CoChat
Sign Up
Cited by 32Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval TechniquesMarine animal studies overview

Extracting identifying contours for African elephants and humpback whales using a learned appearance model

Weideman, Hendrik J., Stewart, Charles V., Parham, Jason R., Holmberg, Jason, Flynn, Kiirsten, Calambokidis, John, Paul, D. Barry, Bedetti, Anka +3 more·2020·2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
DOI 10.1109/wacv45572.2020.9093266
Open this paper in CoChat — free account

Abstract

This paper addresses the problem of identifying individual animals in images based on extracting and matching contours, focusing in particular on the trailing edges of humpback whale flukes and the outline of the ears of African savanna elephants. A coarse-grained FCNN is learned to isolate the contour in an image, and a fine-grained FCNN is learned to provide more precise boundary information. The latter is trained by generating synthetic boundaries from coarse, easily-extracted training data, avoiding tedious manual effort. An A* algorithm extracts the final contour, which is converted to set of digital curvature descriptors and matched against a database of descriptors using local-naive Bayes nearest neighbors. We show that using the learned fine-grained FCNN produces more accurate contours than using image gradients for fine localization, especially for elephant ears where the boundaries are primarily texture. Matching using contours extracted using the fine-grained FCNN improves top-1 accuracy from 80% to 85% for flukes and 78% to 84% for ears.

Integrity signals

Registry

Crossref · 10.1109/wacv45572.2020.9093266

Cites

32 of 32 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Wildbook image analysis can start with images from any source and use ML and computer vision to detect multiple animals, classify their species, and identify individual animals.

    “The image analysis of Wildbook can start with images from any source—scientists, camera traps, drones, community scientists, or social media—and use ML and computer vision to detect multiple animals in the images100 to not only classify their species, but identify individual animals applying a suite of different algorithms101,147.”

    From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [101]

  • Conventional and more recently DL-based methods have reached strong performance for specific taxa, especially across small populations.

    “Yet, conventional101 and more recently DL-based38,54,102 methods have reached strong performance for specific taxa, especially across small populations.”

    From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [101]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Weideman, H. J., Stewart, C. V., Parham, J. R., Holmberg, J., Flynn, K., Calambokidis, J., Paul, D. B., Bedetti, A., Henley, M., Lepirei, J., & Pope, F. G.
Affiliations
  • Bloomberg (United States) (US)
  • Rensselaer Polytechnic Institute (US)
  • Cascadia Research Collective (US)
  • Elephants Alive (ZA)
  • Save the Elephants (KE)
ORCID
Venue
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
Publisher
IEEE
Published
2020-03
Pages
1265-1274
ISSN
2472-6737 (ISSN-L)
DOI
10.1109/wacv45572.2020.9093266
OpenAlex
W3010470656
Cited by
32
References
32
Citations by year
2026: 4 · 2025: 3 · 2024: 8 · 2023: 4 · 2022: 10 · 2021: 3
FWCI
1.31
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
  • Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
Keywords
animal identification, contour detection, African savanna elephant, humpback whale, A* algorithm, nearest neighbor matching

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review