In this research, we aim to realize the improvement of the accuracy of radiometric compensation for moving non-rigid bodies. By predicting the movement of objects using deep learning, the influence of delays in measurement and projection depending on cameras and projectors on correction accuracy is reduced.
Recently, luminance compensation that realizes image projection canceling the in uence of patterned projection surfaces, has attract attention. This technique can cancel the in uence of the patterns, based on the response function representing the input-output relationship between a projector and a camera. On the other hand, it largely depends on the inter-pixel correspondence between the projector and the camera, so projection surfaces is limited to a rigid body. That is, the utilization area of luminance compensation is largely limited. In this research, we realize luminance compensation on swinging curtains by estimating the inter-pixel correspondence in real-time. In addition, the GPU is used to execute the system in real-time. We examine the effectiveness of the constructed system, using curtains of various materials.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.