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ORCID1st CVAUI@ICPR 2014: Stockholm, Sweden
- Alexandra Branzan Albu, Maia Hoeberechts:
2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI@ICPR 2014, Stockholm, Sweden, August 24, 2014. IEEE Computer Society 2014, ISBN 978-1-4799-6709-4
Oral Presentations I
- Lorenzo Corgnati
, Luca Mazzei, Simone Marini, Stefano Aliani, Alessandra Conversi, Annalisa Griffa, Bruno Isoppo, Ennio Ottaviani:
Automated Gelatinous Zooplankton Acquisition and Recognition. 1-8 - Matthew Pugh, Bernard Tiddeman, Hannah Dee, Philip Hughes:
Towards Automated Classification of Seabed Substrates in Underwater Video. 9-16 - Timm Schoening, Thomas Kuhn, Tim W. Nattkemper:
Seabed Classification Using a Bag-of-Prototypes Feature Representation. 17-24 - Marzieh Mehrnejad, Alexandra Branzan Albu, David W. Capson, Maia Hoeberechts:
Towards Robust Identification of Slow Moving Animals in Deep-Sea Imagery by Integrating Shape and Appearance Cues. 25-32 - Meng-Che Chuang, Jenq-Neng Hwang, Kresimir Williams:
Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition. 33-40
Oral Presentations II
- Jonas Osterloff, Timm Schoening, Melanie Bergmann, Jennifer M. Durden, Henry A. Ruhl, Tim W. Nattkemper:
Ranking Color Correction Algorithms Using Cluster Indices. 41-48 - Christian Mulsow, Hans-Gerd Maas:
A Universal Approach for Geometric Modelling in Underwater Stereo Image Processing. 49-56 - Luca Mazzei, Simone Marini, Jessica Craig, Jacopo Aguzzi, Emanuela Fanelli, Imants G. Priede:
Automated Video Imaging System for Counting Deep-Sea Bioluminescence Organisms Events. 57-64
Oral Presentations III
- Hongwei Qin, YiGang Peng, Xiu Li:
Foreground Extraction of Underwater Videos via Sparse and Low-Rank Matrix Decomposition. 65-72 - Thomas Stephan, Jürgen Beyerer:
Computergraphical Model for Underwater Image Simulation and Restoration. 73-79 - Amir M. Rahimi, Robert J. Miller, Dmitri G. Fedorov, Santhoshkumar Sunderrajan, B. M. Doheny, H. M. Page, Bangalore S. Manjunath:
Marine Biodiversity Classification Using Dropout Regularization. 80-87
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