dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to obtain the final detected blood vessels, we first obtain vessel edge lines by linking the detected vessel edge points. Then, the pixels between two vessel edge lines are considered to belong to the blood vessel. By comparing with the ground truth, we can see that the proposed method is able to detect most of the blood vessels. The detection sensitivity, specificity, and accuracy of the proposed method on im0077 are 0. 8020, 0. 9600, and 0. 9426, respectively. Besides, we selected four regions on im0077 as shown in Figure  11 (a), which present four types of vessel structures: line, curve, bifurcation, and   [6]  proposed a filter-based method and made their segmentation results on the STARE database public on their website. Soares'  [13]  method is based on the 2D Gabor wavelet transform, and its test results on the STARE database are presented on the public website (http://retinal.
sourceforge. net/). Mendonca and Campilho  [1]  used morphological operators for vessel segmentation. Table  1  lists the segmentation performance of different methods on the STARE database. As mentioned in the beginning of this section, segmentation results of the first observer are used for the ground truth. In Table  1 , the manual detection results of the second observer are considered as a reference used for comparison. The performance measures of Hoover's  [6]  and Soares'  [13]  methods are calculated using the segmented images from their public websites, respectively. The performance of Mendonca's method is obtained from the original paper  [1]. The experimental results show that our method has a higher sensitivity (0. 7248) than the other three methods. The specificity and accuracy of the proposed method are 0.
9666 and 0. 9412, respectively, which are both higher than those of the second observer and Hoover's method  [6]. (d) (g) (e) (f) (a) (b) (c) (d) (e) (f) (g) As mentioned above, the STARE database includes 10 normal and 10 abnormal retinal images. In Table  2 , we show the performance measures of different methods in the two cases on STARE database. In normal cases, the proposed method outperforms Hoover's method  [6]  and has a similar performance to Mendonca's method  [1]. In abnormal cases, the proposed method is better than the other three methods. The sensitivity of the proposed method is up to 0. 7034, which   is much higher than that of the others. To make the comparison fairer, we show the segmentation results of different methods on two retinal images from the STARE database, one normal im0255 and one abnormal im0044 (see Figures  12  and 13 ).
The ground truth image is the segmentation result of the first observer. Result images of Hoover's  [6]  and Soares'  [13]  methods are downloaded directly from their public website (http://retinal. sourceforge. net/). In the normal case, as shown in Figure  12 , the proposed method detected more details than the others, especially in the area near the fovea. In the abnormal case, as shown in Figure  13 , our method detected most of the vascular tree, while Hoover's method lost some thin blood vessels and the main blood vessels in Soares' result are discontinued. Secondly, we discuss the segmentation performance on DRIVE database. Several popular vessel segmentation methods are considered when testing on the DRIVE database for comparison purposes as shown in Table  3. The performance measures of the compared methods are obtained from the website of DRIVE database (http://www.
isi. uu. nl/Research/Databases/DRIVE/). We can see that the proposed method has higher specificity and accuracy than those of the others. The sensitivity is a bit inferior to some state-of-the-art methods. Because our method seems to be effective in abnormal cases as shown in Table  2 , most of the images are normal in DRIVE database. Figure  14  shows the segmentation results of four different methods on the retinal image 01 test from the DRIVE.
Method Sensitivity Specificity Accuracy The 2nd observer 0. 7761 0. 9725 0. 9473 Al-Diri et al. [30]  0. 7282 0. 9551 0. 9258 Jiang and Mojon  [9]  0. 6478 0. 9625 0. 9212 Martínez-Pérez et al. [31]  0. 7086 0. 9496 0. 9181 Chaudhuri et al. [8]  0. 2716 0. 9794 0. 8773 Proposed 0. 6522 0. 9710 0. 9267 database. As shown in Figure  14 (a), 01 test is a normal retinal image without retinopathy. The ground truth as shown in Figure  14 (b) is manual segmentation result. Figures  14(c ) and 14(d) show the original classification results of the Martnez-Pérez's  [31]  and Chaudhuri's  [8]  algorithms, respectively. For these two methods, the final segmentation results are obtained from the original results by thresholding at a certain value. Jiang and Mojon  [9]  use the adaptive local thresholding and the segmentation result is shown in Figure  14 (e).
