Pattern Recognition via Area-Specific Reference Values
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Solution Overview
Problem
Existing pattern recognition in video images requires significant computing power, making it inefficient and slow on equipment with lower processing capabilities.
Innovation Solution
Divide the image and pattern into areas, calculate area-specific reference values, and compare these values to recognize patterns, reducing the need for high computing capacity by simplifying and speeding up the recognition process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pattern recognition methods are used, then recognition accuracy is maintained, but computing power requirements increase significantly
Solution Approach 1:
The patent divides the image into multiple areas and further segments pattern parts into smaller sub-areas. This segmentation allows the system to process smaller, manageable units rather than analyzing the entire image at once, significantly reducing computing power requirements while maintaining recognition accuracy through systematic comparison of segmented regions.
Solution Approach 2:
The patent pre-calculates reference values for pattern parts and stores them in a database before actual pattern recognition occurs. This preliminary action includes dividing patterns into areas, calculating their reference values, and organizing them for quick retrieval. During runtime, the system only needs to compare incoming image areas against these pre-computed references, dramatically reducing real-time computing requirements.
2Measurement precision
If traditional pattern recognition methods are used, then comprehensive pattern analysis is achieved, but processing speed decreases
Solution Approach 1:
By segmenting both the image and pattern parts into smaller areas, the system can process multiple regions in parallel and quickly compare them against pre-calculated references. This segmentation enables faster processing while maintaining comprehensive analysis through systematic coverage of all image areas.
Solution Approach 2:
The pre-calculation and storage of reference values for pattern parts before runtime operations allows the system to perform rapid comparisons during actual pattern recognition. This preliminary preparation of reference data significantly accelerates processing speed while ensuring complete pattern analysis through thorough comparison against all relevant references.
3Measurement precision
If detailed image analysis is performed, then recognition accuracy improves, but computing capacity requirements increase
Solution Approach 1:
The patent divides the image into multiple areas and each area into sub-areas, allowing detailed analysis to be performed on small, manageable segments rather than the entire image. This segmentation enables comprehensive local analysis with reduced computing capacity requirements, as each segment is processed independently and efficiently.
Solution Approach 2:
The system pre-calculates reference values for pattern parts in advance, storing them for quick comparison. This preliminary action eliminates the need for complex real-time calculations during pattern recognition, allowing detailed image analysis to be performed with simpler, lower-capacity computing equipment while maintaining high recognition accuracy.
Data Source
AI summary
The invention relates to a pattern recognizer, which, in order to recognize the pattern fast and with lowest possible computing power, comprises a memory (12) for storing area-specific reference values (REF) calculated on the basis of image information of image areas containing parts of the pattern to be recognized, and a processor (14) that is configured to divide (15) a received image into areas, to calculate (16) reference values (REF) area-specifically on the basis of the image information of said areas, to compare (17) the calculated reference values (REF) with the reference values (REF) stored in the memory (12) and to indicate (18) that the pattern is recognized, in case in the received image there is found a part consisting of adjacent areas, where the reference values (REF) of the areas correspond, with sufficient accuracy, to the reference values (REF) stored in the memory (12).