Animal recognition in the Mojave Desert: Vision tools for field biologists
By Michael J. Wilber, Walter J. Scheirer, Phil Leitner, Brian Heflin, James P. Zott, Daniel Reinke, David K. Delaney, Terrance E. Boult. 2013
46 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Ecology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Vishal M. Patel
- He Zhang
- Sara Beery
- Ming-Hsuan Yang
- Ben. G. Weinstein
- Yun Raymond Fu
- Siyu Yang
- Shaodi You
In Ecology
- Ben. G. Weinstein
- Georgia Atkinson
- Cameron Trotter
- Kirsten Richardson
- Nick Wright
- Stephen Mcgough
- Matt Sharpe
- Ben Burville
Citations in the corpus
- NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation (2020, background): “Of the datasets which do allow for species identification currently, most primarily focus on the development of landbased camera trap systems (2, 20), although work has also been undertaken in the development of marine life species detection systems (1).”
- Recognition in Terra Incognita (2018, neutral): “[18] showed results classifying squirrels vs.”
- The iWildCam 2019 Challenge Dataset (2019, background): “Automating camera trap labeling is not a new challenge for the computer vision community [1, 3–10, 12–17].”
- Efficient Pipeline for Camera Trap Image Review (2019, methodology): “The computer vision community has been making strides towards automating the species classification challenge in camera traps [1, 2, 4–16], but it has proven difficult to to apply models trained in one region to images collected in different geographic areas.”
- A computer vision for animal ecology. (2018, background): “The majority of applications I reviewed had a particular geographic focus, for example the rodent community of the Mojave desert (Wilber et al., 2013).”
- Classification-Reconstruction Learning for Open-Set Recognition (2018, background): “Closed-set classifiers are error-prone to samples of unknown classes, and this limits their usability [46, 43].”
- Sparse Representation-Based Open Set Recognition (2017, background): “However, if the test image corresponds to an animal which does not match one of the K animals seen during training, then the algorithm should have the capability to ignore or reject the test sample [12].”