Light Flare Quantification for Reliable Machine Vision Imaging
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Solution Overview
Problem
Existing imaging devices struggle to quantify light flare artifacts accurately, which can mislead machine vision systems and affect the reliability of image capture, especially in environments with strong light sources.
Innovation Solution
A method to quantify light flare artifacts by converting images into a flare profile array, determining a peak flare artifact value, and calculating a flare artifact score, allowing comparison of imaging devices based on their flare production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional imaging systems capture images in environments with strong light sources, then the imaging device can record the scene, but light flare artifacts are produced that mislead machine vision systems and reduce image reliability
Solution Approach 1:
The patent converts the harmful light flare artifacts into beneficial information by developing a quantitative metric that measures flare intensity. The system uses the flare patterns themselves to identify and characterize the artifacts, then applies de-flare processing to remove them. This transforms the previously harmful flares into a diagnostic tool for improving image quality and machine vision reliability.
2Measurement precision
If imaging devices operate without flare quantification, then the system remains simple, but there is no way to determine how pronounced light flares are or to compare imaging devices objectively
Solution Approach 1:
The patent introduces a quantitative parameter (flare metric) that measures the intensity and characteristics of light flare artifacts. By defining specific parameters such as flare intensity, spatial distribution, and spectral characteristics, the system enables precise measurement and comparison of flare performance across different imaging devices, transforming a qualitative problem into a quantitative one.
3Productivity
If machine vision systems process images with flare artifacts, then processing can proceed, but the algorithms falsely detect artifacts as real objects leading to incorrect conclusions
Solution Approach 1:
The patent applies de-flare processing as a preliminary step before machine vision algorithms process the image. By removing flare artifacts in advance, the system prevents false detections from occurring in the first place, rather than trying to correct errors after they happen. This preliminary action maintains processing throughput while eliminating the source of information loss.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for identifying light flares in images. An example method may involve receiving an image from an imaging device, the image including data indicative of a flare artifact originating from a region of the image. The example method may also involve determining, based on the image data, a first array of pixels extending radially outwards from the region and a second array of pixels extending radially outwards from the region. The example method may also involve creating, based on the image data, a flare array, the flare array including the first array of pixels and the second array of pixels. The example method may also involve determining, based on the flare array, a peak flare artifact value indicative of a size of the flare artifact; and determining, based on the peak flare artifact value, a flare artifact score for the imaging device.