19.6.3.6.9 Agriculture, Inspection -- Fish, Fish Motion, Detection

Chapter Contents (Back)
Real Time Vision. Application, Inspection. Fish.

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

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

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


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], 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
BibRef

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

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

Huang, T.W., Hwang, J.N., Romain, S., Wallace, F.,
Live Tracking of Rail-Based Fish Catching on Wild Sea Surface,
CVAUI16(25-30)
IEEE DOI 1701
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

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
BibRef

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).
Springer DOI 1611
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
BibRef

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 Măo], Monteiro, J.F.[Joana F.], Cruz, J.A.[José Almeida], Ribeiro, R.[Ricardo], Silva, J.N.[Joăo Nuno],
Automatic System for Zebrafish Counting in Fish Facility Tanks,
ICIAR16(774-782).
Springer DOI 1608
BibRef

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,
ICISP16(30-39).
WWW Link. 1606
BibRef

French, G.[Geoffrey], Fisher, M.[Mark], Mackiewicz, M.[Michal], Needle, C.[Coby],
Convolutional Neural Networks for Counting Fish in Fisheries Surveillance Video,
MVAB15(xx-yy).
DOI Link 1601
BibRef

Hughes, B.[Benjamin], Burghardt, T.[Tilo],
Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery,
MVAB15(xx-yy).
DOI Link 1601
BibRef

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).
DOI Link 1601
BibRef

Puybareau, É.[Élodie], Léonard, M.[Marc], Talbot, H.[Hugues],
An Automated Assay for the Evaluation of Mortality in Fish Embryo,
ISMM15(110-121).
Springer DOI 1506
BibRef

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).
Springer DOI 1506
BibRef

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)
IEEE DOI 1503
They aren't fish, but they are under water. BibRef

Cutter, G.[George], Stierhoff, K.[Kevin], Zeng, J.[Jia_Ming],
Automated Detection of Rockfish in Unconstrained Underwater Videos Using Haar Cascades,
AAVWS15(57-62)
IEEE DOI 1503
BibRef

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)
IEEE DOI 1502
Accuracy BibRef

Westling, F., Sun, C.M.[Chang-Ming], Wang, D.[Dadong],
A Modular Learning Approach for Fish Counting and Measurement Using Stereo Baited Remote Underwater Video,
DICTA14(1-7)
IEEE DOI 1502
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)
IEEE DOI 1412
Deconvolution; Embryo; Microscopy; Noise; TV; Three-dimensional displays 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,
CVAUI14(25-32)
IEEE DOI 1412
Animals BibRef

Dawkins, M., Stewart, C., Gallager, S., York, A.,
Automatic scallop detection in benthic environments,
WACV13(160-167).
IEEE DOI 1303
BibRef

Beyan, C.[Cigdem], Fisher, R.B.[Robert B.],
Classifying imbalanced data sets using similarity based hierarchical decomposition,
PR(48), No. 5, 2015, pp. 1653-1672.
Elsevier DOI 1502
BibRef
Earlier:
Detection of Abnormal Fish Trajectories Using a Clustering Based Hierarchical Classifier,
BMVC13(xx-yy).
DOI Link 1412
BibRef
Earlier:
Detecting abnormal fish trajectories using clustered and labeled data,
ICIP13(1476-1480)
IEEE DOI 1412
BibRef
Earlier:
A filtering mechanism for normal fish trajectories,
ICPR12(2286-2289).
WWW Link. 1302
Class imbalance problem. Abnormal Trajectory BibRef

Amer, M.R.[Mohamed R.], Bilgazyev, E.[Emil], Todorovic, S.[Sinisa], Shah, S.[Shishir], Kakadiaris, I.[Ioannis], Ciannelli, L.[Lorenzo],
Fine-grained categorization of fish motion patterns in underwater videos,
VECTaR11(1488-1495).
IEEE DOI 1201
BibRef

Chuang, M.C.[Meng-Che], Hwang, J.N.[Jenq-Neng], Williams, K.[Kresimir], Towler, R.[Richard],
Automatic fish segmentation via double local thresholding for trawl-based underwater camera systems,
ICIP11(3145-3148).
IEEE DOI 1201
BibRef

Spampinato, C.[Concetto], Giordano, D.[Daniela], di Salvo, R.[Roberto], Chen-Burger, Y.H.J.[Yun-Heh Jessica], Fisher, R.B.[Robert B.], Nadarajan, G.[Gayathri],
Automatic fish classification for underwater species behavior understanding,
ARTEMIS10(45-50).
DOI Link 1111
BibRef

Lillywhite, K.[Kirt], Lee, D.J.[Dah-Jye],
Automated Fish Taxonomy Using Evolution-COnstructed Features,
ISVC11(I: 541-550).
Springer DOI 1109
BibRef

González-Rufino, E.[Encarnación], Carrión, P.[Pilar], Formella, A.[Arno], Fernández-Delgado, M.[Manuel], Cernadas, E.[Eva],
Statistical and Wavelet Based Texture Features for Fish Oocytes Classification,
IbPRIA11(403-410).
Springer DOI 1106
BibRef

