A Novel Image Representation via Local
Frequency Analysis for Illumination Invariant
Stereo Matching
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com
Abstract
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.
Existing Method:
Investigated various factors such as exposure differences, vignetting, varying lighting and noise.
Similarly, other evaluations of sparse feature detection and matching algorithms
Demerits
High complexity
Proposed Method
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.
Merits:
Execution time is less
Further Details Contact: A Vinay 9030333433, 08772261612
Email: takeoffstudentprojects@gmail.com | www.takeoffprojects.com