20.7.3.7.18 Agriculture, Inspection -- Fish, Fish Motion, Detection

Tropical Coral Reef Fish Detection, Tracking And Classification,
Fish4Knowledge project datasets. Online2014
WWW Link. Dataset, Fish.
See also University of Edinburgh.
See also Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data. BibRef 1400

Strachan, N.J.C., Nesvadba, P., Allen, A.R.,
Fish species recognition by shape analysis of images,
PR(23), No. 5, 1990, pp. 539-544.
Elsevier DOI 0401
BibRef

Strachan, N.J.C.,
Recognition of fish species by colour and shape,
IVC(11), No. 1, January-February 1993, pp. 2-10.
Elsevier DOI 0401
BibRef

Pau, L.F., and Olafsson, R.,
Fish Quality Control by Computer Vision,
New York: Marcel Dekker1991. BibRef 9100 Book BibRef

Grigoryan, A.M., Hostetter, G., Kallioniemi, O., Dougherty, E.R.,
Simulation Toolbox for 3D-FISH Spot-Counting Algorithms,
RealTimeImg(8), No. 3, June 2002, pp. 203-212.
DOI Link 0208
BibRef

Guillaud, A.[Anne], Troadec, H.[Herve], Benzinou, A.[Abdesslam], Le Bihan, J.[Jean], Rodin, V.[Vincent],
A Multiagent System for Edge Detection and Continuity Perception on Fish Otolith Images,
JASP(2002), No. 7, July 2002, pp. 746. 0208
BibRef

Benzinou, A., Troadec, H., Le Bihan, J., Rodin, V., de Pontual, H., and Tisseau, J.,
The Locally Deformable B-Bubble Model: An Application to Growth Ring Detection on Fish Otoliths,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Guillaud, A., Troadec, H., Benzinou, A., Rodin, V., Le Bihan, J.,
Continuity Perception Using a Multiagent System. an Application to Growth Ring Detection on Fish Otoliths,
ICPR00(Vol II: 519-522).
IEEE DOI 0009
BibRef

Rodin, V.[Vincent], Troadec, H., de Pontual, H., Benzinou, A., Tisseau, J., Le Bihan, J.,
Growth Ring Detection on Fish Otoliths by a Graph Construction,
ICIP96(II: 685-688).
IEEE DOI BibRef 9600

Cao, F.[Frédéric], Fablet, R.[Ronan],
Automatic morphological detection of otolith nucleus,
PRL(27), No. 6, 15 April 2006, pp. 658-666.
Elsevier DOI Mathematical morphology; A contrario detection; Otolith imaging 0604
BibRef
Earlier: ICPR04(III: 606-609).
IEEE DOI 0409
BibRef

Fablet, R., Le Josse, N., Benzinou, A.,
Automatic fish age estimation from otolith images using statistical learning,
ICPR04(IV: 503-506).
IEEE DOI 0409
BibRef

Fablet, R., Benzinou, A., Doncarli, C.,
Robust time-frequency model estimation in Otolith images for fish age and growth analysis,
ICIP03(III: 593-596).
IEEE DOI 0312
BibRef

Bermejo, S.[Sergio], Monegal, B.[Brais],
Fish age analysis and classification with kernel methods,
PRL(28), No. 10, 15 July 2007, pp. 1164-1171.
Elsevier DOI 0706
Automated fish age classification; Statistical learning; Kernel principal component analysis; Support vector machines; Scientific applications of pattern recognition BibRef

Kawasue, K.[Kikuhito], Nagatomo, S.[Satoshi], and Oya, Y.[Yuichiro],
Three Dimensional Measurement of Aquatic Organisms Using a Single Video Camera,
Sensors(Special: 9), December 2010, pp. 118-126.
HTML Version. BibRef 1012

Enomoto, K.[Koichiro], Toda, M.[Masashi], Kuwahara, Y.[Yasuhiro],
Extraction Method of Scallop Area in Gravel Seabed Images for Fishery Investigation,
IEICE(E93-D), No. 7, July 2010, pp. 1754-1760.
WWW Link. 1008
BibRef
Earlier:
Scallop Detection from Sand-Seabed Images for Fishery Investigation,
CISP09(1-5).
IEEE DOI 0910
BibRef

Enomoto, K.[Koichiro], Toda, M.[Masashi], Kuwahara, Y.[Yasuhiro],
Discussion on a method to extract scallop using line convergence index filter from granule-sand seabed videos,
MVA15(35-40)
IEEE DOI 1507
Aquaculture BibRef

Enomoto, K.[Koichiro], Toda, M.[Masashi], Kuwahara, Y.[Yasuhiro],
Extraction Method of Scallop Area from Sand Seabed Images,
IEICE(E97-D), No. 1, January 2013, pp. 130-138.
WWW Link. 1412
BibRef

Enomoto, K.[Koichiro], Toda, M.[Masashi], Kuwahara, Y.[Yasuhiro], Wada, M.[Masaaki], Hatanaka, K.[Katsumori],
Scallop Detection from Gravel-Seabed Images for Fishery Investigation,
MVA09(479-).
PDF File. 0905
BibRef

Hagisawa, T.[Takeshi], Enomoto, K.[Koichiro], Toda, M.[Masashi], Tamura, M.[Masakatsu], Takeda, S.[Sakae],
The amount of Alaria praelonga Kjellmans analysis method from laminaria bed images for fishery investigation,
FCV11(1-6).
IEEE DOI 1102
BibRef

Lefort, R., Fablet, R., Boucher, J.M.,
Object recognition using proportion-based prior information: Application to fisheries acoustics,
PRL(32), No. 2, 15 January 2011, pp. 153-158.
Elsevier DOI 1101
Weakly supervised learning; Generative classification model; Discriminative classification model BibRef

Fablet, R., Lefort, R., Scalarin, C., Masse, J., Cauchy, P., Boucher, J.M.,
Weakly supervised learning using proportion-based information: An application to fisheries acoustics,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Luengo-Oroz, M.A., Rubio-Guivernau, J.L., Faure, E., Savy, T., Duloquin, L., Olivier, N., Pastor, D., Ledesma-Carbayo, M., Debarre, D., Bourgine, P., Beaurepaire, E., Peyrieras, N., Santos, A.,
Methodology for Reconstructing Early Zebrafish Development From In Vivo Multiphoton Microscopy,
IP(21), No. 4, April 2012, pp. 2335-2340.
IEEE DOI 1204
BibRef

