Friction Ridge Image Region Processing for Distance Compensation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing technologies for friction ridge data, such as fingerprints, face challenges in accurately comparing non-contact images to legacy contact-based images due to variations in object distance and orientation, leading to degraded image quality and scaling issues.
Innovation Solution
A computing device determines and independently processes regions of interest in a friction ridge image by calculating the distance of objects within each region to a plane of interest, allowing for rescaling and perspective correction to ensure accurate comparison with legacy images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-contact imaging is used to capture friction ridge data, then the ease of operation is improved, but the manufacturing precision deteriorates due to variations in object distance and orientation
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs), each corresponding to a specific friction ridge area. Each ROI is processed independently with its own distance and orientation parameters, allowing precise correction of local variations while maintaining the benefits of non-contact imaging.
Solution Approach 2:
Different processing parameters (distance, orientation, scaling) are applied to different regions of the image based on their specific characteristics. This localised approach ensures that each ROI is corrected according to its actual position and orientation relative to the imaging plane, thereby improving manufacturing precision without compromising ease of operation.
2Device complexity
If uniform processing is applied to the entire image, then the device complexity is reduced, but the measurement precision deteriorates due to inability to account for distance variations
Solution Approach 1:
The image processing is segmented into multiple independent ROI processing tasks. Each ROI is identified and processed separately with region-specific parameters, which improves measurement precision while keeping the overall device complexity manageable through modular processing architecture.
Solution Approach 2:
The processing parameters (distance, orientation, scaling factors) are dynamically determined for each ROI based on its position and characteristics. This dynamic adaptation allows the system to achieve high measurement precision without requiring overly complex fixed processing logic.
3Measurement precision
If independent processing of multiple regions is implemented, then the measurement precision is improved, but the productivity decreases due to increased processing time
Solution Approach 1:
By segmenting the image into distinct ROIs, the system can process each region independently and efficiently. This segmentation allows for parallel processing of multiple regions, which maintains measurement precision while reducing overall processing time compared to sequential processing of the entire image.
Solution Approach 2:
The system processes only the necessary regions of interest rather than the entire image. By identifying and processing only the relevant friction ridge areas, the system achieves high measurement precision for the critical regions while minimizing the total processing workload, thereby maintaining productivity.
Data Source
AI summary
In some examples, a method includes determining, by a computing device, a plurality of regions of interest of an image for independently processing relative to a plane of interest; determining, by the computing device and for respective regions of interest of the plurality of regions of interest, a distance from objects within the respective regions of interest to the plane of interest; and processing, by the computing device and independently for the respective regions of interest, the respective regions of interest of the image based on the determined distance from the objects to the plane of interest.