Feature Point Optimization for Image Matching
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Solution Overview
Problem
Current image processing techniques for object recognition and identification in images face challenges in scalability and accuracy, particularly in real-time and on-the-fly processing of multiple images, especially when dealing with transformations such as scale, angle, and skew, and require significant computational resources.
Innovation Solution
An image processing system that generates multiple template images at various sizes and transformations to enhance matching capabilities, using feature point detection and description methods like SIFT, SURF, and GLOH, and applies techniques like Affine-SIFT for fully affine-invariant comparison, while optimizing feature points to improve detection sensitivity and reduce computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image processing techniques are used for object recognition, then basic matching capability is maintained, but speed and accuracy deteriorate when processing multiple images in real-time
Solution Approach 1:
The patent segments the image matching process into distinct phases: feature point detection, feature point description, feature point matching, and verification. By dividing the complex recognition task into manageable segments, the system achieves real-time processing speed while maintaining accuracy through specialized optimization in each segment.
Solution Approach 2:
The patent changes key parameters of the feature point detection and matching process, including using optimized feature detection algorithms, adjusting feature point selection criteria, and modifying matching thresholds. These parameter changes enable faster processing while preserving or improving matching accuracy under various transformations.
2Reliability
If multiple template images at various sizes and transformations are generated to improve matching capability, then matching accuracy under transformations is improved, but computational overhead increases
Solution Approach 1:
The patent performs preliminary actions by pre-detecting and pre-describing feature points in template images before actual matching occurs. Feature points are detected and described in advance, and their descriptors are stored for rapid comparison during runtime. This preliminary processing shifts computational load to offline preparation, reducing real-time computational requirements while improving matching reliability.
Solution Approach 2:
The patent creates multiple transformed copies of template images at various sizes and transformations, but optimizes the process by detecting feature points once in the original image and then transforming only the descriptors rather than re-detecting features in each transformed copy. This copying strategy with optimized feature transformation reduces computational overhead while maintaining comprehensive matching capability.
3Measurement precision
If feature point detection and matching is performed on all images, then comprehensive object recognition is achieved, but processing time increases
Solution Approach 1:
The patent extracts only the essential feature points from images rather than processing all image data. By selecting and extracting only the most discriminative feature points based on specific criteria (such as corner points, edge points, or points with high descriptor variance), the system achieves accurate detection while significantly reducing processing time for batch operations.
Solution Approach 2:
The patent applies partial action by performing feature point detection and matching on a subset of key regions or using a reduced set of feature points when full comprehensiveness is not required. This selective approach reduces processing time while maintaining sufficient detection accuracy for practical applications, allowing the system to scale to batch processing of multiple images.
Data Source
AI summary
An image matching technique locates feature points in a template image such as a logo and then does the same in a test image. Feature points of a template image are determined under various transformations and used to determine a set of composite feature points for each template image. The composite feature points are used to determine if the template image is present in a test image.