Image Region Feature Matching for Object Recognition
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Solution Overview
Problem
Current image processing techniques for object recognition and matching in images face challenges with noise, texture, and repeating patterns, leading to false positives and negatives, especially in real-time and batch processing of multiple images.
Innovation Solution
An image processing system that uses feature point extraction and matching algorithms, such as SIFT and SURF, combined with filters and segmentation methods to reduce noise and texture effects, and employs FLANN for approximate matching, allowing for efficient and accurate identification of template images within test images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image processing techniques are used for object recognition, then the system can identify objects in images, but the speed and accuracy are insufficient for real-time processing of multiple images
Solution Approach 1:
The patent divides the image processing task into multiple stages: feature extraction, candidate generation, and verification. By segmenting the processing pipeline and applying different algorithms at each stage, the system achieves both high speed in candidate generation and high accuracy in final verification, resolving the contradiction between processing speed and recognition accuracy.
Solution Approach 2:
The patent generates a candidate list that may include more candidates than strictly necessary (excessive action), then filters this list through verification steps. This approach allows the system to maintain high processing speed by not exhaustively searching all possible matches, while still achieving high accuracy through subsequent verification of candidates.
2Reliability
If traditional matching algorithms are used, then the system can match images, but false positives and false negatives occur due to noise and texture
Solution Approach 1:
The patent extracts only the most discriminative features from images using algorithms like SIFT and SURF, separating these key features from the noisy background and irrelevant texture information. This extraction process isolates the essential matching information while discarding harmful noise and texture interference, thereby improving match accuracy.
Solution Approach 2:
The patent implements a verification stage that provides feedback on candidate matches, evaluating whether each candidate truly represents a valid match or is a false positive/negative. This feedback mechanism allows the system to correct errors introduced by noise and texture, significantly improving overall match reliability.
3Measurement precision
If comprehensive image analysis is performed to improve accuracy, then the system can reduce false matches, but processing time increases
Solution Approach 1:
The patent segments the comprehensive analysis into two phases: a fast candidate generation phase that screens obvious matches, and a slower verification phase that performs detailed analysis only on promising candidates. This segmentation ensures that most images are processed quickly while maintaining high precision through targeted detailed analysis.
Solution Approach 2:
The patent performs comprehensive analysis partially - only on the candidate list rather than all possible image regions. This partial application of comprehensive analysis maintains high matching precision for relevant areas while avoiding the time cost of analyzing entire images, thus reducing processing time.
Data Source
AI summary
A computer-implemented method for determining whether a first image contains at least a portion of a second image, includes: dividing set first image into multiple image regions; for a particular image region of the multiple image regions, determining a particular set of feature points associated with the particular image region; and attempting to match feature points in the particular set of feature points with second feature points associated with the second image to determine whether the particular image region of the first image contains at least a portion of the second image, wherein the first image is considered to contain at least a portion of a second image when the particular image region of the first image contains at least a portion of the second image.