Publications


2017


  • 이성민, 최민석, 이장호, 이규현, 정원기, and 윤성로, “T-SNE 기반 군집화 분석을 통한 교모세포종 환자의 항암 후보 약물의 반응성 측정,” Korean institute of information scientists and engineers, to appear., 2017.
    [Bibtex]
    @article{ghlee_2017_kcc,
    title = {t-{SNE} 기반 군집화 분석을 통한 교모세포종 환자의 항암 후보 약물의 반응성 측정},
    year = {2017},
    author = {이성민 and 최민석 and 이장호 and 이규현 and 정원기 and 윤성로},
    journal = {Korean Institute of Information Scientists and Engineers, to appear.}
    }

 
 

  • T. M. Quan, J. Choi, H. Jeong, and W. Jeong, “An intelligent system approach for robust volume rendering using hierarchical 3d convolutional sparse coding,,” IEEE transactions on visualization and computer graphics (TVCG), to appear., 2017.
    [Bibtex]
    @article{quan_jun_haejin_vis_2017,
    title = {An Intelligent System Approach for Robust Volume Rendering using Hierarchical 3D Convolutional Sparse Coding,},
    year = {2017},
    author = {Quan, T. M. and JunYoung Choi and Haejin Jeong and Won-Ki Jeong},
    journal = {{IEEE} transactions on visualization and computer graphics ({TVCG}), to appear.}
    }

 

  • [PDF] S. Hong and W. Jeong, “GPU in-memory processing using spark for iterative computation,” 2017 17th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid), 2017.
    [Bibtex]
    @article{hong_ccgrid_2017,
    title = "{GPU} in-memory processing using Spark for iterative computation",
    year = "2017",
    author = "Sumin Hong and Won-Ki Jeong",
    journal = {2017 17th {IEEE}/{ACM} International Symposium on Cluster, Cloud and Grid Computing ({CCG}rid)}
    }

 

  • [DOI] D. G. C. Hildebrand, M. Cicconet, R. M. Torres, W. Choi, T. M. Quan, J. Moon, A. W. Wetzel, A. Scott Champion, B. J. Graham, O. Randlett, G. S. Plummer, R. Portugues, I. H. Bianco, S. Saalfeld, A. D. Baden, K. Lillaney, R. Burns, J. T. Vogelstein, A. F. Schier, W. A. Lee, W. Jeong, J. W. Lichtman, and F. Engert, “Whole-brain serial-section electron microscopy in larval zebrafish,” Nature, vol. 545, iss. 7654, pp. 345-349, 2017.
    [Bibtex]
    @Article{hildebrand_nature_2017,
    author={Hildebrand, David Grant Colburn
    and Cicconet, Marcelo
    and Torres, Russel Miguel
    and Choi, Woohyuk
    and Quan, Tran Minh
    and Moon, Jungmin
    and Wetzel, Arthur Willis
    and Scott Champion, Andrew
    and Graham, Brett Jesse
    and Randlett, Owen
    and Plummer, George Scott
    and Portugues, Ruben
    and Bianco, Isaac Henry
    and Saalfeld, Stephan
    and Baden, Alexander David
    and Lillaney, Kunal
    and Burns, Randal
    and Vogelstein, Joshua Tzvi
    and Schier, Alexander Franz
    and Lee, Wei-Chung Allen
    and Jeong, Won-Ki
    and Lichtman, Jeff William
    and Engert, Florian},
    title={Whole-brain serial-section electron microscopy in larval zebrafish},
    journal={Nature},
    year={2017},
    month={May},
    day={18},
    publisher={Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
    volume={545},
    number={7654},
    pages={345-349},
    note={Letter},
    issn={0028-0836},
    url={http://dx.doi.org/10.1038/nature22356},
    doi = {10.1038/nature22356}
    }

 

  • [PDF] [DOI] S. Hong and W. K. Jeong, “A group-ordered fast iterative method for eikonal equations,” IEEE transactions on parallel and distributed systems, vol. 28, iss. 2, pp. 318-331, 2017.
    [Bibtex]
    @ARTICLE{hong_group_2016,
    author={S. Hong and W. K. Jeong},
    journal={{IEEE} Transactions on Parallel and Distributed Systems},
    title={A Group-Ordered Fast Iterative Method for Eikonal Equations},
    year={2017},
    volume={28},
    number={2},
    pages={318-331},
    keywords={iterative methods;multiprocessing systems;parallel algorithms;parallel architectures;pattern clustering;GO-FIM;blocks clustering;eikonal equations;grid blocks;group-ordered fast iterative method;lock-free local queue approach;multicore parallel architectures;numerical algorithms;parallel algorithm;Algorithm design and analysis;Data structures;Graphics processing units;Iterative methods;Parallel algorithms;Parallel architectures;Eikonal equation;{GPU};parallel computing},
    doi={10.1109/TPDS.2016.2567397},
    ISSN={1045-9219},
    month={Feb},}

 


2016


  • [PDF] [DOI] W. Choi, S. Hong, and W. Jeong, “Vispark: GPU-Accelerated Distributed Visual Computing Using Spark,” SIAM Journal on Scientific Computing (SISC), vol. 38, iss. 5, p. S700-S719, 2016.
    [Bibtex]
    @article{woohyuk_2016_vispark,
    author={Woohyuk Choi and Sumin Hong and Won-Ki Jeong},
    title={{Vispark: {GPU}-Accelerated Distributed Visual Computing Using Spark}},
    journal={{SIAM Journal on Scientific Computing (SISC)}},
    publisher={Society for Industrial and Applied Mathematics},
    volume = {38},
    number = {5},
    pages = {S700-S719},
    year = {2016},
    doi = {10.1137/15M1026407},
    URL = {
    http://dx.doi.org/10.1137/15M1026407
    },
    eprint = {
    http://dx.doi.org/10.1137/15M1026407
    }
    }

 

