{
  "annotation_id": "23d33e53-af53-4d81-b7c3-a5856a94a077",
  "date_created": "2026-03-02T18:01:31.483000Z",
  "date_modified": "2026-03-02T18:01:31.483000Z",
  "file_hash": "82d2dc9f118e7950593f6dd1ecaf44a50dc30e9e0b3f6b60179fc76b58fd2672",
  "private": false,
  "record": {
    "abstract": "As borders between different regions, lines are an important element of\nnatural images. Already at the level of the mammalian primary visual cortex\n(V1), neurons respond best to lines of a given orientation. We reduce a set of\nimages to linear segments and analyze their statistical properties. In\nparticular, appropriately defined Fourier spectra show more power in their\ntransverse component than in the longitudinal one. We then characterize filters\nthat are best suited for extracting information from such images, and find some\nqualitative consistency with neural connections in V1. We also demonstrate that\nsuch filters are efficient in reconstructing missing lines in an image.",
    "arxiv_id": "q-bio/0406051",
    "authors": [
      "Ha Youn Lee",
      "Mehran Kardar"
    ],
    "categories": [
      "q-bio.NC",
      "cond-mat.soft"
    ],
    "title": "Statistics of lines of natural images and implications for visual   detection",
    "url": "https://arxiv.org/abs/q-bio/0406051"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "292b3538-5a15-46ca-9af2-ea2e5a453fbd",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}