Granted Patent B2

US 12,602,925 · App. 18/175,264 · Granted Apr 14, 2026

Inventors: Migel Dileepa Tissera (Coquitlam, CA); Francis George Doumet (Vancouver, CA); Parisa Asgharzadeh (Coquitlam, CA); Ahmed Sigiuk (Burnaby, CA)

Assignee: MLVX TECHNOLOGIES INC.

G06V20/194G06T7/50G06V10/58G06V10/82G06V10/95G06T2207/10048G06T2207/20081G06T2207/20084

Abstract

Claims (59)

1 . A method of using hyperspectral imaging to identify one or more materials in an object, comprising:

illuminating the object with light, wherein at least some of the light is reflected by the object;

using a hyperspectral imaging sensor to capture, based on the reflected light, one or more hyperspectral images of the object, the one or more hyperspectral images comprising hyperspectral data;

inputting the one or more hyperspectral images to a trained machine learning model, comprising inputting to the trained machine learning model, pixel-by-pixel, each pixel of the one or more hyperspectral images, and the hyperspectral data associated with each pixel;

using the trained machine learning model to spectrally un-mix, pixel-by-pixel, the hyperspectral data associated with each pixel so as to extract one or more spectral signatures from the hyperspectral data; and

identifying, based on the one or more extracted spectral signatures, one or more materials comprised in the object.

2 . The method of claim 1 , wherein the object is at least partially transparent to visible light.

3 . The method of claim 1 , wherein the object is a multilayered object, each layer comprising one or more materials.

4 . The method of claim 1 , wherein the light has at least an infrared component.

5 . The method of claim 1 , wherein the trained machine learning model comprises at least one trained neural network.

6 . The method of claim 5 , wherein the neural network is a convolutional neural network (CNN) comprising one or more 1-dimensional convolutions.

7 . The method of claim 5 , wherein the trained neural network is a transformer.

8 . The method of claim 1 , wherein extracting the one or more spectral signatures from the hyperspectral data comprises:

identifying, within the hyperspectral data, one or more spectral signatures, each identified spectral signature corresponding to a respective spectral signature in a set of predefined spectral signatures associated with predefined materials; and

extracting the one or more identified spectral signatures.

9 . The method of claim 1 , wherein inputting the one or more hyperspectral images to the trained machine learning model comprises:

compressing, using a first computer device, the one or more hyperspectral images;

transmitting the compressed one or more hyperspectral images over a computer network to a second computer device;

decompressing, using the second computer device, the compressed one or more hyperspectral images; and

inputting decompressed one or more hyperspectral data images to the trained machine learning model.

10 . The method of claim 9 , wherein the first computer device comprises a Field-Programmable Gate Array (FPGA).

11 . The method of claim 9 , wherein the second computer device comprises an FPGA.

12 . The method of claim 11 , wherein, during the illuminating, the object is moving is relative to the hyperspectral imaging sensor.

13 . The method of claim 1 , wherein the trained machine learning model is a first trained machine learning model, and wherein the method further comprises:

determining, using a second trained machine learning model and based on the one or more hyperspectral images, a shape of the object.

14 . The method of claim 13 , wherein determining the shape of the object comprises:

obtaining visible image data of the object;

inputting the visible image data to the second trained machine learning model; and

using the second trained machine learning model to determine, based on the visible image data, the shape of the object.

15 . The method of claim 1 , wherein identifying the one or more materials comprises:

identifying, for each of the one or more materials, an amount of the material relative to an amount of each other material.

16 . The method of claim 1 , wherein:

spectrally un-mixing the hyperspectral data comprises extracting, for each pixel, one or more spectral signatures from the hyperspectral data associated with the pixel; and

identifying the one or more materials comprises identifying, for each pixel and based on the one or more extracted spectral signatures, one or more dominant materials in a portion of the object corresponding to the pixel.

17 . The method of claim 16 , wherein identifying the one or more dominant materials comprises:

identifying, for at least one pixel of the pixels, multiple materials in the portion of the object corresponding to the at least one pixel; and

identifying, from among the multiple materials, the one or more dominant materials.

18 . The method of claim 17 , wherein identifying the one or more dominant materials comprises:

applying one or more thresholds to each spectral signature associated with each of the multiple materials; and

identifying the one or more dominant materials based on the application of the one or more thresholds.

19 . The method of claim 17 , wherein identifying the one or more dominant materials comprises:

for each of one or more other pixels of the pixels, determining at least one dominant material in a portion of the object corresponding to the other pixel; and

identifying the one or more dominant materials based on each determined dominant material of each other pixel.

20 . The method of claim 1 , wherein inputting each pixel to the trained machine learning model comprises inputting, for each pixel, a 1-dimensional vector representing the pixel and the hyperspectral data associated with the pixel to the trained machine learning model.

21 . A hyperspectral imaging system comprising:

a light source for emitting light;

a hyperspectral imaging sensor;

one or more computer processors; and

a computer-readable medium storing computer program code configured, when executed by the one or more computer processors, to cause the one or more computer processors to perform a method comprising:

controlling the light source to illuminate an object;

receiving, from the hyperspectral imaging sensor, one or more hyperspectral images of the object captured in response to at least some of the emitted light being reflected by the object and being received at the hyperspectral imaging sensor, the one or more hyperspectral images comprising hyperspectral data;

inputting the one or more hyperspectral images to a trained machine learning model, comprising inputting to the trained machine learning model, pixel-by-pixel, each pixel of the one or more hyperspectral images, and the hyperspectral data associated with each pixel;

using the trained machine learning model to spectrally un-mix, pixel-by-pixel, the hyperspectral data associated with each pixel so as to extract one or more spectral signatures from the hyperspectral data; and

identifying, based on the one or more extracted spectral signatures, one or more materials comprised in the object.

22 . A non-transitory computer-readable medium storing computer program code configured, when executed by a processor, to cause the processor to:

receive one or more hyperspectral images of an object, the one or more hyperspectral images comprising hyperspectral data;

input the one or more hyperspectral images to a trained machine learning model, comprising inputting to the trained machine learning model, pixel-by-pixel, each pixel of the one or more hyperspectral images, and the hyperspectral data associated with each pixel;

use the trained machine learning model to spectrally un-mix, pixel-by-pixel, the hyperspectral data associated with each pixel so as to extract one or more spectral signatures from the hyperspectral data; and

identify, based on the one or more extracted spectral signatures, one or more materials comprised in the object.

Assignments (1)

Continuity (1)

References Cited (33)