  • [PDF] G. Lee, T. M. Quan, and W. Jeong, “명시야 현미경 영상에서의 세포 분할을위한 이중 사전 학습 기법,” Journal of the Korea Computer Graphics Society, vol. 22, pp. 21-29, 2016.
    [Bibtex]
    @article{ghlee_2016_dualdictionary,
    author={Gyuhyun Lee and Tran Minh Quan and Won-Ki Jeong},
    title={{명시야 현미경 영상에서의 세포 분할을위한 이중 사전 학습 기법}},
    booltitle={{Vol.22 No.3}},
    journal={{Journal of the Korea Computer Graphics Society}},
    volume={22},
    issur={3},
    publisher={Korea Computer Graphics Society},
    year={2016},
    pages={21-29},
    url={http://www.dbpia.co.kr/Article/NODE06716016
    }
    }

 

  • [PDF] [DOI] T. M. Quan and W. Jeong, “Compressed sensing dynamic MRI reconstruction using GPU-accelerated 3D convolutional sparse coding,” in Medical image computing and computer-assisted intervention — MICCAI 2016: 19th international conference, athens, greece, october 17-21, 2016, proceedings, part iii, S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, Eds., Cham: Springer International Publishing, 2016, pp. 484-492.
    [Bibtex]
    @Inbook{quan_compressed3d_2016,
    author="Quan, Tran Minh
    and Jeong, Won-Ki",
    editor="Ourselin, Sebastien
    and Joskowicz, Leo
    and Sabuncu, Mert R.
    and Unal, Gozde
    and Wells, William",
    title="Compressed Sensing Dynamic {MRI} Reconstruction Using {GPU}-accelerated {3D} Convolutional Sparse Coding",
    bookTitle="Medical Image Computing and Computer-Assisted Intervention -- {MICCAI} 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III",
    year="2016",
    publisher="Springer International Publishing",
    address="Cham",
    pages="484--492",
    isbn="978-3-319-46726-9",
    doi="10.1007/978-3-319-46726-9_56",
    url="http://dx.doi.org/10.1007/978-3-319-46726-9_56"
    }

 

  • [PDF] [DOI] S. Hong and W. Jeong, “A multi-GPU fast iterative method for eikonal equations using on-the-fly adaptive domain decomposition,” Procedia computer science, vol. 80, pp. 190-200, 2016.
    [Bibtex]
    @article{hong_multifim_2016,
    title = "A Multi-{GPU} Fast Iterative Method for Eikonal Equations Using On-the-fly Adaptive Domain Decomposition ",
    journal = "Procedia Computer Science ",
    volume = "80",
    number = "",
    pages = "190 - 200",
    year = "2016",
    note = "International Conference on Computational Science 2016, \{ICCS\} 2016, 6-8 June 2016, San Diego, California, \{USA\} ",
    issn = "1877-0509",
    doi = "http://dx.doi.org/10.1016/j.procs.2016.05.309",
    url = "http://www.sciencedirect.com/science/article/pii/S1877050916306676",
    author = "Sumin Hong and Won-Ki Jeong",
    keywords = "Eikonal equation",
    keywords = "fast iterative method",
    keywords = "{GPU}",
    keywords = "parallel algorithms",
    keywords = "domain decomposition ",
    abstract = "Abstract The recent research trend of Eikonal solver focuses on employing state-of-the-art parallel computing technology, such as {GPU}s. Even though there exists previous work on {GPU}-based parallel Eikonal solvers, only little research literature exists on the multi-{GPU} Eikonal solver due to its complication in data and work management. In this paper, we propose a novel on-the-fly, adaptive domain decomposition method for efficient implementation of the Block-based Fast Iterative Method on a multi-{GPU} system. The proposed method is based on dynamic domain decomposition so that the region to be processed by each \{{GPU}\} is determined on-the-fly when the solver is running. In addition, we propose an efficient domain assignment algorithm that minimizes communication overhead while maximizing load balancing between {GPU}s. The proposed method scales well, up to 6.17x for eight {GPU}s, and can handle large computing problems that do not fit to limited \{{GPU}\} memory. We assess the parallel efficiency and runtime performance of the proposed method on various distance computation examples using up to eight {GPU}s. "
    }

 

  • [PDF] [DOI] T. M. Quan and W. K. Jeong, “A fast discrete wavelet transform using hybrid parallelism on GPUs,” IEEE transactions on parallel and distributed systems, vol. 27, iss. 11, pp. 3088-3100, 2016.
    [Bibtex]
    @ARTICLE{quan_fast_2016,
    author={T. M. Quan and W. K. Jeong},
    journal={{IEEE} Transactions on Parallel and Distributed Systems},
    title={A Fast Discrete Wavelet Transform Using Hybrid Parallelism on {GPU}s},
    year={2016},
    volume={27},
    number={11},
    pages={3088-3100},
    keywords={discrete wavelet transforms;graphics processing units;optimisation;parallel processing;CPU;{GPU} DWT methods;{GPU} optimization strategies;{GPU}-based discrete wavelet transform;Haar DWT;ILP maximization;acceleration techniques;computationally-intensive problem acceleration;fast discrete wavelet transform;graphics processing unit;hybrid parallelism;mixed-band memory layout;multilevel transform;single fused kernel launch;time-critical applications;Acceleration;Discrete wavelet transforms;Graphics processing units;Parallel processing;Registers;{GPU} computing;Wavelet transform;bit rotation;hybrid parallelism;lifting scheme},
    doi={10.1109/TPDS.2016.2536028},
    ISSN={1045-9219},
    month={Nov},}

 

  • [PDF] [DOI] T. M. Quan and W. K. Jeong, “Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding,” in 2016 IEEE 13th international symposium on biomedical imaging (ISBI), 2016, pp. 518-521.
    [Bibtex]
    @INPROCEEDINGS{quan_compressed_2016,
    author={T. M. Quan and W. K. Jeong},
    booktitle={2016 {IEEE} 13th International Symposium on Biomedical Imaging ({ISBI})},
    title={Compressed sensing reconstruction of dynamic contrast enhanced {MRI} using {GPU}-accelerated convolutional sparse coding},
    year={2016},
    pages={518-521},
    keywords={Convolution;Convolutional codes;Dictionaries;Encoding;Fourier transforms;Image reconstruction;Magnetic resonance imaging;Compressed Sensing;Convolutional Sparse Coding;{GPU};{MRI}},
    doi={10.1109/ISBI.2016.7493321},
    month={April},}

 


