公开(公告)号：US20200012889A1

公开(公告)日：2020-01-09

申请号：US16502760

申请日：2019-07-03

Applicant: Tata Consultancy Services Limited

Inventor： Kavya GUPTA ,  Brojeshwar BHOWMICK ,  Angshul MAJUMDAR

IPC: G06K9/62

Abstract: This disclosure relates to systems and methods for solving generic inverse problems by providing a coupled representation architecture using transform learning. Convention solutions are complex, require long training and testing times, reconstruction quality also may not be suitable for all applications. Furthermore, they preclude application to real-time scenarios due to the mentioned inherent lacunae. The methods provided herein require involve very low computational complexity with a need for only three matrix-vector products, and requires very short training and testing times, which makes it applicable for real-time applications. Unlike the conventional learning architectures using inductive approaches, the CASC of the present disclosure can learn directly from the source domain and the number of features in a source domain may not be necessarily equal to the number of features in a target domain.

公开(公告)号：US20190080503A1

公开(公告)日：2019-03-14

申请号：US15898160

申请日：2018-02-15

Applicant: Tata Consultancy Services Limited

Inventor： Brojeshwar BHOWMICK ,  Swapna AGARWAL ,  Sanjana SINHA ,  Balamuralidhar PURUSHOTHAMAN ,  Apurbaa MALLIK

IPC: G06T15/04 ,  G06T15/08 ,  G06T17/00

CPC classification number: G06T15/04 ,  G06T7/254 ,  G06T15/08 ,  G06T17/005 ,  G06T2200/04 ,  G06T2207/10028 ,  G06T2207/20072

Abstract: 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.