Image Matching Apparatus Using Feature-Based Parameter Comparison
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Solution Overview
Problem
Conventional image recognition techniques, such as OCR, face challenges with variations in number plate painting styles, lack of standard fonts and sizes, and require continuous internet connectivity, making them inefficient for recognizing images with specular reflections and low contrast, especially in nighttime conditions.
Innovation Solution
An image matching apparatus and method that uses a processor and memory with modules for receiving, pre-processing, and creating image templates, computing position-based and feature-based matching scores to determine the similarity between test images and templates, allowing for efficient recognition regardless of painting styles and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OCR technique is used for recognizing texts in images, then text recognition capability is provided, but recognition accuracy deteriorates due to variations in painting styles and fonts
Solution Approach 1:
The patent transforms the recognition approach by changing from exact font matching to feature-based parameter comparison. Instead of requiring exact font matches, the system extracts geometric parameters (aspect ratio, stroke width, curvature) and compares them within tolerance ranges, enabling recognition across varied painting styles while maintaining accuracy
Solution Approach 2:
The patent applies local quality analysis by examining specific local features of characters (stroke patterns, geometric proportions, relative positions) rather than requiring global font matching. This allows the system to recognize characters even when overall painting styles differ, as long as local structural features match within defined tolerances
2Measurement precision
If standard fonts and sizes are enforced for number plates, then OCR recognition accuracy improves, but adaptability to real-world variations deteriorates
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the recognition system to adjust to different painting styles and font variations in real-time. Instead of fixed font templates, the system dynamically compares geometric parameters within tolerance ranges, enabling it to adapt to various painting styles while maintaining recognition accuracy
Solution Approach 2:
The patent creates a universal recognition framework that can handle multiple painting styles, fonts, and sizes through a single feature-based comparison mechanism. The system extracts universal geometric parameters that remain consistent across different styles, making the recognition system universally applicable to various number plate formats
3Ease of operation
If image recognition is performed on embedded mobile platforms, then portability and accessibility improve, but processing capability and memory availability deteriorate
Solution Approach 1:
The patent extracts only the essential geometric parameters needed for recognition (aspect ratio, stroke width, curvature, relative positions) from the full images, discarding redundant pixel data. This parameter extraction approach dramatically reduces memory requirements and processing complexity, enabling accurate recognition on resource-constrained mobile platforms
Solution Approach 2:
The patent replaces traditional heavy OCR mechanical processing with a lighter feature-based parameter comparison system. Instead of using complex OCR engines that require significant computational resources, the system uses simple geometric parameter extraction and comparison, reducing the computational burden on embedded mobile platforms
4Measurement precision
If continuous internet connection is required for image recognition, then recognition accuracy improves through cloud processing, but system reliability and offline usability deteriorate
Solution Approach 1:
The patent enables the mobile device to perform recognition independently through self-service parameter extraction and comparison algorithms. The system contains all necessary recognition logic locally, allowing it to function autonomously without external cloud assistance, thereby ensuring reliability and offline usability while maintaining accuracy
Data Source
AI summary
Disclosed is a method and apparatus for computation and processing of an image for image matching. The apparatus here is configured to pre-process plurality of images for creating an image template. Next, the test image is extracted and pre-processed for assessing the degree of match between the test image components and the image components of the images in the image template, based a position based matching score, a feature based matching score or both.