González-Rufino, E., Carrión, P., Cernadas, E., Fernández-Delgado, M., Domínguez-Petit, R.,
Exhaustive comparison of colour texture features and classification methods to discriminate cells categories in histological images of fish ovary,
PR(46), No. 9, September 2013, pp. 2391-2407.
Elsevier DOI 1305
Histological image; Fish ovary; Fecundity; Stereology; Classification; Colour texture analysis; Pyramid decomposition; Multiresolution analysis; Fractal analysis; Local Binary Patterns; Wavelets; Co-ocurrence matrix; Sum and Difference Histogram; Support Vector Machine; Statistical classifiers; Ensembles; Neural networks BibRef

Cernadas, E., Fernández-Delgado, M., González-Rufino, E., Carrión, P.,
Influence of normalization and color space to color texture classification,
PR(61), No. 1, 2017, pp. 120-138.
Elsevier DOI 1705
Color texture classification BibRef

Ardekani, R.[Reza], Greenwood, A.K.[Anna K.], Peichel, C.L.[Catherine L.], Tavaré, S.[Simon],
Automated quantification of the schooling behaviour of sticklebacks,
JIVP(2013), No. 1, 2013, pp. 61.
DOI Link 1312
BibRef

Spampinato, C., Palazzo, S., Kavasidis, I.,
A texton-based kernel density estimation approach for background modeling under extreme conditions,
CVIU(122), No. 1, 2014, pp. 74-83.
Elsevier DOI 1404
BibRef
And: A2, A3, A1:
Covariance based modeling of underwater scenes for fish detection,
ICIP13(1481-1485)
IEEE DOI 1412
Background and foreground modeling BibRef

Chuang, M.C.[Meng-Che], Hwang, J.N.[Jenq-Neng], Williams, K., Towler, R.,
Tracking Live Fish From Low-Contrast and Low-Frame-Rate Stereo Videos,
CirSysVideo(25), No. 1, January 2015, pp. 167-179.
IEEE DOI 1502
aquaculture BibRef

Atoum, Y., Srivastava, S., Liu, X.M.[Xiao-Ming],
Automatic Feeding Control for Dense Aquaculture Fish Tanks,
SPLetters(22), No. 8, August 2015, pp. 1089-1093.
IEEE DOI 1502
aquaculture BibRef

Huang, P.X.[Phoenix X.], Boom, B.J.[Bastiaan J.], Fisher, R.B.[Robert B.],
Hierarchical classification with reject option for live fish recognition,
MVA(26), No. 1, January 2015, pp. 89-102.
WWW Link. 1503
BibRef
Earlier:
GMM improves the reject option in hierarchical classification for fish recognition,
WACV14(371-376)
IEEE DOI 1406
BibRef
Earlier:
Underwater Live Fish Recognition Using a Balance-Guaranteed Optimized Tree,
ACCV12(I:422-433).
Springer DOI 1304
Databases BibRef

Weeks, S.J.[Scarla J.], Magno-Canto, M.M.[Marites M.], Jaine, F.R.A.[Fabrice R. A.], Brodie, J.[Jon], Richardson, A.J.[Anthony J.],
Unique Sequence of Events Triggers Manta Ray Feeding Frenzy in the Southern Great Barrier Reef, Australia,
RS(7), No. 3, 2015, pp. 3138-3152.
DOI Link 1504
Not really detection, analysis of effects. BibRef

Nieuwhof, S.[Sil], Herman, P.M.J.[Peter M. J.], Dankers, N.[Norbert], Troost, K.[Karin], van der Wal, D.[Daphne],
Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery,
RS(7), No. 4, 2015, pp. 3710-3734.
DOI Link 1505
BibRef

Ye, L.N.[Lin-Ning], Hou, Z.[Zujun], Eng, H.L.[How-Lung],
Context aware image enhancement for online fish behaviour monitoring,
IET-IPR(10), No. 2, 2016, pp. 149-157.
DOI Link 1602
Poisson equation BibRef

Chuang, M.C.[Meng-Che], Hwang, J.N.[Jenq-Neng], Williams, K.[Kresimir],
A Feature Learning and Object Recognition Framework for Underwater Fish Images,
IP(25), No. 4, April 2016, pp. 1862-1872.
IEEE DOI 1604
BibRef
Earlier:
Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition,
CVAUI14(33-40)
IEEE DOI 1412
Aquaculture. BibRef

Fisher, R.B., Chen-Burger, Y.H., Giordano, D., Hardman, L., Lin, F.P., (Eds.)
Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data,
Springer2016. ISBN 978-3-319-30206-5
WWW Link.
See also Tropical Coral Reef Fish Detection, Tracking And Classification. BibRef 1600

Boudhane, M.[Mohcine], Nsiri, B.[Benayad],
Underwater image processing method for fish localization and detection in submarine environment,
JVCIR(39), No. 1, 2016, pp. 226-238.
Elsevier DOI 1608
Object detection BibRef

Hughes, B.[Benjamin], Burghardt, T.[Tilo],
Automated Visual Fin Identification of Individual Great White Sharks,
IJCV(122), No. 3, May 2017, pp. 542-557.
Springer DOI 1704
BibRef
Earlier:
Automated Identification of Individual Great White Sharks from Unrestricted Fin Imagery,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Lan, K.W.[Kuo-Wei], Shimada, T.[Teruhisa], Lee, M.A.[Ming-An], Su, N.J.[Nan-Jay], Chang, Y.[Yi],
Using Remote-Sensing Environmental and Fishery Data to Map Potential Yellowfin Tuna Habitats in the Tropical Pacific Ocean,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Chuang, M.C., Hwang, J.N., Ye, J.H., Huang, S.C., Williams, K.,
Underwater Fish Tracking for Moving Cameras Based on Deformable Multiple Kernels,
SMCS(47), No. 9, September 2017, pp. 2467-2477.
IEEE DOI 1708
Cameras, Deformable models, Histograms, Kernel, Object tracking, Target tracking, Deformable part model (DPM), fisheries application, mean-shift (MS) algorithm, moving cameras, object, tracking BibRef

Yi, D.H.[Dong Hoon], Gong, Z.[Zheng], Jech, J.M.[J. Michael], Ratilal, P.[Purnima], Makris, N.C.[Nicholas C.],
Instantaneous 3D Continental-Shelf Scale Imaging of Oceanic Fish by Multi-Spectral Resonance Sensing Reveals Group Behavior during Spawning Migration,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Beyan, C.[Cigdem], Katsageorgiou, V.M.[Vasiliki-Maria], Fisher, R.B.[Robert B.],
Extracting statistically significant behaviour from fish tracking data with and without large dataset cleaning,
IET-CV(12), No. 2, March 2018, pp. 162-170.
DOI Link 1804
BibRef

