Mixed Reality Image Processing Segmentation for Positioning Accuracy
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Solution Overview
Problem
In Mixed Reality (MR) systems, the increased pixel density of imaging devices leads to higher data processing and transmission demands, necessitating appropriate image processing for segmented video images to optimize system performance, but existing technologies do not address specific processing needs for different use purposes within the MR system.
Innovation Solution
An image processing apparatus that generates multiple images with varying resolutions and applies distinct processing methods to each, including segmentation, positioning calculation, and virtual reality image generation, to enhance performance and reduce unnecessary processing loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video images are segmented and different image processing is applied to each segment, then image quality and positioning accuracy are improved, but device complexity increases
Solution Approach 1:
The captured image is divided into multiple segments (first image with lower resolution and second image with higher resolution). Different image processing methods are applied to each segment according to its specific use purpose. This segmentation allows the system to optimize processing for each segment's requirements, improving positioning accuracy for the lower resolution segment while maintaining high image quality for the higher resolution segment, without requiring the entire system to use the most complex processing methods.
Solution Approach 2:
Different image processing techniques are applied to different segments based on their specific requirements. The first image segment receives processing optimized for positioning accuracy, while the second image segment receives processing optimized for image quality. This local quality approach ensures that each segment gets the appropriate level and type of processing, improving overall system performance without uniformly increasing complexity across all processing paths.
2Manufacturing precision
If higher pixel density imaging devices are used, then image quality is improved, but data processing and transmission demands increase
Solution Approach 1:
The image data is segmented into different resolution levels. The first image segment uses lower resolution for applications where high image quality is not critical (such as positioning), while the second image segment maintains higher resolution for applications requiring detailed visual information. This segmentation reduces the total amount of data that needs to be processed and transmitted compared to using full high-resolution data for all purposes.
Solution Approach 2:
The resolution parameter is changed differently for different image segments based on their specific use purposes. The first image segment uses a lower resolution parameter to reduce data amount, while the second image segment maintains a higher resolution parameter to preserve image quality where needed. This parameter adaptation allows the system to optimize the balance between image quality and data processing demands.
Data Source
AI summary
A first image and a second image with resolutions different from each other are generated from an image captured by an imaging unit, and the first image and the second image are subject to different types of image processing. A virtual image is generated by calculating a position and an orientation of the imaging unit based on the processed first image. A composite image is generated by combining the generated virtual image with the second image subjected to the image processing, and the generated composite image is used as a display image.