Projection Surface Color Correction for AR Image Accuracy
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Solution Overview
Problem
Existing augmented reality systems face challenges in accurately projecting images onto non-white, irregularly shaped, and highly reflective surfaces due to surface characteristics, leading to image distortion and poor user experience.
Innovation Solution
The implementation of techniques that sample and modify source images based on the color and shape of the projection surface, using color correction modules and coordinate transformation modules to adjust image projections, ensuring accurate rendering on diverse surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If images are projected onto non-white, irregularly shaped, and highly reflective surfaces, then the augmented reality environment can be created on diverse surfaces, but image distortion occurs and projection accuracy deteriorates
Solution Approach 1:
The system performs preliminary sampling of the projection surface characteristics (color, shape, reflectivity) before projecting the final image. Color correction modules pre-process the source image by sampling surface colors and applying compensatory color transformations. Coordinate transformation modules pre-calculate distortion corrections based on sampled surface geometry, enabling accurate projection on diverse surfaces without real-time adjustment during projection
Solution Approach 2:
The system implements feedback mechanisms by sampling the actual projection surface properties and using this information to adjust the projected image. Color correction modules continuously sample surface colors and adjust color values in real-time. The system measures actual projection results and feeds this information back to refine coordinate transformations and color corrections, maintaining accuracy on irregular and reflective surfaces
2Measurement precision
If color correction modules sample and modify source images based on projection surface color, then image color accuracy is improved, but processing complexity increases
Solution Approach 1:
The color correction system applies local quality adjustments by sampling colors at specific locations on the projection surface and applying targeted color corrections to corresponding image regions. Rather than uniformly processing the entire image, the system identifies specific color deviations in different areas and applies localized color modifications, reducing unnecessary processing while maintaining overall color accuracy
Solution Approach 2:
The system changes color parameters of the source image based on sampled surface characteristics. Color correction modules modify specific color values (RGB, HSV parameters) according to measured surface colors, transforming the image parameters to compensate for surface color effects. This parameter-based approach provides precise control over color corrections without requiring complex reprocessing of the entire image structure
3Manufacturing precision
If coordinate transformation modules adjust image projections based on projection surface shape, then projection geometry accuracy is improved, but computational requirements increase
Solution Approach 1:
Coordinate transformation modules perform preliminary calculations of geometric transformations based on sampled surface geometry. The system pre-computes transformation matrices and coordinate mappings before projection, storing these for reuse. This preliminary action reduces real-time computational requirements during actual projection while maintaining high geometric accuracy on irregular surfaces
Solution Approach 2:
The system creates simplified copies or models of the projection surface geometry for computational processing. Rather than directly processing complex real-world surface variations, the system generates simplified geometric representations that capture essential shape characteristics. These copied models enable efficient coordinate transformations with reduced computational energy while preserving projection accuracy
Data Source
AI summary
Images to be projected onto a projection surface are modified prior to projection based on color values sampled from the projection surface. A camera samples color values from the projection surface and a processor generates a modified source image based at least in part on pixel color values in an original source image and the color values sampled from the projection surface. A projector then projects the modified source image onto the projection surface.