Conclusions In this paper, we have introduced an automatic tracking method for vessel detection in retinal images. The tracking algorithm is able to segment the main vascular tree and obtain local vessel parameters such as vessel edge points, direction, and the vessel width along the retinal vessels. The proposed method starts from a number of seed points selected all over the image. During the tracking process, a dynamic search window is used to get the information of local grey levels' statistics. Different types of configurations are defined to combine the intensity distribution and the geometrical structure of local blood vessel. Vessel's edge points are detected by a Bayesian method with the MAP criterion. Experiments on three publicly available databases show that the proposed method detects blood vessels effectively and outperforms some classical vessel detection methods.
In aerial photogrammetry, a series of data process flows has been industrialized. But in close-range photogrammetry, it cannot normally be implemented, because there are more complex scenes, occlusion, distortion, and the object has various shapes to be taken into account. The National Institute of Geographic and Forest Information (IGN), has adapted and developed algorithms of photogrammetry and computer vision to model objects such as dense point clouds. Its software, called Apero/MicMac (Pierrot-Deseilligny M. et al) has reached a maturity which makes possible the dense correlation procedures. In this paper, we will focus on the experiments carried out for our PHD thesis, which are meant to transfer methods of automated close-range photogrammetry into the field of archeology.
METHODOLOGY All the images have been acquired with a canon EOS 5 D Mark II camera; various lenses have been used, they were all at fixed focal lengths (no zooms); among which, the Canon 100mm macro lens, and a 90mm tilt-shift lens to allow movements of tilt and shift. The Apero/MicMac tools were used for the georeferencing and the Cloud Compare Open Source software (CloudCompare, 2012) was used for meshes of the scenes.
Apero/MicMac IGN has developed a "suite" of free open source photogrammetric software to build 3D models from images. A detailed documentation of these tools can be downloaded on (MicMac 2012). Like several other solutions, IGN's pipeline for computing 3D models out of an unordered collection of images is made of three main following modules: -First module: Pastis (acronym of "Program using Autopano SIFT for the Tie-pointsin the Images") software computes tiepoints from non-oriented images; it uses algorithms focusing on invariant descriptions of salient points like SIFT  (Lowe 2004 ), SURF or MSER; when no information on image acquisition is available. This coputation can be made on all pairs of images. Pastis run the SIFT algorithm on several image pairs in parallel to accelerate the calculation, and can be handle very weighted images by dividing them up among blocks; -Second module: Apero (acronym of "Relatively Operational Experimental Photogrammetric Aerotriangulation")  (Cléry 2011)  software is a computer program that takes as input a set of redundant, and possibly heterogeneous, observations and produces internal and external calibration that minimizes the global incompatibility with these redundant observations.
The longer focal lens (50mm) could then be used to produce 5 convergent images on key areas: a reference image and four images taken by cross-framing the image reference to avoid missing any hidden parts for 3D modelling. For the tie-point module, we used Pastis software to automatically find matching points on different pairs of partially overlapping images. The tool to compute the Sift-match is named Tapioca. To limit the computation time in Tapioca, we had to specify the parameter that defines the strategy to be used for restricting the computation time, to limit the number of image pairs on which the SIFT matching had to be computed (to avoid a computation time in N 2 , with N images). The value of this key word was "MulScale", which specifies a multi-scale strategy: in a preliminary step, all the pairs are computed at a very low resolution.
5°N 9. 5°W, a data acquisition point representative for the Galician Rí as. Monthly rainfall data from a meteorological station at Vigo airport (Peinador) (42°13. 89′N, 8°37. 29′W) were provided by the Spanish Meteorological Agency (www. aemet. es). Outflow of the Adour River and wind measurements (Pointe de Socoa, 43. 4°N-1. 69°W) were provided by Meteo France. Daily SST anomalies over SW Europe, between the English Channel and western Iberia, from January to April 2012 were taken from NOAA  [42]. These data were used to estimate the monthly SST anomaly.