Mohamed, H.[Hassan], Nadaoka, K.[Kazuo], Nakamura, T.[Takashi],
Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
habitat monitoring. BibRef

Guo, Y.H.[Yuan-Hao], Xiong, Z.[Zhan], Verbeek, F.J.[Fons J.],
An efficient and robust hybrid method for segmentation of zebrafish objects from bright-field microscope images,
MVA(29), No. 8, November 2018, pp. 1211-1225.
WWW Link. 1811
BibRef

Kalacska, M.[Margaret], Lucanus, O.[Oliver], Sousa, L.[Leandro], Vieira, T.[Thiago], Arroyo-Mora, J.P.[Juan Pablo],
Freshwater Fish Habitat Complexity Mapping Using Above and Underwater Structure-From-Motion Photogrammetry,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Wang, W., Gu, D., Xie, G.,
Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors,
SMCS(49), No. 5, May 2019, pp. 891-903.
IEEE DOI 1904
Robot sensing systems, Optimization, Biological system modeling, Unmanned underwater vehicles, Aquatic robots, underwater robots BibRef

Huang, T.W., Hwang, J.N., Romain, S., Wallace, F.,
Fish Tracking and Segmentation From Stereo Videos on the Wild Sea Surface for Electronic Monitoring of Rail Fishing,
CirSysVideo(29), No. 10, October 2019, pp. 3146-3158.
IEEE DOI 1910
BibRef
Earlier:
Live Tracking of Rail-Based Fish Catching on Wild Sea Surface,
CVAUI16(25-30)
IEEE DOI 1701
aquaculture, image filtering, image segmentation, Kalman filters, object detection, stereo image processing, stereo video. Computer vision BibRef

Wang, G., Hwang, J.N., Williams, K., Wallace, F., Rose, C.S.,
Shrinking Encoding with Two-Level Codebook Learning for Fine-Grained Fish Recognition,
CVAUI16(31-36)
IEEE DOI 1701
Encoding BibRef

Wang, G., Hwang, J.N., Williams, K., Cutter, G.,
Closed-Loop Tracking-by-Detection for ROV-Based Multiple Fish Tracking,
CVAUI16(7-12)
IEEE DOI 1701
Cameras BibRef

Collas, F.P.L.[Frank P.L.], van Iersel, W.K.[Wimala K.], Straatsma, M.W.[Menno W.], Buijse, A.D.[Anthonie D.], Leuven, R.S.E.W.[Rob S.E.W.],
Sub-Daily Temperature Heterogeneity in a Side Channel and the Influence on Habitat Suitability of Freshwater Fish,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Paspalakis, S.[Stavros], Moirogiorgou, K.[Konstantia], Papandroulakis, N.[Nikos], Giakos, G.[George], Zervakis, M.[Michalis],
Automated fish cage net inspection using image processing techniques,
IET-IPR(14), No. 10, August 2020, pp. 2028-2034.
DOI Link 2008
BibRef

Oleksyn, S.[Semonn], Tosetto, L.[Louise], Raoult, V.[Vincent], Williamson, J.E.[Jane E.],
Drone-Based Tracking of the Fine-Scale Movement of a Coastal Stingray (Bathytoshia brevicaudata),
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Zhao, Z.X.[Zhen-Xi], Liu, Y.[Yang], Sun, X.D.[Xu-Dong], Liu, J.T.[Jin-Tao], Yang, X.T.[Xin-Ting], Zhou, C.[Chao],
Composited FishNet: Fish Detection and Species Recognition From Low-Quality Underwater Videos,
IP(30), 2021, pp. 4719-4734.
IEEE DOI 2105
BibRef

Ding, W.X.[Wen-Xiang], Zhang, C.Y.[Cai-Yun], Hu, J.Y.[Jian-Yu], Shang, S.P.[Shao-Ping],
Unusual Fish Assemblages Associated with Environmental Changes in the East China Sea in February and March 2017,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Berg, H.[Håkan], Mulokozi, D.[Deogratias], Udikas, L.[Lars],
A GIS Assessment of the Suitability of Tilapia and Clarias Pond Farming in Tanzania,
IJGI(10), No. 5, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Li, X.[Xiaoen], Xiao, Y.[Yang], Su, F.Z.[Fen-Zhen], Wu, W.Z.[Wen-Zhou], Zhou, L.[Liang],
AIS and VBD Data Fusion for Marine Fishing Intensity Mapping and Analysis in the Northern Part of the South China Sea,
IJGI(10), No. 5, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Katija, K.[Kakani], Roberts, P.L.D.[Paul L. D.], Daniels, J.[Joost], Lapides, A.[Alexandra], Barnard, K.[Kevin], Risi, M.[Mike], Ranaan, B.Y.[Ben Y.], Woodward, B.G.[Benjamin G.], Takahashi, J.[Jonathan],
Visual tracking of deepwater animals using machine learning-controlled robotic underwater vehicles,
WACV21(859-868)
IEEE DOI 2106
Visualization, Target tracking, Machine learning algorithms, Animals, Oceans, Ecosystems BibRef

Liu, J.[Jincun], Wu, Z.X.[Zheng-Xing], Yu, J.Z.[Jun-Zhi], Xue, Z.B.[Zhi-Bin],
Cooperative Target Tracking in Aquatic Environment Using Dual Robotic Dolphins,
SMCS(51), No. 8, August 2021, pp. 4782-4792.
IEEE DOI 2107
Dolphins, Robot kinematics, Path planning, Target tracking, Turning, Task analysis, Behavior based, biomimetic robotic dolphin, rapidly exploring random tree (RRT) BibRef

Zang, X.Q.[Xiao-Qin], Yin, T.Z.[Tian-Zhixi], Hou, Z.S.[Zhang-Shuan], Mueller, R.P.[Robert P.], Deng, Z.Q.D.[Zhi-Qun Daniel], Jacobson, P.T.[Paul T.],
Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Zhang, M.[Miao], Rock, S.[Stephen],
Augmenting Discriminative Correlation Filters with Stereo Blob Tracking for Long-Term Tracking of Underwater Animals,
MVA21(1-5)
DOI Link 2109
Resistance, Target tracking, Correlation, Memory management, Filtering algorithms, Information filters BibRef

Zhuang, P.Q.[Pei-Qin], Wang, Y.[Yali], Qiao, Y.[Yu],
Wildfish++: A Comprehensive Fish Benchmark for Multimedia Research,
MultMed(23), 2021, pp. 3603-3617.
IEEE DOI 2110
Task analysis, Benchmark testing, Fish, Visualization, Feature extraction, Morphology, WildFish++, Automatic Fish Classification BibRef

