Barcode Decoding Motion Blur Compensation via Velocity
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Solution Overview
Problem
Handheld barcode readers face challenges in capturing clear images due to motion blur, especially with low-cost image sensors and inadequate lighting, leading to misdetected edges and decoding errors, as existing solutions like optical stabilization and delay-based systems either increase device complexity or result in unacceptable delays.
Innovation Solution
A method and system that utilize a motion sensor to determine velocity between the image sensor and barcode, adjusting edge detection parameters based on this velocity to compensate for blurring, thereby improving barcode decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical stabilization techniques are used to reduce blurring, then image quality is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical optical stabilization systems with a computational approach. A motion sensor (accelerometer) detects device movement, and a processor calculates motion blur parameters to adjust edge detection algorithms. This substitutes complex mechanical lens/sensor stabilization mechanisms with sensor-based motion detection and software-based compensation, reducing device complexity while maintaining image quality.
Solution Approach 2:
The patent introduces a motion sensor as an intermediary between the physical motion and the image processing system. The accelerometer captures motion data that serves as input for calculating blur parameters, which then guide the edge detection algorithm adjustments. This intermediary enables the system to compensate for motion blur without requiring complex optical stabilization hardware.
2Measurement precision
If optical stabilization techniques are used to reduce blurring, then image quality is improved, but power consumption increases
Solution Approach 1:
The patent replaces power-intensive mechanical optical stabilization systems with a low-power computational approach. The motion sensor and processor consume significantly less power than motors, lenses, or complex optical mechanisms. By using software-based edge detection adjustment rather than hardware stabilization, the system reduces power consumption while maintaining image quality.
3Measurement precision
If delay-based motion detection is used to capture images during pauses, then motion blur is reduced, but decoding time increases
Solution Approach 1:
The patent performs preliminary action by continuously monitoring motion sensor data and pre-calculating blur parameters before image capture. Rather than waiting for motion pauses, the system proactively determines the appropriate edge detection parameters based on current motion conditions. This allows immediate image processing without delay, as the compensation parameters are ready in advance.
Solution Approach 2:
The patent implements feedback by continuously monitoring motion sensor data and using it to dynamically adjust edge detection parameters. The system creates a closed-loop control where motion information feeds back into the image processing algorithm, enabling real-time adaptation to motion conditions without requiring pause-based capture strategies.
4Illumination intensity
If exposure time is increased to capture images in low light, then image signal quality is improved, but motion blur increases
Solution Approach 1:
The patent changes the parameters of the edge detection algorithm based on motion data to compensate for motion blur. By adjusting edge detection sensitivity and threshold parameters according to the calculated blur amount, the system can successfully decode barcodes from longer exposure images that would otherwise be too blurred. This parameter adaptation allows the system to maintain image sharpness in the processing stage even when physical exposure time is extended for low-light conditions.
Solution Approach 2:
The patent converts the harmful effect of motion blur into beneficial information. Instead of treating motion blur as a defect to be avoided, the system uses motion sensor data to quantify the blur and adjusts edge detection parameters accordingly. The motion information that causes blur becomes the key to compensating for it, allowing the system to successfully decode barcodes in low-light conditions where longer exposure is necessary.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively compensates for motion-induced blurring, enhancing the accuracy of barcode decoding even in low-light conditions and with low-cost image sensors, without increasing device complexity or power consumption.
Implementation Method 1
a motion sensor for collecting acceleration data for motion between the image sensor and the barcode; and a processor configured to determine a velocity of the motion between the image sensor and the barcode based on the acceleration data
Data Source
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AI summary
The barcode decoding system (300) includes an imager for capturing an image of the barcode, a motion sensor (318) for collecting acceleration data and a processor (302) that is configured to determine a velocity of the image sensor (320) during the exposure period based on the acceleration data and a periodic motion model. The determined velocity is used to adjust the edge detection algorithm used to detect the barcode features in order to decode the barcode. The orientation of the captured barcode can also be determined in order to determine the velocity in a direction perpendicular to the barcode features.