Computer vision using a prior probability distribution selected based on an image capture condition
US11468275B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 16, 2020 |
| Grant date | Oct 11, 2022 |
| Priority date | — |
| Expiry date | Dec 18, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A machine learning (ML) model is trained and used to produce a probability distribution associated with a computer vision task. The ML model uses a prior probability distribution associated with a particular image capture condition determined based on sensor data. For example, given that an image was captured by an image capture device at a particular height above the floor and angle relative to the vertical world axis, a prior probability distribution for that particular image capture device condition can be used in performing a computer vision task on the image. Accordingly, the machine learning model is given the image as input as well as the prior probability distribution for the particular image capture device condition. The use of the prior probability distribution can improve the accuracy, efficiency, or effectiveness of the ML learning model for the computer vison task.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.