Regional Sharpness Mapping for Projector Focus Identification
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Solution Overview
Problem
Conventional focus identification methods for projectors are inaccurate as they calculate optimal sharpness for the entire picture, failing to reflect regional sharpness variations, making it difficult for remote maintainers to assess focus quality effectively.
Innovation Solution
A focus identification method that divides the captured picture into regions, calculates sharpness values for each region, and displays them on the projection picture, enabling remote maintainers to easily identify focus quality and recognize changes post-maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire captured picture is used to calculate optimal sharpness, then the calculation process is simple, but the focus identification accuracy is insufficient due to regional sharpness variations
Solution Approach 1:
The captured picture is divided into multiple image regions, and sharpness values are calculated separately for each region. This segmentation approach enables accurate identification of regional focus quality differences while maintaining a systematic calculation process that is manageable and efficient.
Solution Approach 2:
Different image regions are evaluated independently for their own sharpness characteristics. This allows the system to identify and report focus quality specific to each region, providing localized accuracy information that reflects the actual optical uniformity variations across the projection picture.
2Measurement precision
If regional sharpness values are calculated and displayed, then the focus quality identification becomes accurate, but the system complexity increases
Solution Approach 1:
Sharpness values are displayed on the projection picture using visual indicators such as color coding or numerical overlays. This visual presentation method enables remote maintainers to quickly and easily identify focus quality issues across different regions without requiring complex data interpretation, thus maintaining operational simplicity while achieving accurate focus identification.
3Loss of information
If multiple sharpness values are calculated for different regions, then the focus quality assessment becomes comprehensive, but the data processing time increases
Solution Approach 1:
The picture is divided into multiple image regions with sharpness calculated for each region. This segmentation provides comprehensive focus quality information across the entire projection picture, ensuring no regional variations are missed while maintaining efficient processing through structured regional analysis.
Data Source
AI summary
A focus identification method adaptable for a focus identification system is provided. The focus identification method includes: capturing a projection picture to generate a captured picture; dividing the captured picture into a plurality of image regions; calculating a plurality of sharpness values corresponding to the plurality of image regions respectively according to image data of the plurality of image regions; and displaying the plurality of sharpness values on the projection picture corresponding to the plurality of image regions respectively the to generate a first focus identification picture. Moreover, the disclosure further discloses a focus identification system applying the focus identification method. The focus identification method and the focus identification system using the same in the disclosure may improve the remote maintenance efficiency.