公开(公告)号：US20240078784A1

公开(公告)日：2024-03-07

申请号：US18272858

申请日：2021-09-23

Applicant: SeeChange Technologies Limited

Inventor： David Packwood ,  Ariel Edgar Ruiz-Garcia

IPC: G06V10/426 ,  G06T3/40 ,  G06V10/44 ,  G06V10/82

CPC classification number: G06V10/426 ,  G06T3/40 ,  G06V10/44 ,  G06V10/82 ,  G06V2201/07

Abstract: A computer implemented method of processing image data by an image processing system comprising a directed acyclic graph of nodes for receiving and processing image data is disclosed. The method comprises: at a compute node in the graph: receiving image data; performing an image processing operation on the image data to produce a compute node output based on the data; and transmitting the compute node output to another node in the graph. The method also comprises, at a control node in the graph: receiving a control node input, wherein the control node input is the image data or is based on the image data; and, if the control node determines that a control condition is satisfied, transmitting the control node input to another node in the graph. A computer implemented image processing system is also disclosed.

公开(公告)号：US11631019B2

公开(公告)日：2023-04-18

申请号：US16834920

申请日：2020-03-30

Applicant: SEECHANGE TECHNOLOGIES LIMITED

Inventor： David Packwood

IPC: G06N5/04 ,  G06N20/20 ,  G06N3/08 ,  G06N3/04 ,  H04L29/08 ,  G06N5/046 ,  H04L67/12

Abstract: A computing network has a sensor, a first processor in a first computing network location, and a second processor in a second computing network location, the second computing network location further from the sensor than the first computing network location. The first processor is configured to receive sensor data from the sensor and configured to operate a first machine learning model to make a first inference based on the sensor data. The second processor is configured to receive the sensor data and to operate a second machine learning model to make a second inference based on the sensor data in response to a trigger. The computing network is configured to collate and process the first and second inferences to make an aggregated inference.

公开(公告)号：US20220319177A1

公开(公告)日：2022-10-06

申请号：US17544400

申请日：2021-12-07

Applicant: SeeChange Technologies Limited

Inventor： Ariel Edgar RUIZ-GARCIA ,  David Packwood ,  Michael Andrew Pallister

IPC: G06V20/50 ,  G06V10/764 ,  G06V10/82 ,  G06V10/44 ,  G06T7/11 ,  G06T7/194 ,  G06V10/774

Abstract: System, apparatus and method of image processing to detect a substance spill on a solid surface such as a floor is disclosed. First data representing a first image, captured by an image sensor, of a region including a solid surface, is received. A trained semantic segmentation neural network is applied to the first image data to determine, for each pixel of the first image, a spill classification value associated with the pixel, the determined spill classification value for a given pixel indicating the extent to which the trained semantic segmentation neural network estimates, based on its training, that the given pixel illustrates a substance spill. The presence of a substance spill on the solid surface is detected based on the determined spill classification values of the pixels of the first image.

公开(公告)号：US12154338B2

公开(公告)日：2024-11-26

申请号：US17544400

申请日：2021-12-07

Applicant: SeeChange Technologies Limited

Inventor： Ariel Edgar Ruiz-Garcia ,  David Packwood ,  Michael Andrew Pallister

IPC: G06V20/50 ,  G06T7/11 ,  G06T7/194 ,  G06V10/44 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82

Abstract: System, apparatus and method of image processing to detect a substance spill on a solid surface such as a floor is disclosed. First data representing a first image, captured by an image sensor, of a region including a solid surface, is received. A trained semantic segmentation neural network is applied to the first image data to determine, for each pixel of the first image, a spill classification value associated with the pixel, the determined spill classification value for a given pixel indicating the extent to which the trained semantic segmentation neural network estimates, based on its training, that the given pixel illustrates a substance spill. The presence of a substance spill on the solid surface is detected based on the determined spill classification values of the pixels of the first image.