{"url":"/dataset/mdbd","name":"MDBD","full_name":"Multicue Dataset for Edge Detection","description_markdown":"In order to study the interaction of several early visual cues (luminance, color, stereo, motion) during boundary detection in challenging natural scenes, we have built a multi-cue video dataset composed of short binocular video sequences of natural scenes using a consumer-grade Fujifilm stereo camera (Mély, Kim, McGill, Guo and Serre, 2016). We considered a variety of places (from university campuses to street scenes and parks) and seasons to minimize possible biases. We attempted to capture more challenging scenes for boundary detection by framing a few dominant objects in each shot under a variety of appearances. Representative sample keyframes are shown on the figure below. The dataset contains 100 scenes, each consisting of a left and right view short (10-frame) color sequence. Each sequence was sampled at a rate of 30 frames per second. Each frame has a resolution of 1280 by 720 pixels.","description_withheld":null,"homepage":"https://serre-lab.clps.brown.edu/resource/multicue/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Edge Detection","url":"/task/edge-detection","datasets_with_task":"/datasets/task/edge-detection"},{"name":"Boundary Detection","url":"/task/boundary-detection","datasets_with_task":"/datasets/task/boundary-detection"}],"languages":[],"variants":["MDBD"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/edge-detection-on-mdbd","task":"Edge Detection","dataset_variant":"MDBD","rows":6,"metrics":["ODS","Number of parameters (M)"],"first_row_in_archive_order":{"model":"DexiNed-a","paper":"/paper/dense-extreme-inception-network-for-edge","metrics":{"ODS":"0.894"},"code_links":[{"title":"xavysp/DexiNed","url":"https://github.com/xavysp/DexiNed"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ldc-lightweight-dense-cnn-for-edge-detection","title":"LDC: Lightweight Dense CNN for Edge Detection","date":"2022-06-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dense-extreme-inception-network-for-edge","title":"Dense Extreme Inception Network for Edge Detection","date":"2021-12-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/unmixing-convolutional-features-for-crisp","title":"Unmixing Convolutional Features for Crisp Edge Detection","date":"2020-11-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bi-directional-cascade-network-for-perceptual","title":"Bi-Directional Cascade Network for Perceptual Edge Detection","date":"2019-02-28","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/richer-convolutional-features-for-edge","title":"Richer Convolutional Features for Edge Detection","date":"2016-12-07","rows_on_this_dataset":1,"code_links":3,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}