2015


  • J. Kim, W. Jeong, and B. Nam, “Exploiting massive parallelism for indexingmulti-dimensional datasets on the GPU,” IEEE transactions on parallel and distributed systems, vol. 26, iss. 8, pp. 2258-2271, 2015.
    [Bibtex]
    @article{kim2015exploiting,
    title={Exploiting Massive Parallelism for IndexingMulti-Dimensional Datasets on the {GPU}},
    author={Kim, Jinwoong and Jeong, Won-Ki and Nam, Beomseok},
    journal={{IEEE} Transactions on Parallel and Distributed Systems},
    volume={26},
    number={8},
    pages={2258--2271},
    year={2015},
    publisher={IEEE}
    }

 

  • [PDF] [DOI] T. M. Quan, S. Han, H. Cho, and W. Jeong, “Multi-GPU Reconstruction of Dynamic Compressed Sensing MRI,” in Proceedings of the 18th international conference on medical image computing and computer-assisted intervention (MICCAI), Springer International Publishing, 2015, pp. 484-492.
    [Bibtex]
    @incollection{quan_multi_2015,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {Multi-{GPU} {Reconstruction} of {Dynamic} {Compressed} {Sensing} {MRI}},
    copyright = {©2015 Springer International Publishing Switzerland},
    isbn = {978-3-319-24573-7 978-3-319-24574-4},
    url = {http://link.springer.com/chapter/10.1007/978-3-319-24574-4_58},
    abstract = {Magnetic resonance imaging (MRI) is a widely used in-vivo imaging technique that is essential to the diagnosis of disease, but its longer acquisition time hinders its wide adaptation in time-critical applications, such as emergency diagnosis. Recent advances in compressed sensing (CS) research have provided promising theoretical insights to accelerate the MRI acquisition process, but CS reconstruction also poses computational challenges that make MRI less practical. In this paper, we introduce a fast, scalable parallel CS-MRI reconstruction method that runs on graphics processing unit ({GPU}) cluster systems for dynamic contrast-enhanced (DCE) MRI. We propose a modified Split-Bregman iteration using a variable splitting method for CS-based DCE-MRI. We also propose a parallel {GPU} Split-Bregman solver that scales well across multiple {GPU}s to handle large data size. We demonstrate the validity of the proposed method on several synthetic and real DCE-MRI datasets and compare with existing methods.},
    language = {en},
    number = {9351},
    urldate = {2015-10-12},
    booktitle = {Proceedings of the 18th international conference on Medical image computing and computer-assisted intervention ({MICCAI})},
    publisher = {Springer International Publishing},
    author = {Quan, Tran Minh and Han, Sohyun and Cho, Hyungjoon and Jeong, Won-Ki},
    year = {2015},
    doi = {10.1007/978-3-319-24574-4\_58},
    keywords = {Artificial Intelligence (incl. Robotics), computer graphics, Health Informatics, Image Processing and Computer Vision, Imaging / Radiology, Pattern Recognition},
    pages = {484--492},
    file = {Full Text PDF:files/227/Quan et al. - 2015 - Multi-{GPU} Reconstruction of Dynamic Compressed Sen.pdf:application/pdf;Snapshot:files/234/978-3-319-24574-4_58.html:text/html}
    }

 


2014


  • [PDF] [DOI] H. Choi, W. Choi, T. M. Quan, D. G. C. Hildebrand, H. Pfister, and W. Jeong, “Vivaldi: a domain-specific language for volume processing and visualization on distributed heterogeneous systems,” IEEE transactions on visualization and computer graphics, vol. 20, iss. 12, pp. 2407-2416, 2014.
    [Bibtex]
    @article{choi_vivaldi_2014,
    title = {Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems},
    volume = {20},
    issn = {1077-2626},
    shorttitle = {Vivaldi},
    doi = {10.1109/TVCG.2014.2346322},
    abstract = {As the size of image data from microscopes and telescopes increases, the need for high-throughput processing and visualization of large volumetric data has become more pressing. At the same time, many-core processors and {GPU} accelerators are commonplace, making high-performance distributed heterogeneous computing systems affordable. However, effectively utilizing {GPU} clusters is difficult for novice programmers, and even experienced programmers often fail to fully leverage the computing power of new parallel architectures due to their steep learning curve and programming complexity. In this paper, we propose Vivaldi, a new domain-specific language for volume processing and visualization on distributed heterogeneous computing systems. Vivaldi's Python-like grammar and parallel processing abstractions provide flexible programming tools for non-experts to easily write high-performance parallel computing code. Vivaldi provides commonly used functions and numerical operators for customized visualization and high-throughput image processing applications. We demonstrate the performance and usability of Vivaldi on several examples ranging from volume rendering to image segmentation.},
    number = {12},
    journal = {{IEEE} Transactions on Visualization and Computer Graphics},
    author = {Choi, H. and Choi, W. and Quan, T.M. and Hildebrand, D.G.C. and Pfister, H. and Jeong, W.},
    month = dec,
    year = {2014},
    keywords = {computational modeling, Data models, Data visualization, distributed heterogeneous systems, Domain-specific language, {GPU} computing, graphics processing units, image classification, parallel processing, rendering (computer graphics), volume rendering},
    pages = {2407--2416},
    file = {{IEEE} Xplore Abstract Record:H\:\\Zotero\\storage\\ED5RN7ME\\articleDetails.html:text/html;{IEEE} Xplore Full Text PDF:H\:\\Zotero\\storage\\XKKZSRWV\\Choi et al. - 2014 - Vivaldi A Domain-Specific Language for Volume Pro.pdf:application/pdf}
    }

 

