Randomized Point Set Geometry Verification for Image Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video signal processing techniques are computationally expensive and incur high communication costs, making them unsuitable for real-time operation in content delivery networks (CDNs) and other applications, and they struggle with noise and imperfections in image matching, leading to inaccurate visual object recognition.
Innovation Solution
A system and method for randomized point set geometry verification for image identification, which involves computing interesting points sets, determining matching pairs, sorting by distance, calculating a topology code distance vector, and using a decision tree classifier to verify geometric consistency and improve matching accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video signal processing techniques are used, then image matching accuracy can be improved, but computational cost and communication costs increase significantly
Solution Approach 1:
The patent segments the image matching process into two stages: a fast initial matching stage using simplified criteria, and a verification stage using geometric consistency checks only on candidate matches. This segmentation reduces overall computational cost by avoiding expensive operations on all image data, while maintaining high matching accuracy through the two-stage approach.
Solution Approach 2:
The patent applies partial verification by checking geometric consistency only for a subset of matching point pairs rather than all possible pairs. By selecting a representative sample of matches for verification, the system achieves sufficient accuracy without the full computational burden of exhaustive verification, thus reducing computational cost while maintaining precision.
2Measurement precision
If existing video signal processing techniques are used, then image matching accuracy can be improved, but communication costs increase
Solution Approach 1:
The patent extracts and processes only the essential geometric relationships between matching points rather than transmitting complete image data. By focusing communication and computation on the critical geometric consistency information of matched point pairs, the system reduces communication costs while maintaining the accuracy needed for reliable image matching.
3Productivity
If traditional image matching methods are used, then processing speed can be maintained, but accuracy deteriorates due to noise and imperfections
Solution Approach 1:
The patent implements feedback through geometric consistency verification that corrects and refines initial matching results. By checking whether matched point pairs maintain consistent geometric relationships and using this feedback to reject incorrect matches, the system improves accuracy without significantly reducing processing speed, as the verification operates on already-identified candidates rather than performing exhaustive analysis.
Data Source
AI summary
Object recognition can be improved by verifying a geometric consistency between matching interesting points pairs of two images, as objects with the images are more likely to match when the matching interesting points pairs are geometrically consistent. The geometric consistency between matching interesting points pairs can be verified in accordance with a topology code distance vector (D). The topology code distance vector (D) may be evaluated in accordance with a decision tree classifier, which may be trained in accordance with previous or historic topology code distance vectors. The topology code distance vector (D) may be computed from a subset of matching interesting points pairs having the shortest matching distance.