Active contours with a novel distribution metric for complex object segmentation

Shu Juan Peng*, Xin Liu, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, we present the efficient region-based active contours with a novel distribution metric for complex object segmentation problems. Unlike most conventional approaches, we model the regional statistics using probability distribution function and propose a simple but effective distribution metric to drive the active contours. Subsequently, the proposed approach speeds up the segmentation process without initializing the zero level set in terms of a sign distance function (SDF) and re-initializing it periodically during the evolution as used in the traditional methods. Some challenging synthetic and real-world images are utilized to evaluate the proposed segmentation algorithm. The experiments show its promising result in comparison with the existing methods.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages3345-3348
Number of pages4
DOIs
Publication statusPublished - 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period11/09/1114/09/11

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

User-Defined Keywords

  • Active contours
  • complex object segmentation
  • distribution metric
  • zero level set

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