  • [PDF] [DOI] T. M. Quan and W. Jeong, “A fast Mixed-Band lifting wavelet transform on the GPU,” in IEEE International Conference on Image Processing, 2014, pp. 1238-1242.
    [Bibtex]
    @INPROCEEDINGS{quan_fast_2013,
    AUTHOR="Tran Minh Quan and Won-Ki Jeong",
    TITLE="A Fast {Mixed-Band} Lifting Wavelet Transform on the {GPU}",
    BOOKTITLE="{{IEEE} International Conference on Image Processing}",
    PAGES="1238-1242",
    DAYS=27,
    MONTH=oct,
    YEAR=2014,
    KEYWORDS="Mixed-band, Wavelet, Denoising, {GPU}, Parallel Computing, Compressive
    Sensing, MRI",
    DOI = {10.1109/ICIP.2014.7025247},
    ABSTRACT="Discrete wavelet transform (DWT) has been widely used in many image
    compression applications, such as JPEG2000 and compressive sensing MRI.
    Even though a lifting scheme has been widely adopted to accelerate DWT,
    only a handful of research has been done on its efficient implementation on
    many-core accelerators, such as graphics processing units ({GPU}s). Moreover,
    we observe that rearranging the spatial locations of wavelet coefficients
    at every level of DWT significantly impairs the performance of memory
    transaction on the {GPU}. To address these problems, we propose a mixed-band
    lifting wavelet transform that reduces uncoalesced global memory access on
    the {GPU} and maximizes on-chip memory bandwidth by implementing in-place
    operations using registers. We assess the performance of the proposed
    method by comparing with the state-of-the-art DWT libraries, and show its
    usability in a compressive sensing (CS) MRI application."
    }

 


2013


  • [PDF] [DOI] J. Beyer, M. Hadwiger, A. Al-Awami, W. Jeong, N. Kasthuri, J. W. Lichtman, and H. Pfister, “Exploring the connectome: petascale volume visualization of microscopy data streams,” IEEE computer graphics and applications, vol. 33, iss. 4, pp. 50-61, 2013.
    [Bibtex]
    @article{beyer_exploring_2013,
    title = {Exploring the Connectome: Petascale Volume Visualization of Microscopy Data Streams},
    volume = {33},
    issn = {0272-1716},
    shorttitle = {Exploring the Connectome},
    doi = {10.1109/MCG.2013.55},
    abstract = {Recent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams. The system can concurrently handle multiple volumes and can support the simultaneous visualization of high-resolution voxel segmentation data. Its visualization-driven design restricts most computations to a small subset of the data. It employs a multiresolution virtual-memory architecture for better scalability than previous approaches and for handling incomplete data. Researchers have employed it for a 1-teravoxel mouse cortex volume, of which several hundred axons and dendrites as well as synapses have been segmented and labeled.},
    number = {4},
    journal = {{IEEE} Computer Graphics and Applications},
    author = {Beyer, J. and Hadwiger, M. and Al-Awami, A. and Jeong, Won-Ki and Kasthuri, N. and Lichtman, J.W. and Pfister, H.},
    month = jul,
    year = {2013},
    keywords = {1-teravoxel mouse cortex volume, axon, computer graphics, data processing, data storage, data visualisation, Data visualization, dendrite, high-resolution microscopy, high-resolution voxel segmentation, high-throughput electron microscopy, high-throughput imaging, image resolution, incomplete data handling, medical computing, medical image processing, microscopy, microscopy data stream, multiresolution virtual-memory architecture, neural structure, neural-tissue volume data, neurophysiology, neuroscience, neuroscientist, petascale volume visualization, petascale-volume exploration, petavoxel volume, rendering (computer graphics), segmented volume data, Streaming media, visualization-driven design},
    pages = {50--61},
    file = {{IEEE} Xplore Abstract Record:E:\Zotero\storage\EQHG5J25\articleDetails.html:text/html}
    }

 

  • [PDF] [DOI] W. Jeong, J. Schneider, A. Hansen, M. Lee, S. G. Turney, B. E. Faulkner-Jones, J. L. Hecht, R. Najarian, E. Yee, J. W. Lichtman, and H. Pfister, “A collaborative digital pathology system for multi-touch mobile and desktop computing platforms,” Computer graphics forum, vol. 32, iss. 6, pp. 227-242, 2013.
    [Bibtex]
    @article{jeong_collaborative_2013,
    title = {A Collaborative Digital Pathology System for Multi-Touch Mobile and Desktop Computing Platforms},
    volume = {32},
    copyright = {© 2013 The Authors Computer Graphics Forum © 2013 The Eurographics Association and John Wiley \& Sons Ltd.},
    issn = {1467-8659},
    url = {http://onlinelibrary.wiley.com/doi/10.1111/cgf.12137/abstract},
    doi = {10.1111/cgf.12137},
    abstract = {Collaborative slide image viewing systems are becoming increasingly important in pathology applications such as telepathology and E-learning. Despite rapid advances in computing and imaging technology, current digital pathology systems have limited performance with respect to remote viewing of whole slide images on desktop or mobile computing devices. In this paper we present a novel digital pathology client–server system that supports collaborative viewing of multi-plane whole slide images over standard networks using multi-touch-enabled clients. Our system is built upon a standard {HTTP} web server and a {MySQL} database to allow multiple clients to exchange image and metadata concurrently. We introduce a domain-specific image-stack compression method that leverages real-time hardware decoding on mobile devices. It adaptively encodes image stacks in a decorrelated colour space to achieve extremely low bitrates (0.8 bpp) with very low loss of image quality. We evaluate the image quality of our compression method and the performance of our system for diagnosis with an in-depth user study.},
    language = {en},
    number = {6},
    urldate = {2014-04-05},
    journal = {Computer Graphics Forum},
    author = {Jeong, W. and Schneider, J. and Hansen, A. and Lee, M. and Turney, S. G. and Faulkner-Jones, B. E. and Hecht, J. L. and Najarian, R. and Yee, E. and Lichtman, J. W. and Pfister, H.},
    month = sep,
    year = {2013},
    keywords = {biomedical image visualization, collaborative visualization, Computer Graphics [I.3.2]: Graphics systems, distributed/network graphics, Computer Graphics [I.3.8]: Applications, digital pathology, {GPU}, image compression},
    pages = {227--242},
    file = {Jeong et al. - 2013 - A Collaborative Digital Pathology System for Multi-Touch Mobile and Desktop Computing Platforms:E:\Zotero\storage\EXCZR9DN\Jeong et al. - 2013 - A Collaborative Digital Pathology System for Multi-Touch Mobile and Desktop Computing Platforms.pdf:application/pdf;Snapshot:E:\Zotero\storage\I923MWWD\abstract.html:text/html}
    }

 


