Ambiguity Detection in 2D Structure Matrices
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Solution Overview
Problem
Current methods for detecting ambiguities in 2D structures, such as images, are inefficient and power-intensive, particularly in real-time systems like driver assistance systems, as they often rely on complex transformations like Fourier transforms and require significant computational resources.
Innovation Solution
A method that compares a matrix of a 2D structure with a comparison matrix using a similarity algorithm, forming a similarity matrix and evaluating it to generate an ambiguity measure, which can be implemented in hardware with low power consumption, enabling efficient processing of large numbers of images in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex transformation methods like Fourier transforms are used for ambiguity detection, then measurement precision is improved, but use of energy increases and productivity decreases
Solution Approach 1:
The patent extracts only the essential information needed for ambiguity detection from the full image data by comparing selected regions (first image region and second image region) rather than processing entire images through complex transforms. This selective extraction maintains detection precision while significantly reducing computational energy requirements.
Solution Approach 2:
The patent segments the image processing task into specific comparable regions rather than processing the entire image. By dividing the task into comparing a first image region with a second image region, the method reduces the data volume requiring complex transformation while preserving the ability to detect ambiguities in the relevant areas.
2Measurement precision
If complex transformation methods like Fourier transforms are used for ambiguity detection, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent extracts only the essential information needed for ambiguity detection from the full image data by comparing selected regions (first image region and second image region) rather than processing entire images through complex transforms. This selective extraction maintains detection precision while significantly reducing computational energy requirements.
Solution Approach 2:
The patent segments the image processing task into specific comparable regions rather than processing the entire image. By dividing the task into comparing a first image region with a second image region, the method reduces the data volume requiring complex transformation while preserving the ability to detect ambiguities in the relevant areas.
3Productivity
If simple comparison algorithms are used, then use of energy decreases and productivity increases, but measurement precision worsens
Solution Approach 1:
The patent introduces an intermediary evaluation step that assesses the comparison results between the first image region and second image region. This intermediary evaluation function analyzes the similarity or differences between regions to determine ambiguity, bridging the gap between simple comparison operations and accurate ambiguity detection without requiring complex transformations.
Data Source
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AI summary
The present invention relates to a method for detecting ambiguities in a structure, comprising several steps, and a device, for example a chip, such as in a driver assistance system, for detecting ambiguities in a structure. In a first step (S100) of the method, a matrix of a 2D structure is provided. The matrix has several matrix elements. In a further step (S200), a comparison matrix with several comparison matrix elements is provided. A step (S300) comprises comparing the matrix with the comparison matrix using a comparison algorithm. In a subsequent step (S400), a similarity matrix with similarity elements is formed from the result of the comparison. A step (S500) comprises evaluating the similarity matrix using an evaluation function along a path that includes a plurality of similarity elements.One step (S600) involves generating an evaluation result as a measure of any ambiguity present in the matrix of the first 2D structure.