Multi-Scale Stereo Analysis Based on Local Multi-Model Monogenic Image Feature Descriptors

Abstract

A multi-scale method based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal and local mean colors in the multi-scale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation and instantaneous phase of 2D image signal. The color monogenic signal is the extension of monogenic signal to color image based on Clifford algebras. The local color phase can be estimated by computing geometric product between the color monogenic signal and a unit reference vector in RGB color space. Because the proposed feature descriptors contain local geometric, structure and color information, it is robust against noise and brightness change in feature matching and 3D reconstruction. Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.