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Proceedings Paper

Multi-polarimetric textural distinctiveness for outdoor robotic saliency detection
Author(s): S. A. Haider; C. Scharfenberger; F. Kazemzadeh; A. Wong; D. A. Clausi
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Paper Abstract

Mobile robots that rely on vision, for navigation and object detection, use saliency approaches to identify a set of potential candidates to recognize. The state of the art in saliency detection for mobile robotics often rely upon visible light imaging, using conventional camera setups, to distinguish an object against its surroundings based on factors such as feature compactness, heterogeneity and/or homogeneity. We are demonstrating a novel multi- polarimetric saliency detection approach which uses multiple measured polarization states of a scene. We leverage the light-material interaction known as Fresnel reflections to extract rotationally invariant multi-polarimetric textural representations to then train a high dimensional sparse texture model. The multi-polarimetric textural distinctiveness is characterized using a conditional probability framework based on the sparse texture model which is then used to determine the saliency at each pixel of the scene. It was observed that through the inclusion of additional polarized states into the saliency analysis, we were able to compute noticeably improved saliency maps in scenes where objects are difficult to distinguish from their background due to color intensity similarities between the object and its surroundings.

Paper Details

Date Published: 8 February 2015
PDF: 7 pages
Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 94060A (8 February 2015); doi: 10.1117/12.2082860
Show Author Affiliations
S. A. Haider, Univ. of Waterloo (Canada)
C. Scharfenberger, Univ. of Waterloo (Canada)
F. Kazemzadeh, Univ. of Waterloo (Canada)
A. Wong, Univ. of Waterloo (Canada)
D. A. Clausi, Univ. of Waterloo (Canada)


Published in SPIE Proceedings Vol. 9406:
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

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