Neuro-physiological Visual Detection for Threat Identification
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Solution Overview
Problem
Conventional visual aid devices, such as binoculars and night vision viewing devices, do not effectively detect regions of interest within an image using neuro-physiological characteristics of the user, limiting their ability to provide early warnings or reduce user fatigue in threat detection.
Innovation Solution
A visual detection system incorporating an image processing unit coupled with neuro-physiological sensors, which generates neuro-physiological signals to identify regions of interest by combining EEG, eye tracker, and other sensor data to provide real-time alerts and enhance situational awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional visual aid devices (binoculars, night vision devices) are used, then users can view images of objects, but the devices cannot automatically detect or identify regions of interest, requiring manual scanning and increasing user fatigue
Solution Approach 1:
The patent combines multiple independent subsystems (visual capture device, neuro-physiological sensors, image processing unit, and alert generation unit) into an integrated automated detection system. The image processing unit merges computer vision algorithms with neuro-physiological signal processing to automatically identify regions of interest, eliminating the need for manual scanning while managing system complexity through modular integration.
Solution Approach 2:
The patent introduces neuro-physiological sensors as intermediary components that bridge the gap between visual input and automated detection. These sensors capture neural responses and eye movement data, serving as mediators that enable the system to infer user attention and automatically identify regions of interest without requiring complex manual intervention or overly complicated processing algorithms.
2Productivity
If manual scanning is used to detect threats, then users can identify objects of interest, but the process is time-consuming and increases user fatigue
Solution Approach 1:
The patent implements a feedback mechanism where neuro-physiological sensors continuously monitor user visual attention and neural responses. The image processing unit uses this real-time feedback to dynamically identify and alert users to regions of interest, significantly accelerating threat detection speed compared to manual scanning while reducing the time users must maintain active surveillance.
Solution Approach 2:
The system enables self-service detection by automatically processing visual information and neuro-physiological signals to identify threats without requiring continuous active user engagement. The automated alert generation unit notifies users of regions of interest, allowing the system to perform detection functions independently while minimizing user cognitive load and time investment.
3Measurement precision
If neuro-physiological sensors and image processing units are added, then automatic region of interest detection is enabled, but the device complexity and cost increase
Solution Approach 1:
The patent segments the detection system into distinct functional modules: visual capture device, neuro-physiological sensors, image processing unit, and alert generation unit. This segmentation allows each component to be optimized independently for its specific function while maintaining overall system manageability. The image processing unit is further divided into separate processing streams for visual data and neuro-physiological signals, enabling precise region of interest identification through coordinated analysis of multiple data sources.
Data Source
AI summary
According to one embodiment, a visual detection system includes an image processing unit coupled to a display and one or more neuro-physiological sensors. The display generates an image that may be viewed by a user. The neuro-physiological sensors generate neuro-physiological signals representing neuro-physiological characteristics of the user while viewing the display. The image processing unit receives the neuro-physiological signals from the neuro-physiological sensors, determines a region of interest in the image according to the neuro-physiological signals, and provides an indication of the presence of the region of interest in the image.