Authors:
Nadia Tamayo
1
and
V. Javier Traver
2
Affiliations:
1
Universidad de Oriente, Cuba
;
2
Universitat Jaume I, Spain
Keyword(s):
Log-polar images, Entropy-based saliency, Space-variant sampling, Adaptive scale.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Early Vision and Image Representation
;
Image and Video Analysis
Abstract:
Visual saliency provides a filtering mechanism to focus on a set of interesting areas in the scene, but these mechanisms often overload the computational resources of many computer vision tasks. In order to reduce such an overload and improve the computational performance, we propose to exploit the advantages of log-polar vision to detect salient regions with economy of computational resources and quite stable results. Particularly, in this paper we study the application of the entropy-based saliency to log-polar images. Some interesting considerations are presented in reference to the concept of “scale” and the effects of space-variant sampling on scale selection. We also propose a necessary border extension to detect objects present in peripheral areas. The original entropy-based saliency algorithm can be used in log-polar images, but the results show that our adaptations allow to detect with more precision log-polar salient forms because they consider the information redundancy of
space-variant sampling. Compared with cartesian, log-polar salient results allow a significant saving of computational resources.
(More)