Imaging Reader Search Routine for Bar Code Detection
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Solution Overview
Problem
Imaging readers face inefficiencies in locating and decoding bar codes due to the time-consuming process of searching the entire image, especially with high-resolution megapixel sensors, where the target is more likely to be found near the center, and existing systems waste time scanning outer extremes.
Innovation Solution
A strategic search routine that initiates at the center of the image, analyzing data blocks along sequential scan paths progressing outward, with a step function that increases the search area based on distance or data block density, terminating when the target is detected or a limit is reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire image is searched sequentially from the beginning, then all areas are covered ensuring target detection, but the search time increases significantly
Solution Approach 1:
The image search space is segmented into concentric annular regions centered at the image middle point. The search algorithm divides the image into multiple search zones (first annular region, second annular region, etc.) and searches them in sequence from the center outward. This segmentation allows the system to prioritize high-probability areas while systematically covering the entire image, resolving the contradiction between complete detection and search time efficiency.
Solution Approach 2:
The search algorithm performs preliminary action by first examining the central region of the image where target objects are most likely to be located based on probability analysis. By starting the search at the middle point and expanding outward through annular regions, the system performs the most valuable search actions first, before committing resources to less probable areas, thus reducing overall search time while maintaining detection completeness.
2Loss of time
If the search focuses only on the center of the image, then search time is reduced, but the target object might be missed if located in outer regions
Solution Approach 1:
The image is segmented into multiple annular search regions expanding from the center. The algorithm systematically searches the first annular region, then the second annular region, and continues outward. This segmented approach ensures that while the center is prioritized, outer regions are still thoroughly searched, maintaining detection completeness while optimizing search time through hierarchical processing.
Solution Approach 2:
The search algorithm dynamically adjusts the search pattern based on probability distribution. It starts with a focused search at the center where targets are most likely, then dynamically expands to outer regions if needed. This dynamic search strategy adapts to the actual target location while maintaining efficiency, resolving the contradiction between focused search speed and comprehensive coverage.
3Measurement precision
If high-resolution megapixel sensors are used, then image quality and detail are improved, but the time required to process and decode bar codes increases
Solution Approach 1:
The algorithm extracts and focuses processing efforts on the most promising regions of the high-resolution image first. By searching annular regions from the center outward and terminating early when a target is detected, the system extracts only the necessary portion of the high-resolution image data needed for decoding, avoiding the need to process the entire large dataset, thus reducing processing time while maintaining the benefits of high resolution.
Solution Approach 2:
The search algorithm performs partial action by examining only a portion of the image data at each stage, starting with the central annular region and proceeding outward only as necessary. This partial processing approach allows the system to utilize high-resolution sensors for improved target identification while avoiding the excessive processing time that would result from analyzing the entire high-resolution image, resolving the contradiction between image quality and processing speed.
Data Source
AI summary
A method and apparatus for reading a target object with an image reader that includes an illumination source that illuminates an area corresponding to an indicia area when the imager is acting within an operating range of the indicia, a two-dimensional array sensor capable of collecting data from a captured image corresponding to an image reflected from the indicia area by the illumination source when the imager is acting within the operating range of the indicia, a starting coordinate located substantially about the center of the captured image, and a processing source including a search routine originating at the starting coordinate capable of analyzing the captured image by employing a plurality of scan paths associated to an algebraic equation.