Multi-Projector Image Alignment via Feature-Point Calibration
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Solution Overview
Problem
Current projection technologies face challenges in projecting high-quality images using multiple monochromatic projectors due to alignment and distortion issues, leading to inaccuracies and reduced image quality.
Innovation Solution
An image processing apparatus and method that generates projection images with a content image region and a feature-point image region, using correction parameters to align and correct the images projected by multiple monochromatic projectors, allowing for accurate overlap and high-quality color image projection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple monochromatic projectors are used to project color images, then image quality and color accuracy are improved, but alignment accuracy and image distortion deteriorate due to positioning errors and lens distortions
Solution Approach 1:
The system performs preliminary calibration by projecting a calibration image containing feature points before normal operation. The correction parameters are calculated in advance based on the detected feature point positions, allowing the system to pre-compensate for alignment errors and lens distortions. This preliminary action ensures that when color images are projected using multiple monochromatic projectors, the pre-calculated correction parameters are applied to maintain accurate alignment and positioning.
Solution Approach 2:
The system implements a feedback mechanism where the actual positions of feature points in the projected calibration image are detected and compared with their expected positions. Based on this feedback, correction parameters are dynamically calculated and applied to adjust the projection images from each monochromatic projector. This closed-loop feedback ensures continuous alignment accuracy and compensates for positioning errors and lens distortions in real-time.
2Measurement precision
If correction parameters are applied to align projection images from multiple monochromatic projectors, then alignment accuracy is improved, but device complexity increases due to calibration and parameter management
Solution Approach 1:
The system performs self-calibration by automatically detecting feature points in the projected calibration image and calculating correction parameters without requiring manual intervention. The apparatus autonomously manages the entire calibration process, from projecting the calibration pattern to detecting feature points and generating correction parameters. This self-service approach reduces operational complexity while maintaining high alignment accuracy through automated parameter management.
3Measurement precision
If feature-point images are projected in the second pixel region for calibration, then alignment precision is improved, but loss of useful projection area increases
Solution Approach 1:
The projection image is segmented into two distinct regions: the first pixel region for normal content image projection and the second pixel region for calibration feature-point projection. This segmentation allows the system to dedicate specific areas for their respective functions without interfering with each other. The feature-point images are projected only in the second pixel region, enabling accurate alignment calibration while preserving the first pixel region for useful content projection, thus minimizing the loss of projection area.
Data Source
AI summary
An image processing apparatus according to an embodiment of the present technology includes a first generator and a second generator. The first generator generates projection images correspondingly to respective monochromatic projectors of a plurality of monochromatic projectors using respective correction parameters, each projection image including a first pixel region that includes a content image, and a second pixel region that is a region other than the first pixel region, the second pixel region including a feature-point image in at least a portion of the second pixel region. The second generator detects the feature-point image in a captured image obtained by capturing the projection image projected by each of the plurality of monochromatic projectors, and generates the correction parameter on the basis of a result of the detection of the feature-point image.