Robust Binarization of Gray-Coded Pattern Images for Smart Projectors

Abstract

A smart projector, equipped with a camera, automatically adjusts its keystone and color transformation according to the shape, position, orientation, and reflectance of projection surfaces. To realize the automatic adjustment, smart projectors build a mapping between pixel locations in a projector image and their corresponding locations in the camera image of the projected surface. Complementary gray-coded patterns play an important role in building such correspondences; corresponding pixels share the same code, so that correspondence search becomes as simple as reading out the codes. If the camera views a wider area than the projection region, a significant number of camera pixels capture surfaces outside the projection region. To build correct correspondences, detection and rejection of them is indispensable. In this paper, we build a robust method for detecting and rejecting those pixels, so that the camera images of the patterns will be correctly binarized. Experimental results show that the proposed method effectively rejects outliers while preserving accurately binarized pixels.