Label Image Subregion Transformation for Distortion-Robust Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine-learning models for automated checkout systems struggle to accurately identify item identifiers from machine-readable labels in real-world images due to the need for extensive and costly training data to handle distortions and imperfections, as training data is typically cleaner than real-world conditions.
Innovation Solution
The automated checkout system transforms subregions of images of machine-readable labels using boundary points to create standardized subregions and applies a transformation matrix, and identifies readable regions within the images to improve the performance of label detection models by modifying the images to resemble clean training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning models are trained with extensive real-world distorted images, then identification accuracy improves, but training cost and data acquisition difficulty increase prohibitively
Solution Approach 1:
The system performs preliminary image transformation before label decoding by detecting boundary points, generating subregions, and applying transformation matrices to correct distortions. This preliminary processing prepares the image in advance, allowing the label decoding model to work with corrected images rather than requiring extensive training on distorted images.
Solution Approach 2:
The system introduces an intermediary image transformation process between image capture and label decoding. The transformation module acts as a mediator that converts distorted real-world images into corrected images with proper geometry, enabling the label decoding model to achieve high accuracy without requiring extensive training on distorted images.
2Ease of manufacture
If training data is made cleaner and easier to obtain, then training cost decreases, but the model's ability to handle real-world distorted images worsens
Solution Approach 1:
Instead of training the model on distorted images, the system performs preliminary distortion correction on input images before they reach the label decoding model. This allows the model to be trained on clean, undistorted images while still handling real-world distorted images through the preliminary correction step.
Solution Approach 2:
The image transformation module serves as an intermediary that bridges clean training data and distorted real-world images. It transforms distorted input images into a format suitable for the model trained on clean data, allowing the model to maintain high adaptability to real-world conditions while being trained on easier-to-obtain clean data.
3Measurement precision
If the system processes entire images through transformation matrices, then all regions are corrected, but processing time and computational complexity increase
Solution Approach 1:
The system segments the image into multiple subregions based on detected boundary points before applying transformation matrices. By processing only relevant subregions rather than the entire image, the system maintains correction accuracy while significantly reducing computational complexity and processing time.
Solution Approach 2:
The system applies different processing quality to different parts of the image by focusing transformation operations on specific subregions containing the label. This local quality approach ensures high correction accuracy where needed while avoiding unnecessary processing in other areas, reducing overall processing time.
Data Source
AI summary
An automated checkout system modifies received images of machine-readable labels to improve the performance of a label detection model that the system uses to decode item identifiers encoded in the machine-readable labels. For example, the automated checkout system may transform subregions of an image of a machine-readable label to adjust for distortions in the image's depiction of the machine-readable label. Similarly, the automated checkout system may identify readable regions within received images of machine-readable labels and apply a label detection model to those readable regions. By modifying received images of machine-readable labels, these techniques improve on existing computer-vision technologies by allowing for the effective decoding of machine-readable labels based on real-world images using relatively clean training data.