A Multi-Model Stereo Similarity Function Based on Monogenic Signal Analysis in Poisson Scale Space

Abstract EN

A stereo similarity function 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 multiscale 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.

Experiment results on the synthetic and natural stereo images show the performance of the proposed approach.