ROI Image Sensor Capture for Low-Power Barcode Decoding
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Solution Overview
Problem
Conventional machine vision and barcode scanning techniques require high-resolution image sensors operating at high frame rates, leading to computational intensity, high power consumption, and inclusion of non-decodable static artifacts and areas of non-interest.
Innovation Solution
A computing system that captures low-resolution images to identify regions of interest, followed by high-resolution images of those regions, reducing computational load and data volume, while focusing on areas of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution image frames are captured at high frame rate, then image quality and decoding accuracy are improved, but computational resources and power consumption increase significantly
Solution Approach 1:
The patent divides the image capture process into two stages: first capturing low resolution frames to identify regions of interest, then capturing high resolution frames only for those identified regions. This segmentation reduces the overall computational load and power consumption while maintaining image quality for relevant areas.
Solution Approach 2:
The patent performs preliminary low resolution image capture and region of interest identification before capturing high resolution images. This preliminary action filters out non-interest areas, ensuring that high resolution capture resources are allocated only to meaningful regions, thereby reducing power consumption.
2Measurement precision
If high resolution image frames are captured continuously, then decoding accuracy is improved, but data streaming volume and processing complexity increase
Solution Approach 1:
The patent extracts and processes only the region of interest from the full image frame. By taking out the relevant portion identified from low resolution frames and capturing high resolution data only for that extracted region, the system reduces data volume while maintaining decoding accuracy for the barcode or target object.
Solution Approach 2:
The patent segments the full image frame into region of interest and non-interest areas. Only the segmented region of interest is captured at high resolution, while other areas are processed at low resolution or discarded, thereby reducing overall data volume while preserving decoding accuracy for relevant content.
3Speed
If high resolution image sensors operate at high frame rate, then object tracking capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements dynamic resolution adjustment where the sensor operates at low resolution for most frames and switches to high resolution only when a region of interest is identified. This dynamic operation maintains object tracking capability at high frame rates while reducing the effective complexity and cost requirements of the sensor system.
Solution Approach 2:
The patent uses periodic low resolution frame capture interspersed with high resolution frame capture based on detected regions of interest. This periodic switching between resolution modes enables high frame rate operation for tracking while reducing the continuous demand for high resolution sensing, thereby lowering device complexity.
4Measurement precision
If low resolution images are captured first to identify regions of interest, then high resolution images are captured for those regions, but the total capture time may increase
Solution Approach 1:
The patent performs preliminary low resolution image capture and region of interest identification in advance before high resolution capture. Although this adds a preliminary step, it prevents unnecessary high resolution capture of non-interest areas, and the low resolution processing is computationally lighter, potentially offsetting the additional capture time.
Solution Approach 2:
The patent segments the capture process into rapid low resolution scanning followed by targeted high resolution capture. The segmentation allows the system to quickly identify regions of interest using computationally efficient low resolution processing, then focus high resolution resources only where needed, balancing total capture time against identification accuracy.
Data Source
AI summary
Methods for optimal region of interest frame acquisition are disclosed herein. An example computing system includes: one or more memories including computer-executable instructions stored thereon that, when executed by one or more processors cause the computing system to: capture, by an image acquisition assembly, a first low resolution image dataset; determine a first region of interest from the first low resolution image dataset; capture, by the image acquisition assembly, a first high resolution image dataset based on the first region of interest; capture, by the image acquisition assembly, a second low resolution image dataset; determine a second region of interest from the second low resolution image dataset; capture, by the image acquisition assembly, a second high resolution image dataset based on the second region of interest; and identify, based on one or more of: the first high resolution image dataset or the second high resolution image dataset, an image feature.