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

  • Sara MemarEmail author
  • Riadh Ksantini
  • Boubakeur Boufama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)

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.

Keywords

Active contour Fuzzy c-mean Microsoft Kinect Depth clue Object detection 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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