Noise Robustness of Irregular Graph Pyramids using LBP and Combinatorial Maps

Christoph Körner; Ines Janusch; Walter Kropatsch

Noise Robustness of Irregular Graph Pyramids using LBP and Combinatorial Maps

Christoph Körner, Ines Janusch, Walter Kropatsch

Vienna University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In this paper, we briefly introduce the SCIS algorithm - a hierarchical image segmentation approach based on LBP pyramids - and evaluate its robustness to uniform, Gaussian, and Poisson distributed additive chromatic noise. Moreover, we study the influence of image properties such as the amount of details and SNR on the segmentation performance. Our evaluation shows that SCIS is robust to Gaussian and Poisson noise for our testing environment.

Original language	English
Title of host publication	Vision Meets Robotics, Proceedings of the ÖAGM Workshop 2016
Editors	Kurt Niel, Willhelm Burger
Pages	101-108
Number of pages	8
Publication status	Published - 2016
Externally published	Yes

Fields of science

102 Computer Sciences

Cite this

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title = "Noise Robustness of Irregular Graph Pyramids using LBP and Combinatorial Maps",

abstract = "In this paper, we briefly introduce the SCIS algorithm - a hierarchical image segmentation approach based on LBP pyramids - and evaluate its robustness to uniform, Gaussian, and Poisson distributed additive chromatic noise. Moreover, we study the influence of image properties such as the amount of details and SNR on the segmentation performance. Our evaluation shows that SCIS is robust to Gaussian and Poisson noise for our testing environment.",

author = "Christoph K{\"o}rner and Ines Janusch and Walter Kropatsch",

year = "2016",

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AU - Janusch, Ines

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AB - In this paper, we briefly introduce the SCIS algorithm - a hierarchical image segmentation approach based on LBP pyramids - and evaluate its robustness to uniform, Gaussian, and Poisson distributed additive chromatic noise. Moreover, we study the influence of image properties such as the amount of details and SNR on the segmentation performance. Our evaluation shows that SCIS is robust to Gaussian and Poisson noise for our testing environment.

M3 - Conference proceedings

SP - 101

EP - 108

BT - Vision Meets Robotics, Proceedings of the ÖAGM Workshop 2016

A2 - Niel, Kurt

A2 - Burger, Willhelm

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