Duane, D.[Daniel], Godø, O.R.[Olav Rune], Makris, N.C.[Nicholas C.],
Quantification of Wide-Area Norwegian Spring-Spawning Herring Population Density with Ocean Acoustic Waveguide Remote Sensing (OAWRS),
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Duane, D.[Daniel], Zhu, C.Y.[Chen-Yang], Piavsky, F.[Felix], Godø, O.R.[Olav Rune], Makris, N.C.[Nicholas C.],
The Effect of Attenuation from Fish on Passive Detection of Sound Sources in Ocean Waveguide Environments,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Berg, P.[Paul], Maia, D.S.[Deise Santana], Pham, M.T.[Minh-Tan], Lefèvre, S.[Sébastien],
Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Zhang, W.B.[Wen-Bo], Wu, C.Y.[Chao-Yi], Bao, Z.S.[Zhen-Shan],
DPANet: Dual Pooling-aggregated Attention Network for fish segmentation,
IET-CV(16), No. 1, 2022, pp. 67-82.
DOI Link 2202
convolutional neural nets, image segmentation, learning (artificial intelligence) BibRef

Li, L.[Lin], Dong, B.[Bo], Rigall, E.[Eric], Zhou, T.[Tao], Dong, J.Y.[Jun-Yu], Chen, G.[Geng],
Marine Animal Segmentation,
CirSysVideo(32), No. 4, April 2022, pp. 2303-2314.
IEEE DOI 2204
Marine animals, Annotations, Animals, Task analysis, Image segmentation, Image enhancement, Feature extraction, camouflaged marine animals BibRef

Elise, S.[Simon], Guilhaumon, F.[François], Mou-Tham, G.[Gérard], Urbina-Barreto, I.[Isabel], Vigliola, L.[Laurent], Kulbicki, M.[Michel], Bruggemann, J.H.[J. Henrich],
Combining Passive Acoustics and Environmental Data for Scaling Up Ecosystem Monitoring: A Test on Coral Reef Fishes,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Mammel, M.[Mubarak], Naimullah, M.[Muhamad], Vayghan, A.H.[Ali Haghi], Hsu, J.[Jhen], Lee, M.A.[Ming-An], Wu, J.H.[Jun-Hong], Wang, Y.C.[Yi-Chen], Lan, K.W.[Kuo-Wei],
Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Liu, T.[Tao], He, S.Y.[Shuang-Yan], Liu, H.Y.[Hao-Yang], Gu, Y.Z.[Yan-Zhen], Li, P.L.[Pei-Liang],
A Robust Underwater Multiclass Fish-School Tracking Algorithm,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Bigal, E.[Eyal], Galili, O.[Ori], van Rijn, I.[Itai], Rosso, M.[Massimiliano], Cleguer, C.[Christophe], Hodgson, A.[Amanda], Scheinin, A.[Aviad], Tchernov, D.[Dan],
Reduction of Species Identification Errors in Surveys of Marine Wildlife Abundance Utilising Unoccupied Aerial Vehicles (UAVs),
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Chen, Z.[Ziwen], Cao, L.J.[Li-Jie], Wang, Q.H.[Qi-Hua], Cai, Y.[Yu],
FishNet: Fish visual recognition with one stage multi-task learning,
IET-IPR(16), No. 12, 2022, pp. 3237-3246.
DOI Link 2209
BibRef

Zhang, S.[Shuang], Qian, X.Y.[Xin-Yu], Liu, Z.J.[Zhi-Jie], Li, Q.[Qing], Li, G.[Guang],
PDE Modeling and Tracking Control for the Flexible Tail of an Autonomous Robotic Fish,
SMCS(52), No. 12, December 2022, pp. 7618-7627.
IEEE DOI 2212
FIsh, Robots, Mathematical models, Robot kinematics, Regulators, Actuators, Propulsion, Tracking, Autonomous robotic fish, tracking control BibRef

Many, G.[Gaël], Escoffier, N.[Nicolas], Ferrari, M.[Michele], Jacquet, P.[Philippe], Odermatt, D.[Daniel], Mariethoz, G.[Gregoire], Perolo, P.[Pascal], Perga, M.E.[Marie-Elodie],
Long-Term Spatiotemporal Variability of Whitings in Lake Geneva from Multispectral Remote Sensing and Machine Learning,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Yin, X.Q.[Xiao-Qing], Yang, D.T.[Ding-Tian], Zhao, L.H.[Lin-Hong], Zhong, R.[Rong], Du, R.R.[Ran-Ran],
Fishery Resource Evaluation with Hydroacoustic and Remote Sensing in Yangjiang Coastal Waters in Summer,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Xu, Y.[Yuan], Jin, Y.C.[Yi-Chao], Zhang, Y.[Yang], Zhu, Q.[Qunxiong], He, Y.L.[Yan-Lin], Sheng, H.[Hao],
3D zebrafish tracking with topology association,
IET-IPR(17), No. 4, 2023, pp. 1044-1059.
DOI Link 2303
Multi-object tracking, topology association, 3D tracking, Zebrafish BibRef

McEver, R.A.[R. Austin], Zhang, B.[Bowen], Levenson, C.[Connor], Iftekhar, A.S.M., Manjunath, B.S.,
Context-Driven Detection of Invertebrate Species in Deep-Sea Video,
IJCV(131), No. 6, June 2023, pp. 1367-1388.
Springer DOI 2305
I know they aren't fish, but same environment. BibRef

Cai, L.[Levi], McGuire, N.E.[Nathan E.], Hanlon, R.[Roger], Mooney, T.A.[T. Aran], Girdhar, Y.[Yogesh],
Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles,
IJCV(131), No. 6, June 2023, pp. 1406-1427.
Springer DOI 2305
BibRef

Zhao, M.[Meng], Wu, J.F.[Jun-Feng], Yu, H.[Hong], Li, H.Q.[Hai-Qing], Xu, J.W.[Jing-Wen], Cheng, S.Q.[Si-Qi], Gu, L.S.[Li-Shuai], Meng, J.[Juan],
Fish Detecting Using YOLOv4 and CVAE in Aquaculture Ponds with a Non-Uniform Strong Reflection Background,
IEICE(E106-D), No. 5, May 2023, pp. 715-725.
WWW Link. 2305
BibRef

