@book{
 title = {Deep learning for polar bear detection},
 type = {book},
 year = {2017},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 identifiers = {[object Object]},
 keywords = {Deep learning,Marine mammals,Thermal imaging},
 volume = {10269 LNCS},
 id = {c4afe036-99ab-3351-8d7d-9939d29004ef},
 created = {2018-01-25T21:12:05.684Z},
 file_attached = {false},
 profile_id = {5cd3e5c4-6fbf-3497-86a2-c05887b8b938},
 last_modified = {2018-01-25T21:12:05.684Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
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 abstract = {© Springer International Publishing AG 2017. Marine mammals in the Arctic are threatened by a changing climate and increasing human activity in the region. International laws protect these animals, however detecting and identifying them is not always easy. We have developed a multimodal approach using an omnidirectional thermal camera system, and an optical band stereo system operating in parallel. Using a unified framework for transfer learning with convolutional neural networks in both modalities we have trained a system to detect and classify mammals as well as habitat indicators in the images from both camera systems. Our experiments show that mammal habitat can be identified reliably using these techniques, and our analysis provides a framework for real world use cases.},
 bibtype = {book},
 author = {Sorensen, S. and Treible, W. and Hsu, L. and Wang, X. and Mahoney, A.R. and Zitterbart, D.P. and Kambhamettu, C.}
}

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