Fisheye Image Effective Area Calibration via Azimuthal Gradient Analysis
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Solution Overview
Problem
Current methods for preprocessing fisheye images struggle to adapt to different shooting scenes and exposure parameters, leading to unstable accuracy in determining the effective imaging area.
Innovation Solution
The method involves acquiring a fisheye image, performing color space transformation to generate a grayscale image, calculating gradient images in horizontal and perpendicular directions, determining azimuthal angles, and generating an azimuthal image to accurately determine the effective imaging area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current preprocessing methods are used on fisheye images, then the processing speed is maintained, but the accuracy of determining effective imaging area becomes unstable across different scenes and exposure parameters
Solution Approach 1:
The patent transforms the fisheye image from color space to grayscale space, then computes gradient magnitudes and azimuthal angles to create a normalized angular distribution image. This parameter transformation approach extracts invariant features that remain consistent across different shooting scenes and exposure parameters, thereby improving measurement precision while maintaining adaptability.
Solution Approach 2:
The patent introduces intermediate processing steps including gradient computation, azimuthal angle calculation, and normalization to create a mediator representation (the angular distribution image). This intermediary representation serves as a bridge between the raw fisheye image and the effective imaging area determination, enabling accurate measurement across varying conditions by filtering out scene-specific variations.
2Reliability
If traditional preprocessing methods are applied, then the device complexity remains low, but the reliability of effective imaging area determination deteriorates under varying shooting conditions
Solution Approach 1:
The patent segments the image processing into distinct stages: color space transformation to grayscale, gradient computation in horizontal and vertical directions, azimuthal angle calculation, and normalization to generate the angular distribution image. This segmentation allows each stage to focus on extracting specific invariant features, improving reliability while keeping the complexity manageable through modular processing.
Solution Approach 2:
The patent replaces traditional mechanical or simple threshold-based preprocessing methods with a computational approach using gradient analysis and angular normalization. This substitution enables the system to adapt to varying shooting conditions through mathematical transformations rather than relying on fixed mechanical parameters, thereby improving reliability despite increased computational complexity.
Data Source
AI summary
This application provides a fisheye image processing method, an electronic device, and a computer-readable storage medium. The method includes: acquiring a fisheye image; generating a grayscale image according to the fisheye image; generating a first gradient image in a horizontal direction and a second gradient image in a perpendicular direction; generating an azimuthal image according to the first gradient image and the second gradient image; and determining an effective imaging area according to the azimuthal image. The anti-distortion correction for the fisheye camera is performed in the process of shooting images according to the effective imaging area of the fisheye camera, and thus the imaging area of the fisheye camera is accurately calibrated when the images are shot by the fisheye camera.