Fish species classification in unconstrained underwater environments based on deep learning

QR Code

Fish species classification in unconstrained underwater environments based on deep learning

Underwater video and digital still cameras are rapidly being adopted by marine scientists and managers as a tool for non-destructively quantifying and measuring the relative abundance, cover and size of marine fauna and flora. Imagery recorded of fish can be time consuming and costly to process and...

Full description

Bibliographic Details
Main Authors:	Salman, A., Jalal, A., Shafait, F., Mian, A., Shortis, M., Seager, J., Harvey, Euan
Format:	Journal Article
Published:	Association for the Sciences of Limnology and Oceanography 2016
Online Access:	http://hdl.handle.net/20.500.11937/26863

Similar Items

Automatic fish species classification in underwater videos: Exploiting pre-trained deep neural network models to compensate for limited labelled data
by: Siddiqui, S., et al.
Published: (2018)

Automated Fish Detection in Underwater Images Using Shape-Based level Sets
by: Ravanbakhsh, M., et al.
Published: (2015)

An Application of Shape-Based Level Sets to Fish Detection in Underwater Images
by: Ravanbakhsh, M., et al.
Published: (2014)

Fish identification from videos captured in uncontrolled underwater environments
by: Shafait, F., et al.
Published: (2013)

Towards automating underwater measurement of fish length: A comparison of semi-automatic and manual stereo-video measurements
by: Shafait, F., et al.
Published: (2017)

A review of underwater stereo-image measurement for marine biology and ecology applications
by: Shortis, M., et al.
Published: (2009)

A comparison of calibration methods and system configurations of underwater stereo-video systems for applications in marine ecology
by: Boutros, N., et al.
Published: (2015)

Accelerating Species Recognition and Labelling of Fish From Underwater Video With Machine-Assisted Deep Learning
by: Marrable, Daniel, et al.
Published: (2022)

Does fish behaviour bias abundance and length information collected by baited underwater video?
by: Coghlan, A., et al.
Published: (2017)

Combining underwater video methods improves effectiveness of demersal fish assemblage surveys across habitats
by: Logan, J., et al.
Published: (2017)

MS-Net: multi-segmentation network for the Iris region using deep learning in an unconstrained environment
by: Malgheet, Jasem Rahman, et al.
Published: (2023)

Spatial prediction of demersal fish distributions: Enhancing our understanding of species-environment relationships
by: Moore, Cordelia, et al.
Published: (2009)

Response of diurnal and nocturnal coral reef fish to protection from fishing: An assessment using baited remote underwater video
by: Harvey, Euan, et al.
Published: (2012)

Diversity and Composition of Demersal Fishes along a Depth Gradient Assessed by Baited Remote Underwater Stereo-Video
by: Zintzen, V., et al.
Published: (2012)

Multimodal analysis of depression in unconstrained environments
by: Kusumam, Keerthy
Published: (2023)

Face detection and recognition in an unconstrained environment
by: Chin, Ariana Sue Rei
Published: (2022)

Localized deep extreme learning machines for efficient RGB-D object recognition
by: Mohd Zaki, Hasan Firdaus, et al.
Published: (2015)

Deep Learning for Coral Classification
by: Mahmood, A., et al.
Published: (2017)

Underwater species-constrained fish detection using multi-frame image information
by: Ling, Yi Jun
Published: (2023)

Comparison of baited longlines and baited underwater cameras for assessing the composition of continental slope deepwater fish assemblages off southeast Australia
by: McLean, D., et al.
Published: (2015)

Baited remote underwater stereo-video outperforms baited downward-facing single-video for assessments of fish diversity, abundance and size composition
by: Cundy, M., et al.
Published: (2017)

Baited remote underwater video as a promising nondestructive tool to assess fish assemblages in clearwater Amazonian rivers: Testing the effect of bait and habitat type
by: Schmid, K., et al.
Published: (2017)

Multi-camera face detection and recognition in an unconstrained environment
by: Lee, Kian Huang
Published: (2023)

Shining a light on fish at night: An overview of fish and fisheries in the dark of night, and in deep and polar seas
by: Hammerschlag, N., et al.
Published: (2017)

Influence of the Leeuwin Current on the distribution of fishes and the composition of fish assemblages
by: Watson, D., et al.
Published: (2009)

Effects of protection from fishing on the lengths of targeted and non-targeted fish species at the Houtman Abrolhos Islands, Western Australia
by: Watson, D., et al.
Published: (2009)

The Use of Stationary Underwater Video for Sampling Sharks
by: Harvey, Euan, et al.
Published: (2018)

Flow Field Measurement Of Fish And Development Of Fish Like Underwater Robot
by: Soh, Ling Xin
Published: (2018)

Deep learning for image classification
by: Koi, Chin Chong
Published: (2024)

Effects of a spatial closure on highly mobile fish species: an assessment using pelagic stereo-BRUVs
by: Santana-Garcon, J., et al.
Published: (2014)

Targeted demersal fish species exhibit variable responses to long-term protection from fishing at the Houtman Abrolhos Islands
by: Bornt, K., et al.
Published: (2015)

Seasonal changes in a deep-water fish assemblage in response to monsoon-generated upwelling events
by: McIlwain, Jennifer, et al.
Published: (2011)

Silent fish surveys: Bubble-free diving highlights inaccuracies associated with SCUBA-based surveys in heavily fished areas
by: Lindfield, S., et al.
Published: (2014)

Applications of Underwater Acoustics in Polar Environments
by: Gavrilov, Alexander, et al.
Published: (2017)

Evaluation of fish behaviour and aggregation by underwater videography in an artificial reef in Tioman Island, Malaysia
by: T. Yoshida,, et al.
Published: (2010)

Deep Image Representations for Coral Image Classification
by: Mahmood, A., et al.
Published: (2018)

An underwater electrosensory membrane bio-inspired by weakly electric fish
by: Wang, Ke, et al.
Published: (2016)

Effects of fishing on fish assemblages at the reefscape scale
by: Fitzpatrick, B., et al.
Published: (2015)

Within and between day variability in temperate reef fish assemblages: Learned response to baited video
by: Birt, M., et al.
Published: (2012)

Integration of Enhanced Background Filtering and Wavelet Fusion for High Visibility and Detection Rate of Deep Sea Underwater Image of Underwater Vehicle
by: Ahmad Shahrizan, Abdul Ghani, et al.
Published: (2017)

Cannot write session to /tmp/vufind_sessions/sess_odd7m3g8vf6d836evkpp3od9gs