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469 results for “Image analysis”
Figures 1-5 from: Schmidt J, Scholz S, Will K (2022) Character analysis and descriptions of Eocene sphodrine fossils (Coleoptera, Carabidae) using light microscopy, micro-CT scanning, and 3D imaging. Deutsche Entomologische Zeitschrift 69(1): 19-44. https://doi.org/10.3897/dez.69.79931
Figures 1-5 Quasicalathus elpis (Ortuño and Arillo 2009), light microscopic images of specimens "Groehn 4879" (1–3.) and "Groehn 7814" (4, 5.). 1, 5. General view of the amber pieces; 2. Ventral side of head (the white arrow points to the mentum tooth; note that the mentum is somewhat detached from the head capsule); 3. Pronotum and anterior part of elytra showing the markedly concave basal margin and projected humeri; 4. Right lateral view of body.
Figures 25-30 from: Schmidt J, Scholz S, Will K (2022) Character analysis and descriptions of Eocene sphodrine fossils (Coleoptera, Carabidae) using light microscopy, micro-CT scanning, and 3D imaging. Deutsche Entomologische Zeitschrift 69(1): 19-44. https://doi.org/10.3897/dez.69.79931
Figures 25-30 Quasicalathus agonicollis sp. nov., light microscopic images of specimen "GZG 16188" (25–26.) and the holotype (28–30.). 25. Posterior part of pronotum and anterior part of elytra, right side; 26. Anterior part of fifth interval of left elytra showing microsculpture; 27. Posterior part of elytra (the arrows point to the insertions of the discal setae); 28. General view of the amber piece; 29. Right dorsal view of beetle body (the arrow points to the insertion of the discal seta on left elytron); 30. Left ventral view. Abbreviations: bs – insertion of the pronotal laterobasal seta; hm – humerus; m – mite (syninclusion).
Figure 1 from: Huntenburg J, Wagstyl K, Steele C, Funck T, Bethlehem R, Foubet O, Larrat B, Borrell V, Bazin P (2017) Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. Research Ideas and Outcomes 3: e12346. https://doi.org/10.3897/rio.3.e12346
Figure 1 - Laminar python pipeline, demonstrated using high-resolution MR data of a ferret brain. a) Binary images demarcating inner (grey-white matter interface, top) and outer (pial surface, bottom) boundaries of the cortex. b) Levelset representations of the same surfaces, where positive values are assigned to voxels outside of the volume deliminated by the surface, and negative values to voxels inside, each increasing in value with euclidean distance from the surface. c) Continuous equivolumetric intracortical depth, which models the positions of laminae relative to cortical morphology. d) Discrete representations of equivolumetric depth levels. e) T2 values, sampled at the six equivolumetric intracortical depths. Note that the equivolumetric laminae do not represent architectonic layers, but provide an anatomically meaningful coordinate system of cortical depth.
MATLAB code for processing images of the in-situ etching process and analysis of etching kinetics for MAX phase to MXene transformation
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Data from "SynBot: An open-source image analysis software for automated quantification of synapses"
<p>Primary image datasets and associated tables from the paper "SynBot: An open-source image analysis software for automated quantification of synapses". </p>
A group of images used for fractal analysis in Grading Evaluation of Marbling in Wagyu Beef Using Fractal Analysis.
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LBPA/Cholesterol, Prestwick screen image analysis data (Z-score)
<p>LBPA-Cholesterol, Prestwick library screening image analysis data</p>
ClariPhy: Physics-Informed Image Deblurring with Transformers for Hydrodynamic Instability Analysis
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Images used in Leaf Vein Analysis of Different Plant Species Using Multifrac-tal Analysis
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Data from: The systematic position of the stonefly †culonga Sinitshenkova, 2011 (Plecoptera: Leuctrida) reassessed using Reflectance Transforming Imaging and cladistic analysis
The phylogenetic position of the insect species †culonga Sinitshenkova, 2011, recovered from the Khasurty locality (Transbaikalia, Russia; Early Cretaceous), is re-investigated. This fossil needle stonefly has been considered an Exeleuctrida nec Mioleuctrida & Eleuctrida based on the organization of the hind wing venation opposite the arculus. We reconsidered this interpretation based on a direct observation, complemented by a Reflectance Transforming Imaging file. Our investigation indicates that the species displays the defining character state of Eleuctrida while it lacks the defining character state of Mioleuctrida. Due to inconsistencies in the character state combination displayed by the species, we carried out a cladistics analysis. The species †culonga is recovered as an Eleuctrida nec Mioleuctrida, and therefore represents a suitable calibration point for a node more recent than previously assumed, and incidentally demonstrates that Mioleuctrida belongs to Eleuctrida.