Prado, E.[Elena], Abad-Uribarren, A.[Alberto], Ramo, R.[Rubén], Sierra, S.[Sergio], González-Pola, C.[César], Cristobo, J.[Javier], Ríos, P.[Pilar], Graña, R.[Rocío], Aierbe, E.[Eneko], Rodríguez, J.M.[Juan Manuel], Rodríguez-Cabello, C.[Cristina], Modica, L.[Larissa], Rodríguez-Basalo, A.[Augusto], Sánchez, F.[Francisco],
Describing Polyps Behavior of a Deep-Sea Gorgonian, Placogorgia sp., Using a Deep-Learning Approach,
RS(15), No. 11, 2023, pp. 2777.
DOI Link 2306
BibRef

Tsuda, M.E.[Masaki E.], Miller, N.A.[Nathan A.], Saito, R.[Rui], Park, J.[Jaeyoon], Oozeki, Y.[Yoshioki],
Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea,
RS(15), No. 11, 2023, pp. 2911.
DOI Link 2306
BibRef

Monteiro, F.[Filipe], Bexiga, V.[Vasco], Chaves, P.[Paulo], Godinho, J.[Joaquim], Henriques, D.[David], Melo-Pinto, P.[Pedro], Nunes, T.[Tiago], Piedade, F.[Fernando], Pimenta, N.[Nelson], Sustelo, L.[Luis], Fernandes, A.M.[Armando M.],
Classification of Fish Species Using Multispectral Data from a Low-Cost Camera and Machine Learning,
RS(15), No. 16, 2023, pp. 3952.
DOI Link 2309
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Kelaher, B.P.[Brendan P.], Monteforte, K.I.[Kim I.], Morris, S.G.[Stephen G.], Schlacher, T.A.[Thomas A.], March, D.T.[Duane T.], Tucker, J.P.[James P.], Butcher, P.A.[Paul A.],
Drone-Based Assessment of Marine Megafauna off Wave-Exposed Sandy Beaches,
RS(15), No. 16, 2023, pp. 4018.
DOI Link 2309
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Cheng, J.G.[Jin-Guang], Wu, Z.W.[Zong-Wei], Wang, S.[Shuo], Demonceaux, C.[Cédric], Jiang, Q.P.[Qiu-Ping],
Bidirectional Collaborative Mentoring Network for Marine Organism Detection and Beyond,
CirSysVideo(33), No. 11, November 2023, pp. 6595-6608.
IEEE DOI Code:
WWW Link. 2311
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Yu, Y.Z.[Yao-Zhen], Zhang, H.[Hao], Yuan, F.[Fei],
Key point detection method for fish size measurement based on deep learning,
IET-IPR(17), No. 14, 2023, pp. 4142-4158.
DOI Link 2312
image processing, length measurement, measurement systems BibRef

Zhao, Z.X.[Zhen-Xi], Yang, X.T.[Xin-Ting], Liu, J.T.[Jin-Tao], Zhou, C.[Chao], Zhao, C.J.[Chun-Jiang],
GCVC: Graph Convolution Vector Distribution Calibration for Fish Group Activity Recognition,
MultMed(26), 2024, pp. 1776-1789.
IEEE DOI 2402
Fish, Feature extraction, Activity recognition, Calibration, Adhesives, Training, Convolution, fish activity dataset BibRef

Zhang, P.P.[Ping-Ping], Yan, T.Y.[Tian-Yu], Liu, Y.[Yang], Lu, H.C.[Hu-Chuan],
Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM,
CVPR24(2578-2587)
IEEE DOI Code:
WWW Link. 2410
Representation learning, Location awareness, Image segmentation, Codes, Feature extraction, Marine Animal Segmentation BibRef

Lu, Z.X.[Zhao-Xuan], Zhu, X.L.[Xiao-Long], Guo, H.T.[Hai-Tao], Xie, X.G.[Xin-Gang], Chen, X.Z.[Xiang-Zi], Quan, X.Q.[Xiang-Qian],
FishFocusNet: An improved method based on YOLOv8 for underwater tropical fish identification,
IET-IPR(18), No. 12, 2024, pp. 3634-3649.
DOI Link 2411
convolutional neural nets, object detection, object recognition BibRef

Zhang, Z.K.[Zhen-Kai], Li, W.[Wanghua], Seet, B.C.[Boon-Chong],
A lightweight underwater fish image semantic segmentation model based on U-Net,
IET-IPR(18), No. 12, 2024, pp. 3143-3155.
DOI Link 2411
computer vision, convolutional neural nets, image segmentation, oceanographic techniques BibRef

Xu, J.W.[Jia-Wei], Chen, F.[Fen], Huang, L.[Lian], Liu, T.[Tingna], Peng, Z.[Zongju],
Underwater organisms detection algorithm based on multi-scale perception and representation enhancement,
IET-IPR(18), No. 13, 2024, pp. 4032-4046.
DOI Link 2411
image enhancement, object detection BibRef

Dawkins, M.[Matthew], Prior, J.[Jack], Lewis, B.[Bryon], Faillettaz, R.[Robin], Banez, T.[Thompson], Salvi, M.[Mary], Rollo, A.[Audrey], Simon, J.[Julien], Campbell, M.[Matthew], Lucero, M.[Matthew], Chaudhary, A.[Aashish], Richards, B.[Benjamin], Hoogs, A.[Anthony],
FishTrack23: An Ensemble Underwater Dataset for Multi-Object Tracking,
WACV24(7152-7161)
IEEE DOI 2404
Target tracking, Annotations, Snow, Heuristic algorithms, Organizations, Detectors, Applications, Animals / Insects, Datasets and evaluations BibRef

O'Keeffe, H.[Hamish], Xue, B.[Bing], Zhang, M.J.[Meng-Jie], Hawes, N.[Nicola], Lovell-Smith, C.[Cris],
Real-Time Instance Segmentation Techniques using Neural Networks for the Assessment of Green-Lipped Mussels,
IVCNZ23(1-6)
IEEE DOI 2403
Instance segmentation, Performance evaluation, Quantization (signal), Neural networks, Streaming media, CenterMask BibRef

Khan, F.F.[Faizan Farooq], Li, X.[Xiang], Temple, A.J.[Andrew J.], Elhoseiny, M.[Mohamed],
FishNet: A Large-scale Dataset and Benchmark for Fish Recognition, Detection, and Functional Trait Prediction,
ICCV23(20439-20449)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lin, Y.[Yuan], Chu, Z.Q.[Zhao-Qi], Korhonen, J.[Jari], Xu, J.Y.[Jia-Yi], Liu, X.R.[Xiang-Rong], Liu, J.[Juan], Fang, L.[Lvping], Yang, W.[Weidi], Ghose, D.[Debasish], You, J.Y.[Jun-Yong],
Fast Accurate Fish Recognition with Deep Learning Based on a Domain-Specific Large-Scale Fish Dataset,
MMMod23(I: 515-526).
Springer DOI 2304
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Kumar, N.[Navdeep], di Biagio, C.[Claudia], Dellacqua, Z.[Zachary], Raman, R.[Ratish], Martini, A.[Arianna], Boglione, C.[Clara], Muller, M.[Marc], Geurts, P.[Pierre], Marée, R.[Raphaël],
Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages,
BioImage22(470-486).
Springer DOI 2304
BibRef

