Illumination Invariant Road Marking Classification
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Solution Overview
Problem
Current computer vision systems for automotive driver-assistance devices face challenges in accurately identifying painted road markings due to variations in illumination, which affect the accuracy of image processing and analysis.
Innovation Solution
A method and system that transform image pixels from N band color values to log color space, generate a log chromaticity plane, and orient it based on a bi-illuminant dichromatic reflection model to create an illumination-invariant representation of road images, allowing for accurate identification of road markings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard image processing is used to identify road markings, then the system can operate with simple processing algorithms, but the accuracy of road marking identification deteriorates under varying illumination conditions
Solution Approach 1:
The patent transforms image data from standard color space to log chromaticity space, changing the parameter representation to make it illumination-invariant. This allows the system to maintain high identification accuracy under varying lighting conditions while using a well-defined mathematical transformation rather than complex adaptive algorithms
Solution Approach 2:
The patent performs preliminary orientation of the log chromaticity plane based on the BIDR model before actual road marking identification. By pre-aligning the color space orientation according to illumination geometry, the system simplifies subsequent identification tasks and improves accuracy without requiring complex real-time adaptation
2Reliability
If illumination-invariant processing is implemented to eliminate lighting effects, then road marking identification accuracy improves, but the computational complexity of image processing increases
Solution Approach 1:
The patent applies a mathematical transformation to convert standard color values to log chromaticity coordinates, creating illumination-invariant parameters. This transformation provides consistent identification results under varying lighting while maintaining a well-defined computational process rather than requiring complex adaptive algorithms
Solution Approach 2:
The system performs preliminary orientation of the log chromaticity plane using the BIDR model before road marking identification. This pre-processing step establishes the correct color space orientation based on illumination geometry, ensuring reliable identification results while simplifying the main identification algorithm
Data Source
AI summary
An automated, computerized method is provided for processing an image. The method includes the steps of arranging a digital camera on a vehicle body, operating the digital camera to provide an image file depicting an image of a scene related to vehicle operation on a road, in a computer memory, receiving from the memory the image file depicting pixels of an image of the scene related to vehicle operation on a road, and using an analysis of the pixels to generate an illumination invariant image of the scene. A further process step includes using the illumination invariant image to analyze the road scene for painted road markings.