5 papers
Anomaly detection in radio galaxy data with trainable COSFIRE filters
Steven Ndung'u, Trienko Grobler, Stefan J. Wijnholds +1
Detecting anomalies in radio astronomy is challenging due to the vast amounts of data and the rarity of labeled anomalous examples. Addressing this challenge requires efficient met…
Content-Based Image Retrieval Using COSFIRE Descriptors with application to Radio Astronomy
Steven Ndungu, Trienko Grobler, Stefan J. Wijnholds +1
The morphologies of astronomical sources are highly complex, making it essential not only to classify the identified sources into their predefined categories but also to determine…
Classification of Radio Galaxies with trainable COSFIRE filters
Steven Ndungu, Trienko Grobler, Stefan J. Wijnholds Dimka Karastoyanova +1
Radio galaxies exhibit a rich diversity of characteristics and emit radio emissions through a variety of radiation mechanisms, making their classification into distinct types based…
Deep supervised hashing for fast retrieval of radio image cubes
Steven Ndung'u, Trienko Grobler, Stefan J. Wijnholds +2
The shear number of sources that will be detected by next-generation radio surveys will be astronomical, which will result in serendipitous discoveries. Data-dependent deep hashing…
Advances on the classification of radio image cubes
Steven Ndung'u, Trienko Grobler, Stefan J. Wijnholds +2
Modern radio telescopes will daily generate data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare…