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18 results for “image rotation”
Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training
<p>Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].</p> <p><strong>Format</strong>:</p> <p>HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]</li> <li>scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Annotations to direct and indirect image rotation estimation methods of orthopedic X-ray images
<p>The annotation file contains labels for AP wrist images of the MURA dataset on the center line of the radius bone. The annotations are stored in json format. For each annotated image file of the MURA dataset an entry is provided with the coordinates of the start and end point of the radius' center line.</p>
TLS Z+F Imager 5010 point clouds of hybrid poplar trees from short-rotation crops after 5, 6, and 7 growing seasons
<p>The point clouds are obtained from hybrid poplar crops installed in NE Romania, managed in short rotation (SRWCs) between 5, 6, and 7 growing seasons. The crops were planted every spring, outside the growing season, at a depth of 0.6 m in the ground with two clones: AF8 and Pannonia. Rods (2-meter-long cuttings) were used as planting material at a density of 1667 trees per ha (3 x 2 m). The scanning of the sample areas (3 x 10 trees for each variant, about 6 x 10 m) was outside the growing seasons.</p><p>The 3D model was obtained using the Z+F Imager 5010 (Zoller and Fröhlich, Wangen, Germany), phase-shift type, providing a distance estimation accuracy of ±1 mm at 25 m and a nominal range of 187 m, and the tree individualization was done in CloudCompare v.2.12 (public license). A total of six station points and eight fixed targets or remarks (200 mm spheres) for co-registration were adopted for scanning. Trees included in the survey (without leaves) were marked with a ring of adhesive tape (black with yellow, 50 mm wide) at 1.4 m height on the tree spindle to adjust the results for calibration. Individually segmented trees can be sent on request, the database has a limit of 100 files. They can be converted into different formats via the CloudCompare application.</p><p>File code: clone type _ number of growing seasons _ plot number</p>
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings.
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
<p><strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below.</p> <p><em><strong>1. Structure</strong></em></p> <p>The repository contains the following items:</p> <ol> <li>"data" - the results from our experiments</li> <li>"lib" - some external functions used in the experiments</li> <li>"make_data" - the training and test data</li> <li>"fmt-vgg.py" - the FMT-RAN model</li> <li>"stn.py" - the STN module of ST-RAN</li> <li>"st_ran.py" - the ST-RAN model</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p><strong><em>2. License</em></strong></p> <p>The following licenses apply for the files and folders:</p> <ul> <li>The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository <a href="https://github.com/GHamrouni/stn-tuto">https://github.com/GHamrouni/stn-tuto</a> and therefore are under the copyright of its repository owner Ghassen Hamrouni.</li> <li>All other files are under the <a href="https://mit-license.org/">MIT License</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><em><strong>3. Contact</strong></em></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, <a href="mailto:wuzz@hfuu.edu.cn">wuzz@hfuu.edu.cn</a><br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a>, <a href="http://mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a></p> <p><a href="http://iao.hfuu.edu.cn">Institute of Applied Optimization</a>, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China</p>
Data from: Real-time imaging of rotation during synthesis by the replisome
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Microchamber slide design for cell confinement during imaging- cyst rotation
<p>The data shows a <i>Colpoda steinii</i> resting cyst rotating with internal vesicles.</p><p>We performed Imaging on a Nikon Ti2-E & Yokogawa CSU W1-SoRa microscope. The microscope was equipped with an ORCA-Fusion BT digital C-MOS camera. We used a 100x silicone objective for differential interference contrast (DIC) imaging. More microscopy information is detailed in the metadata file associated with each .nd2 files. The repository contains timelapse data. <br> </p><p> </p>
Image Content Analysis of Dural-axis Rotational Versus Standard Coronary Angiography
ClinicalTrials.gov study NCT01776866. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Data from: A rotational cylindrical fMRI phantom for image quality control
Purpose: A novel phantom for image quality testing for functional magnetic resonance imaging (fMRI) scans is described. Methods: The cylindrical, rotatable, ~4.5L phantom, with eight wedge-shaped compartments, is used to simulate rest and activated states. The compartments contain NiCl2 doped agar gel with alternating concentrations of agar (1.4%, 1.6%) to produce T1 and T2 values approximating brain grey matter. The Jacard index was used to compare the image distortions for echo planar imaging (EPI) and gradient recalled echo (GRE) scans. Contrast to noise ratio (CNR) was compared across the imaging volume for GRE and EPI. Results: The mean T2 for the two agar concentrations were found to be 106.5±4.8, 94.5±4.7 ms, and T1 of 1500±40 and 1485±30 ms, respectively. The Jacard index for GRE was generally found to be higher than for EPI (0.95 versus 0.8). The CNR varied from 20 to 50 across the slices and echo times used for EPI scans, and from 20 to 40 across the slices for the GRE scans. The phantom provided a reproducible CNR over 25 days. Conclusions: The phantom provides a quantifiable signal change over a head-size imaging volume with EPI and GRE sequences, which was used for image quality assessment.
Data for: Artificial squares, rectangles and Xray images in random rotational orientations, centered and in different sizes
<p>SORFAC-CT (Cylindrical-Topology Self-Organizing Reference-Free Alignment and Classification; pronounced sôr-fakt or sôr-fak-si-ti) is an efficient method of reference-free rotational image alignment. SORFAC-CT circumvents the dependence of subjective user- or computer-generated external or internal reference images used by other reference-based techniques. Alignment is performed using a Kohonen self-organizing map (SOM), configured on a cylindrical array of artificial neurons. Although alternative alignment protocols often depend on an expert to adjust many ad hoc parameters, SORFAC-CT instead achieves the minimization of one objectively calculated target function by varying only two free parameters. Because SOMs preserve the topological properties of the training (dataset) images, the relative in-plane rotational orientations of dataset images are obtained directly from the array's intrinsic cylindrical coordinate system by noting each mapped dataset image's respective azimuthal angle coordinate placement around the cylinder. Dataset images are not rotated into alignment with internally or externally generated reference images. Instead, SORFAC-CT starts with a cylindrical array of proto-models consisting of random pixel values. The proto-models gradually morph to become the rotational class models by an unsupervised process. The dataset images are then mapped to the class models according to greatest similarity; the alignment angles directly read off the cylinder. This was tested on datasets including square, rectangular and hexagonal geometrical shapes in six different sizes, to introduce heterogeneity. It was also tested using noisy 2-dimensional projections of the AQP-1 x-ray model. The results were near-perfect alignments that could be improved by increasing cylinder circumference, until the resolution limits of the dataset images are reached.</p>
Data from: A rotational cylindrical fMRI phantom for image quality control
Open the record for dataset details and reuse information.
Data for: Artificial squares, rectangles and Xray images in random rotational orientations, centered and in different sizes
Open the record for dataset details and reuse information.
Rotating Frame Relaxation Imaging in Patients With Multiple Sclerosis
ClinicalTrials.gov study NCT02403570. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Optimal Dosing of IC-Green for Visualization of Rotator Cuff Vascularity Using Advanced Imaging Modality Arthroscopy
ClinicalTrials.gov study NCT04340752. IPD Sharing: YES. Countries: 1. Publications: 0.
3D Imaging Assessment of Scoliosis: Back Height Difference vs. Trunk Rotation Angle
ClinicalTrials.gov study NCT06643104. IPD Sharing: NO. Countries: 1. Publications: 0.
Rotating Frame Relaxation and Diffusion Weighted Imaging of Human Gliomas
ClinicalTrials.gov study NCT02186262. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Frequency Analysis of the Ultrasound Images in Rotator Cuff Tendon
ClinicalTrials.gov study NCT04859413. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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