Phytoplankton data for Arcachon Bay (1988-2012) were obtained from the French HAB monitoring programme (REPHY, IFREMER) database  [45]. Hydrobios bottle samples were collected at 5 m depth biweekly at 14 different stations between the south of the Gironde Estuary and the Spanish coast. Quantitative phytoplankton analyses in both countries were carried out according to the Utermöhl  [46]  method. Samples were counted after sedimentation (4-24 h) of 10-25 mL columns under an inverted light microscope fitted with 100×, 200× and 400× objectives and phase-contrast optics. To enumerate large species, such as Dinophysis spp. , the whole surface of the chamber is scanned at a magnification of 100 × (detection level of 100-40 cell L -1 ). Average cell densities of D. acuminata were used for the time series in the two study areas to smooth variability associated with tidal changes and local wind forcing.
Acknowledgments We thank the  Galician Monitoring Programme (INTECMAR)  for weekly reports and IEO-RADIALES programme for monthly reports on phytoplankton distributions. This work was funded by project  ASIMUTH  ( EC FP7-SPACE-2010-1  grant agreement number  261860 ). Patricio A. Dí az was supported by a PhD student fellowship from  BECAS-CHILE , of the  National Commission for Scientific and Technological Research (CONICYT) ,  Chile. L. Velo-Suá  rez was supported by a  Marie Curie International Outgoing Fellowship (IOF ; grant agreement:  MOHAB PIOF-GA-252260 ). Thanks to  M. M. Danielou  for her help on the data processing. This is a contribution to the SCOR and IOC GEOHAB programme, Core Research Projects on " HABs and Stratification " and " HABs in Upwelling Systems ".
Background: In crops, inflorescence complexity and the shape and size of the seed are among the most important characters that influence yield. For example, rice panicles vary considerably in the number and order of branches, elongation of the axis, and the shape and size of the seed. Manual low-throughput phenotyping methods are time consuming, and the results are unreliable. However, high-throughput image analysis of the qualitative and quantitative traits of rice panicles is essential for understanding the diversity of the panicle as well as for breeding programs. Results: This paper presents P-TRAP software (Panicle TRAit Phenotyping), a free open source application for high-throughput measurements of panicle architecture and seed-related traits. The software is written in Java and can be used with different platforms (the user-friendly Graphical User Interface (GUI) uses Netbeans Platform 7.
Given the importance of gene discovery and crop improvement, there is thus an urgent need to automate such tedious and time-consuming tasks. The development of an easy high-throughput panicle phenotyping method should aim to standardize the measurement and extraction of panicle traits. In recent years, plant phenotyping research has led to the development of software for plant screening facilities. Recent image processing solutions, such as TraitMill and HTPheno, offer general analysis for the measurement of plant height, volume and colorimetry  [5, 6]. Other software provides 2-D image-based semiautomated processing for leaf phenotyping (Phenopsis or LAMINA) and root data monitoring (GROWSCREEN)  [7] [8] [9]. Specific rice image-based solutions have been developed for phenotyping and involve the measurement of parameters such as grain size (length, width, and thickness), panicle length, and the number of tillers  [10, 11].
However, these methods could not be adapted to rice panicle structure phenotyping and require expensive equipment. Ikeda et al  [12]  developed a software named PASTAR (Panicle Structure Analyzer for Rice) and PASTA Viewer, to automatically extract values for length, number of branches, and number of grains from scanned panicle images. However, this software is under license, thus limiting access by the scientific community. Recently, a program named Smartgrain was developed to quantify seed shape and size. However, this software does not process the grain attached to panicles but only individual grains  [13]. In this context, it was important to develop an easy-to-use freely available open-source software based on 2-D image processing for the analysis of rice panicle structure. Here, we propose a Java-based stand-alone application named P-TRAP (for Panicle TRAit Phenotyping) to easily quantify 2-D panicle traits.
