Class Reference Image Generation for Bank Note Classification
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Solution Overview
Problem
Existing bank note classification methods require significant specialist knowledge and experience to set reliable class reference parameters, especially when dealing with numerous security features, and often rely on manual specification which can be error-prone and time-consuming.
Innovation Solution
A method and apparatus for creating a class reference data record by generating a class reference image from multiple reference images of classified value documents, where pixel values are derived from corresponding pixels of the reference images, allowing for automated or user-assisted creation of class reference parameters with tolerance ranges, enabling accurate classification of value documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual specification of class reference parameters is used, then reliability of classification can be achieved with sufficient expertise, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by automatically generating class reference parameters from reference value documents before the actual classification process. The control and evaluation device extracts features from multiple reference documents of the same class, computes reference parameters and tolerance ranges, and stores them in a database. This preliminary automated parameter setup eliminates the need for time-consuming manual specification while ensuring reliability through systematic processing of multiple reference samples.
Solution Approach 2:
The system enables self-service by allowing it to automatically create and update its own classification reference data without external human intervention. The control and evaluation device autonomously processes reference value documents, computes statistical parameters including mean values and tolerance ranges, and maintains the reference database. This self-service capability reduces dependency on manual expertise while maintaining classification reliability through consistent automated processing.
2Measurement precision
If manual specification of class reference parameters is used, then expert knowledge can ensure accuracy, but the process becomes complex and requires significant expertise
Solution Approach 1:
The system replaces the mechanical process of manual expert analysis with an automated computational system. The control and evaluation device uses image processing algorithms to extract features from reference value documents, applies statistical computations to determine reference parameters and tolerance ranges, and automatically updates the database. This substitution of manual expert operations with automated computational processes maintains measurement precision while significantly reducing system complexity and the need for specialized human expertise.
Solution Approach 2:
The system creates accurate copies of the classification criteria by extracting features from multiple reference value documents and computing statistical representations. Instead of relying on individual expert judgment, the system copies and synthesizes information from multiple standardized reference samples, computing mean values and tolerance ranges that represent the class characteristics. This copying approach from standardized references ensures precision while simplifying the system by eliminating the need for complex expert knowledge structures.
3Reliability
If multiple reference images are processed to create class reference data, then accuracy and reliability are enhanced, but the data processing complexity increases
Solution Approach 1:
The system merges information from multiple reference value documents by processing several images of the same class together. The control and evaluation device extracts features from each reference document, combines the extracted data, and computes statistical parameters including mean values and tolerance ranges that represent the consolidated class characteristics. This merging approach enhances reliability by incorporating multiple samples while managing processing complexity through systematic integration of the reference data.
Solution Approach 2:
The system transforms the raw image data from multiple reference documents into standardized statistical parameters. Instead of processing individual image features separately, the system changes the parameter representation by computing aggregated statistics (mean values, tolerance ranges) that capture the essential class characteristics. This parameter transformation reduces processing complexity by converting complex image data into manageable statistical representations while maintaining or enhancing classification reliability.
Data Source
AI summary
A method and apparatus for determining a class reference data record for classifying documents of value includes creating a class reference image using a multiplicity of reference images of already classified documents of value in the same class; and creating a class reference data record having at least one class reference parameter using the class reference image. The pixel and intensity values of the respective pixel in the class reference image are a function of the pixel values of the relevant pixels in the multiplicity of reference images of already classified documents of value in the same class. The method includes determining at least one quantitative property of a document of value to be classified; and classifying the document of value to be classified on the basis of a comparison between the quantitative property of the document of value to be classified and the class reference data record.