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1.发明授权公开(公告)号：US11790518B2
公开(公告)日：2023-10-17
申请号：US17357210
申请日：2021-06-24
发明人： Jayavardhana Rama Gubbi Lakshminarasimha , Mahesh Rangarajan , Rishin Raj , Vishnu Hariharan Anand , Vishal Bajpai , Vishwa Chethan Dandenahalli Venkatappa , Pradeep Kumar Mishra , Gourav Singh Jat , Meghala Mani , Gangadhar Shankarappa , Dinesh Sasidharan Nair , Shashank Lipate , Vineet Lall , Kavita Sara Mathew , Karthik Seemakurthy , Balamuralidhar Purushothaman
IPC分类号： G06T7/00 , G06T7/11 , G06V10/44 , G01N21/88 , G06T5/00 , G06T7/136 , G06T7/168 , G06V10/25 , G06T5/20
CPC分类号： G06T7/0006 , G01N21/8851 , G06T5/007 , G06T5/20 , G06T7/11 , G06T7/136 , G06T7/168 , G06V10/25 , G06V10/443 , G01N2021/8877 , G01N2021/8893 , G06T2207/10016
摘要： Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.
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公开(公告)号：US10964076B2
公开(公告)日：2021-03-30
申请号：US16504196
申请日：2019-07-05
发明人： Jayavardhana Rama Gubbi Lakshminarasimha , Karthik Seemakurthy , Sandeep Nk , Ashley Varghese , Shailesh Shankar Deshpande , Mariaswamy Girish Chandra , Balamuralidhar Purushothaman , Angshul Majumdar
摘要： This disclosure relates generally to image processing, and more particularly to method and system for image reconstruction using deep dictionary learning (DDL). The system collects the degraded image as test image and processes the test image to extract sparse features from the test image, at different levels, using dictionaries. The extracted sparse features and data from the dictionaries are used by the system to reconstruct the HR image corresponding to the test image.
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公开(公告)号：US11657590B2
公开(公告)日：2023-05-23
申请号：US17187929
申请日：2021-03-01
发明人： Jayavardhana Rama Gubbi Lakshminarasimha , Akshaya Ramaswamy , Karthik Seemakurthy , Balamuralidhar Purushothaman
IPC分类号： G06V10/25 , G06N3/08 , G06V20/40 , G06F18/214 , G06N3/045 , G06V10/764 , G06V10/82
CPC分类号： G06V10/25 , G06F18/214 , G06N3/045 , G06N3/08 , G06V10/764 , G06V10/82 , G06V20/41 , G06V20/46
摘要： State of the art techniques in the domain of video analysis have limitations in terms of capability to extract the spatial and temporal information. This limitation in turn affects interpretation of the video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to extract spatio-temporal information from a video being analyzed. The system uses a neural network architecture which has multiple layers to extract spatial and temporal information from the video being analyzed. The method of training the neural network that extracts a micro-scale information from a latent representation of the video is presented. This is generated using an attention network, which is then used to extract spatio-temporal information corresponding to the collected video, which is then used in multiple video analysis applications such as searching actions in videos, action detection and localization.
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公开(公告)号：US12100124B2
公开(公告)日：2024-09-24
申请号：US17552868
申请日：2021-12-16
发明人： Jayavardhana Rama Gubbi Lakshminarasimha , Vartika Sengar , Vivek Bangalore Sampathkumar , Aparna Kanakatte Gurumurthy , Murali Poduval , Balamuralidhar Purushothaman , Karthik Seemakurthy , Avik Ghose , Srinivasan Jayaraman
CPC分类号： G06T5/70 , G06T5/50 , G06T7/269 , G06T2207/10016 , G06T2207/10024 , G06T2207/20081 , G06T2207/20084
摘要： The disclosure herein relates to methods and systems for generating an end-to-end de-smoking model for removing smoke present in a video. Conventional data-driven based de-smoking approaches are limited mainly due to lack of suitable training data. Further, the conventional data-driven based de-smoking approaches are not end-to-end for removing the smoke present in the video. The de-smoking model of the present disclosure is trained end-to-end with the use of synthesized smoky video frames that are obtained by source aware smoke synthesis approach. The end-to-end de-smoking model localize and remove the smoke present in the video, using dynamic properties of the smoke. Hence the end-to-end de-smoking model simultaneously identifies the regions affected with the smoke and performs the de-smoking with minimal artifacts. localized smoke removal and color restoration of a real-time video.