2012


  • [PDF] [DOI] M. Hadwiger, J. Beyer, W. Jeong, and H. Pfister, “Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach,” IEEE transactions on visualization and computer graphics, vol. 18, iss. 12, pp. 2285-2294, 2012.
    [Bibtex]
    @article{hadwiger_interactive_2012,
    title = {Interactive Volume Exploration of Petascale Microscopy Data Streams Using a Visualization-Driven Virtual Memory Approach},
    volume = {18},
    issn = {1077-2626},
    doi = {10.1109/TVCG.2012.240},
    number = {12},
    journal = {{IEEE} Transactions on Visualization and Computer Graphics},
    author = {Hadwiger, Markus and Beyer, Johanna and Jeong, Won-Ki and Pfister, Hanspeter},
    year = {2012},
    keywords = {{2D} microscope image tiles, {3D} blocks, {3D} multiresolution representation, anisotropic petascale volume, best-of-breed system, cache misses, continuous stream, data acquisition, data visualisation, Data visualization, decouples construction, electron microscopes, Graphics processing unit, high resolution electron microscopy image, high-resolution microscopy, high-throughput imaging, Image resolution, interactive volume exploration, microscopes, microscopy, multiresolution hierarchy, multiresolution virtual memory architecture, neuroscience, octree, Octrees, petascale microscopy data streams, Petascale volume exploration, petascale volumes, ray-casting, real microscopy data, rendering (computer graphics), system design, virtual storage, visible volume data, visualization-driven virtual memory, volume ray casting, volume visualization system},
    pages = {2285--2294}
    }

 


2011


  • [PDF] [DOI] Z. Fu, W. Jeong, Y. Pan, R. M. Kirby, and R. T. Whitaker, “A fast iterative method for solving the eikonal equation on triangulated surfaces,” SIAM j. sci. comput., vol. 33, iss. 5, p. 2468–2488, 2011.
    [Bibtex]
    @article{fu_fast_2011,
    title = {A Fast Iterative Method for Solving the Eikonal Equation on Triangulated Surfaces},
    volume = {33},
    issn = {1064-8275},
    url = {http://dx.doi.org/10.1137/100788951},
    doi = {10.1137/100788951},
    number = {5},
    urldate = {2013-04-20},
    journal = {{SIAM} J. Sci. Comput.},
    author = {Fu, Zhisong and Jeong, Won-Ki and Pan, Yongsheng and Kirby, Robert M. and Whitaker, Ross T.},
    month = oct,
    year = {2011},
    keywords = {Eikonal equation, Graphics processing unit, Hamilton-Jacobi equation, parallel algorithm, shared memory multiple-processor computer system, triangular mesh},
    pages = {2468–2488}
    }

 

  • [PDF] [DOI] Y. Pan, W. Jeong, and R. Whitaker, “Markov surfaces: a probabilistic framework for user-assisted three-dimensional image segmentation,” Computer vision and image understanding, vol. 115, iss. 10, pp. 1375-1383, 2011.
    [Bibtex]
    @article{pan_markov_2011,
    title = {Markov surfaces: A probabilistic framework for user-assisted three-dimensional image segmentation},
    volume = {115},
    issn = {1077-3142},
    shorttitle = {Markov surfaces},
    url = {http://www.sciencedirect.com/science/article/pii/S1077314211001408},
    doi = {10.1016/j.cviu.2011.06.003},
    number = {10},
    urldate = {2013-04-20},
    journal = {Computer Vision and Image Understanding},
    author = {Pan, Yongsheng and Jeong, Won-Ki and Whitaker, Ross},
    month = oct,
    year = {2011},
    keywords = {{GPU}, Image segmentation, Markov chain, Probabilistic framework},
    pages = {1375--1383},
    }

 

  • [PDF] [DOI] W. Jeong, M. K. Johnson, I. Yu, J. Kautz, H. Pfister, and S. Paris, “Display-aware image editing,” in 2011 IEEE international conference on computational photography (ICCP), 2011, pp. 1-8.
    [Bibtex]
    @inproceedings{jeong_display-aware_2011,
    title = {Display-aware image editing},
    doi = {10.1109/ICCPHOT.2011.5753125},
    abstract = {We describe a set of image editing and viewing tools that explicitly take into account the resolution of the display on which the image is viewed. Our approach is twofold. First, we design editing tools that process only the visible data, which is useful for images larger than the display. This encompasses cases such as multi-image panoramas and high-resolution medical data. Second, we propose an adaptive way to set viewing parameters such brightness and contrast. Because we deal with very large images, different locations and scales often require different viewing parameters. We let users set these parameters at a few places and interpolate satisfying values everywhere else. We demonstrate the efficiency of our approach on different display and image sizes. Since the computational complexity to render a view depends on the display resolution and not the actual input image resolution, we achieve interactive image editing even on a 16 gigapixel image.},
    booktitle = {2011 {IEEE} International Conference on Computational Photography ({ICCP)}},
    author = { Jeong, Won-Ki and Johnson, Micah K. and Yu, Insu and Kautz, J. and Pfister, H. and Paris, S.},
    month = apr,
    year = {2011},
    keywords = {Brightness, Cloning, Computational complexity, data visualisation, display resolution, display sizes, display-aware image editing, Histograms, image render, image resolution, image sizes, image viewing tools, Interpolation, Pixel, rendering (computer graphics), Tiles, viewing parameters},
    pages = {1--8},
    file = {{IEEE} Xplore Abstract Record:E:\Zotero\storage\CZQ7GWEI\abs_all.html:text/html;{IEEE} Xplore Full Text PDF:E:\Zotero\storage\INNKVUSB\Jeong et al. - 2011 - Display-aware image editing.pdf:application/pdf}
    }

 

  • [PDF] M. Roberts, W. Jeong, A. Vázquez-Reina, M. Unger, H. Bischof, J. Lichtman, and H. Pfister, “Neural process reconstruction from sparse user scribbles,” in Proceedings of the 14th international conference on medical image computing and computer-assisted intervention – volume part i, Berlin, Heidelberg, 2011, p. 621–628.
    [Bibtex]
    @inproceedings{roberts_neural_2011,
    address = {Berlin, Heidelberg},
    series = {{MICCAI'11}},
    title = {Neural process reconstruction from sparse user scribbles},
    isbn = {978-3-642-23622-8},
    url = {http://dl.acm.org/citation.cfm?id=2044656.2044742},
    urldate = {2013-04-20},
    booktitle = {Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I},
    publisher = {Springer-Verlag},
    author = {Roberts, Mike and Jeong, Won-Ki and Vázquez-Reina, Amelio and Unger, Markus and Bischof, Horst and Lichtman, Jeff and Pfister, Hanspeter},
    year = {2011},
    pages = {621–628}
    }

