• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.372-374
• /
• 2003

The optical flow theory can be utilized for automatically tracking and positioning homologous points in digital video (DV) image sequences. In this paper, the Lucas-Kanade optical flow estimation (LKOFE) method and the normalized cross-correlation (NCC) method are compared and analyzed using the DV image sequences acquired by our SONY DCRPC115 DV camera. Thus, an improved optical flow estimation procedure, called 'Iterated Optical Flow Estimation (IOFE)', is presented. Our test results show that the trackable range of 3${\sim}$4 pixels in the LKOFE procedure can be apparently enlarged to 30 pixels in the IOFE.

• Journal of The Korean Astronomical Society
• /
• v.56 no.2
• /
• pp.253-262
• /
• 2023

This work presents four high-amplitude variable YSOs (≃3 mag at near- or mid-IR wavelengths) arising from the SPICY catalog. Three outbursts show a duration that is longer than 1 year, and are still ongoing. And additional YSO brightened over the last two epochs of NEOWISE observations and the duration of the outburst is thus unclear. Analysis of the spectra of the four sources confirms them as new members of the eruptive variable class. We find two YSOs that can be firmly classified as bona fide FUors and one object that falls in the V1647 Ori-like class. Given the uncertainty in the duration of its outburst, an additional YSO can only be classified as a candidate FUor. Continued monitoring and follow-up of these particular sources is important to better understand the accretion process of YSOs.

• Journal of Korean Society for Geospatial Information Science
• /
• v.16 no.2
• /
• pp.49-56
• /
• 2008

Image registration is one of the critical techniques of image mosaic which has many applications such as generating panoramas, video monitoring, image rendering and reconstruction, etc. The fundamental tasks of image registration are point features extraction and tracking which take much computation time. KLT(Kanade-Lucas-Tomasi) feature tracker has proposed for extracting and tracking features through image sequences. The aim of this study is to demonstrate the usage of effective and robust KLT feature detector and tracker for an image registration using the sequence image frames captured by UAV video camera. In result, by using iterative implementation of the KLT tracker, the features extracted from the first frame of image sequences could be successfully tracked through all frames. The process of feature tracking in the various frames with rotation, translation and small scaling could be improved by a careful choice of the process condition and KLT pyramid implementation.