Image Flare Quantification Using Radial Pixel Profiles
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Solution Overview
Problem
Imaging devices suffer from light flare artifacts that can mislead machine vision systems and lack a quantitative metric for comparing flare severity across devices, especially in environments with strong light sources.
Innovation Solution
A method to quantify light flare artifacts by capturing images at multiple orientations, converting them to a flare profile array, and calculating a flare artifact score based on median pixel brightness and peak flare detection, allowing comparison and quality assurance of imaging devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used, then general image analysis is possible, but light flare artifacts cannot be quantitatively measured or compared across different imaging devices
Solution Approach 1:
The patent segments the image processing task into distinct components: detecting the light source position, extracting radial streak artifacts, and calculating flare metrics. This segmentation allows for targeted processing of flare artifacts without requiring complete reprocessing of entire images, thereby improving measurement precision while managing system complexity
Solution Approach 2:
The patent performs preliminary actions by pre-defining flare artifact characteristics (radial streaks from light sources) and pre-processing images to identify and isolate these specific artifacts before general analysis. This preliminary segmentation of flare components enables quantitative measurement without significantly increasing overall system complexity
2Illumination intensity
If imaging devices operate in environments with strong light sources, then they can capture bright scenes, but light flare artifacts are generated that mislead machine vision systems
Solution Approach 1:
The patent converts the harmful flare artifacts into beneficial information by using the detected flare patterns and quantitative metrics to identify and correct machine vision errors. The harmful radial streaks are transformed into diagnostic data that enables the system to distinguish between real objects and flare-induced false detections, thereby improving reliability while maintaining operation in bright environments
Solution Approach 2:
The patent implements feedback by using detected flare artifacts to adjust machine vision algorithm behavior. The quantitative flare metrics provide feedback signals that enable the vision system to compensate for flare effects, filter false detections, and maintain accurate object recognition even when operating in environments with strong light sources
3Adaptability or versatility
If no quantitative metric for flare artifacts is provided, then imaging devices can be used without standardized testing, but there is no way to determine how pronounced flares are relative to other devices
Solution Approach 1:
The patent changes the measurement parameter from subjective visual assessment to objective quantitative metrics by calculating flare intensity, streak length, and artifact density. These standardized parameters enable direct comparison between different imaging devices and provide a universal framework for evaluating flare performance across various device types and operating conditions
Data Source
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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. The example method may also involve determining, based on the peak flare artifact value, a flare artifact score for the imaging device.