Barcode Decoder Using Blur Estimation and Geometric Modeling
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Solution Overview
Problem
Existing barcode reading technologies face inefficiencies in decoding deformed linear barcodes due to defocus and motion blur, leading to high resource expenditure and inaccurate decoding results.
Innovation Solution
A processor-implemented system that includes a camera module, barcode blur estimation, localization, geometric modeling, and decoding modules to estimate defocus blur, identify barcode regions, generate geometric models, and decode barcodes efficiently by using backward extraction techniques and known barcode formats, even in conditions of defocus, motion, curvature, skew, and tilt.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional barcode reading technologies are used to decode deformed linear barcodes, then decoding can be attempted, but resource expenditure is high and decoding accuracy is low due to defocus and motion blur
Solution Approach 1:
The system performs preliminary actions by estimating defocus blur amounts and generating geometric models of barcode deformities (curvature, skew, tilt) before attempting decoding. This preliminary characterization allows the system to preprocess images appropriately and select suitable decoding strategies, avoiding wasted resources on clearly undecodable barcodes while improving success rates for recoverable ones.
Solution Approach 2:
The system changes parameters by estimating defocus blur amounts and using these to adjust decoding parameters and image processing settings. The geometric model parameters (curvature, skew, tilt angles) are used to transform the barcode image into a corrected coordinate system, effectively changing the parameter space to make decoding feasible.
2Productivity
If the system attempts to decode barcodes with significant deformity, then more decoding attempts may be made, but resource usage increases without guaranteed improvement in success rate
Solution Approach 1:
The system uses feedback by estimating the degree of barcode deformity (curvature, skew, tilt) and defocus blur, then using this information to determine whether decoding is likely to succeed. This feedback mechanism allows the system to adapt its decoding strategy and resource allocation based on the actual state of each barcode, avoiding wasted attempts on severely deformed codes while intensifying efforts on recoverable ones.
Solution Approach 2:
The system applies partial action by performing geometric correction and specialized decoding only for barcodes that meet certain deformity thresholds, rather than applying the same intensive processing to all barcodes. This selective approach ensures resources are concentrated on cases where they are most likely to be effective.
3Measurement precision
If the system uses simple decoding methods, then resource usage is low, but accuracy in decoding deformed barcodes is insufficient
Solution Approach 1:
The system segments the barcode decoding process into distinct stages: defocus estimation, geometric deformity detection (curvature, skew, tilt), image correction, and decoding. Each stage uses specialized algorithms appropriate to that specific task, allowing the system to achieve high accuracy through a series of simple, focused operations rather than one complex monolithic process.
Solution Approach 2:
The system introduces intermediary processing steps between image capture and decoding, including defocus estimation and geometric modeling. These intermediaries transform the raw deformed barcode image into a corrected representation that is more amenable to standard decoding algorithms, effectively bridging the gap between simple decoding methods and the need for high accuracy.
4Reliability
If the system processes every video frame, then no frames are missed, but processing time and resource consumption increase significantly
Solution Approach 1:
The system extracts only the relevant information from each video frame by first estimating defocus blur and detecting geometric deformities. Based on these extracted characteristics, it determines whether the frame contains a decodable barcode. This extraction approach allows the system to quickly eliminate unsuitable frames without performing full decoding operations, maintaining reliability while reducing overall processing time.
Data Source
AI summary
In a system and method of recognizing a barcode from a stream of video frames, a processor-implemented camera module receives a stream of video frames, with at least one video frame including a barcode. A processor-implemented barcode blur estimate module estimates an amount of defocus blur in a video frame. The processor-implemented barcode blur estimate module further estimates an identity of the barcode. A processor-implemented barcode localization module identifies a region of the video frame containing the barcode. A processor-implemented barcode geometric modeler module generates a geometric model of the barcode that includes an identified barcode deformity. A processor-implemented barcode decoder module decodes the barcode from the video frame using the estimated amount of defocus blur, the estimated identity of the barcode, and the geometric model of the barcode.