Image Stitching via Feature Group Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image stitching technologies for monitoring cameras are limited by the need for identification patterns, which restrict the installation height and detectable distance of camera units, making it difficult to stitch images effectively over large ranges.
Innovation Solution
An image stitching method that detects and groups features without special identification patterns, allowing for increased detectable distance and adaptability by analyzing and matching feature groups between images to compute transformation parameters for stitching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image stitching technology uses marking features with identification patterns, then the stitching direction and sequence can be determined, but the installation height and detectable distance of camera units are limited
Solution Approach 1:
The patent extracts and removes the identification pattern requirement from the feature marking process. Instead of using predefined patterns, the system detects and uses natural feature points directly from the image content, eliminating the constraint that limited installation height and detectable distance.
Solution Approach 2:
The patent changes the parameter of feature representation from structured identification patterns to unstructured feature points with geometric and photometric properties. This parameter change enables the system to work with diverse natural features rather than constrained artificial markers.
2Adaptability or versatility
If marking features with identification patterns are used for stitching, then the stitching process can be controlled, but the system complexity and adaptability to different environments are reduced
Solution Approach 1:
The patent creates a universal feature detection and matching system that can operate across diverse environments without requiring environment-specific identification patterns. The feature-based approach is universally applicable to various scenes, making the system highly adaptable while reducing complexity.
Solution Approach 2:
The system uses the image content itself to provide all necessary information for stitching. Natural features in the images serve their own purpose for alignment and registration, eliminating the need for external identification patterns and reducing system complexity.
3Measurement precision
If features are divided into multiple groups for matching, then the stitching accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the feature matching process into distinct stages: feature detection, feature description, feature matching, and transformation computation. This segmentation improves accuracy by allowing specialized processing at each stage while managing computational complexity through systematic organization.
Solution Approach 2:
The patent performs preliminary feature detection and description before the matching process. By pre-computing feature descriptors and organizing features by location and type, the system reduces the complexity of the subsequent matching stage while maintaining high stitching accuracy.
Data Source
AI summary
An image stitching method applied to a monitoring camera apparatus with a first image receiver and a second image receiver for acquiring a first image and a second image. The image stitching method includes detecting a plurality of first features in the first image and a plurality of second features in the second image, dividing the plurality of first features at least into a first group and a second group and further dividing the plurality of second features at least into a third group, analyzing the plurality of first features and the plurality of second features via an identification condition to determine whether one of the first group and the second group is matched with the third group, and utilizing two matched groups to stitching the first image and the second image.