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Multiple Object Detection with Occlusion Using Active Contour Model and Fuzzy C-Mean

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Image Analysis and Recognition (ICIAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8814))

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Abstract

This paper presents a novel two-stage unsupervised method using Active Contour Model (ACM) and Fuzzy C-mean (FCM) for image segmentation and object detection. In the first stage, ACM is applied to identify the regions of interest, making it possible to subtract the background. Then, an FCM-based algorithm is used to detect the objects in a given image. Unlike existing techniques where the number of clusters is typically set manually, the proposed method is able to automatically estimate the cluster number. Moreover, the proposed method can effectively handle the multi-object case, even in the presence of occlusions where, images may contain an arbitrary number of unknown objects. Experimental results on several images have shown the success and effectiveness of our method in detecting the salient objects.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Windsor, Windsor, ON, Canada

    Sara Memar, Riadh Ksantini & Boubakeur Boufama

Authors
  1. Sara Memar
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  2. Riadh Ksantini
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  3. Boubakeur Boufama
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Corresponding author

Correspondence to Sara Memar .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Dept. of Electrical and Computer Eng., University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

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Memar, S., Ksantini, R., Boufama, B. (2014). Multiple Object Detection with Occlusion Using Active Contour Model and Fuzzy C-Mean. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8814. Springer, Cham. https://doi.org/10.1007/978-3-319-11758-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-11758-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11757-7

  • Online ISBN: 978-3-319-11758-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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