jose Pio Pereira , Mihailo M. Stojancic , Peter Wendt
IPC: G06F17/00 , G06F3/16 , G10L19/022 , G10L19/26 , G10L19/018 , G10L25/51 , G06F17/30
CPC classification number: G06F3/165 , G06F17/30743 , G10L19/018 , G10L19/022 , G10L19/26 , G10L25/51
Abstract: Content identification methods for consumer devices determine robust audio fingerprints that are resilient to audio distortions. One method generates signatures representing audio content based on a constant Q-factor transform (CQT). A 2D spectral representation of a 1D audio signal facilitates generation of region based signatures within frequency octaves and across the entire 2D signal representation. Also, points of interest are detected within the 2D audio signal representation and interest regions are determined around selected points of interest. Another method generates audio descriptors using an accumulating filter function on bands of the audio spectrum and generates audio transform coefficients. A response of each spectral band is computed and transform coefficients are determined by filtering, by accumulating derivatives with different lags, and computing second order derivatives. Additionally, time and frequency based onset detection determines audio descriptors at events and enhances descriptors with information related to an event.
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公开(公告)号：US20160307037A1
公开(公告)日：2016-10-20
申请号：US15186901
申请日：2016-06-20
Applicant: Gracenote, Inc.
Inventor： Mihailo M. Stojancic , Prashant Ramanathan , Peter Wendt , Jose Pio Pereira
CPC classification number: G06K9/00503 , G06F17/30799 , G06F17/30805 , G06F17/30814 , G06K9/0053 , G06K9/00744 , G06K9/4676 , H04N5/14
Abstract: Video sequence processing is described with various filtering rules applied to extract dominant features for content based video sequence identification. Active regions are determined in video frames of a video sequence. Video frames are selected in response to temporal statistical characteristics of the determined active regions. A two pass analysis is used to detect a set of initial interest points and interest regions in the selected video frames to reduce the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence. Extracted features and descriptors are robust with respect to image scaling, aspect ratio change, rotation, camera viewpoint change, illumination and contrast change, video compression/decompression artifacts and noise. Compact, representative signatures are generated for video sequences to provide effective query video matching and retrieval in a large video database.
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公开(公告)号：US11861888B2
公开(公告)日：2024-01-02
申请号：US17672963
申请日：2022-02-16
Applicant: Gracenote, Inc.
Inventor： Jose Pio Pereira , Kyle Brocklehurst , Sunil Suresh Kulkarni , Peter Wendt
CPC classification number: G06V10/82 , G06F18/24 , G06T7/11 , G06T7/337 , G06T7/60 , G06V10/462 , G06V10/764 , G06T2207/20052 , G06V10/50 , G06V2201/09
Abstract: Accurately detection of logos in media content on media presentation devices is addressed. Logos and products are detected in media content produced in retail deployments using a camera. Logo recognition uses saliency analysis, segmentation techniques, and stroke analysis to segment likely logo regions. Logo recognition may suitably employ feature extraction, signature representation, and logo matching. These three approaches make use of neural network based classification and optical character recognition (OCR). One method for OCR recognizes individual characters then performs string matching. Another OCR method uses segment level character recognition with N-gram matching. Synthetic image generation for training of a neural net classifier and utilizing transfer learning features of neural networks are employed to support fast addition of new logos for recognition.
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Patent Agency Ranking
公开(公告)号：US20200372662A1
公开(公告)日：2020-11-26
申请号：US16841681
申请日：2020-04-07
Applicant: Gracenote, Inc.
Inventor： Jose Pio Pereira , Kyle Brocklehurst , Sunil Suresh Kulkarni , Peter Wendt
Abstract: Methods, apparatus, systems and articles of manufacture of logo recognition in images and videos are disclosed. An example method to detect a specific brand in images and video streams comprises accepting luminance images at a scale in an x direction Sx and a different scale in a y direction Sy in a neural network, and training the neural network with a set of training images for detected features associated with a specific brand.
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公开(公告)号：US20180307942A1
公开(公告)日：2018-10-25
申请号：US16018011
申请日：2018-06-25
Applicant: Gracenote, Inc.
Inventor： Jose Pio Pereira , Kyle Brocklehurst , Sunil Suresh Kulkarni , Peter Wendt
CPC classification number: G06K9/6215 , G06K9/4642 , G06K9/4671 , G06K9/52 , G06K9/6267 , G06K9/66 , G06K2009/4666 , G06T7/0081 , G06T7/11 , G06T7/337 , G06T7/60 , G06T2207/20052
Abstract: Methods, apparatus, systems and articles of manufacture of logo recognition in images and videos are disclosed. An example method to detect a specific brand in images and video streams comprises accepting luminance images at a scale in an x direction Sx and a different scale in a y direction Sy in a neural network, and training the neural network with a set of training images for detected features associated with a specific brand.
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公开(公告)号：US20240096082A1
公开(公告)日：2024-03-21
申请号：US18507560
申请日：2023-11-13
Applicant: Gracenote, Inc.
Inventor： Jose Pio Pereira , Kyle Brocklehurst , Sunil Suresh Kulkarni , Peter Wendt
CPC classification number: G06V10/82 , G06F18/24 , G06T7/11 , G06T7/337 , G06T7/60 , G06V10/462 , G06V10/764 , G06T2207/20052 , G06V10/50
Abstract: Accurately detection of logos in media content on media presentation devices is addressed. Logos and products are detected in media content produced in retail deployments using a camera. Logo recognition uses saliency analysis, segmentation techniques, and stroke analysis to segment likely logo regions. Logo recognition may suitably employ feature extraction, signature representation, and logo matching. These three approaches make use of neural network based classification and optical character recognition (OCR). One method for OCR recognizes individual characters then performs string matching. Another OCR method uses segment level character recognition with N-gram matching. Synthetic image generation for training of a neural net classifier and utilizing transfer learning features of neural networks are employed to support fast addition of new logos for recognition.
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公开(公告)号：US20220172384A1
公开(公告)日：2022-06-02
申请号：US17672963
申请日：2022-02-16
Applicant: Gracenote, Inc.
Inventor： Jose Pio Pereira , Kyle Brocklehurst , Sunil Suresh Kulkami , Peter Wendt
Abstract: Accurately detection of logos in media content on media presentation devices is addressed. Logos and products are detected in media content produced in retail deployments using a camera. Logo recognition uses saliency analysis, segmentation techniques, and stroke analysis to segment likely logo regions. Logo recognition may suitably employ feature extraction, signature representation, and logo matching. These three approaches make use of neural network based classification and optical character recognition (OCR). One method for OCR recognizes individual characters then performs string matching. Another OCR method uses segment level character recognition with N-gram matching. Synthetic image generation for training of a neural net classifier and utilizing transfer learning features of neural networks are employed to support fast addition of new logos for recognition.
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公开(公告)号：US20190251112A1
公开(公告)日：2019-08-15
申请号：US16385575
申请日：2019-04-16
Applicant: Gracenote, Inc.
Inventor： Mihailo M. Stojancic , Prashant Ramanathan , Peter Wendt , Jose Pio Pereira
IPC: G06F16/48 , G06F16/901 , G06F16/783 , G06F16/22 , G06F16/951 , G06K9/00 , G06F16/28 , G06F16/41 , G06K9/46
CPC classification number: G06F16/48 , G06F16/2255 , G06F16/285 , G06F16/41 , G06F16/783 , G06F16/7847 , G06F16/9014 , G06F16/951 , G06K9/00744 , G06K9/4671 , Y10S707/913
Abstract: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.
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