{
  "id": "1709.00408",
  "version": "v1",
  "published": "2017-09-03T23:42:29.000Z",
  "updated": "2017-09-03T23:42:29.000Z",
  "title": "Lensless-camera based machine learning for image classification",
  "authors": [
    "Ganghun Kim",
    "Stefan Kapetanovic",
    "Rachael Palmer",
    "Rajesh Menon"
  ],
  "categories": [
    "cs.CV",
    "physics.optics"
  ],
  "abstract": "Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-09-03T23:42:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "image classification",
      "machine learning",
      "lensless-camera",
      "standard cmos image sensor",
      "handwritten digits"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}