 



Before 2011


Journal Papers


  • [PDF] [DOI] W. Jeong, J. Schneider, S. Turney, B. E. Faulkner-Jones, D. Meyer, R. Westermann, C. R. Reid, J. Lichtman, and H. Pfister, “Interactive histology of large-scale biomedical image stacks,” IEEE transactions on visualization and computer graphics, vol. 16, iss. 6, p. 1386–1395, 2010.
    [Bibtex]
    @article{jeong_interactive_2010,
    title = {Interactive Histology of Large-Scale Biomedical Image Stacks},
    volume = {16},
    issn = {1077-2626},
    url = {http://dx.doi.org/10.1109/TVCG.2010.168},
    doi = {10.1109/TVCG.2010.168},
    abstract = {Histology is the study of the structure of biological tissue using microscopy techniques. As digital imaging technology advances, high resolution microscopy of large tissue volumes is becoming feasible; however, new interactive tools are needed to explore and analyze the enormous datasets. In this paper we present a visualization framework that specifically targets interactive examination of arbitrarily large image stacks. Our framework is built upon two core techniques: display-aware processing and {{GPU}-accelerated} texture compression. With display-aware processing, only the currently visible image tiles are fetched and aligned on-the-fly, reducing memory bandwidth and minimizing the need for time-consuming global pre-processing. Our novel texture compression scheme for {{GPU}s} is tailored for quick browsing of image stacks. We evaluate the usability of our viewer for two histology applications: digital pathology and visualization of neural structure at nanoscale-resolution in serial electron micrographs.},
    number = {6},
    urldate = {2013-04-20},
    journal = {{IEEE} Transactions on Visualization and Computer Graphics},
    author = {Jeong, Won-Ki and Schneider, Jens and Turney, Stephen and Faulkner-Jones, Beverly E and Meyer, Dominik and Westermann, Rudiger and Reid, R. Clay and Lichtman, Jeff and Pfister, Hanspeter},
    month = nov,
    year = {2010},
    keywords = {biomedical image processing, Gigapixel viewer, {GPU}, texture compression},
    pages = {1386–1395}
    }

 

  • [PDF] [DOI] W. Jeong, J. Beyer, M. Hadwiger, R. Blue, C. Law, A. Vázquez-Reina, C. R. Reid, J. Lichtman, and H. Pfister, “Ssecrett and NeuroTrace: interactive visualization and analysis tools for large-scale neuroscience data sets,” IEEE comput. graph. appl., vol. 30, iss. 3, p. 58–70, 2010.
    [Bibtex]
    @article{jeong_ssecrett_2010,
    title = {Ssecrett and {NeuroTrace:} Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets},
    volume = {30},
    issn = {0272-1716},
    shorttitle = {Ssecrett and {NeuroTrace}},
    url = {http://dx.doi.org/10.1109/MCG.2010.56},
    doi = {10.1109/MCG.2010.56},
    number = {3},
    urldate = {2013-04-20},
    journal = {{IEEE} Comput. Graph. Appl.},
    author = {Jeong, Won-Ki and Beyer, Johanna and Hadwiger, Markus and Blue, Rusty and Law, Charles and Vázquez-Reina, Amelio and Reid, R. Clay and Lichtman, Jeff and Pfister, Hanspeter},
    month = may,
    year = {2010},
    keywords = {computer graphics, connectome, graphics and multimedia, graphics hardware, implicit surface rendering, neuroscience, Segmentation, volume rendering},
    pages = {58–70}
    }

 

  • [DOI] W. Jeong, J. Beyer, M. Hadwiger, A. Vazquez, H. Pfister, and R. T. Whitaker, “Scalable and interactive segmentation and visualization of neural processes in EM datasets,” IEEE transactions on visualization and computer graphics, vol. 15, iss. 6, p. 1505–1514, 2009.
    [Bibtex]
    @article{jeong_scalable_2009,
    title = {Scalable and Interactive Segmentation and Visualization of Neural Processes in {EM} Datasets},
    volume = {15},
    issn = {1077-2626},
    url = {http://dx.doi.org/10.1109/TVCG.2009.178},
    doi = {10.1109/TVCG.2009.178},
    number = {6},
    urldate = {2013-04-20},
    journal = {{IEEE} Transactions on Visualization and Computer Graphics},
    author = {Jeong, Won-Ki and Beyer, Johanna and Hadwiger, Markus and Vazquez, Amelio and Pfister, Hanspeter and Whitaker, Ross T.},
    month = nov,
    year = {2009},
    keywords = {connectome, graphics hardware, implicit surface rendering, neuroscience, Segmentation, volume rendering},
    pages = {1505–1514}
    }

 

  • [PDF] [DOI] J. Seong, W. Jeong, and E. Cohen, “Curvature-based anisotropic geodesic distance computation for parametric and implicit surfaces,” The visual computer, vol. 25, iss. 8, pp. 743-755, 2009.
    [Bibtex]
    @article{seong_curvature-based_2009,
    title = {Curvature-based anisotropic geodesic distance computation for parametric and implicit surfaces},
    volume = {25},
    issn = {0178-2789, 1432-2315},
    url = {http://link.springer.com/article/10.1007/s00371-009-0362-0},
    doi = {10.1007/s00371-009-0362-0},
    language = {en},
    number = {8},
    urldate = {2013-04-20},
    journal = {The Visual Computer},
    author = {Seong, Joon-Kyung and Jeong, Won-Ki and Cohen, Elaine},
    month = aug,
    year = {2009},
    keywords = {Anisotropy, Artificial Intelligence (incl. Robotics), computer graphics, Geodesic, {H–J} equation, Image Processing and Computer Vision, Normal curvature, Parametric and implicit surface, Tensor},
    pages = {743--755},
    file = {Seong et al. - 2009 - Curvature-based anisotropic geodesic distance comp.pdf:E:\Zotero\storage\FSZ6VKMT\Seong et al. - 2009 - Curvature-based anisotropic geodesic distance comp.pdf:application/pdf;Snapshot:E:\Zotero\storage\427GD4WC\10.html:text/html}
    }

