Image Partitioning for Distortion Correction and Recognition
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Solution Overview
Problem
Current image processing methods for object recognition in distorted images require extensive correction steps, leading to reduced precision and increased complexity, especially when directly recognizing objects without prior distortion correction.
Innovation Solution
The method involves partitioning an original image into two parts based on distortion thresholds, correcting only the heavily distorted part, and using neural networks with training data for finer vector-level recognition, thereby simplifying processing steps and improving precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire distortion image is corrected before object recognition, then the object recognition precision is improved, but the processing steps become complicated and the processing time increases
Solution Approach 1:
The image is divided into multiple regions based on distortion characteristics. Only regions with distortion exceeding a threshold are selected for correction, while regions with acceptable distortion are processed directly. This segmentation approach reduces the number of correction operations needed while maintaining recognition precision in critical areas.
Solution Approach 2:
Different processing strategies are applied to different regions of the image based on their local distortion characteristics. High-distortion regions undergo correction processing, while low-distortion regions are processed directly. This local quality approach optimizes the balance between recognition precision and processing complexity by applying corrections only where necessary.
2Measurement precision
If the entire distortion image is corrected before object recognition, then the object recognition precision is improved, but the processing time and computational resources increase
Solution Approach 1:
The image is divided into multiple regions based on distortion characteristics. Only regions with distortion exceeding a threshold are selected for correction, while regions with acceptable distortion are processed directly. This segmentation approach reduces the number of correction operations needed while maintaining recognition precision in critical areas.
Solution Approach 2:
Instead of correcting the entire image, only the necessary portions (regions with high distortion) are corrected. This partial action approach achieves sufficient recognition precision without the excessive processing time and computational resources required for full-image correction.
3Device complexity
If object recognition is performed directly on the distortion image without correction, then the processing steps are simplified, but the object recognition precision becomes too low
Solution Approach 1:
Different processing strategies are applied to different regions of the image based on their local distortion characteristics. High-distortion regions undergo correction processing, while low-distortion regions are processed directly. This local quality approach optimizes the balance between recognition precision and processing complexity by applying corrections only where necessary.
Data Source
AI summary
An image processing method includes obtaining an original image; partitioning the original image into a first part and a second part such that distortion of at least a part of an image in the first part of the original image is smaller than a predetermined threshold, and distortion of at least a part of an image in the second part of the original image is greater than or equal to the predetermined threshold; correcting the second part of the original image so as to obtain a distortion-corrected image corresponding to the second part; and recognizing the first part of the original image and the distortion-corrected image so as to recognize an object in the original image.