Automatic Distortion Correction via Line Detection and Perspective Transformation
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Solution Overview
Problem
Conventional distortion correction methods require manual intervention and often result in images with significant distortion, especially due to limitations in taking angle or position, making it inconvenient for image processing and recognition.
Innovation Solution
A method involving line detection using a Line Segment Detector (LSD) to identify straight lines, grouping them, selecting optimal boundary lines, determining initial vertices from intersection points, and performing perspective transformation to correct the image automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used in distortion correction, then correction accuracy can be improved, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically detects straight lines, groups them, selects optimal boundary lines, and determines vertexes without requiring manual intervention. The algorithm self-services the correction process by using computational geometry and image processing to identify and correct distortion automatically, eliminating the need for user interaction while maintaining high accuracy through sophisticated line detection and grouping mechanisms
Solution Approach 2:
The patent replaces manual mechanical operations (user interaction, manual vertex selection) with automated computational systems. The line detection algorithm, grouping mechanism, and automatic vertex determination replace what would traditionally require human hands and eyes, substituting mechanical human operation with electronic-computational processes that achieve both speed and precision
2Ease of operation
If conventional distortion correction methods are used without manual intervention, then operation simplicity is improved, but correction accuracy deteriorates resulting in significant distortion
Solution Approach 1:
The correction process is segmented into distinct automated stages: line detection, line grouping, optimal boundary line selection, and vertex determination. Each segment handles a specific aspect of the correction task, with the line detection segment identifying all straight lines, the grouping segment organizing them by spatial relationship, the selection segment choosing optimal boundaries, and the vertex segment calculating intersection points. This segmentation enables full automation while maintaining accuracy through specialized processing at each stage
Solution Approach 2:
The patent introduces intermediate computational structures (line groups, boundary line selections, intersection point calculations) that mediate between the input distorted image and the final corrected output. These intermediaries serve as computational bridges that automatically process the transformation, replacing the need for direct manual vertex specification while ensuring accurate geometric relationships are maintained throughout the correction process
3Quantity of substance
If all detected straight lines are used for correction, then line coverage is improved, but processing complexity and identification of optimal boundaries deteriorates
Solution Approach 1:
The system extracts only the relevant straight lines from the complete set of detected lines by grouping them according to their spatial relationships and selecting optimal boundary lines from each group. This extraction process removes unnecessary lines and processing steps, keeping only those lines that contribute to defining the image boundaries, thereby reducing processing complexity while maintaining comprehensive coverage of the relevant geometric features
Solution Approach 2:
The patent performs preliminary grouping of detected lines before selecting optimal boundaries. By pre-organizing lines into groups based on their spatial relationships and characteristics, the system prepares the data structure in advance to facilitate efficient selection of boundary lines. This preliminary action reduces the complexity of the subsequent selection process by having lines already organized and categorized, making optimal boundary identification more straightforward
Data Source
AI summary
The present disclosure provides a distortion correction method, a distortion correction apparatus, an electronic device, and a computer-readable storage medium, the distortion correction method including: performing a line detection on the distorted image to obtain multiple straight lines in the distorted image; grouping the straight lines into multiple groups of straight lines; selecting optimal boundary lines of the distorted image from the groups of straight lines; determining multiple initial vertexes of the distorted image according to intersection points between the optimal boundary lines; and performing a perspective transformation processing on the distorted image according to the initial vertexes to obtain a corrected image.