 

  • [PDF] [DOI] W. Jeong and R. T. Whitaker, “A fast iterative method for eikonal equations,” SIAM j. sci. comput., vol. 30, iss. 5, p. 2512–2534, 2008.
    [Bibtex]
    @article{jeong_fast_2008,
    title = {A Fast Iterative Method for Eikonal Equations},
    volume = {30},
    issn = {1064-8275},
    url = {http://dx.doi.org/10.1137/060670298},
    doi = {10.1137/060670298},
    number = {5},
    urldate = {2013-04-20},
    journal = {{SIAM} J. Sci. Comput.},
    author = {Jeong, Won-Ki and Whitaker, Ross T.},
    month = jul,
    year = {2008},
    keywords = {Eikonal equation, graphics processing unit ({{GPU})}, Hamilton-Jacobi equation, label-correcting method, parallel algorithm, viscosity solution},
    pages = {2512–2534}
    }

 

  • [PDF] [DOI] W. Jeong, T. P. Fletcher, R. Tao, and R. Whitaker, “Interactive visualization of volumetric white matter connectivity in DT-MRI using a parallel-hardware hamilton-jacobi solver,” IEEE transactions on visualization and computer graphics, vol. 13, iss. 6, p. 1480–1487, 2007.
    [Bibtex]
    @article{jeong_interactive_2007,
    title = {Interactive Visualization of Volumetric White Matter Connectivity in {DT-MRI} Using a Parallel-Hardware Hamilton-Jacobi Solver},
    volume = {13},
    issn = {1077-2626},
    url = {http://dx.doi.org/10.1109/TVCG.2007.70571},
    doi = {10.1109/TVCG.2007.70571},
    number = {6},
    urldate = {2013-04-20},
    journal = {{IEEE} Transactions on Visualization and Computer Graphics},
    author = {Jeong, Won-Ki and Fletcher, P. Thomas and Tao, Ran and Whitaker, Ross},
    month = nov,
    year = {2007},
    keywords = {Diffusion tensor visualization, graphics hardware, interactivity.},
    pages = {1480–1487}
    }

 

  • [DOI] W. Jeong and C. Kim, “Direct reconstruction of a displaced subdivision surface from unorganized points,” Graphical models, vol. 64, iss. 2, pp. 78-93, 2002.
    [Bibtex]
    @article{jeong_direct_2002,
    title = {Direct Reconstruction of a Displaced Subdivision Surface from Unorganized Points},
    volume = {64},
    issn = {1524-0703},
    url = {http://www.sciencedirect.com/science/article/pii/S1524070302905722},
    doi = {10.1006/gmod.2002.0572},
    number = {2},
    urldate = {2013-04-20},
    journal = {Graphical Models},
    author = {Jeong, Won-Ki and Kim, Chang-Hun},
    month = mar,
    year = {2002},
    pages = {78--93},
    file = {Jeong and Kim - 2002 - Direct Reconstruction of a Displaced Subdivision S.pdf:E:\Zotero\storage\JR6NZB5M\Jeong and Kim - 2002 - Direct Reconstruction of a Displaced Subdivision S.pdf:application/pdf;ScienceDirect Snapshot:E:\Zotero\storage\3D8MKH94\S1524070302905722.html:text/html}
    }

 


Peer Reviewed Conference Papers


  • [PDF] Y. Pan, W. Jeong, and R. T. Whitaker, “Markov surfaces: a probabilistic framework for user-assisted three-dimensional image segmentation,” in Proceedings of medical image computing and computer-assisted intervention workshop on probabilistic models for medical image analysis, 2009, p. 57–68.
    [Bibtex]
    @inproceedings{pan_probabilistic_2009,
    address = {},
    series = {{MICCAI'09}},
    title = {Markov Surfaces: A Probabilistic Framework for User-Assisted Three-Dimensional Image Segmentation},
    isbn = {},
    url = {},
    urldate = {2013-04-20},
    booktitle = {Proceedings of Medical image computing and computer-assisted intervention Workshop on Probabilistic Models for Medical Image Analysis},
    publisher = {},
    author = {Pan, Yongsheng and Jeong, Won-Ki and Whitaker, Ross T.},
    year = {2009},
    pages = {57–68}
    }

 

  • [PDF] [DOI] J. Seong, W. Jeong, and E. Cohen, “Anisotropic geodesic distance computation for parametric surfaces,” in IEEE international conference on shape modeling and applications, 2008. SMI 2008, 2008, pp. 179-186.
    [Bibtex]
    @inproceedings{seong_anisotropic_2008,
    title = {Anisotropic geodesic distance computation for parametric surfaces},
    doi = {10.1109/SMI.2008.4547968},
    booktitle = {{IEEE} International Conference on Shape Modeling and Applications, 2008. {SMI} 2008},
    author = {Seong, Joon-Kyung and Jeong, Won-Ki and Cohen, Elaine},
    year = {2008},
    keywords = {{AG} distance map, anisotropic geodesic distance computation, Anisotropic magnetoresistance, Application software, computational geometry, computer graphics, convex Hamilton-Jacobi equation solver, curve fitting, difference curvature tensor, differential geometry, Distributed computing, Equations, Euclidean distance, geometric feature distribution, Geophysics computing, local distance function, minimisation, parametric surface, Shape, surface fitting, Tensile stress, tensor speed function, tensors, total distance minimization, Vehicles, wave propagation control},
    pages = {179--186},
    }

 

  • [PDF] T. P. Fletcher, R. Tao, W. Jeong, and R. T. Whitaker, “A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI,” in Proceedings of the 20th international conference on information processing in medical imaging, Berlin, Heidelberg, 2007, p. 346–358.
    [Bibtex]
    @inproceedings{fletcher_volumetric_2007,
    address = {Berlin, Heidelberg},
    series = {{IPMI'07}},
    title = {A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor {MRI}},
    isbn = {978-3-540-73272-3},
    url = {http://dl.acm.org/citation.cfm?id=1770424.1770457},
    urldate = {2013-04-20},
    booktitle = {Proceedings of the 20th international conference on Information processing in medical imaging},
    publisher = {Springer-Verlag},
    author = {Fletcher, P. Thomas and Tao, Ran and Jeong, Won-Ki and Whitaker, Ross T.},
    year = {2007},
    pages = {346–358}
    }