Slonimer, A.L.[Alex L.], Cote, M.[Melissa], Marques, T.P.[Tunai Porto], Rezvanifar, A.[Alireza], Dosso, S.E.[Stan E.], Albu, A.B.[Alexandra Branzan], Ersahin, K.[Kaan], Mudge, T.[Todd], Gauthier, S.[Stéphane],
Instance Segmentation of Herring and Salmon Schools in Acoustic Echograms using a Hybrid U-Net,
CRV22(8-15)
IEEE DOI 2301
Satellites, Oceans, Semantics, Ecosystems, Distributed databases, Fish, Encoding, Unet, instance-segmentation, fish, echogram BibRef

Adolfo, G.[Gonçalo], Bernardino, A.[Alexandre], Baylina, N.[Núria], Pinto, H.S.[H. Sofia],
Comparison of Methodologies for Detecting Feeding Activity in Aquatic Environment,
ICPR22(706-712)
IEEE DOI 2212
Measurement, Training, Sea surface, Training data, Fish, Cameras, Convolutional neural networks BibRef

Kay, J.[Justin], Kulits, P.[Peter], Stathatos, S.[Suzanne], Deng, S.Q.[Si-Qi], Young, E.[Erik], Beery, S.[Sara], van Horn, G.[Grant], Perona, P.[Pietro],
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting,
ECCV22(VIII:290-311).
Springer DOI 2211
Dataste, Fish. BibRef

Mei, J.[Jie], Romain, S.[Suzanne], Rose, C.[Craig], Magrane, K.[Kelsey], Hwang, J.N.[Jenq-Neng],
HCIL: Hierarchical Class Incremental Learning for Longline Fishing Visual Monitoring,
ICIP22(3662-3666)
IEEE DOI 2211
Visualization, Training data, Fish, Cameras, Monitoring, Strain, Hierarchical Classification, Class Incremental Learning, Longline Fishing BibRef

Ospici, M.[Matthieu], Sys, K.[Klaas], Guegan-Marat, S.[Sophie],
Prediction of Fish Location by Combining Fisheries Data and Sea Bottom Temperature Forecasting,
CIAP22(III:437-448).
Springer DOI 2205
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Moskvyak, O.[Olga], Maire, F.[Frederic], Dayoub, F.[Feras], Armstrong, A.O.[Asia O.], Baktashmotlagh, M.[Mahsa],
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings,
DICTA21(1-8)
IEEE DOI 2201
Location awareness, Visualization, Databases, Wildlife, Whales, Lighting, Morphology BibRef

Yang, Y.H.[Yun-Han], Xue, B.[Bing], Jesson, L.[Linley], Wylie, M.[Matthew], Zhang, M.J.[Meng-Jie], Wellenreuther, M.[Maren],
Deep Convolutional Neural Networks for Fish Weight Prediction from Images,
IVCNZ21(1-6)
IEEE DOI 2201
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Pradana, H.[Hilmil], Horio, K.[Keiichi],
Tuna Nutriment Tracking using Trajectory Mapping in Application to Aquaculture Fish Tank,
DICTA20(1-8)
IEEE DOI 2201
Tracking, Production, Fish, Trajectory, Standards, Aquaculture, Videos, Productivity, Fish Feeding, Nutriment, Tracking Algorithm, Real Environment Datasets BibRef

Shen, Z.[Zhou], Nguyen, C.[Chuong],
Temporal 3D RetinaNet for fish detection,
DICTA20(1-5)
IEEE DOI 2201
Convolutional codes, Solid modeling, Fish, Feature extraction, Sports, Fish detection, 3D convolution, Temporal feature, 3D-subnets RetinaNet BibRef

Kumar, N.[Navdeep], Carletti, A.[Alessio], Gavaia, P.J.[Paulo J.], Muller, M.[Marc], Cancela, M.L.[M. Leonor], Geurts, P.[Pierre], Marée, R.[Raphaël],
Deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images,
CAIP21(I:154-164).
Springer DOI 2112
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Marques, T.P.[Tunai Porto], Cote, M.[Melissa], Rezvanifar, A.[Alireza], Albu, A.B.[Alexandra Branzan], Ersahin, K.[Kaan], Mudge, T.[Todd], Gauthier, S.[Stéphane],
Instance Segmentation-based Identification of Pelagic Species in Acoustic Backscatter Data,
PBVS21(4373-4382)
IEEE DOI 2109
Training, Image segmentation, Semantics, Object detection, Standardization, Biology BibRef

Fontana, I.[Ignazio], Giacalone, G.[Giovanni], Rizzo, R.[Riccardo], Barra, M.[Marco], Mangoni, O.[Olga], Bonanno, A.[Angelo], Basilone, G.[Gualtiero], Genovese, S.[Simona], Mazzola, S.[Salvatore], Lo Bosco, G.[Giosuè], Aronica, S.[Salvatore],
Unsupervised Classification of Acoustic Echoes from Two Krill Species in the Southern Ocean (Ross Sea),
MAES20(65-74).
Springer DOI 2103
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Marques, T.P.[Tunai Porto], Rezvanifar, A.[Alireza], Cote, M.[Melissa], Albu, A.B.[Alexandra Branzan], Ersahin, K.[Kaan], Mudge, T.[Todd], Gauthier, S.[Stéphane],
Detecting Marine Species in Echograms via Traditional, Hybrid, and Deep Learning Frameworks,
ICPR21(5928-5935)
IEEE DOI 2105
Training, Deep learning, Visualization, Scalability, Object detection, Manuals, Feature extraction BibRef

Martija, M.A.M.[Mygel Andrei M.], Naval, P.C.[Prospero C.],
SynDHN: Multi-Object Fish Tracker Trained on Synthetic Underwater Videos,
ICPR21(8841-8848)
IEEE DOI 2105
Video tracking, Video sequences, Estimation, Detectors, Fish, Prediction algorithms, Approximation algorithms BibRef

