Image Processing Annotation Merging by Relevance Detection
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Solution Overview
Problem
Existing image processing systems face challenges in accurately and efficiently integrating annotations recorded by different devices, leading to inconsistent and redundant annotations in image files.
Innovation Solution
An image processing apparatus and method that integrates and displays annotations from multiple devices by determining relevance and integrating overlapping or related annotations, using a neural network for subject detection and metadata management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple recording devices record annotations in one image file, then the annotation coverage and detection capabilities are improved, but the annotation consistency and redundancy increase
Solution Approach 1:
The system performs preliminary actions by establishing annotation integration rules and device identification mechanisms before multiple devices record annotations. This includes setting up the framework for determining device identities, comparing annotation regions, and applying integration strategies to prevent redundancy before it occurs
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring annotations from multiple devices, comparing them against existing annotations, and adjusting the integration process based on overlap detection and device reliability assessment. This feedback loop ensures consistent annotation quality while maintaining versatility
2Loss of substance
If annotations from multiple devices are integrated, then the redundancy is reduced, but the processing complexity increases
Solution Approach 1:
The system extracts and separates the annotation integration function from the overall image processing workflow. By isolating the comparison and integration logic into a dedicated module, it reduces redundancy efficiently while managing processing complexity through functional decomposition
Solution Approach 2:
The system changes parameters by adjusting the integration threshold and device identification criteria based on the specific application context. This allows flexible control over the balance between redundancy reduction and processing complexity without requiring complete redesign of the integration architecture
3Quantity of substance
If annotation integration is performed, then the storage efficiency is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing integration rules and device profiles before annotation processing. This preparation work enables faster real-time integration by avoiding repeated analysis of device characteristics and integration strategies during the actual annotation merging process
Solution Approach 2:
The system applies discarding and recovering by identifying and discarding redundant annotations through comparison algorithms, then recovering only the unique and valuable annotation data. This selective process reduces storage requirements while minimizing processing time by avoiding unnecessary analysis of duplicate information
Data Source
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AI summary
An image processing apparatus includes acquisition means (201, 300) configured to acquire an image file including a plurality of annotations, determination means (201, 1000) configured to determine a relevance between the plurality of annotations, and control means (201, 1002) configured to perform control to integrate annotations determined to be relevant by the determination means among the plurality of annotations into an integrated annotation and output the integrated annotation.