Eye Location Identification Using Fourier Phase Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle systems face challenges in accurately locating a driver's eye for effective head-up display information presentation and vehicle control, as existing methods lack precision and efficiency in eye detection.
Innovation Solution
The method involves obtaining a camera image of the vehicle occupant, performing Fourier transforms to generate a phase model, and identifying the eye location using this model, with additional steps including inverse Fourier transforms and enhanced image generation with highlighted edges to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional eye detection methods are used, then the system is simpler to implement, but the eye location detection precision is insufficient
Solution Approach 1:
The patent replaces traditional mechanical or simple algorithmic eye detection methods with Fourier transform-based image processing. By transforming the image to frequency domain and analyzing phase information, the system achieves superior eye location precision without requiring complex hardware modifications, effectively substituting advanced signal processing for simpler detection mechanisms.
Solution Approach 2:
The patent changes the parameter space by performing Fourier transforms on the captured image, converting spatial domain information to frequency domain representation. This parameter transformation enables extraction of phase information that is not readily visible in the original image, thereby improving eye location detection precision through mathematical transformation rather than hardware complexity.
2Measurement precision
If Fourier transforms and phase modeling are applied, then eye detection accuracy improves, but processing time increases
Solution Approach 1:
The patent applies Fourier transforms and generates phase models in advance during the image processing pipeline, before final eye location identification is required. By pre-computing the frequency domain representation and phase information, the system reduces real-time processing requirements, allowing accurate eye detection without excessive delay during critical driving moments.
3Reliability
If multiple Fourier transforms and enhanced image generation are performed, then detection reliability improves, but device complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct stages: initial Fourier transform, phase model generation, enhanced image creation with edge highlighting, and final eye location identification. By dividing the complex processing into modular segments, the system achieves high detection reliability through multiple processing passes while organizing processor complexity into manageable, sequential operations rather than monolithic complexity.
Data Source
AI summary
In various embodiments, methods, systems, and vehicles are provided for locating an eye of an occupant of a vehicle. In an exemplary embodiment, a system is provided that includes: (i) a camera configured to generate a camera image of an occupant for a vehicle; and (ii) a processor coupled to the camera and configured to at least facilitate: (a) performing one or more Fourier transforms of the camera image; and (b) identifying a location for an eye of the occupant of the vehicle, using a phase model generated via the one or more Fourier transforms.