Image Verification Engine for Recognition Reliability
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Solution Overview
Problem
Current image recognition technologies face challenges in reliability due to poor image capture conditions and lack of image references, leading to failures in recognizing subjects or false identifications.
Innovation Solution
A verification engine is introduced that selects appropriate image processing and matching techniques based on various factors to verify the results of image recognition, using down-sampled signatures, regions of interest, and rectification to improve matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image recognition is performed under poor image capture conditions, then the system can still attempt recognition, but the reliability of recognition results deteriorates due to poor image quality and lack of references
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate recognition results before final verification. The verification engine receives a set of candidate images from the image recognition process and selects appropriate verification techniques in advance based on image characteristics, allowing the system to prepare multiple potential matches before committing to a final result.
Solution Approach 2:
The verification engine implements feedback by comparing candidate images against multiple verification criteria and using the results to confirm or reject initial recognition outcomes. The system uses match scores from multiple verification techniques to provide feedback on the reliability of each candidate, allowing iterative refinement of recognition results.
2Reliability
If multiple verification techniques are applied to improve matching accuracy, then false positives are reduced, but the processing time and computational complexity increase
Solution Approach 1:
The verification engine applies partial verification by selecting and applying only the most appropriate verification techniques for each specific candidate image based on its characteristics. Rather than applying all possible verification methods to every candidate, the system performs selective verification using techniques such as down-sampled signature comparison, region of interest analysis, and rectification only when needed.
Solution Approach 2:
The system changes verification parameters dynamically by selecting different verification techniques based on image characteristics. The verification engine adjusts the level and type of verification applied to each candidate image, using parameters such as image quality metrics, candidate ranking positions, and image feature analysis to determine the appropriate verification depth.
3Measurement precision
If the verification engine selects and applies multiple image processing techniques, then verification accuracy is improved, but the device complexity increases
Solution Approach 1:
The verification engine implements multi-functionality by consolidating multiple verification techniques (down-sampled signature matching, region of interest analysis, rectification, feature extraction) into a single unified system. This universal verification engine can adaptively select and apply different verification methods based on the specific characteristics of each candidate image, reducing the need for separate specialized systems for each verification technique.
Data Source
AI summary
Systems and methods of verifying the results of an initial image recognition process are presented. A verification engine can receive a set of candidate images corresponding to the results of an image recognition process performed on a captured query image. The verification engine can determine an appropriate verification technique to apply to the images of the candidate set, and classify, re-rank or otherwise re-organize the candidate set such that the best match from the candidate set is confirmed as a proper match.