Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFreeLabel: A Publicly Available Annotation Tool based on Freehand Traces
85
0
0.0
(
0
)
Added by
Amy Tabb
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed to benefit the computer vision community, FreeLabel can be used for both crowdsourced or private annotation and has a modular structure that can be easily adapted for any image dataset.
rate research
Read More
We present a simple, physically-motivated model to interpret consistently the emission from galaxies at ultraviolet, optical and infrared wavelengths. We combine this model with a Bayesian method to obtain robust statistical constraints on key parameters describing the stellar content, star formation activity and dust content of galaxies. Our model is now publicly available via a user-friendly code package, MAGPHYS at www.iap.fr/magphys. We present an application of this model to interpret a sample of ~1400 local (z<0.5) galaxies from the H-ATLAS survey. We find that, for these galaxies, the diffuse interstellar medium, powered mainly by stars older than 10 Myr, accounts for about half the total infrared luminosity. We discuss the implications of this result to the use of star formation rate indicators based on total infrared luminosity.
Electrocardiography plays an essential role in diagnosing and screening cardiovascular diseases in daily healthcare. Deep neural networks have shown the potentials to improve the accuracies of arrhythmia detection based on electrocardiograms (ECGs). However, more ECG records with ground truth are needed to promote the development and progression of deep learning techniques in automatic ECG analysis. Here we propose a web-based tool for ECG viewing and annotating, LabelECG. With the facilitation of unified data management, LabelECG is able to distribute large cohorts of ECGs to dozens of technicians and physicians, who can simultaneously make annotations through web-browsers on PCs, tablets and cell phones. Along with the doctors from four hospitals in China, we applied LabelECG to support the annotations of about 15,000 12-lead resting ECG records in three months. These annotated ECGs have successfully supported the First China ECG intelligent Competition. La-belECG will be freely accessible on the Internet to support similar researches, and will also be upgraded through future works.
In this manuscript, we introduce a semi-automatic scene graph annotation tool for images, the GeneAnnotator. This software allows human annotators to describe the existing relationships between participators in the visual scene in the form of directed graphs, hence enabling the learning and reasoning on visual relationships, e.g., image captioning, VQA and scene graph generation, etc. The annotations for certain image datasets could either be merged in a single VG150 data-format file to support most existing models for scene graph learning or transformed into a separated annotation file for each single image to build customized datasets. Moreover, GeneAnnotator provides a rule-based relationship recommending algorithm to reduce the heavy annotation workload. With GeneAnnotator, we propose Traffic Genome, a comprehensive scene graph dataset with 1000 diverse traffic images, which in return validates the effectiveness of the proposed software for scene graph annotation. The project source code, with usage examples and sample data is available at https://github.com/Milomilo0320/A-Semi-automatic-Annotation-Software-for-Scene-Graph, under the Apache open-source license.
Context. The solar chromosphere is the interface between the solar surface and the solar corona. Modelling of this region is difficult because it represents the transition from optically thick to thin radiation escape, from gas-pressure domination to magnetic-pressure domination, from a neutral to an ionised state, from MHD to plasma physics, and from near-equilibrium (LTE) to non-equilibrium conditions. Aims. Our aim is to provide the community with realistic simulations of the magnetic solar outer atmosphere. This will enable detailed comparison of existing and upcoming observations with synthetic observables from the simulations, thereby elucidating the complex interactions of magnetic fields and plasma that are crucial for our understanding of the dynamic outer atmosphere. Methods. We used the radiation magnetohydrodynamics code Bifrost to perform simulations of a computational volume with a magnetic field topology similar to an enhanced network area on the Sun. Results. The full simulation cubes are made available online. The general properties of the simulation are discussed, and limitations are discussed.
I highlight several concerns regarding the consistency of Type Ia supernova data in the publicly available Pantheon and JLA compilations. The measured heliocentric redshifts (zhel) of $sim$150 SNe Ia as reported in the Pantheon catalogue are significantly discrepant from those in JLA - with 58 having differences amounting to between 5 and 137 times the quoted measurement uncertainty. The discrepancy seems to have been introduced in the process of rectifying a previously reported issue. The Pantheon catalogue until very recently had the redshifts of all SNe Ia up to z $sim$ 0.3 modified under the guise of peculiar velocity corrections - although there is no information on peculiar velocities at such high redshifts. While this has reportedly been rectified on Github by removing peculiar velocity corrections for z > 0.08, the impact of this on the published cosmological analysis of the Pantheon catalogue is not stated. In JLA, the effect of these corrections is to significantly bias the inferred value of $Omega_{Lambda}$ towards higher values, while the equivalent effect on Pantheon cannot be ascertained due to the unavailability of the individual components of the covariance matrix in the public domain. I provide Jupyter notebooks and URLs in order to allow the reader to ascertain the veracity of these assertions.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
