{
  "id": "1405.2362",
  "version": "v1",
  "published": "2014-05-09T21:53:05.000Z",
  "updated": "2014-05-09T21:53:05.000Z",
  "title": "Image Segmentation Using Frequency Locking of Coupled Oscillators",
  "authors": [
    "Yan Fang",
    "Matthew J. Cotter",
    "Donald M. Chiarulli",
    "Steven P. Levitan"
  ],
  "comment": "7 pages, 14 figures, the 51th Design Automation Conference 2014, Work in Progress Poster Session",
  "categories": [
    "cs.CV",
    "q-bio.NC"
  ],
  "abstract": "Synchronization of coupled oscillators is observed at multiple levels of neural systems, and has been shown to play an important function in visual perception. We propose a computing system based on locally coupled oscillator networks for image segmentation. The system can serve as the preprocessing front-end of an image processing pipeline where the common frequencies of clusters of oscillators reflect the segmentation results. To demonstrate the feasibility of our design, the system is simulated and tested on a human face image dataset and its performance is compared with traditional intensity threshold based algorithms. Our system shows both better performance and higher noise tolerance than traditional methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-05-09T21:53:05.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "C.1.3"
    ],
    "keywords": [
      "image segmentation",
      "frequency locking",
      "human face image dataset",
      "higher noise tolerance",
      "traditional intensity threshold"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 7,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1405.2362F"
    }
  }
}