Dot Code Image Processing on Glass Containers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to reliably read dot codes on glass containers, especially in consumer settings where specialized optical hardware is not available, due to the challenges posed by the glass substrate and ambient light, which affect image quality and detection accuracy.
Innovation Solution
A method and system that convert images of dot codes on glass substrates to grayscale, detect edges, filter images, apply a Close transform to convert open instances to closed instances, and identify the quantity and position of circular dots, using consumer electronic devices and a central server for effective decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard imaging equipment is used to capture dot code images on glass containers, then the system complexity is reduced and consumer devices can be utilized, but image quality deteriorates due to glass substrate interference and ambient light effects
Solution Approach 1:
The patent introduces an image processing system that acts as an intermediary between the consumer device camera and the dot code decoding process. This intermediary system applies specialized algorithms including grayscale conversion with adaptive thresholding, morphological operations (closing, opening), and dot pattern recognition specifically designed to compensate for glass substrate interference and ambient light variations, thereby maintaining measurement precision without increasing device complexity
Solution Approach 2:
The patent transforms the image data from color RGB space to grayscale intensity space, applying adaptive thresholding parameters that dynamically adjust to ambient light conditions. The image processing system modifies key parameters including brightness thresholds, contrast enhancement levels, and morphological operation parameters to optimize dot code detection under varying glass substrate and lighting conditions
2Measurement precision
If image processing is performed to remove glass color and ambient light effects, then detection accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent divides the image processing task into distinct sequential stages: initial grayscale conversion, adaptive thresholding to separate dots from background, morphological closing operations to fill dot interiors, morphological opening operations to remove noise, and final dot pattern recognition. This segmentation allows each processing stage to be optimized independently and enables early termination if detection confidence is sufficient
Solution Approach 2:
The patent applies preliminary grayscale conversion and adaptive thresholding operations that quickly establish a binary representation of the dot code pattern before more computationally intensive morphological operations are applied. This preliminary action creates a simplified intermediate representation that reduces the computational burden of subsequent processing steps while maintaining detection accuracy
3Reliability
If specialized optical hardware is used to improve image quality for dot code reading, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces specialized optical hardware (such as polarizing filters, specialized lighting systems, or optical compensators) with computational image processing methods. The image processing system uses software-based techniques including adaptive thresholding, morphological operations, and pattern recognition algorithms to achieve the same reliability benefits that would otherwise require complex optical hardware, thereby maintaining detection reliability while eliminating hardware complexity
Data Source
AI summary
A system and method for use in decoding a dot code carried by a glass substrate or container. The method comprises converting an image of a dot code to a grayscale image to remove effects of at least one of color of the glass substrate or color of ambient light, where the image includes a plurality of circular dots; detecting edges of the circular dots in the grayscale image to produce an edge-detected image; filtering the edge-detected image to produce a filtered image; applying a Close transform to convert open instances of the circular dots to closed instances of the circular dots; and identifying quantity and position of all of the circular dots. The method may further include applying a transform to remove background noise from the filtered image and/or filling all the closed instances of the circular dots.