By Topic

Scale-Invariant Features and Polar Descriptors in Omnidirectional Imaging

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Arican, Z. ; Signal Process. Lab. (LTS4), Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland ; Frossard, P.

We propose a method to compute scale-invariant features in omnidirectional images. We present a formulation based on the Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that adapt to the mirror and lens structures in omnidirectional imaging. These operators lead to a scale-space analysis that preserves the geometry of the visual information in omnidirectional images. We then build a novel scale-invariant feature detection framework for omnidirectional images that can be mapped on the sphere. We further present a new descriptor and feature matching solution for these omnidirectional images. The descriptor builds on the log-polar planar descriptors and adapts the descriptor computation to the specific geometry and the nonuniform sampling density of omnidirectional images. We also propose a rotation-invariant matching method that eliminates the orientation computation during the feature detection phase and thus decreases the computational complexity. Experimental results demonstrate that the new feature computation method combined with the adapted descriptors offers promising detection and matching performance, i.e., it improves on the common scale-invariant feature transform (SIFT) features computed on the unwrapped omnidirectional images, as well as spherical SIFT features. Finally, we show that the proposed framework also permits to match features between images with different native geometry.

Published in:

Image Processing, IEEE Transactions on  (Volume:21 ,  Issue: 5 )