Quaternion-type moments combining both color and depth information for RGB-D object recognition

Beijing Chen, Jianhao Yang, Mengru Ding, Tianliang Liu, Xinpeng Zhang

Abstract: The existing quaternion-type moments (QTMs) are based on the quaternion representation (QR) of color images. However, this representation creates redundancy when using four-dimensional quaternions to represent color images with three components. In this paper, for RGB-D images, the QR is improved by combining both color and depth information, which is invariant to lighting and color variations. The improved QR fully utilizes the four-dimensional quaternion domain. The new QTMs (NQTMs) are defined using the improved QR. They are combined with the quaternion back-propagation neural network (QBPNN) for RGB-D object recognition. The experimental results demonstrate that the NQTMs outperform our previous QTMs considering only color information.