Seong-Gyun Jeong

Ph. D., INRIA Sophia Antipolis

Keywords: Curvilinear structure modeling, Stochastic process, Structure inference, Machine learning

Contact:
Mail: seong-gyundotjeongatinriadotfr
Phone: (33)4-92-38-76-65
Fax: (33)4-92-38-78-58
Postal address: INRIA Sophia Antipolis , 2004, route des Lucioles, 06902 Sophia Antipolis Cedex, France

Abstract:

My work at INRIA deals with the development of curvilinear structure reconstruction models based on stochastic modeling and machine learning. I assume that the curvilinear structure can be decomposed into a set of line segments. This assumption enables us to reconstruct arbitrary shapes of curvilinear structure for different types of datasets, e.g., retinal images, DNA filaments, facial wrinkles, road networks. I look for an optimal set of line segments which correspond to the latent curvilinear structure. For the stochastic model, I propose a probability density of the line segments for a given image data which constrains geometric interaction of line segments. Also, I employ a machine learning technique to infer structured information and to predict the correspondence of the given line segment and the latent curvilinear structures.

Short Bio:

Seong-Gyun Jeong received the B.E. degree in Electrical Engineering and M.E. degree in Electrical & Computer engineering from the Korea University, Seoul, Korea, in 2010 and 2012, respectively. Currently he is working as a Ph.D. student with AYIN team, INRIA. In particular, his research concerns stochastic image modeling, structure inference, machine learning, and graph theory.

Last publications:

Publications HAL de Seong-Gyun,Jeong du labo/EPI ayin

2016

Preprints, Working Papers, …

ref_biblio
Seong-Gyun Jeong, Yuliya Tarabalka, Nicolas Nisse, Josiane Zerubia. Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm. 2016. <hal-01414864>
Accès au texte intégral et bibtex
https://hal.inria.fr/hal-01414864/file/jeong2016arxiv.pdf BibTex

2015

Conference papers

ref_biblio
Seong-Gyun Jeong, Yuliya Tarabalka, Josiane Zerubia. Stochastic model for curvilinear structure reconstruction using morphological profiles. ICIP 2015 – IEEE International Conference on Image Processing, Sep 2015, Quebec City, Canada. IEEE. <hal-01152932>
Accès au texte intégral et bibtex
https://hal.inria.fr/hal-01152932/file/2015_ICIP_SGJEONG_CAMREADY.pdf BibTex
ref_biblio
Seong-Gyun Jeong, Yuliya Tarabalka, Josiane Zerubia. Marked Point Process Model for Curvilinear Structures Extraction. EMMCVPR 2015, Jan 2015, Hong Kong, Hong Kong SAR China. pp.436-449, 2015, LNCS 8932. <10.1007/978-3-319-14612-6_32>. <hal-01084939>
Accès au bibtex
BibTex

Reports

ref_biblio
Seong-Gyun Jeong, Yuliya Tarabalka, Nicolas Nisse, Josiane Zerubia. Inference of Curvilinear Structure based on Learning a Ranking Function and Graph Theory. [Research Report] RR-8789, Inria Sophia Antipolis. 2015. <hal-01214932>
Accès au texte intégral et bibtex
https://hal.inria.fr/hal-01214932/file/RR-8789.pdf BibTex

Theses

ref_biblio
Seong-Gyun Jeong. Curvilinear structure modeling and its applications in computer vision. Computer Science [cs]. Université Nice Sophia Antipolis, 2015. English. <NNT : 2015NICE4086>. <tel-01243028v2>
Accès au texte intégral et bibtex
https://hal.inria.fr/tel-01243028/file/2015NICE4086.pdf BibTex

2014

Conference papers

ref_biblio
Seong-Gyun Jeong, Yuliya Tarabalka, Josiane Zerubia. Marked point process model for facial wrinkle detection. IEEE ICIP – International Conference on Image Processing, Oct 2014, Paris, France. pp.1391-1394, 2014. <hal-01066231>
Accès au texte intégral et bibtex
https://hal.inria.fr/hal-01066231/file/2014_ICIP_SGJEONG_Submitted.pdf BibTex