Fractal Analysis for Global Pattern Characterization
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Solution Overview
Problem
The identification and matching of patterns, such as fingerprints, are time-intensive and cumbersome due to the sensitivity of direct comparison algorithms to position and variability in resolution, requiring extensive data-processing infrastructure.
Innovation Solution
A method involving the generation of quantitative characterizations of patterns through fractal analysis, where a random walk is performed on the pattern to create fractals, allowing for the extraction of scaling parameters that provide a global characterization, enabling efficient pattern matching regardless of orientation or position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct comparison algorithms are used for pattern identification, then matching accuracy can be maintained, but the process becomes time-intensive and requires extensive data-processing infrastructure
Solution Approach 1:
The patent extracts the essential matching information from complex patterns by generating quantitative characterizations that capture only the necessary features for identification. This extraction process removes redundant data while preserving matching accuracy, enabling faster comparison without requiring extensive infrastructure.
Solution Approach 2:
The invention transforms pattern data from raw image or signal form into quantitative characterization parameters through fractal analysis. This parameter transformation converts complex spatial patterns into simplified numerical descriptors that maintain discriminative power while reducing processing complexity and time requirements.
2Measurement precision
If direct comparison algorithms are used for pattern matching, then detailed feature analysis can be performed, but the device complexity and data-processing infrastructure requirements increase
Solution Approach 1:
The patent extracts only the essential matching features from complete pattern data by using quantitative characterizations. This selective extraction removes unnecessary computational complexity while retaining the critical information needed for accurate pattern identification, thereby simplifying the overall system infrastructure.
Solution Approach 2:
The invention creates simplified quantitative representations (copies) of complex patterns that preserve matching information. These quantitative characterizations serve as efficient substitutes for original pattern data, enabling accurate comparison with reduced computational requirements and simpler processing infrastructure.
3Measurement precision
If traditional pattern matching methods are used, then position-specific details can be captured, but the ease of operation decreases due to sensitivity to position and resolution variability
Solution Approach 1:
The patent creates quantitative characterizations that are universally applicable across different pattern instances regardless of position or resolution variations. These characterizations serve multiple functions including matching, identification, and verification in a unified framework that eliminates the need for separate position-specific processing, thereby improving ease of operation.
Solution Approach 2:
The invention transforms position-dependent pattern data into position-independent quantitative parameters through fractal analysis. This parameter transformation removes sensitivity to translation, rotation, and resolution changes, making the matching process more robust and easier to operate without requiring precise position alignment.
Data Source
AI summary
Various systems, methods, and programs embodied in computer-readable mediums are provided for the global quantitative characterization of patterns. In one representative embodiment, a method is provided in which fractal analysis is performed on a pattern to generate a global quantitative characterization of the pattern in a computer system.