Hsu, H.M.[Hung-Min], Xie, Z.[Ziyi], Hwang, J.N.[Jenq-Neng], Berdahl, A.[Andrew],
Robust Fish Enumeration by Multiple Object Tracking in Overhead Videos,
CVAUI20(434-442).
Springer DOI 2103
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Mei, J.[Jie], Hwang, J.N.[Jenq-Neng], Romain, S.[Suzanne], Rose, C.[Craig], Moore, B.[Braden], Magrane, K.[Kelsey],
Video-based Hierarchical Species Classification for Longline Fishing Monitoring,
CVAUI20(422-433).
Springer DOI 2103
BibRef

Zhang, Z., Yu, L., Zhang, J., Wu, Q.,
A Vision Based Fish Processing System,
VCIP20(260-260)
IEEE DOI 2102
aquaculture, fishing industry, food processing industry, food products, supply chain management, Servers BibRef

Lochhead, I., Hedley, N.,
3D Modelling in Temperate Waters: Building Rigs and Data Science to Support Glass Sponge Monitoring Efforts In Coastal British Columbia,
ISPRS20(B2:969-976).
DOI Link 2012
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Pedersen, M., Haurum, J.B., Hein Bengtson, S., Moeslund, T.B.,
3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset,
CVPR20(2423-2433)
IEEE DOI 2008
Tracking, Cameras, Visualization, Videos, Complexity theory BibRef

Castelo, J.[José], Pinto, H.S.[H. Sofia], Bernardino, A.[Alexandre], Baylina, N.[Núria],
Video Based Live Tracking of Fishes in Tanks,
ICIAR20(I:161-173).
Springer DOI 2007
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Haurum, J.B., Karpova, A., Pedersen, M., Bengtson, S.H., Moeslund, T.B.,
Re-Identification of Zebrafish using Metric Learning,
WACVWS20(1-11)
IEEE DOI 2006
Image color analysis, Measurement, Cameras, Task analysis, Head BibRef

Liu, P.[Ping], Yang, H.B.[Hong-Bo], Fu, J.N.[Jing-Nan],
Marine Biometric Recognition Algorithm Based on YOLOV3-GAN Network,
MMMod20(I:581-592).
Springer DOI 2003
TRacking fish resources. BibRef

Zamzuri, N.A.A., Hassan, M.I.,
Potential Fish Ground Breeding Area Based On Localized Criteria For Sustainable Food Security,
GGT19(719-728).
DOI Link 1912
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Huang, T., Hwang, J., Romain, S., Wallace, F.,
Recognizing Fish Species Captured Live on Wild Sea Surface in Videos by Deep Metric Learning with a Temporal Constraint,
ICIP19(3407-3411)
IEEE DOI 1910
Image sequence classification, deep metric learning, fine-grained classification, fishery species BibRef

Kokaki, Y., Tawara, N., Kobayashi, T., Hashimoto, K., Ogawa, T.,
Sequential Fish Catch Forecasting Using Bayesian State Space Models,
ICPR18(776-781)
IEEE DOI 1812
aquaculture, Bayes methods, fishing industry, Monte Carlo methods, parameter estimation, state-space methods, Industries BibRef

Möller, T.[Torben], Nilssen, I.[Ingunn], Nattkemper, T.W.[Tim Wilhelm],
Tracking Sponge Size and Behaviour with Fixed Underwater Observatories,
CVAUI18(45-54).
Springer DOI 1901
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Shevchenko, V., Eerola, T., Kaarna, A.,
Fish Detection from Low Visibility Underwater Videos,
ICPR18(1971-1976)
IEEE DOI 1812
Videos, Task analysis, Fish, Lighting, Adaptation models, Optical distortion, Adaptive optics BibRef

Chou, Y., Chen, C., Liu, K., Chen, C.,
Changing Background to Foreground: An Augmentation Method Based on Conditional Generative Network for Stingray Detection,
ICIP18(2740-2744)
IEEE DOI 1809
Training, Object detection, Sea surface, Generators, Detectors, Task analysis, Object detection, aerial image, generative latent optimization BibRef

Liu, L., Lu, H., Cao, Z., Xiao, Y.,
Counting Fish in Sonar Images,
ICIP18(3189-3193)
IEEE DOI 1809
Sonar, Fish, Training, Visualization, Imaging, Task analysis, Estimation, Fish counting, sonar images, sample imbalance, local regression, convolutional neural networks BibRef

Li, X., Wei, Z., Huang, L., Nie, J., Zhang, W., Wang, L.,
Real-Time Underwater Fish Tracking Based on Adaptive Multi-Appearance Model,
ICIP18(2710-2714)
IEEE DOI 1809
Fish, Adaptation models, Target tracking, Correlation, Real-time systems, Strain, visual tracking, underwater vision BibRef

Guo, Y., van Wijk, R.C., Krekels, E.H.J., Spaink, H.P., van der Graaf, P.H., Verbeek, F.J.,
Multi-modal 3d reconstruction and measurements of zebrafish larvae and its organs using axial-view microscopy,
ICIP17(2194-2198)
IEEE DOI 1803
Cameras, Liver, Microscopy, Shape, 3D measurements, Multi-modal 3D reconstruction, zebrafish and organ BibRef

Nilssen, I., Möller, T., Nattkemper, T.W.,
Active Learning for the Classification of Species in Underwater Images from a Fixed Observatory,
Wildlife17(2891-2897)
IEEE DOI 1802
Image color analysis, Learning systems, Monitoring, Observatories, Support vector machines, Training, Wildlife BibRef

Rasmussen, C., Zhao, J., Ferraro, D., Trembanis, A.,
Deep Census: AUV-Based Scallop Population Monitoring,
Wildlife17(2865-2873)
IEEE DOI 1802
Cameras, Image color analysis, Manuals, Object detection, Sociology, Statistics BibRef

Li, T.[Teng], Wang, X.F.[Xue-Feng], Sun, M.Z.[Ming-Zhu], Zhao, X.[Xin],
Design and Implementation of the Three-Dimensional Observation System for Adult Zebrafish,
CVS17(553-563).
Springer DOI 1711
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Hsiao, Y.H., Chen, C.C.,
Over-atoms accumulation orthogonal matching pursuit reconstruction algorithm for fish recognition and identification,
ICPR16(1071-1076)
IEEE DOI 1705
Databases, Feature extraction, Fish, Image reconstruction, Matching pursuit algorithms, Reconstruction algorithms, Testing, compressive sensing, orthogonal matching pursuit, pattern, recognition BibRef

Hasija, S., Buragohain, M.J., Indu, S.,
Fish Species Classification Using Graph Embedding Discriminant Analysis,
CMVIT17(81-86)
IEEE DOI 1704
aquaculture BibRef