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公开(公告)号：US12094035B2
公开(公告)日：2024-09-17
申请号：US17645116
申请日：2021-12-20
发明人： Jayavardhana Rama Gubbi Lakshminarasimha , Karthik Seemakurthy , Vartika Sengar , Aparna Kanakatte Gurumurthy , Avik Ghose , Balamuralidhar Purushothaman , Murali Poduval , Jayeeta Saha , Srinivasan Jayaraman , Vivek Bangalore Sampathkumar
CPC分类号： G06T11/001 , G06T7/90 , G06V10/56 , G06T2207/10016
摘要： The disclosure herein relates to methods and systems for localized smoke removal and color restoration of a real-time video. Conventional techniques apply the de-smoking process only on a single image, by finding the regions having the smoke, based on manual air-light estimation. In addition, regaining original colors of de-smoked image is quite challenging. The present disclosure herein solves the technical problems. In the first stage, video frames having the smoky and smoke-free video frames are identified, from the video received in the real-time. In the second stage, an air-light is estimated automatically using a combined feature map. An intermediate de-smoked video frame for each smoky video frame is generated based on the air-light using a de-smoking algorithm. In the third and the last stage, a smoke-free video reference frame is used to compensate for color distortions introduced by the de-smoking algorithm in the second stage.
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公开(公告)号：US20230047937A1
公开(公告)日：2023-02-16
申请号：US17552868
申请日：2021-12-16
发明人： Jayavardhana Rama GUBBI LAKSHMINARASIMHA , Vartika Sengar , Vivek Bangalore Sampathkumar , Aparna Kanakatte Gurumurthy , Murali Poduval , Balamuralidhar Purushothaman , Karthik Seemakurthy , Avik Ghose , Srinivasan Jayaraman
摘要： The disclosure herein relates to methods and systems for generating an end-to-end de-smoking model for removing smoke present in a video. Conventional data-driven based de-smoking approaches are limited mainly due to lack of suitable training data. Further, the conventional data-driven based de-smoking approaches are not end-to-end for removing the smoke present in the video. The de-smoking model of the present disclosure is trained end-to-end with the use of synthesized smoky video frames that are obtained by source aware smoke synthesis approach. The end-to-end de-smoking model localize and remove the smoke present in the video, using dynamic properties of the smoke. Hence the end-to-end de-smoking model simultaneously identifies the regions affected with the smoke and performs the de-smoking with minimal artifacts. localized smoke removal and color restoration of a real-time video.
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7.发明申请公开(公告)号：US20220036541A1
公开(公告)日：2022-02-03
申请号：US17357210
申请日：2021-06-24
发明人： Jayavardhana Rama Gubbi Lakshminarasimha , Mahesh Rangarajan , Rishin Raj , Vishnu Hariharan Anand , Vishal Bajpai , Vishwa Chethan Dandenahalli Venkatappa , Pradeep Kumar Mishra , Gourav Singh Jat , Meghala Mani , Gangadhar Shankarappa , Dinesh Sasidharan Nair , Shashank Lipate , Vineet Lall , Kavita Sara Mathew , Karthik Seemakurthy , Balamuralidhar Purushothaman
IPC分类号： G06T7/00 , G06K9/32 , G06K9/46 , G06T5/20 , G06T5/00 , G06T7/11 , G06T7/168 , G06T7/136 , G01N21/88
摘要： Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.
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