Image Tile Segmentation for Uniform Feature Point Distribution
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Solution Overview
Problem
Current image merging techniques face inefficiencies in processing large amounts of data and achieving uniform distribution of feature points, leading to suboptimal image merging performance.
Innovation Solution
The method involves dividing input images into tiles to generate feature point and descriptor maps, which are then used to determine matching relationships and reduce processing time, enhancing image merging performance by improving feature point distribution and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image matching is performed on entire input images without division, then comprehensive feature point coverage is achieved, but processing time and computational load increase significantly
Solution Approach 1:
The patent divides each input image into multiple image tiles, and further divides each image tile into multiple regions for feature point extraction. This segmentation approach processes smaller regions independently and combines results, reducing the computational complexity and processing time while maintaining uniform feature point distribution across the entire image.
2Reliability
If feature points are extracted from entire images, then all matching opportunities are captured, but processing complexity and memory requirements increase
Solution Approach 1:
The patent segments images into tiles and regions to manage feature point extraction in smaller units. Each region is processed independently with simplified algorithms, and results are aggregated to form complete matching relationships. This reduces memory requirements and processing complexity while maintaining matching accuracy.
Solution Approach 2:
The patent introduces a hierarchical processing dimension by first dividing images into tiles, then further dividing tiles into regions. This multi-level division creates an additional processing dimension that organizes the complex matching task into manageable stages, reducing overall system complexity.
3Loss of information
If descriptors are generated for all feature points in entire images, then complete matching information is obtained, but memory usage and processing load increase
Solution Approach 1:
The patent generates descriptors for feature points in segmented regions rather than processing entire images at once. This divides the large volume of descriptor generation into smaller, manageable batches processed in sequence or parallel, reducing peak memory usage and processing load while ensuring all matching relationships are captured through aggregation of regional results.
Data Source
AI summary
Each input image from a plurality of input images is divided into a plurality of image tiles. A feature point map including a plurality of feature point tiles respectively corresponding to the plurality of image tiles is generated by extracting feature points included in each image tile of the plurality of image tiles. A descriptor map including a plurality of descriptor tiles respectively corresponding to the plurality of feature point tiles is generated by generating descriptors of feature points included in the feature point map. Mapping information containing matching relationships between feature points included in different input images of the plurality of input images is generated based on a plurality of descriptor maps respectively corresponding to the plurality of input images. Image merging performance may be enhanced by dividing the input image into the plurality of image tiles to increase distribution uniformity of the feature points.