Data from: Towards a framework for agent-based image analysis of remote-sensing data
Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).
Data from: Studying developmental variation with Geometric Morphometric Image Analysis (GMIA)
The ways in which embryo development can vary across individuals of a population determine how genetic variation translates into adult phenotypic variation. The study of developmental variation has been hampered by the lack of quantitative methods for the joint analysis of embryo shape and the spatial distribution of cellular activity within the developing embryo geometry. By drawing from the strength of geometric morphometrics and pixel/voxel-based image analysis, we present a new approach for the biometric analysis of two-dimensional and three-dimensional embryonic images. Well-differentiated structures are described in terms of their shape, whereas structures with diffuse boundaries, such as emerging cell condensations or molecular gradients, are described as spatial patterns of intensities. We applied this approach to microscopic images of the tail fins of larval and juvenile rainbow trout. Inter-individual variation of shape and cell density was found highly spatially structured across the tail fin and temporally dynamic throughout the investigated period.
Image dataset from Automatic analysis of Trypanosoma cruzi: A machine learning approach for the detection of blood trypomastigotes in low resolution images
<p>Image files of mice blood smear containing <em>T. cruzi</em> trypomastigotes. The images were used to train and test the classification model of the objects (nuclei and/or kinetoplasts) present in the images.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>This is an open dataset.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as 'Cascaded Object Detector' (COD) and the other is a composition of traditional customised methods, individually known as 'Colour Transformation': 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola-Jones) COD generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of <i>O</i>(<i>n</i><sup>3</sup>) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.</p>
Figure 2 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 2 - Virtual configurations used for software validation. Virtual configurations used to compile the data presented in the Table 1. Part 2.8 is one of the 10 replicates obtained with a random distribution. All other configurations have been designed in order to reach the desired level of aggregation and affinity between groups. The filled and empty shapes represented two virtual groups in the population.
Figure 1 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 1 - Flow chart of the creation of a new NEIGHBOUR-IN file. This figure presents the different steps in the creation of a new file, from the importation of the snapshot to the calculation of the statistics of dispersion.
Figure 4 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 4 - Spatial distribution in woodlice. Graphic outputs of spatial distribution patterns obtained in three configurations with monospecific or bispecific populations including two groups of eight individuals: a PD-PD: The two groups are Porcellio dilatatus (red and green) b PD-PS: Porcellio dilatatus (red) and Porcellio scaber (green) c PD-AV: Porcellio dilatatus (red) and Armadillidium vulgare (green). The outputs show 64 cells. Each cell is represented with a colour corresponding to the individual(s) in that cell. The colour is mixed using green and red proportional to the number of green and red individuals. If the cell is empty, the colour is black. The intensity of the colour reflects the number of individuals. The position of the individual is determined by its point G (centre-point).
Figure 3 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 3 - Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcellio dilatatus, PS: Porcellio scaber, CC: Cylisticus convexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
DATASETS - Integration of Non-Destructive Acoustic Imaging investigation with Photogrammetric and Morphological Analysis to Study the "Graecia Vetus" in the Chigi Palace of Ariccia
<p>Datasets of an integrated investigation on the monochrome painting "Graecia Vetus" located in the Ariosto Room in the historical Chigi Palace of Ariccia.</p> <p>Three datasets including:</p> <p>- <strong>Dense Cloud from the surface morphological investigation of the surface by Photogrammetric Survey:</strong></p> <p>1. Zip file containing the "*.obj", "*.mtl", "*.jpg" files to open the Model.</p> <p><strong>- Four matrices from structural damage investigation of the painting by Frequency Resolved Acoustic Imaging:</strong></p> <p>1. Integrated Acoustic Image (IAI) in the wideband (500 - 12,000) Hz;</p> <p>2. Frequency Resolved Acoustic Image (FRAI) in the narrow frequency band 1/3 octave band centred at 1000 Hz;</p> <p>3. Frequency Resolved Acoustic Image (FRAI) in the narrow frequency band 1/3 octave band centred at 6300 Hz;</p> <p>4. Frequency Resolved Acoustic Image (FRAI) in the narrow frequency band 1/3 octave band centred at 10,000 Hz.</p> <p><strong>- One profile and one matrix from structural damage investigation of the wall by Sonic Tests with Impact Hammer:</strong></p> <p>1. Relative variation of pulse velocity along the horizontal axis;</p> <p>2. Tomographic map of the relative variation of pulse velocity inside the wall</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.