@incollection{eprints2972,
       publisher = {Springer},
          author = {Mohammed Abdelsamea and Giorgio Gnecco and Mohamed Medhat Gaber},
       booktitle = {Advances in Self-Organizing Maps and Learning Vector Quantization},
          volume = {295},
           pages = {199--208},
           title = {A Concurrent SOM-Based Chan-Vese Model for Image Segmentation},
          series = {Advances in Intelligent Systems and Computing},
            year = {2014},
        abstract = {Concurrent Self Organizing Maps (CSOMs) deal with the pattern classification problem in a parallel processing way, aiming to minimize a suitable objective function. Similarly, Active Contour Models (ACMs) (e.g., the Chan-Vese (CV) model) deal with the image segmentation problem as an optimization problem by minimizing a suitable energy functional. The effectiveness of ACMs is a real challenge in many computer vision applications. In this paper, we propose a novel regional ACM, which relies on a CSOM to approximate the foreground and background image intensity distributions in a supervised way, and to drive the active-contour evolution accordingly. We term our model Concurrent Self Organizing Map-based Chan-Vese (CSOM-CV) model. Its main idea is to concurrently integrate the global information extracted by a CSOM from a few supervised pixels into the level-set framework of the CV model to build an effective ACM. Experimental results show the effectiveness of CSOM-CV in segmenting synthetic and real images, when compared with the stand-alone CV and CSOM models.},
             url = {http://eprints.imtlucca.it/2972/},
        keywords = {Image segmentation; Chan-Vese model; Concurrent Self Organizing Maps; global active contours; neural networks}
}