The interface allows the two analyses to be performed at the same time (or separately) and extracts the different traits in different output formats (CSV and XML) to facilitate data analysis and access to OpenAlea platform facilities  [14]. In this study, we used this program to analyze the panicle structure of various accessions of O. sativa,O. glaberrima and O. barthii and to compare the results with manual measurements to check the robustness of the software.
Implementation P-TRAP is written in Java with a user-friendly GUI. The GUI is built on top of the Netbeans Platform (version 7. 3), which provides a modular underlay for the system's architecture. The software provides different features for users to conduct their experiments and edit and collect the final results. It offers an editor for the input image, the panicle structure and the grains. The user interaction is mostly performed by using the mouse or keyboard shortcuts. In addition, developers can easily add new features to the application, as it is very modular.
P-TRAP output files The data collected from the processed images are exported in two different formats: XML and CSV. The XML format is used to store the panicle structure and the grains particles, and can be exported for other applications such as OpenAlea after conversion to the MTG (Multiscale Tree Graph) format  [14]. Each analysis run produces two files:. ricepr and. ricegr for the structure and the grains, respectively. More information on the structure of the output files is available in Additional file 2. A CSV file is also generated to allow direct visualization of the results and easy transfer to spreadsheet software (e. g. Microsoft Excel). The results of the quantification of the panicle and grains are stored in files with two different levels of details. These CSV files are: • MainTraits.
Panicle structure For evaluation the panicle structure, only the main manually measurable traits (length of the primary axes, number of nodes, secondary axes, and the number and The input images and the software options are provided by the user. These images are then binarized and passed to the graph and particle processing modules to identify the structure and the grains on the panicle. The resulting graphs and particles are stored in separate XML files for visualization and additional editing. These editors are part of the Workspace module, which translates the XML files into editable widgets supported by the WidgetFactory GUI helper module. The user can easily edit these visual widgets and send the changes back to the XML files for storage. All interactions between the user and the system are performed using the Workspace module.
Figure7P-TRAP architecture and the processing pipeline. The input images and the software options are provided by the user. These images are then binarized and passed to the graph and particle processing modules to identify the structure and the grains on the panicle. The resulting graphs and particles are stored in separate XML files for visualization and additional editing. These editors are part of the Workspace module, which translates the XML files into editable widgets supported by the WidgetFactory GUI helper module. The user can easily edit these visual widgets and send the changes back to the XML files for storage. All interactions between the user and the system are performed using the Workspace module. The final reports are based on the contents of the XML files. The contents of these reports are stored in CSV files with different levels of detail.
Acknowledgements HR Shahbazkia would like to thank  D Fontenille  (head of  UMR MIVEGEC, in IRD, Montpellier France ) for the reception facilities. The authors would like to thank  Joe Tohme  ( CIAT, Cali, Colombia ) for the field phenotyping facilities and for the production of spread out panicle images. Harold Chrestin  is thanked for the spread out seed images. This work was supported by  IRD institutional funding, GRiSP funding  ( MENERGEP  project),  Agropolis fonudation and Cariplo foundation co-funding  ( FIRST/EVOREPRICE 2011 ).
Availability and requirements Project name: P-TRAP Project home page: http://bioinfo. mpl. ird. fr/index. php?option=com_ content&view=article&id=102&Itemid=2. Several video tutorials can be found at this URL. Operating system(s): Platform independent Programming language: Java Other requirements: JRE ≥ 1. 6 to run the application. To compile the source code, the Netbeans Platform ≥ V. 7. 3 IDE, Java Matrix Package (JAMA) ≥ V. 1. 0. 2 and Java Advanced Imaging (JAI) ≥ V. 1. 3 libraries are needed. License: GPL V3 Any restrictions to use by non-academics: As specified by GPL V3 license.
