Two-Dimensional Code Boundary Detection in Low-Resolution Images
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Solution Overview
Problem
Conventional methods for detecting the boundary line of two-dimensional codes captured with low-resolution images, such as those from inexpensive digital cameras, often result in incorrect detection due to defocus and distortion, leading to inaccurate positioning of the code boundary.
Innovation Solution
An image processing apparatus that detects candidate points on the boundary of a two-dimensional code, calculates a first boundary line, selects points within a threshold distance from this line, and refines the boundary line detection using selected points, improving precision even in defocused or distorted images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional boundary line detection methods are used on low-resolution images, then processing speed is maintained, but measurement precision deteriorates due to defocus and distortion
Solution Approach 1:
The boundary line detection process is divided into multiple stages: initial candidate point detection, first boundary line calculation, point selection based on distance threshold, and second boundary line detection. This segmentation allows each stage to focus on specific tasks, improving overall precision while maintaining manageable complexity through modular processing
Solution Approach 2:
The method performs preliminary actions by first detecting candidate points and calculating a first boundary line before final detection. This preliminary processing prepares the data for more accurate final boundary line detection, addressing defocus and distortion issues before they compromise the final measurement
2Measurement precision
If multiple processing steps are added to improve boundary detection precision, then measurement precision improves, but processing time increases
Solution Approach 1:
The method applies partial action by selecting only candidate points within a threshold distance from the first boundary line for the final detection. This selective processing reduces the number of points requiring detailed analysis, maintaining high precision while limiting the time cost of additional processing steps
Solution Approach 2:
The distance threshold selection provides feedback control: candidate points are evaluated based on their distance from the first boundary line, and only those meeting the threshold criterion proceed to final detection. This feedback mechanism ensures precision while automatically filtering out unnecessary processing of distant points
Data Source
AI summary
An image processing apparatus includes a candidate point detecting section configured to detect a plurality of candidate points on a boundary of a code region of a two-dimensional code in an image capturing the two-dimensional code, by scanning the image starting from an internal point of the two-dimensional code in the image, the two-dimensional code including a plurality of modules being arranged in two dimensions; a calculating section configured to calculate a first boundary line using the candidate points; a selecting section configured to select the candidate points located within a distance less than a threshold value from the first boundary line; and a boundary line detecting section configured to detect a second boundary line calculated using the selected candidate points selected by the selecting section.