Multi-Scale Frequency Analysis for Precise Image Region Detection
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Solution Overview
Problem
Existing image processing devices, such as those described in WO 2008/139825, fail to accurately identify target regions due to insufficient reflection of wide-range features in region identification, leading to low precision in detecting target regions from detection object images.
Innovation Solution
A region detecting method that hierarchizes both reference and detection object images using different reduction rates, calculates frequency information, compares these hierarchies, and synthesizes matching degree regions to enhance precision by aligning frequency information across multiple hierarchies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If processing is executed for each unit pixel block only, then processing simplicity is maintained, but region identification precision is insufficient
Solution Approach 1:
The patent segments the image processing into multiple hierarchies (first hierarchy, second hierarchy, third hierarchy) with different reduction rates. Each hierarchy processes image data at a different scale, allowing the system to capture both local unit pixel block features and global wide-range features. This multi-scale segmentation resolves the contradiction by maintaining processing simplicity at each level while achieving high precision through hierarchical integration.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional single-scale processing. By creating multiple hierarchies with different reduction rates (first, second, and third hierarchies), the system adds a dimensional aspect that captures image features at various scales. This dimensional expansion enables the system to reflect both local and global features, thereby improving region identification precision without overly complicating the processing architecture.
2Loss of information
If only unit pixel block processing is used, then computational load is reduced, but detection of wide-range features is lost
Solution Approach 1:
The patent divides the processing into three distinct hierarchies with different reduction rates. The first hierarchy uses a higher reduction rate for coarse-grained wide-range feature extraction, while the second and third hierarchies use lower reduction rates for detailed local feature analysis. This segmentation allows the system to efficiently process both wide-range and local features without requiring all processing to operate at full detail level, thus reducing overall computational load while preventing information loss.
Solution Approach 2:
The patent applies different processing qualities to different parts of the analysis. The first hierarchy provides a simplified overview for wide-range feature detection, while the second and third hierarchies provide detailed local analysis where needed. This local quality differentiation ensures that computational resources are allocated efficiently, maintaining high productivity while preserving wide-range feature information through the first hierarchy's coarse-grained processing.
Data Source
AI summary
A region detecting method detects, from a detection object image, a target region corresponding to a reference image. The region detecting method includes: hierarchizing the reference image into N hierarchies by processing using different reduction rates and generating a plurality of reference hierarchical images, calculating a first frequency information from each of the plurality of reference hierarchical images, hierarchizing the detection object image into N hierarchies by processing using different reduction rates and generating a plurality of object hierarchical images, calculating a second frequency information from each of the plurality of object hierarchical images, comparing the respective second frequency information of the plurality of object hierarchical images and the respective first frequency information of the plurality of reference hierarchical images, and detecting, from the detection object image, the target region corresponding to the reference image based on the comparison result.