Adaptive ROI Barcode Detection Reduces Processing Time
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Solution Overview
Problem
Current machine vision systems are inefficient in locating barcodes within an image, as they require searching the entire field of view, which is unnecessary and time-consuming.
Innovation Solution
The method involves capturing an initial image, analyzing it to detect and locate a barcode, and then capturing subsequent images with a region of interest (ROI) defined based on the location of the first barcode, thereby focusing the search and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire field of view is searched for barcodes, then comprehensive detection is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent divides the field of view into multiple regions and processes them in a hierarchical manner. First, a coarse search identifies potential barcode locations, then a refined search focuses only on those specific regions. This segmentation allows the system to maintain comprehensive detection while reducing the overall processing time by avoiding exhaustive search of the entire field of view.
Solution Approach 2:
The patent performs preliminary processing by capturing a first image to identify potential barcode regions before conducting the main barcode detection. This preliminary action creates a region of interest that guides the subsequent detailed search, eliminating the need to process the entire field of view and thereby reducing processing time while maintaining detection effectiveness.
2Measurement precision
If the entire field of view is analyzed, then all barcodes are detected, but computational resources and processing power increase
Solution Approach 1:
The patent extracts and isolates the region of interest containing the barcode from the rest of the field of view. By taking out only the relevant portion of the image for detailed analysis, the system maintains high detection accuracy while significantly reducing the computational resources required, as the expensive barcode recognition algorithms are applied only to the extracted ROI rather than the entire image.
Solution Approach 2:
The patent performs preliminary image capture and region identification before executing the computationally intensive barcode detection algorithms. This preliminary action prepares the data in advance and defines the search space, allowing the main processing to focus only on relevant regions and thereby reducing overall computational resource consumption.
3Productivity
If a fixed region of interest is used for all images, then processing efficiency improves, but adaptability to different barcode locations decreases
Solution Approach 1:
The patent implements a dynamic region of interest that adapts to the content of each image. Rather than using a fixed ROI, the system determines the ROI based on the actual barcode location detected in each image, allowing the processing region to move and resize as needed. This dynamic approach maintains high processing efficiency by avoiding unnecessary analysis of irrelevant areas while preserving adaptability to different barcode positions.
Solution Approach 2:
The patent uses feedback from the barcode detection process to adjust the region of interest for subsequent processing. The detected barcode location informs the definition of the ROI, creating a feedback loop that optimizes the search region based on actual findings. This ensures both efficiency (by focusing on relevant areas) and adaptability (by adjusting to different barcode positions).
Data Source
AI summary
Machine vision techniques for determining a region of interest (ROI) are disclosed herein. An example implementation includes a computing device for executing an application, the application operable to: (1) receive a first image; (2) set an first ROI of the first image to a field of view (FOV) of the first image; (3) determine a barcode within the first ROI; (4) determine a bounding box of the barcode; (5) form a second ROI based on the bounding box; (6) receive a second image; and (7) set an ROI of the second image to be the second ROI of the first image.