Camera-Assisted Keystone Distortion Correction via Homography
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Solution Overview
Problem
Existing projector systems face challenges in accurately fitting projected images to screens without keystone distortion, which often requires iterative user adjustments and is prone to errors due to non-orthogonal optical axes, leading to suboptimal image quality.
Innovation Solution
The implementation of a camera-assisted planar homography transformation process that determines the corner coordinates of user-defined sub-screens and corrects for keystone distortion by using homography matrices to transform image coordinates, reducing the need for user intervention and improving accuracy through analytical transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual iterative adjustment is used to fit projected images to screens, then user control and flexibility are maintained, but setup time increases and measurement precision deteriorates
Solution Approach 1:
The patent replaces manual mechanical adjustment of projector position and angle with an automated optical measurement system. A camera captures the projected pattern, and computational algorithms automatically calculate the homography matrix and keystone distortion parameters, eliminating the need for iterative manual positioning while achieving sub-pixel measurement precision.
Solution Approach 2:
The system projects a test pattern and captures its image through the camera to create a digital copy of the projected geometry. By analyzing this captured image and comparing it with the known test pattern coordinates, the system derives accurate transformation parameters without physical adjustment, significantly reducing setup time while maintaining high precision.
2Extent of automation
If camera-assisted homography transformation is implemented, then measurement precision and automation are improved, but device complexity increases
Solution Approach 1:
The camera serves multiple functions: it captures the projected test pattern for homography calculation, detects screen boundaries, and verifies projection geometry. This multi-functionality reduces the need for separate calibration devices and simplifies the overall system architecture while enabling automatic screen fitting across various projector configurations.
Solution Approach 2:
The system performs self-calibration by automatically capturing images, calculating homography matrices, and adjusting projection parameters without user intervention. The projector and camera work together in a closed-loop system where the camera provides feedback data that the processor uses to automatically correct keystone distortion and fit the image to the screen.
3Manufacturing precision
If keystone distortion is not corrected, then device complexity remains low, but image quality and perceived quality deteriorate
Solution Approach 1:
The system corrects keystone distortion by calculating and applying a homography transformation matrix that maps distorted projection coordinates to undistorted screen coordinates. This parameter-based correction approach handles both 1D (vertical/horizontal offset) and 2D (combined offset) keystone distortion through mathematical transformation rather than physical adjustment, achieving high geometric accuracy.
Data Source
AI summary
In described examples, solid field patterns are projected onto screens via at least one projector. The screens include user-defined sub-screens. The projected solid field patterns are image captured as imaged solid patterns via at least one camera. For camera perspective planes of the at least one camera, first corner coordinates of the sub-screens are determined, based on differences in pixel values of the imaged solid patterns. Homography matrices are associated with the camera perspective planes and associated with projector perspective planes of the at least one projector. For the projector perspective planes, the first corner coordinates are transformed to second corner coordinates of the sub-screens, based on the homography matrices. Images are projected based on the second corner coordinates.