Barcode Detection Using Parallel Segment Analysis
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Solution Overview
Problem
Existing barcode scanning techniques face challenges in accurately detecting barcodes within general images, particularly when the barcode is not well-framed, leading to false positive lines and misdetection, especially in user-wearable camera scenarios.
Innovation Solution
The method employs local segment detection (LSD) and parallel segment detection (PSD) to identify candidate barcode lines, followed by central bisector analysis and pixel intensity evaluation to determine the barcode's start and end points, reducing false positives and improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional barcode scanning techniques are used, then barcodes can be read in standard scenarios, but false positive lines and misdetection occur when barcodes are not well-framed
Solution Approach 1:
The detection process is divided into multiple stages: initial line detection, candidate filtering, geometric validation, and final verification. Each stage segments the detection task to progressively eliminate false positives while maintaining computational efficiency.
Solution Approach 2:
The patent applies preliminary geometric constraints and validation rules to filter candidate lines before full barcode verification. By pre-establishing acceptance criteria for line geometry and arrangement, the system eliminates obvious false positives early in the detection pipeline.
2Measurement precision
If advanced detection algorithms are applied to reduce false positives, then detection precision improves, but computational load increases
Solution Approach 1:
The system applies geometric validation and candidate filtering to only the most promising line segments identified in earlier detection stages. By focusing computational resources on partial candidates rather than processing all detected lines equally, the system achieves high precision with reduced overall computational load.
Solution Approach 2:
Different validation criteria and computational approaches are applied to different regions of the image based on local characteristics. Areas with high-confidence barcode patterns receive more rigorous validation, while other regions use lighter checking, optimizing the balance between precision and energy consumption.
Data Source
AI summary
A method of detecting a barcode that in one embodiment includes performing a line segment detection of a barcode image to provide a plurality of line segments, and analyzing the line segments using parallel segment detection to determine a best candidate line segment having a greatest similarity to a remainder of adjacent line segments. The method may further include providing a central bisector of the best candidate line segment, and forming a plurality of parallel lines offset from the central bisector. In a following step, a pixel map is from the central bisector and the plurality of the parallel lines, and an end and a start of the barcode is determined from changes in intensity of the pixels in the pixel map.