A Novel Image Representation via Local Frequency Analysis for Illumination Invariant Stereo Matching

In this paper, we propose a novel image representation approach to tackle illumination variations in stereo matching problems. Images are mapped using their Fourier transforms which are convolved with a set of monogenic filters. Frequency analysis is carried out at different scales to account for most image content. The phase congruency and the local weighted mean phase angle are then computed over all the scales. The original image is transformed into a new representation using these two mappings. This representation is invariant to illumination and contrast variations. More importantly, it is generic and can be used with most sparse as well as dense stereo matching algorithms. In addition, sequential feature matching or tracking can also benefit from our approach in varying radiometric conditions. We demonstrate the improvements introduced with our image mappings on well-established data sets in the literature as well as on our own experimental scenarios that include high dynamic range imagery. The experiments include both dense and sparse stereo and sequential matching algorithms where the latter is considered in the very challenging visual odometry framework.