Reliable Feature Point Detection and Object Pose Estimation using Photometric Quasi-Invariant SIFT

Object pose estimation from stereo images with unknown correspondence is a thoroughly studied problem in the computer vision and robot engineering literatures. Especially, it is important to detect the desirable corresponding points from images for object pose estimation. For this, many approaches have been proposed. Among them, the local feature descriptor, which describe the feature points that are robust to image deformations in an object or image, is one of the most promising approaches that has been applied to the stable feature detection problem successfully. Although any descriptors including the SIFT represent superior performance, these are based on luminance information rather than color information thereby resulting in instability to photometric variations such as shadows, highlights, and illumination changes. Therefore, we propose a novel method which extracts the interest points that are insensitive to both geometric and photometric variations in order to estimate more accurate and desirable object pose. In this method, we use photometric quasi-invariant features based on the dichromatic reflection model in order to achieve photometric invariance, and the SIFT is used for geometric invariance as well. The performance of the proposed method is evaluated with other local descriptors. Experimental results show that our method gives similar performance or outperforms them with respect to various imaging conditions. Finally, we estimate object pose by using the features extracted via the proposed method.