[go: up one dir, main page]

Avestruz et al., 2019 - Google Patents

Automated lensing learner: automated strong lensing identification with a computer vision technique

Avestruz et al., 2019

View PDF @Full View
Document ID
13973396991003055292
Author
Avestruz C
Li N
Zhu H
Lightman M
Collett T
Luo W
Publication year
Publication venue
The Astrophysical Journal

External Links

Snippet

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from …
Continue reading at iopscience.iop.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light

Similar Documents

Publication Publication Date Title
Avestruz et al. Automated lensing learner: automated strong lensing identification with a computer vision technique
Lehmer et al. X-ray binary luminosity function scaling relations for local galaxies based on subgalactic modeling
Li et al. New high-quality strong lens candidates with deep learning in the kilo-degree survey
Rojas et al. Search of strong lens systems in the Dark Energy Survey using convolutional neural networks
Goldstein et al. Automated transient identification in the dark energy survey
Pourrahmani et al. LensFlow: A convolutional neural network in search of strong gravitational lenses
Hayes et al. Spectral Shapes of the Lyα Emission from Galaxies. I. Blueshifted Emission and Intrinsic Invariance with Redshift
Jacobs et al. Finding high-redshift strong lenses in DES using convolutional neural networks
Rojas-Ruiz et al. Probing the Bright End of the Rest-frame Ultraviolet Luminosity Function at z= 8–10 with Hubble Pure-parallel Imaging
Buchner et al. Obscuration-dependent evolution of active galactic nuclei
Fossati et al. Galaxy Environment in the 3D-HST Fields: Witnessing the Onset of Satellite Quenching at z∼ 1–2
Sazonova et al. Are all post-starbursts mergers? HST reveals hidden disturbances in the majority of PSBs
Yang et al. Timely and accurate national-scale mapping of urban land in China using Defense Meteorological Satellite Program’s Operational Linescan System nighttime stable light data
Marshall et al. Automated detection of galaxy-scale gravitational lenses in high-resolution imaging data
Tohill et al. Quantifying non-parametric structure of high-redshift galaxies with deep learning
McQuinn et al. Pegasus W: An Ultrafaint Dwarf Galaxy Outside the Halo of M31 Not Quenched by Reionization
Pérez et al. StarcNet: Machine learning for star cluster identification
Galvin et al. Radio galaxy zoo: Knowledge transfer using rotationally invariant self-organizing maps
Williams et al. Population mixtures and searches of lensed and extended quasars across photometric surveys
Anand et al. Archetype-based redshift estimation for the dark energy spectroscopic instrument survey
Morgan et al. DeepZipper: a novel deep-learning architecture for lensed supernovae identification
Ferreira et al. Galaxy mergers in UNIONS–I. A simulation-driven hybrid deep learning ensemble for pure galaxy merger classification
Teimoorinia et al. Assessment of astronomical images using combined machine-learning models
Hughes et al. The GALAH Survey: A New Sample of Extremely Metal-poor Stars Using a Machine-learning Classification Algorithm
Angora et al. Searching for strong galaxy-scale lenses in galaxy clusters with deep networks-I. Methodology and network performance