US 12,444,184 B2
System and method for generating hyperspectral artificial vision for machines
Shailesh Shankar Deshpande, Pune (IN); Karan Sharad Owalekar, Thane West (IN); Apoorva Khanna, New Delhi (IN); Mahesh Kshirsagar, Mumbai (IN); and Balamuralidhar Purushothaman, Bangalore (IN)
Assigned to TATA CONSULTANCY SERVICES LIMITED, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Aug. 17, 2023, as Appl. No. 18/234,913.
Claims priority of application No. 202221053465 (IN), filed on Sep. 19, 2022.
Prior Publication US 2024/0096080 A1, Mar. 21, 2024
Int. Cl. G06V 10/82 (2022.01); G06V 10/58 (2022.01); G06V 10/776 (2022.01)
CPC G06V 10/82 (2022.01) [G06V 10/58 (2022.01); G06V 10/776 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A processor-implemented method comprising steps of:
receiving, via an input/output interface, a hyperspectral signal of a target material to be detected as an input to a neural network model, wherein the hyperspectral signal comprises a plurality of spectral bands;
reshaping, via one or more hardware processors, each signal vector of the plurality of spectral bands to a predefined shape;
dividing, via the one or more hardware processors, the reshaped signal vector of the plurality of spectral bands into a plurality of batches;
training, via the one or more hardware processors, the neural network model iteratively for each of the plurality of batches of the reshaped signal vector to update weight for each unsuccessful material class prediction;
initializing, via the one or more hardware processors, two or more chromatic primitives of the trained neural network model;
optimizing, via the one or more hardware processors, a chromatic primitive sensitivity function using an adaptive moment estimation to achieve optimized weights of the initialized two or more chromatic primitives;
evaluating, via the one or more hardware processors, performance of the neural network model at each iteration to obtain two or more chromatic primitives from the plurality of initialized artificial color primitives, wherein halting the training of the neural network model when convergence of the two or more chromatic primitives is obtained;
generating, via the one or more hardware processors, a new artificial color value for one or more pixels by combining nodes of the each of the obtained two or more chromatic primitives, wherein the new artificial color is a combination of signals created by a linear combination of two or more chromatic primitives; and
predicting, via the one or more hardware processors, an image for the generated new color using the learned two or more chromatic primitive sensitivity functions to detect the target material.