Gravity-Aligned Gradient Computation for Vision Localization
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Solution Overview
Problem
Existing image processing methods for edge detection in computer vision are computationally expensive and inefficient, particularly when dealing with gravity-aligned edges, as they require additional post-processing steps or image rectification, which complicates the processing and increases computational cost.
Innovation Solution
The proposed method constrains image processing using degrees of freedom with high confidence, such as gravity orientation from sensors, to directly compute gravity-aligned gradients, avoiding the need for post-processing and rectification, and adapts kernel properties based on confidence levels for more efficient edge detection and image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image-axis aligned kernel based filters are used for edge detection, then the filtering process is simple and direct, but additional post-processing steps are required to detect gravity-aligned edges, increasing computational cost
Solution Approach 1:
The patent changes the parameter of kernel orientation from fixed image-axis alignment to dynamic alignment based on gravity direction. By adapting the kernel orientation parameter to match the gravity vector direction, the system directly computes gravity-aligned gradients without requiring post-processing steps, thus resolving the contradiction between filtering simplicity and computational efficiency
Solution Approach 2:
The patent introduces dynamic adaptation of kernel properties based on confidence levels of degree of freedom information. The kernel orientation and other properties are no longer static but dynamically adjusted according to the reliability of sensor data, allowing the system to optimize computational resources while maintaining accuracy in gravity-aligned edge detection
2Measurement precision
If the whole image is rectified to align with gravity vector before filtering, then gravity-aligned gradients can be computed directly, but the processing becomes computationally more expensive and depends on intermediate rectification results
Solution Approach 1:
The patent extracts only the essential information needed for gravity-aligned filtering (the gravity direction vector) without performing full image rectification. By taking out just the orientation information from the gravity sensor and applying it directly to the filtering kernels, the system achieves accurate gravity-aligned gradient computation without the computational overhead of complete image rectification
Solution Approach 2:
The patent performs preliminary alignment of the filtering kernels with the gravity direction before the actual gradient computation. By pre-configuring the kernel orientation based on gravity vector information, the system prepares the filtering operation in advance, eliminating the need for time-consuming image rectification while maintaining measurement precision
3Measurement precision
If additional post-processing steps are applied to compute edge orientation and compare with gravity orientation, then gravity-aligned edges can be detected, but the processing complexity and computational cost increase
Solution Approach 1:
The patent merges the edge detection process with the gravity alignment constraint by incorporating the gravity direction directly into the filtering kernels. This combination eliminates the need for separate post-processing steps to compute and compare edge orientations, reducing processing complexity while maintaining detection accuracy through the constrained filtering approach
Data Source
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AI summary
An image processing method comprises the steps of providing at least one image of at least one object or part of the at least one object, and providing a coordinate system in relation to the image, providing at least one degree of freedom in the coordinate system or at least one sensor data in the coordinate system, and computing image data of the at least one image or at least one part of the at least one image constrained or aligned by the at least one degree of freedom or the at least one sensor data.