Serra-Toro, C.[Carlos], Montoliu, R.[Raul], Traver, V.J.[V. Javier], Hurtado-Melgar, I.M.[Isabel M.], Nunez-Redo, M.[Manuela], Cascales, P.[Pablo],
Assessing Water Quality by Video Monitoring Fish Swimming Behavior,
ICPR10(428-431).
IEEE DOI 1008
BibRef

Mery, D., Lillo, I., Loebel, H., Riffo, V., Soto, A., Cipriano, A., Aguilera, J.M.,
Automated Detection of Fish Bones in Salmon Fillets Using X-ray Testing,
PSIVT10(46-51).
IEEE DOI 1011
BibRef

Thida, M.[Myo], Remagnino, P.[Paolo], Eng, H.L.[How-Lung],
A particle swarm optimization approach for multi-objects tracking in crowded scene,
VS09(1209-1215).
IEEE DOI 0910
BibRef

Thida, M.[Myo], Eng, H.L.[How-Lung], Chew, B.F.[Boon Fong],
Automatic Analysis of Fish Behaviors and Abnormality Detection,
MVA09(278-).
PDF File. 0905
BibRef

Chew, B.F.[Boon Fong], Eng, H.L.[How-Lung], Thida, M.[Myo],
Vision-Based Real-Time Monitoring on the Behavior of Fish School,
MVA09(90-).
PDF File. 0905
Not walking, clusters of fish. BibRef

Zhao, H.F.[Hai-Feng], Zhou, J.[Jun], Robles-Kelly, A.[Antonio], Lu, J.F.[Jian-Feng], Yang, J.Y.[Jing-Yu],
Automatic Detection of Defective Zebrafish Embryos via Shape Analysis,
DICTA09(431-438).
IEEE DOI 0912
BibRef

Pinkiewicz, T., Williams, R., Purser, J.,
Application of the Particle Filter to Tracking of Fish in Aquaculture Research,
DICTA08(457-464).
IEEE DOI 0812
BibRef

Clausen, S.[Sigmund], Greiner, K.[Katharina], Andersen, O.[Odd], Lie, K.A.[Knut-Andreas], Schulerud, H.[Helene], Kavli, T.[Tom],
Automatic Segmentation of Overlapping Fish Using Shape Priors,
SCIA07(11-20).
Springer DOI 0706
BibRef

Zhou, J.[Jun], Clark, C.M.,
Autonomous fish tracking by ROV using Monocular Camera,
CRV06(68-68).
IEEE DOI 0607
BibRef

Alén, S., Cernadas, E., Formella, A., Domínguez, R., Saborido-Rey, F.,
Comparison of Region and Edge Segmentation Approaches to Recognize Fish Oocytes in Histological Images,
ICIAR06(II: 853-864).
Springer DOI 0610
BibRef

Stewman, J.[John], Debure, K.[Kelly], Hale, S.[Scott], Russell, A.[Adam],
Iterative 3-D Pose Correction and Content-Based Image Retrieval for Dorsal Fin Recognition,
ICIAR06(I: 648-660).
Springer DOI 0610
BibRef

Larsen, R.[Rasmus], Olafsdottir, H.[Hildur], Ersbřll, B.K.[Bjarne Kjćr],
Shape and Texture Based Classification of Fish Species,
SCIA09(745-749).
Springer DOI 0906
BibRef

Evans, F.H.,
Detecting fish in underwater video using the EM algorithm,
ICIP03(III: 1029-1032).
IEEE DOI 0312
BibRef

di Gesu, V., Isgro, F., Tegolo, D., Trucco, E.,
Finding essential features for tracking star fish in a video sequence,
CIAP03(504-509).
IEEE DOI 0310
BibRef

Lundgren, B., Nielsen, H., Nielsen, R., Faber, P.,
Estimation of 3D Position, Angle of Attitude and Orientation of Free-swimming Fish in a Hydroacoustic Beam Field under Extreme Lighting Conditions,
SCIA01(P-W4B). 0206
BibRef

Rife, J., Rock, S.,
Visual Tracking of Jellyfish in Situ,
ICIP01(I: 289-292).
IEEE DOI 0108
BibRef

Chan, D., Hockaday, S., Tillett, R.D., Ross, L.G.,
Factors Affecting the Training of a WISARD Classifier for Monitoring Fish Underwater,
BMVC99(Posters/Exhibition/Demos).
PDF File. BibRef 9900

Naiberg, A., and Little, J.J.,
A Unified Recognition and Stereo Vision System for Size Assessment of Fish,
WACV94(2-9).
IEEE Abstract. BibRef 9400

Nagashima, Y., Ishimatsu, T.,
A Morphological Approach to Fish Discrimination,
MVA98(xx-yy). BibRef 9800

Han, K.J.[Keesook J.], Tewfik, A.H.[Ahmed H.],
Expert Computer Vision Based Crab Recognition System,
ICIP96(II: 649-652).
IEEE DOI BibRef 9600

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Insects, Detection, Identification .


Last update:May 25, 2017 at 22:18:08