We present a global database on the subaerial morphometry of composite volcanoes. Data was extracted from the 90-m resolution Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The 759 volcanoes included in the database are the composite (i. e. , polygenetic) volcanoes listed in the Smithsonian Institution Global Volcanism Program (GVP) database that are covered by the SRTM DEM, have a constructional topography and a basal width larger than 2 km. The extent of each volcano edifice was defined using the NETVOLC algorithm, which computes outlines by minimizing a cost function based on breaks in slope around the edifices. Morphometric parameters were then calculated using the MORVOLC algorithm. The parameters characterize and quantify volcano size (basal width, summit width, height, and volume), profile shape (height/basal width and summit width/basal width ratios), plan shape (ellipticity and irregularity indexes), and slopes.
e. , volcano morphometry) is of interest because it can supply information concerning the processes that interact during the growth history and evolution of volcanoes. Although shape and size are basic volcano properties, there is to date no comprehensive database of volcano morphometry at a global scale. We here present a systematic near-global database on the morphometry of composite volcanoes. We use the term 'composite volcano' as defined by  Davidson and De Silva (2000) , i. e. , all constructional and polygenetic volcanoes, including both 'composite cones' (synonymous to stratovolcano) and parameters that thoroughly characterize the shape and size of volcano edifices in a systematic and comparable way. The morphometric data was extracted from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM).
Considered volcanoes are the composite constructional volcanoes listed in the Smithsonian Institution Global Volcanism Program database  (Siebert et al. 2010 ) covered by the SRTM DEM. Thus, the database includes most of the subaerial active or potentially active composite volcanoes of the world. The goal of this contribution is to present the database, describing the applied methodology and the database design. We also perform a general analysis of the data, although more detailed analyses are beyond the scope of this contribution. We anticipate that the database will become a useful tool for research on a variety of volcanological processes and at several scales.
Compilations and studies on composite volcanoes containing morphometric data There are many studies on monogenetic cone fields containing morphometric measurements (e. g. ,  Favalli et al. 2009; Kervyn et al. 2012 , and references therein), but relatively few on composite volcanoes. The most complete existing database at a global scale containing volcano morphometric parameters is that of  Pike (1978)  and its revision,  Pike and Clow (1981). This compilation contains 697 entries, including composite volcanoes, calderas, and monogenetic cones. It considers five measurements, three of which relate to the summit crater (edifice height and flank width; crater diameter, depth, and circularity). Several regional compilations or catalogues contain varied volcano morphometric data. Compilations include those of Japan (Committee for Catalog of Quaternary Volcanoes in Japan 1999), Kamchatka  (Fedotov and Masurenkov 1991; Kozhemyaka 1995) , Alaska  (Miller et al.
2007; Völker et al. 2011) , shape characterization of symmetrical cones  (Karátson et al. 2010) , or calculation of stratovolcano erosion rates  (Karátson et al. 2012). Again, data quality and methods vary and comparisons are difficult. Detailed studies on individual or a few volcanoes often give some morphometric data such as height, volume, slopes, etc. (e. g. ,  Favalli et al. 2005; Mathieu et al. 2013) , but again data are drawn from diverse sources, reducing their value for entry into a global database. Of special notice is the morphometric characterization of Martian shields by  Plescia (2004)  because, by using a planet-wide dataset, the Mars Orbiter Laser Altimeter (MOLA) DEM, it has the advantage, as we seek here, to be truly comparable. There is also considerable morphometric data on seamounts derived from bathymetric DEMs (e.
DEM source and volcano selection We have used the C-band 3 arc seconds (∼90 m spatial resolution) SRTM DEM (e. g. ,  Rabus et al. 2003)  to compile the database. It is at present the most adequate DEM dataset because it has a near-global coverage, a spatial resolution that is sufficient for morphometric analysis of composite volcanoes (e. g. ,  Wright et al. 2006; Kervyn et al. 2008; Grosse et al. 2009 ) and good accuracy (e. g. ,  Rodríguez et al. 2006). We used the seamless SRTM DEM dataset from CGIAR-CSI (Consultative Group on International Agricultural Research-Consortium for Spatial Information;  Jarvis et al. 2008). Although the ASTER Global Digital Elevation Model (G-DEM) has a better spatial resolution of 30 m, it suffers from numerous errors and artifacts that strongly limit its use in morphometric studies (e.