paper-with-me

Papers

Polar Shapelets

2004-08-24 · Richard Massey, Alexandre Refregier

The shapelets method for image analysis is based upon the decomposition of localised objects into a series of orthogonal components with convenient mathematical properties. We extend the "Cartesian shapelet" formalism from earlier work, and construct "polar shapelet" basis functions that separate an image into components with explicit rotational symmetries. These frequently provide a more compact parameterisation, and can be interpreted in an intuitive way. Image manipulation in shapelet space is simplified by the concise expressions for linear coordinate transformations; and shape measures (including object photometry, astrometry and galaxy morphology estimators) take a naturally elegant form. Particular attention is paid to the analysis of astronomical survey images, and we test shapelet techniques upon real data from the Hubble Space Telescope. We present a practical method to automatically optimise the quality of an arbitrary shapelet decomposition in the presence of observational noise, pixellisation and a Point-Spread Function. A central component of this procedure is the adaptive choice of the shapelet expansion's scale size and truncation order. A complete software package to perform shapelet image analysis is made available on the world-wide web at http://www.astro.caltech.edu/~rjm/shapelets/ .

📄 PDF Abstract BibTeX arXiv:astro-ph/0408445

Code (1)

mr-superonion/fpfs jax

Tasks

Image Manipulation

Similar Papers 제목 키워드 기반

SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets

2023-04-06 · Borui Cai, Guangyan Huang, Shuiqiao Yang, Yong Xiang 외

Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they disc…

ClusteringTime SeriesTime Series Clustering

Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method

2016-06-20 · Qiuyan Yan, Qifa Sun, Xinming Yan

ELM (Extreme Learning Machine) is a single hidden layer feed-forward network, where the weights between input and hidden layer are initialized randomly. ELM is efficient due to its utilization of the analytical approach …

DiversityGeneral ClassificationTime SeriesTime Series Analysis+1

Ultra-Fast Shapelets for Time Series Classification

2015-03-17 · Martin Wistuba, Josif Grabocka, Lars Schmidt-Thieme

Time series shapelets are discriminative subsequences and their similarity to a time series can be used for time series classification. Since the discovery of time series shapelets is costly in terms of time, the applica…

ClassificationGeneral ClassificationTime SeriesTime Series Analysis+1

GENDIS: GENetic DIscovery of Shapelets

2019-09-13 · Gilles Vandewiele, Femke Ongenae, Filip De Turck

In the time series classification domain, shapelets are small time series that are discriminative for a certain class. It has been shown that classifiers are able to achieve state-of-the-art results on a plethora of data…

Outlier DetectionTime SeriesTime Series AnalysisTime Series Classification

Shapelets for earthquake detection

2019-11-20 · Monica Arul, Ahsan Kareem

This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embedded in machine learning to autonomously detect earthquakes. It promises to overcome the challenges in the field of seismolo…

Time SeriesTime Series Analysis