Body Scanner Automated Target Recognition via Bilateral Symmetry
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Solution Overview
Problem
Current automated target recognition (ATR) systems in body scanners perform poorly in distinguishing anatomical and non-anatomical features, leading to low detection probability and high false alarm rates, which limits their effectiveness in security screening.
Innovation Solution
The system separates anatomical from non-anatomical features by leveraging the bilateral symmetry of the human body, using computer algorithms to identify and match features on one side of the body's vertical axis with corresponding features on the other side, and employs image warping techniques to create a symmetrical coordinate system, thereby eliminating anatomical features and highlighting non-anatomical objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated target recognition systems are used to eliminate human intervention in image analysis, then productivity increases and security officer workload decreases, but measurement precision deteriorates leading to high false alarm rates and low detection probability
Solution Approach 1:
The patent applies asymmetry by exploiting the bilateral symmetry of human anatomy as a reference framework. The system identifies that anatomical features exhibit symmetric patterns across the body's midline, while concealed objects create asymmetric disruptions. By analyzing symmetry deviations from the anatomical baseline, the system achieves both high automation and high precision in detecting concealed objects.
2Reliability
If automated target recognition systems analyze all features in body scanner images, then comprehensive detection is achieved, but false alarm rates increase due to inability to distinguish anatomical from non-anatomical features
Solution Approach 1:
The patent extracts and removes anatomical features from the analysis by using bilateral symmetry as a filtering mechanism. The system identifies symmetric anatomical structures and effectively subtracts them from the image data, leaving only the asymmetric concealed objects for further analysis. This extraction process eliminates false alarms caused by misidentifying anatomical features while maintaining reliable detection of actual threats.
3Measurement precision
If human image analysts manually evaluate body scanner images, then detection accuracy is maintained through trained recognition, but productivity decreases due to time-consuming manual analysis
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform the analytical function previously requiring human expertise. The bilateral symmetry analysis algorithm autonomously distinguishes anatomical from non-anatomical features and identifies concealed objects without human intervention, achieving both high accuracy and high throughput simultaneously.
4Speed
If automated systems use simple feature detection algorithms, then processing speed increases, but detection precision decreases due to inability to distinguish anatomical features from concealed objects
Solution Approach 1:
The patent changes the fundamental parameter of feature analysis from direct object detection to symmetry pattern recognition. By transforming the problem into analyzing bilateral symmetry deviations rather than directly detecting objects, the system achieves both rapid processing and high precision discrimination between anatomical and non-anatomical features.
Data Source
AI summary
This Invention is directed at the automated analysis of body scanner images. Body scanners are used in airports and other secured facilities to detect weapons, explosives, and other security threats hidden under persons' clothing. These devices use x-rays, millimeter waves and other radiant energy to produce an electronic image of the person's body and any concealed objects. Examination of these images by human analysts is slow, expensive, and subject to privacy concerns. The Invention provides automated analysis of body scanner images by recognizing that human anatomy is bilaterally symmetric to a high degree, while concealed objects are asymmetric. Digital techniques are used to separate the scanned image into its symmetric and asymmetric parts, thereby effectively separating anatomic from non-anatomic image features.