A computer vision approach for monitoring the spatial and temporal shrimp distribution at the LoVe observatory

Abstract This paper demonstrates how computer vision can be applied for the automatic detection of shrimp in smaller areas of interest with a high temporal resolution for long time periods. A recorded sequence of digital HD camera images from fixed underwater observatories provides unique opportunities to study shrimp behavior in their natural environment, such as number of shrimp and their abundance at different locations (micro habitats) over time. Temporal color contrast features were applied to enable the detection of the semi-transparent shrimp. To study the spatial–temporal characteristics of the shrimp, pseudo-color visualizations referred to as shrimp abundance maps (SAM) are introduced. SAMs for different time periods are presented, to show the potential of the methodology.

[1]  Tim W. Nattkemper,et al.  Ranking Color Correction Algorithms Using Cluster Indices , 2014, 2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  Marc Picheral,et al.  Digital zooplankton image analysis using the ZooScan integrated system , 2010 .

[5]  Tim W. Nattkemper,et al.  Biigle - Web 2.0 enabled labelling and exploring of images from the Arctic deep-sea observatory HAUSGARTEN , 2009, OCEANS 2009-EUROPE.

[6]  J. Gutt,et al.  Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN , 2012, PloS one.

[7]  Rüdiger Henrich,et al.  Grounding Pleistocene icebergs shape recent deep-water coral reefs , 1999 .

[8]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[9]  Tomas Lundälv,et al.  Distributional patterns of macro- and megafauna associated with a reef of the cold-water coral Lophelia pertusa on the Swedish west coast , 2004 .

[10]  David M. Lane,et al.  Implementation of a fast coral detector using a supervised machine learning and Gabor Wavelet feature descriptors , 2014, 2014 IEEE Sensor Systems for a Changing Ocean (SSCO)..

[11]  P. Anderson.  Pandalid Shrimp as Indicators of Ecosystem Regime Shift , 2000 .

[12]  J. Schanda,et al.  Colorimetry : understanding the CIE system , 2007 .

[13]  Phil Culverhouse,et al.  Time to automate identification , 2010, Nature.

[14]  Pål Buhl-Mortensen,et al.  Effects of water flow and drilling waste exposure on polyp behaviour in Lophelia pertusa , 2015 .

[15]  Tim W. Nattkemper,et al.  BIIGLE Tools – A Web 2.0 Approach for Visual Bioimage Database Mining , 2009, 2009 13th International Conference Information Visualisation.

[16]  Alf Harbitz.  Estimation of shrimp (Pandalus borealis) carapace length by image analysis , 2007 .

[17]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Olav Rune Godø,et al.  The LoVe Ocean Observatory is in Operation , 2014 .

[19]  Daniel O.B. Jones,et al.  A Southeast Atlantic deep‐ocean observatory: first experiences and results , 2013 .

[20]  Øyvind Ødegård,et al.  Integrated environmental mapping and monitoring, a methodological approach to optimise knowledge gathering and sampling strategy. , 2015, Marine pollution bulletin.

[21]  Jörg Ontrup,et al.  Use of machine-learning algorithms for the automated detection of cold-water coral habitats: a pilot study , 2009 .

[22]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[23]  P. Utgoff,et al.  RAPID: Research on Automated Plankton Identification , 2007 .

[24]  Ruiju Tong,et al.  Microhabitat and shrimp abundance within a Norwegian cold-water coral ecosystem , 2013 .