Interactive Image Sequences for Cross-Scanner TSO Training
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Solution Overview
Problem
Existing training systems for Transportation Security Officers (TSOs) face challenges due to varying image quality and characteristics across different scanner systems, making it impractical to provide consistent training images, and machine learning models require specific scanner data for effective training.
Innovation Solution
A system and method for generating a sequence definition of images using a user interface that allows customization and emulation of target scanner systems, enabling the creation, modification, and visualization of images with defined areas of interest and user-selectable parameters, ensuring compatibility and consistency across different scanner models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training images are generated using the same scanner system as the target deployment scanner, then training reliability and realism are improved, but device complexity and resource requirements increase due to needing multiple scanner systems
Solution Approach 1:
The patent creates virtual copies of scanner systems through software emulation. Instead of requiring multiple physical scanner systems to generate training images for different scanner models, the system uses a single scanner connected to software that emulates the imaging characteristics of multiple scanner types. This allows training images to be generated that match target deployment scanners without acquiring additional hardware.
Solution Approach 2:
The patent modifies image parameters through post-processing to match the characteristics of different scanner systems. By adjusting parameters such as resolution, noise patterns, contrast enhancement, and artifact characteristics, the system transforms images from one scanner model to resemble images from other scanner models, enabling versatile training image generation from a single hardware source.
2Adaptability or versatility
If training images are generated from multiple different scanner systems, then adaptability to various scanner models is improved, but manufacturing precision and image consistency deteriorate due to varying image quality across scanners
Solution Approach 1:
The software emulation system creates consistent virtual representations of different scanner models, ensuring that training images maintain uniform quality standards regardless of the target scanner model. The emulation process applies standardized processing pipelines that preserve image consistency while adapting to the specific characteristics of each scanner type.
Solution Approach 2:
The system applies controlled parameter transformations to adapt images for different scanner models while maintaining quality consistency. By systematically adjusting resolution, noise characteristics, and enhancement parameters according to predefined profiles for each scanner model, the system achieves both adaptability to various scanners and consistency in image quality.
3Reliability
If a large number of training images are presented to TSOs, then training completeness is improved, but loss of time and training duration increase
Solution Approach 1:
The training system dynamically adapts the sequence and selection of images based on the trainee's performance and progress. Rather than presenting a fixed large set of images, the system adjusts the training sequence in real-time, presenting additional images when needed and skipping or reviewing images based on demonstrated competence, thereby reducing overall training time while maintaining completeness.
Solution Approach 2:
The system incorporates immediate feedback mechanisms that allow trainees to receive instant evaluation of their identifications. This feedback loop enables more efficient learning by immediately correcting misconceptions and reinforcing correct identifications, reducing the number of images needed to achieve comprehensive training compared to traditional methods without feedback.
Data Source
AI summary
In an approach to generating playlists/sequences of images, a system includes: a memory configured to store at least a first image; a one or more computer processors; one or more non-transitory computer readable storage media; and program instructions. The program instructions include: receive the first image; determine at least one characteristic associated with an object represented within the first image and an expected user input associated with the at least one characteristic; and generate a sequence definition, the sequence definition including an identifier of the first image and an identifier of the at least one characteristic, where the sequence definition is configured to cause the first image to be interactively visualized to a user via a user interface to determine if a user input matches an expected user input based on the identifier of the at least one characteristic.