Keypoint Matching via Statistical Distance Ratio Analysis
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Solution Overview
Problem
Existing image comparison methods for object recognition are time-consuming and require processing large amounts of data, particularly in applications involving large reference databases, such as online shopping scenarios with millions of items.
Innovation Solution
A method that generates keypoint matches between query and reference images, statistically processes these matches through geometric consistency checks, and uses a goodness-of-fit test to assess similarity, with optional phases to estimate the number of inliers and identify specific keypoint matches as inliers or outliers, employing techniques like Pearson's chi-square test and log distance ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional iterative algorithms (e.g., RANSAC) are used for keypoint matching verification, then reliability of matching results is improved, but processing time increases significantly
Solution Approach 1:
The patent extracts and removes incorrect keypoint matches (outliers) from the matching set using statistical analysis of distance ratios. By identifying and eliminating outliers through histogram comparison and goodness-of-fit tests, the method achieves reliable matching results without requiring time-consuming iterative verification procedures like RANSAC.
Solution Approach 2:
The patent replaces the mechanical iterative approach of RANSAC with a statistical analysis method. Instead of repeatedly sampling and testing hypotheses through iteration, the method uses histogram-based statistical tests to directly identify and remove outliers in a single pass, substituting computational iteration with mathematical analysis.
2Reliability
If the reference database contains a large number of images for comprehensive object recognition, then recognition completeness is improved, but the number of comparing operations and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for matching by computing distance ratios between keypoint pairs and analyzing their statistical distribution. Instead of processing and comparing all image data comprehensively, the method extracts key geometric relationships and uses histogram-based statistical tests to identify matching images, dramatically reducing data processing volume while maintaining recognition completeness.
Solution Approach 2:
The patent performs partial processing by focusing only on the statistical distribution of distance ratios rather than analyzing all aspects of image data. By computing histograms of distance ratios and performing goodness-of-fit tests, the method processes a simplified representation of the data that is sufficient for identification purposes, avoiding excessive processing of redundant information.
3Productivity
If statistical processing with goodness-of-fit tests is used to verify keypoint matches, then processing speed is improved, but measurement precision of matching verification may be reduced
Solution Approach 1:
The patent changes the parameter being measured from individual keypoint match accuracy to the statistical distribution of distance ratios across multiple keypoint pairs. By analyzing the histogram of distance ratios and comparing it against expected distributions using goodness-of-fit tests, the method transforms the verification task into a statistical parameter analysis that maintains precision while enabling faster parallel processing.
Data Source
AI summary
A method for comparing a first image with a second image is provided. The method comprises identifying first keypoints in the first image and second keypoints in the second image and associating each first keypoint with a corresponding second keypoint in order to form a corresponding keypoint match. For each pair of first keypoints, the method further comprises calculating the distance therebetween for obtaining a corresponding first length. Similarly, for each pair of second keypoints, the method comprises calculating the distance therebetween for obtaining a corresponding second length. The method further comprises calculating a plurality of distance ratios; each distance ratio is based on a length ratio between a selected one between a first length and a second length and a corresponding selected one between a second length and a first length, respectively. The method still further includes calculating a statistical distribution of the plurality of distance ratios and generating a model function expressing a statistical distribution of further distance ratios corresponding to a random selection of keypoints in the first and second images. The method includes comparing said statistical distribution of the plurality of distance ratios with said model function, and assessing whether the first image contains a view of an object depicted in the second image based on said comparison.