Unoccupied Road Area Detection Using Pascal Triangle Wavelet Filters
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Solution Overview
Problem
Current image processing methods for vehicle-mounted cameras struggle to efficiently detect unoccupied road areas under varying lighting and contrast conditions, requiring complex and computationally expensive filter banks for accurate boundary detection.
Innovation Solution
The method employs a discrete wavelet transform (DWT) decomposition with iterative low-pass and high-pass filtering, using symmetrical filters based on Pascal's triangle values to generate sub-band images, allowing for efficient detection of unoccupied road areas without the need for orthogonal filter banks, thereby reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex orthogonal filter banks are used for accurate boundary detection, then measurement precision is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent replaces complex orthogonal filter banks with simple symmetrical filters based on Pascal's triangle. These simpler filters achieve sufficient boundary detection accuracy without requiring the computational overhead of orthogonal filter banks, effectively using 'cheaper' filtering mechanisms to accomplish the detection task.
Solution Approach 2:
The patent changes the filter design parameters from orthogonal filter coefficients to symmetrical Pascal's triangle-based coefficients. This parameter change simplifies the filter structure while maintaining effectiveness in boundary detection, reducing computational complexity without significantly sacrificing measurement precision.
2Reliability
If iterative low-pass filtering and down-sampling is performed to generate sub-band images, then reliability under varying lighting conditions is improved, but processing time increases
Solution Approach 1:
The patent segments the image processing into discrete wavelet transform levels, where each level processes specific frequency components. By dividing the image analysis into separate sub-band images (LL, LH, HL, HH), the system can efficiently process only the necessary components at each iteration, improving reliability while controlling processing time through structured segmentation.
Solution Approach 2:
The patent performs preliminary down-sampling and filtering in the discrete wavelet transform to create simplified sub-band representations before boundary detection. This preliminary action reduces the complexity of subsequent processing while maintaining the essential features needed for reliable detection under varying lighting conditions.
3Device complexity
If symmetrical filters based on Pascal's triangle are used instead of orthogonal filter banks, then device complexity is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent employs simple symmetrical filters based on Pascal's triangle coefficients as a substitute for complex orthogonal filter banks. These simpler filters are computationally cheaper and easier to implement, achieving sufficient boundary detection precision without the overhead of orthogonal filtering, thus resolving the trade-off between complexity and precision.
Data Source
AI summary
A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating an LL sub-band image of an Nth level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating a sub-band image of an (N+1)th level by high-pass filtering the LL sub-band image of the Nth level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1)th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating the boundary data by determining a boundary of the pixel region.