Method, System, Medium, Device and Terminal for Detecting Strong Light Region in Image
Through image chunking processing and PCA dimensionality reduction combined with machine learning image classifier, efficient and accurate detection of strong light areas is achieved, and the problems of difficult distinction between strong light images and complex calculations in the prior art are solved.
CN114638785BActive Publication Date: 2025-07-15SHANGHAI INSTITUTE OF TECHNICAL PHYSICS CHINESE ACADEMY OF SCIENCES
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Patent Information
- Application Number
- CN202210148004.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-02-17
- Publication Date
- 2025-07-15
- Estimated Expiration
- 2042-02-17
AI Technical Summary
Technical Problem
The prior art cannot effectively distinguish between strong and non-strong light images, resulting in unreliable results of the visual task processing of strong light images, and the traditional methods have high computational complexity and poor adaptability.
Method used
Image blocking processing is used to reduce dimensionality by combining PCA method, and machine learning image classifiers are used to detect strong light areas, including image preprocessing, block feature calculation, PCA dimensionality reduction and image classifier judgment.
Benefits of technology
It improves the accuracy and efficiency of bright light area detection, simplifies the calculation process, and enhances the adaptability and robustness of the model.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention belongs to the technical field of image processing, and discloses a method, a system, a medium, a device and a terminal for detecting a strong light area in an image, including: after preprocessing the image acquired by a camera to complete pixel gray value normalization, performing block processing on the image, calculating the image gray value features, and performing dimensionality reduction processing on the image features by using the PCA method; inputting the image features into an image classifier model to determine the types of strong light and non-strong light in the image; for the image classified as the strong light type, using the gray feature information of the image block to determine the position and size of the strong light area in the image, and completing the detection and positioning of the strong light area in the image. The present invention combines a machine learning image classification algorithm with a traditional image processing method, improves the detection efficiency and accuracy of strong light images, and completes the positioning of the strong light area in the image. The present invention uses the PCA principal component analysis method to optimize the image features and simplifies the operation process of the model.
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