ZooCAMNet: plankton images captured with the ZooCAM
| Type | : | ACL |
|---|---|---|
| Nature | : | Production scientifique |
| Au bénéfice du Laboratoire | : | Oui |
| Statut de publication | : | Publié |
| Année de publication | : | 2024 |
| Auteurs (16) | : | ROMAGNAN Jean-baptiste PANAIOTIS Thelma BOURRIAU P DANIELOU M,m DORAY M DUPUY C FOREST B GRANDREMY N HURET M LE MESTRE S NOWACZYK A PETITGAS P PINEAU P ROUXEL J TARDIVEL M IRISSON Jean-olivier |
| Revue scientifique | : | SEANOE |
| Volume | : | |
| Fascicule | : | |
| Pages | : | |
| DOI | : | 10.17882/101928 |
| URL | : | https://www.seanoe.org/data/00907/101928/data/113094.tar |
| Abstract | : | Plankton was sampled with a Continuous Underway Fish Egg Sampler (CUFES, 315µm mesh size) at 4 m below the surface, and a WP2 net (200µm mesh size) from 100m to the surface, or 5 m above the sea floor to the surface when the depth was < 100 m, in the Bay of Biscay. The full images were processed with the ZooCAM software and the embedded Matrox Imaging Library (Colas et a., 2018) which generated regions of interest (ROIs) around each individual object and a set of features measured on the object. The same objects were re-processed to compute features with the scikit-image library http://scikit-image.org. The 1, 286, 590 resulting objects were sorted by a limited number of operators, following a common taxonomic guide, into 93 taxa, using the web application EcoTaxa http://ecotaxa.obs-vlfr.fr. For the purpose of training machine learning classifiers, the images in each class were split into training, validation, and test sets, with proportions 70%, 15% and 15%. The archive contains : taxa.csv.gz Table of the classification of each object in the dataset, with columns : objid : unique object identifier in EcoTaxa (integer number). taxon_level1 : taxonomic name corresponding to the level 1 classification lineage_level1 : taxonomic lineage corresponding to the level 1 classification taxon_level2 : name of the taxon corresponding to the level 2 classification plankton : if the object is a plankton or not (boolean) set : class of the image corresponding to the taxon (train : training, val : validation, or test) img_path : local path of the image corresponding to the taxon (of level 1), named according to the object id features_native.csv.gz Table of morphological features computed by ZooCAM. All features are computed on the object only, not the background. All area/length measures are in pixels. All grey levels are in encoded in 8 bits (0=black, 255=white). With columns : area : object's surface area_exc : object surface excluding white pixels area_based_diameter : object's Area Based Diameter: 2 * (object_area/pi)^(1/2) meangreyobjet : mean image grey level modegreyobjet : modal object grey level sigmagrey : object grey level standard deviation mingrey : minimum object grey level maxgrey : maximum object grey level sumgrey : object grey level integrated density: object_mean*object_area breadth : breadth of the object along the best fitting ellipsoid minor axis length : breadth of the object along the best fitting ellipsoid majorr axis elongation : elongation index: object_length/object_breadth perim : object's perimeter minferetdiam : minimum object's feret diameter maxferetdiam : maximum object's feret diameter meanferetdiam : average object's feret diameter feretelongation : elongation index: object_maxferetdiam/object_minferetdiam compactness : Isoperimetric quotient: the ration of the object's area to the area of a circle having the same perimeter intercept0, intercept45 , intercept90, intercept135 : the number of times that a transition from background to foreground occurs a the angle 0ø, 45ø, 90ø and 135ø for the entire object convexhullarea : area of the convex hull of the object convexhullfillratio : ratio object_area/convexhullarea convexperimeter : perimeter of the convex hull of the object n_number_of_runs : number of horizontal strings of consecutive foreground pixels in the object n_chained_pixels : number of chained pixels in the object n_convex_hull_points : number of summits of the object's convex hull polygon n_number_of_holes : number of holes (as closed white pixel area) in the object roughness : measure of small scale variations of amplitude in the object's grey levels rectangularity : ratio of the object's area over its best bounding rectangle's area skewness : skewness of the object's grey level distribution kurtosis : kurtosis of the object's grey level distribution fractal_box : fractal dimension of the object's perimeter hist25, hist50, hist75 : grey level value at quantile 0.25, 0.5 and 0.75 of the object's grey levels normalized cumulative histogram valhist25, valhist50, valhist75 : sum of grey levels at quantile 0.25, 0.5 and 0.75 of the object's grey levels normalized cumulative histogram nobj25, nobj50, nobj75 : number of objects after thresholding at the object_valhist25, object_valhist50 and object_valhist75 grey level symetrieh :index of horizontal symmetry symetriev : index of vertical symmetry skelarea : area of the object skeleton thick_r : maximum object's thickness/mean object's thickness cdist : distance between the mass and the grey level object's centroids features_skimage.csv.gz Table of morphological features recomputed with skimage.measure.regionprops on the ROIs produced by ZooCAM. See http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops for documentation. inventory.tsv Tree view of the taxonomy and number of images in each taxon, displayed as text. With columns : lineage_level1 : taxonomic lineage corresponding to the level 1 classification taxon_level1 : name of the taxon corresponding to the level 1 classification n : number of objects in each taxon group map.png Map of the sampling locations, to give an idea of the diversity sampled in this dataset. imgs Directory containing images of each object, named according to the object id objid and sorted in subdirectories according to their taxon. |
| Mots-clés | : | 101928; plankton;image;ZooCAM;WP2;CUFES |
| Commentaire | : | - |
| Tags | : | - |
| Fichier attaché | : | - |
| Citation | : |
Romagnan J-B, Panaiotis T, Bourriau P, Danielou MM, Doray M, Dupuy C, Forest B, Grandremy N, Huret M, Le Mestre S, Nowaczyk A, Petitgas P, Pineau P, Rouxel J, Tardivel M, Irisson J-O (2024) ZooCAMNet: plankton images captured with the ZooCAM. SEANOE | doi: 10.17882/101928
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