Interest Point Matching via Worm Objects and Spatial Coherency
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Solution Overview
Problem
Conventional image matching techniques require computationally intensive descriptor generation and do not effectively account for spatial positioning and orientation of interest points, leading to inefficiencies in real-time processing and bandwidth issues.
Innovation Solution
The method involves creating 'worm' objects that relate base interest points to satellite points, normalizing their positions and orientations relative to the base, and matching these objects across images without the need for descriptor computation, using arrays to track and update matches based on similarity measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local descriptors are computed for each interest point to enable matching, then matching accuracy is improved, but computational load and processing time increase significantly
Solution Approach 1:
The patent extracts only the essential geometric properties (position, scale, orientation) of interest points and their pairwise spatial relationships, discarding the computationally intensive local descriptor computation. This extraction approach maintains matching capability while dramatically reducing computational load by focusing only on the minimal necessary features.
Solution Approach 2:
The patent segments the matching problem into two distinct stages: first detecting interest points with their geometric properties, then matching them based on spatial relationships. This segmentation eliminates the need for descriptor computation entirely, as matching is achieved through geometric constraints alone, thereby improving processing speed while maintaining accuracy.
2Reliability
If comprehensive local descriptors are generated for each interest point, then matching reliability is improved, but data size and bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential geometric properties (position, scale, orientation) of interest points and their pairwise spatial relationships, discarding the computationally intensive local descriptor computation. This extraction approach maintains matching capability while dramatically reducing computational load by focusing only on the minimal necessary features.
Solution Approach 2:
Instead of computing descriptors and then matching, the patent inverts the approach by directly matching interest points based on their geometric properties and spatial relationships. This inversion eliminates the need for large descriptor data structures, reducing data size while maintaining matching reliability through geometric constraints.
3Measurement precision
If conventional descriptor-based matching is used, then spatial positioning information is captured, but the spatial relationships between multiple interest points are not effectively utilized
Solution Approach 1:
The patent merges the position, scale, and orientation information of multiple interest points into unified spatial relationship constraints. By combining these geometric properties across multiple points and using them jointly in the matching process, the system effectively utilizes spatial relationships while simplifying the algorithm compared to descriptor-based methods.
Data Source
AI summary
Interest points are markers anchored to a specific position in a digital image of an object. They are mathematically extracted in such a way that, in another image of the object, they will appear in the same position on the object, even though the object may be presented at a different position in the image, a different orientation, a different distance or under different lighting conditions. The goal is to match interest points in one image with corresponding interest points in another image. Typically, this involves the construction of a descriptor, which is both computationally expensive and resource-intensive. Methods and devices are described that match interest points without the construction of conventional descriptors and that permit the use of spatial coherency information to increase the accuracy of the match.