Zhou, Q.[Qian], Miller, G.[Gregor], Wu, K.[Kai], Correa, D., Fels, S.[Sidney],
Automatic Calibration of a Multiple-Projector Spherical Fish Tank VR Display,
WACV17(1072-1081)
IEEE DOI 1609
Calibration, Cameras, Distortion, Image reconstruction, Image resolution, Shape, Three-dimensional, displays BibRef

Zhou, Q.[Qian], Miller, G.[Gregor], Wu, K.[Kai], Stavness, I.[Ian], Fels, S.[Sidney],
Analysis and Practical Minimization of Registration Error in a Spherical Fish Tank Virtual Reality System,
ACCV16(IV: 519-534).
Springer DOI 1704
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Seese, N., Myers, A., Smith, K., Smith, A.O.,
Adaptive Foreground Extraction for Deep Fish Classification,
CVAUI16(19-24)
IEEE DOI 1701
Computer vision BibRef

Podila, S.[Sahithi], Zhu, Y.[Ying],
Simulating a Predator Fish Attacking a School of Prey Fish in 3D Graphics,
ISVC16(II: 586-594).
Springer DOI 1701
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Koreitem, K.[Karim], Girdhar, Y.[Yogesh], Cho, W.[Walter], Singh, H.[Hanumant], Pineda, J.[Jesus], Dudek, G.[Gregory],
Subsea Fauna Enumeration Using Vision-Based Marine Robots,
CRV16(101-108)
IEEE DOI 1612
Marine Robotics; Visual Learning BibRef

Sansone, C.[Carmine], Pucher, D.[Daniel], Artner, N.M.[Nicole M.], Kropatsch, W.G.[Walter G.], Saggese, A.[Alessia], Vento, M.[Mario],
Shape Normalizing and Tracking Dancing Worms,
SSSPR16(390-400).
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Marine worms. BibRef

Villon, S.[Sébastien], Chaumont, M.[Marc], Subsol, G.[Gérard], Villéger, S.[Sébastien], Claverie, T.[Thomas], Mouillot, D.[David],
Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods,
ACIVS16(160-171).
Springer DOI 1611
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Hsiao, Y.H., Chen, C.C.,
A sparse sample collection and representation method using re-weighting and dynamically updating OMP for fish tracking,
ICIP16(3494-3497)
IEEE DOI 1610
Computers BibRef

Jovanovic, V., Risojevic, V., Babic, Z., Svendsen, E., Stahl, A.,
Splash detection in surveillance videos of offshore fish production plants,
WSSIP16(1-4)
IEEE DOI 1608
aquaculture BibRef

Silvério, F.J.[Francisco J.], Certal, A.C.[Ana C.], de Ferro, C.M.[Carlos Mão], Monteiro, J.F.[Joana F.], Cruz, J.A.[José Almeida], Ribeiro, R.[Ricardo], Silva, J.N.[João Nuno],
Automatic System for Zebrafish Counting in Fish Facility Tanks,
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Zhang, D., Kopanas, G., Desai, C., Chai, S., Piacentino, M.,
Unsupervised underwater fish detection fusing flow and objectiveness,
AAVWS16(1-7)
IEEE DOI 1606
image fusion BibRef

El Habouz, Y.[Youssef], Es-Saady, Y.[Youssef], El Yassa, M.[Mostafa], Mammass, D.[Driss], Fathallah, N.[Nouboud], Chalifour, A.[Alain], Manchih, K.[Khalid],
Otolith Recognition System Using a Normal Angles Contour,
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French, G.[Geoffrey], Fisher, M.[Mark], Mackiewicz, M.[Michal], Needle, C.[Coby],
Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video,
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Hughes, B.[Benjamin], Burghardt, T.[Tilo],
Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery,
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Jäger, J.[Jonas], Simon, M.[Marcel], Denzler, J.[Joachim], Wolff, V.[Viviane], Fricke-Neuderth, K.[Klaus], Kruschel, C.[Claudia],
Croatian Fish Dataset: Fine-grained classification of fish species in their natural habitat,
MVAB15(xx-yy).
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Puybareau, É.[Élodie], Léonard, M.[Marc], Talbot, H.[Hugues],
An Automated Assay for the Evaluation of Mortality in Fish Embryo,
ISMM15(110-121).
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Pintor, J.M., Carrión, P., González-Rufino, E., Formella, A., Fernández-Delgad, M., Cernadas, E., Domínguez-Petit, R., Rábade-Uberos, S.,
A Multi-platform Graphical Software for Determining Reproductive Parameters in Fishes Using Histological Image Analysis,
IbPRIA15(743-750).
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Mendes, A.[Andre], Hoeberechts, M.[Maia], Albu, A.B.[Alexandra Branzan],
Evolutionary Computational Methods for Optimizing the Classification of Sea Stars in Underwater Images,
AAVWS15(44-50)
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Cutter, G.[George], Stierhoff, K.[Kevin], Zeng, J.[Jia_Ming],
Automated Detection of Rockfish in Unconstrained Underwater Videos Using Haar Cascades,
AAVWS15(57-62)
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Chuang, M.C.[Meng-Che], Hwang, J.N.[Jenq-Neng], Kuo, F.F.[Fang-Fei], Shan, M.K.[Man-Kwan], Williams, K.[Kresimir],
Recognizing live fish species by hierarchical partial classification based on the exponential benefit,
ICIP14(5232-5236)
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A Modular Learning Approach for Fish Counting and Measurement Using Stereo Baited Remote Underwater Video,
DICTA14(1-7)
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aquaculture BibRef

Dong, B.[Bo], Shao, L.[Ling], Frangi, A.F.[Alejandro F.], Bandmann, O.[Oliver], da Costa, M.[Marc],
Three-Dimensional Deconvolution of Wide Field Microscopy with Sparse Priors: Application to Zebrafish Imagery,
ICPR14(865-870)
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Deconvolution; Embryo; Microscopy; Noise; TV BibRef

Mehrnejad, M.[Marzieh], Albu, A.B.[Alexandra Branzan], Capson, D.[David], Hoeberechts, M.[Maia],
Towards Robust Identification of Slow Moving Animals in Deep-Sea Imagery by Integrating Shape and Appearance Cues,
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Automatic scallop detection in benthic environments,
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ICIP13(1476-1480)
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Amer, M.R.[Mohamed R.], Bilgazyev, E.[Emil], Todorovic, S.[Sinisa], Shah, S.[Shishir], Kakadiaris, I.[Ioannis], Ciannelli, L.[Lorenzo],
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Chuang, M.C.[Meng-Che], Hwang, J.N.[Jenq-Neng], Williams, K.[Kresimir], Towler, R.[Richard],
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Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Insects, Other Pests, Detection, Identification .