Semi-automatic Segmentation of Scattered and Distributed Objects

  • Muhammad Shahid Farid
  • Maurizio Lucenteforte
  • Muhammad Hassan Khan
  • Marco Grangetto
Conference paper

DOI: 10.1007/978-3-319-59162-9_12

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)
Cite this paper as:
Farid M.S., Lucenteforte M., Khan M.H., Grangetto M. (2018) Semi-automatic Segmentation of Scattered and Distributed Objects. In: Kurzynski M., Wozniak M., Burduk R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham

Abstract

This paper presents a novel object segmentation technique to extract objects that are potentially scattered or distributed over the whole image. The goal of the proposed approach is to achieve accurate segmentation with minimum and easy user assistance. The user provides input in the form of few mouse clicks on the target object which are used to characterize its statistical properties using Gaussian mixture model. This model determines the primary segmentation of the object which is refined by performing morphological operations to reduce the false positives. We observe that the boundary pixels of the target object are potentially misclassified. To obtain an accurate segmentation, we recast our objective as a graph partitioning problem which is solved using the graph cut technique. The proposed technique is tested on several images to segment various types of distributed objects e.g. fences, railings, flowers. We also show some remote sensing application examples, i.e. segmentation of roads, rivers, etc. from aerial images. The obtained results show the effectiveness of the proposed technique.

Keywords

Object segmentation Gaussian mixture model Graph-cuts 

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Muhammad Shahid Farid
    • 1
    • 2
  • Maurizio Lucenteforte
    • 2
  • Muhammad Hassan Khan
    • 3
  • Marco Grangetto
    • 2
  1. 1.College of Information TechnologyUniversity of the PunjabLahorePakistan
  2. 2.Dipartimento di InformaticaUniversità Degli Studi di TorinoTorinoItaly
  3. 3.Pattern Recognition GroupUniversity of SiegenSiegenGermany

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