Automated Image Region Identification for Text Overlay
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Solution Overview
Problem
Current document composition methods require manual intervention by graphics designers to identify regions of interest in images for overlaying text or objects, making the process expensive and inefficient, especially when trying to avoid important image areas.
Innovation Solution
An automated method for determining regions of interest in images by segmenting them into smaller regions, analyzing their importance based on contrast metrics, and grouping similar regions to identify areas suitable for overlaying text or objects, ensuring that important image areas are not obstructed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify regions of interest in images for overlay, then the quality and accuracy of avoiding important image areas is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The system performs automatic image analysis and region identification without human intervention. The computer executes algorithms that autonomously segment the image, evaluate regions based on multiple criteria (contrast, color saturation, edge detection, texture analysis), and identify safe overlay areas, making the system self-sufficient in replacing manual designer work
Solution Approach 2:
The patent replaces the mechanical manual process of graphic designers visually inspecting and marking important areas with an automated computational system. The system uses electronic image processing algorithms including contrast calculation, color space transformation, edge detection, and texture analysis to automatically identify regions of interest, substituting human visual inspection with machine-based automated analysis
2Measurement precision
If manual methods are used to identify regions of interest in images for overlay, then the quality and accuracy of avoiding important image areas is improved, but the production cost increases
Solution Approach 1:
The system performs automatic image analysis and region identification without human intervention. The computer executes algorithms that autonomously segment the image, evaluate regions based on multiple criteria (contrast, color saturation, edge detection, texture analysis), and identify safe overlay areas, making the system self-sufficient in replacing manual designer work
Solution Approach 2:
The patent replaces the mechanical manual process of graphic designers visually inspecting and marking important areas with an automated computational system. The system uses electronic image processing algorithms including contrast calculation, color space transformation, edge detection, and texture analysis to automatically identify regions of interest, substituting human visual inspection with machine-based automated analysis
3Productivity
If automated methods are used to identify overlay regions, then productivity and efficiency are improved, but the precision of avoiding important image details may deteriorate
Solution Approach 1:
The patent divides the image into multiple discrete regions or zones for independent evaluation. Each region is analyzed separately using image processing algorithms to calculate contrast ratios, color saturation, edge density, and texture characteristics. This segmentation allows the system to process large images efficiently while maintaining detailed analysis of individual areas, balancing automated speed with precise local evaluation
Solution Approach 2:
The system employs multiple evaluation parameters and thresholds that can be adjusted to optimize both speed and precision. By changing parameters such as contrast thresholds, color space transformations, edge detection sensitivity, and region size criteria, the system adapts to different image types and requirements, maintaining high precision while operating automatically
4Area of stationary object
If large image areas are selected for overlay to ensure sufficient space, then the likelihood of finding suitable overlay regions is improved, but the risk of obscuring important small details increases
Solution Approach 1:
The patent divides the image into multiple discrete regions or zones for independent evaluation. Each region is analyzed separately using image processing algorithms to calculate contrast ratios, color saturation, edge density, and texture characteristics. This segmentation allows the system to process large images efficiently while maintaining detailed analysis of individual areas, balancing automated speed with precise local evaluation
Solution Approach 2:
The system applies different evaluation criteria and weighting to different regions of the image based on their local characteristics. Areas with high contrast, strong edges, or rich textures are automatically identified as important and given higher protection, while uniform low-information areas are identified as suitable for overlay. This local quality assessment ensures that each region is evaluated according to its specific properties rather than applying a uniform standard
Data Source
AI summary
Provided is a method for the automatic determination of a region of interest in an image, comprising the steps of: segmenting the image into a plurality of smaller regions, each region extending over a plurality of pixels; performing an analysis on each of said regions to characterize an aspect of the region relating to its level of importance in communicating information to a viewer; grouping adjacent regions having similar aspect characteristics; and identifying at least one group as a region of interest. Also provided is a method employing the steps above for use in an automated document composition process, where overlaid content is not placed over regions of interest that are identified.