Programmable Logic 2D Indicia Decoder Using Density Maps
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Solution Overview
Problem
Existing systems for decoding encoded data markings on objects are processing-intensive, often requiring complex processors or multiple processors, which increase costs and power consumption, and struggle with efficient identification of encoded data markings in high-throughput environments.
Innovation Solution
A decoding system utilizing a programmable logic device that convolves a 2D indicia stencil to generate per-pixel and per-tile density maps, employing noise filtering and subsampling to efficiently locate and decode 2D indicia within captured images without the need for complex or multiple processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex processors or multiple processors are used to identify encoded data markings, then identification accuracy and speed are improved, but device complexity and power consumption increase
Solution Approach 1:
The patent segments the image processing task into distinct stages: preprocessing (grayscale conversion, noise filtering), feature detection (edge detection, corner detection), and decoding. Each stage is handled by optimized algorithms rather than brute-force processing, reducing the computational burden on processors while maintaining identification speed and accuracy.
Solution Approach 2:
The patent transforms the image data through various parameter changes including grayscale conversion, adaptive thresholding, and coordinate transformations. These parameter transformations simplify the data structure and reduce processing complexity while enabling efficient identification of encoded markings.
2Productivity
If complex processors or multiple processors are used to identify encoded data markings, then identification accuracy and speed are improved, but power consumption increases
Solution Approach 1:
The patent applies partial action by selectively processing only regions of the image that contain encoded markings rather than analyzing the entire image. The system uses feature detection to identify relevant areas and focuses processing resources only on those regions, reducing overall power consumption while maintaining identification speed.
Solution Approach 2:
Through parameter transformations such as grayscale conversion and thresholding, the patent reduces the data complexity and processing requirements. These changes enable efficient processing with lower power consumption by simplifying the computational tasks needed for identification.
3Reliability
If traditional processing methods are used to search through captured images, then processing completeness is maintained, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the image data before full analysis. Steps such as grayscale conversion, noise filtering, and edge detection are executed beforehand to simplify the main decoding task. This preliminary processing reduces the time required for complete image analysis while maintaining processing thoroughness.
Solution Approach 2:
The patent replaces traditional mechanical search methods with optimized algorithms including adaptive thresholding, edge detection, and corner detection. These algorithmic substitutions enable faster processing while maintaining comprehensive analysis of the image data for encoded marking identification.
Data Source
AI summary
An apparatus includes a programmable logic configured to: convolve a stencil in a non-rotated orientation about a 2D array of pixels of a captured image received as image data from a camera to generate non-rotated stencil data; generate rotated stencil data based on a rotation of the stencil into a rotated orientation; generate, based on the non-rotated and rotated stencil data, a per-pixel density map indicative of a location of a corner of a 2D indicia within the captured image; employ noise filtering and subsampling to generate, based on the per-pixel density map, a per-tile density map indicative of the location of the corner of the 2D indicia within a 2D array of tiles that corresponds to the 2D array of pixels of the captured image; and generate, based on the per-tile density map, 2D indicia metadata indicative of the location of the 2D indicia within the captured image.