Tata Consultancy Services Limited

Swapna Agarwal

Brojeshwar Bhowmick

Balamuralidhar Purushothaman

Avik Ghose

Sanjay Madhukar Kimbahune

Dibyanshu Jaiswal

Sujit Shinde

Sanjana Sinha

Hrishav Bakul Barua

Soumyadip Maity

Dipanjan Das

Apurbaa Mallik

Traditional machine learning (ML) based systems used for scene recognition and object recognition have the disadvantage that they require huge quantity of labeled data to generate data models for the purpose of aiding the scene and object recognition. The disclosure herein generally relates to image processing, and, more particularly, to method and system for generating 3D mesh generation using planar and non-planar data. The system extracts planar point cloud and non-planar point cloud from each RGBD image in a sequence of RGBD images fetched as input, and then generates a planar mesh and a non-planar mesh for planar and non-planar objects in the image. A mesh representation is generated by merging the planar mesh and the non-planar mesh. Further, an incremental merging of the mesh representation is performed on the sequence of RGBD images, based on an estimated camera pose information, to generate representation of the scene.

Method and system for generating 3D mesh of a scene using RGBD image sequence

A method and system for generating 2D animated lip images synchronizing to an audio signal for an unseen subject. The system receives an audio signal and a target lip image of an unseen target subject as inputs from a user and processes these inputs to extract a plurality of high dimensional audio image features. The lip generator system is meta-trained with training dataset which consists of large variety of subjects' ethnicity and vocabulary. The meta-trained model generates realistic animation for previously unseen face and unseen audio when finetuned with only a few-shot samples for a predefined interval of time. Additionally, the method protects intrinsic features of the unseen target subject.

Method and system for generating 2D animated lip images synchronizing to an audio signal

Embodiments herein provide a method and system for real time ROI detection in thermal face images based on a heuristic approach. The ROI of the thermal images, once detected, is then further used to detect temperature of a subject corresponding to the ROI. Unlike state of the art techniques, the heuristic approach is computationally less intensive and provides fast and accurate ROI detection even in case of occluded faces in a crowd with a single thermal image having a plurality of subject being scanned. The heuristics applied does not focus on face detection but directly on point of interest detection. Once the point of interest (ROI) is detected, it may be used for plurality of applications such as subject tracking and the like, not limited to subject or object temperature sensing since the method disclosed herein is easily implementable on low power devices.

Real time region of interest (ROI) detection in thermal face images based on heuristic approach

Systems and methods of the present disclosure facilitate rigid point cloud registration with characteristics including shape constraint, translation proportional to distance and spatial point-set distribution model for handling scale. The method of the present disclosure enables registration of a rigid template point cloud to a given reference point cloud. Shape-constrained gravitation, as induced by the reference point cloud, controls movement of the template point cloud such that at each iteration, the template point cloud better aligns with the reference point cloud in terms of shape. This enables alignment in difficult conditions introduced by change such as presence of outliers and/or missing parts, translation, rotation and scaling. Also, systems and methods of the present disclosure provide an automated method as against conventional methods that depended on manually adjusted parameters.

Systems and methods for shape constrained 3D point cloud registration

Methods and systems for change detection utilizing three dimensional (3D) point-cloud processing are provided. The method includes detecting changes in the surface based on a surface fitting approach with a locally weighted Moving Least Squares (MLS) approximation. The method includes acquiring and comparing surface geometry of a reference point-cloud defining a reference surface and a template point-cloud defining a template surface at local regions or local surfaces using the surface fitting approach. The method provides effective change detection for both, rigid as well as non-rigid changes, reducing false detections due to presence of noise and is independent of factors such as texture or illumination of an object or scene being tracked for changed detection.

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Kolkata, India

Swapna Agarwal