Tata Consultancy Services Limited

Karthik Seemakurthy

Balamuralidhar Purushothaman

Jayavardhana Rama Gubbi Lakshminarasimha

Avik Ghose

Murali Poduval

Vartika Sengar

Aparna Kanakatte Gurumurthy

Vivek Bangalore Sampathkumar

Srinivasan Jayaraman

Shailesh Shankar Deshpande

Akshaya Ramaswamy

Angshul Majumdar

Rishin Raj

Mahesh Rangarajan

Ashley Varghese

Vishnu Hariharan Anand

Vishal Bajpai

Vishwa Chethan Dandenahalli Venkatappa

Gourav Singh Jat

Meghala Mani

Gangadhar Shankarappa

Pradeep Kumar Mishra

Dinesh Sasidharan Nair

Vineet Lall

Kavita Sara Mathew

Sandeep Nk

Mariaswamy Girish Chandra

Shashank Lipate

Jayeeta Saha

Sasidharan Dinesh Nair

Srinivsan Jayaraman

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.

Methods and systems for generating end-to-end de-smoking model

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.

Methods and systems for localized smoke removal and color restoration of real-time video

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.

Identification of defect types in liquid pipelines for classification and computing severity thereof

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.

Method and system for video analysis

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.

Method and system for solving inverse problems in image processing using deep dictionary learning (DDL)

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Bengaluru, India

Karthik Seemakurthy