Object Recognition Using Edge Point Extraction and Weighted Gradient Analysis
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Solution Overview
Problem
Existing image processing systems face challenges in accurately recognizing objects in images due to noise, geometric transformations, and varying brightness conditions, which affect the reliability of object recognition results.
Innovation Solution
A system and method for object recognition that involves obtaining an image with a search region and a model featuring multiple points, determining a match metric for similarity between the model and sub-regions, and identifying the instance of the model based on these metrics, using techniques such as edge point extraction and weighted gradient analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object recognition techniques are used, then the system can process images, but the recognition accuracy deteriorates due to noise, geometric transformations, and brightness variations
Solution Approach 1:
The patent segments the object recognition process into multiple stages: template generation from reference images, feature point extraction and matching, geometric transformation calculation, and final recognition decision. This segmentation allows each stage to be optimized independently, improving overall accuracy while maintaining reliability under varying conditions.
Solution Approach 2:
The system dynamically adjusts recognition parameters including similarity thresholds, matching criteria, and geometric transformation tolerances based on image quality assessment. When noise or brightness variations are detected, the system modifies these parameters to maintain reliable recognition decisions across diverse imaging conditions.
2Measurement precision
If the system processes all image regions uniformly, then processing is simple, but recognition accuracy deteriorates in regions with noise or geometric variations
Solution Approach 1:
The patent implements local quality assessment by evaluating image characteristics (noise level, brightness, geometric stability) in different regions and applying region-specific recognition strategies. High-quality regions use standard matching, while low-quality regions trigger alternative processing paths, improving local accuracy without uniformly increasing complexity across the entire system.
3Measurement precision
If multiple feature points are used for matching, then recognition accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing reference images to generate templates with pre-identified feature points and characteristic patterns before actual recognition occurs. This preliminary preparation enables faster real-time matching while maintaining high precision, as the computationally intensive template generation is done in advance rather than during processing.
Data Source
AI summary
The present disclosure relates to systems and methods for object recognition. The system may obtain an image and a model. The image may include a search region in which the object recognition process is performed. In the objection recognition process, for each of one or more sub-regions of the search region, the system may determine a match metric indicating a similarity between the model and the sub-region of the search region. Further, the system may determine an instance of the model among the one or more sub-regions of the search region based on the match metrics.