Matching CCD images to a stellar catalog using locality-sensitive hashing

Liu, Bo; Yu, Jia-Zong; Peng, Qingyu

doi:10.1088/1674-4527/18/2/22

Cited by 1 publication

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The astrometry of CCD images includes the geometry distortion correction (Peng et al 2017), and matching reference stars (Liu et al 2018), but they have the same prerequisite: no false detected image star. In most cases, Saturn and its satellites are disk-resolve objects in the ISS images, which will bring some false detected image stars in the disk.…”

Section: Introductionmentioning

confidence: 99%

Contour detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

Yang

Zhang

Ли

2020

Res. Astron. Astrophys.

View full text Add to dashboard Cite

In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved object to accurately locate their center. Thus, the contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, often extract the contour with too much interior details and noise. Although the deep convolutional neural network has been applied successfully in many image tasks, such as classification and object detection, it needs more time and computer resources. In the paper, a contour detection algorithm based on H-ELM (Hierarchical Extreme Learning Machine) and DenseCRF (Dense Conditional Random Field) is proposed for Cassini ISS images. The experimental results show that this algorithms performance is better than both traditional machine learning methods such as SVM, ELM and even deep convolutional neural network. And the extracted contour is closer to the actual contour. Moreover, it can be trained and tested quickly on the general configuration of PC, so can be applied to contour detection for Cassini ISS images.

show abstract

Section: Introductionmentioning

confidence: 99%