 

  • [PDF] W. Jeong, R. Whitaker, and M. Dobin, “Interactive 3D seismic fault detection on the graphics hardware,” in Proceedings of international workshop on volume graphics, 2006, pp. 111-118.
    [Bibtex]
    @inproceedings{jeong_interactive_2006,
    title = {Interactive {3D} seismic fault detection on the Graphics Hardware},
    url = {},
    author = {Jeong, Won-Ki and Whitaker, Ross and Dobin, Mark},
    booktitle = {Proceedings of International Workshop on Volume Graphics},
    year = {2006},
    pages = {111--118}
    }

 

  • [PDF] I. Ivrissimtzis, W. Jeong, R. Whitaker, and M. Dobin, “Surface reconstruction based on neural meshes,” in Proceedings of mathematical methods for curves and surfaces, 2005, pp. 223-242.
    [Bibtex]
    @inproceedings{ivrissimtzis_surface_2005,
    title = {SURFACE RECONSTRUCTION BASED ON NEURAL MESHES},
    url = {},
    author = {Ivrissimtzis, Ioannis and Jeong, Won-Ki and Whitaker, Ross and Dobin, Mark},
    booktitle = {Proceedings of Mathematical Methods for Curves and Surfaces},
    year = {2005},
    pages = {223--242}
    }

 

  • [PDF] [DOI] I. Ivrissimtzis, Y. Lee, S. Lee, W. Jeong, and H. Seidel, “Neural mesh ensembles,” in Proceedings of the 3D data processing, visualization, and transmission, 2nd international symposium, Washington, {DC}, {USA}, 2004, pp. 308-315.
    [Bibtex]
    @inproceedings{ivrissimtzis_neural_2004,
    address = {Washington, {DC}, {USA}},
    series = {{3DPVT} '04},
    title = {Neural Mesh Ensembles},
    isbn = {0-7695-2223-8},
    url = {http://dx.doi.org/10.1109/3DPVT.2004.87},
    doi = {10.1109/3DPVT.2004.87},
    urldate = {2013-04-20},
    booktitle = {Proceedings of the {3D} Data Processing, Visualization, and Transmission, 2nd International Symposium},
    publisher = {{IEEE} Computer Society},
    author = {Ivrissimtzis, Ioannis and Lee, Yunjin and Lee, Seungyong and Jeong, Won-Ki and Seidel, Hans-Peter},
    year = {2004},
    pages = {308--315}
    }

 

  • [PDF] W. Jeong, I. Ivrissimtzis, and H. Seidel, “Neural meshes: statistical learning based on normals,” in Proceedings of the 11th pacific conference on computer graphics and applications, Washington, {DC}, {USA}, 2003, p. 404.
    [Bibtex]
    @inproceedings{jeong_neural_2003,
    address = {Washington, {DC}, {USA}},
    series = {{PG} '03},
    title = {Neural Meshes: Statistical Learning Based on Normals},
    isbn = {0-7695-2028-6},
    shorttitle = {Neural Meshes},
    url = {http://dl.acm.org/citation.cfm?id=946250.946985},
    urldate = {2013-04-20},
    booktitle = {Proceedings of the 11th Pacific Conference on Computer Graphics and Applications},
    publisher = {{IEEE} Computer Society},
    author = {Jeong, Won-Ki and Ivrissimtzis, Ioannis and Seidel, Hans-Peter},
    year = {2003},
    pages = {404}
    }

 

  • [PDF] [DOI] I. Ivrissimtzis, W. Jeong, and H. Seidel, “Using growing cell structures for surface reconstruction,” in Shape modeling international, 2003, 2003, pp. 78-86.
    [Bibtex]
    @inproceedings{ivrissimtzis_using_2003,
    title = {Using growing cell structures for surface reconstruction},
    doi = {10.1109/SMI.2003.1199604},
    booktitle = {Shape Modeling International, 2003},
    author = {Ivrissimtzis, Ioannis and Jeong, Won-Ki and Seidel, Hans-Peter},
    year = {2003},
    keywords = {Application software, Biological neural networks, Clouds, Computer networks, concavity, evolutionary computation, growing cell structure, Humans, image reconstruction, mesh generation, network connectivity, neural nets, neural network algorithm, random sampling, Shape, shape modeling, sharp feature, signal processing, Signal processing algorithms, solid modelling, surface fitting, surface meshing, surface reconstruction, surface sampling, target space, unorganized point cloud},
    pages = {78--86},
    file = {{IEEE} Xplore Abstract Record:E:\Zotero\storage\KHMVTMGD\articleDetails.html:text/html;{IEEE} Xplore Full Text PDF:E:\Zotero\storage\CKUTIX23\Ivrissimtzis et al. - 2003 - Using growing cell structures for surface reconstr.pdf:application/pdf}
    }

 

  • [PDF] [DOI] W. Jeong, K. Kahler, and H. Seidel, “Subdivision surface simplification,” in 10th pacific conference on computer graphics and applications, 2002. proceedings, 2002, pp. 477-480.
    [Bibtex]
    @inproceedings{jeong_subdivision_2002,
    title = {Subdivision surface simplification},
    doi = {10.1109/PCCGA.2002.1167907},
    abstract = {A modified quadric error metric ({QEM)} for simplification of Loop subdivision surfaces is presented The suggested error metric not only measures the geometric difference but also controls the smoothness and well-shapedness of the triangles that result from the decimation process. Minimizing the error with respect to the original limit surface, our method allows for drastic simplification of Loop control meshes with convenient control over the reproduction of sharp features.},
    booktitle = {10th Pacific Conference on Computer Graphics and Applications, 2002. Proceedings},
    author = {Jeong, Won-Ki and Kahler, Kolja and Seidel, Hans-Peter},
    year = {2002},
    keywords = {Application software, Bridges, computer graphics, Control systems, decimation, Displays, Error correction, error metric, errors, Loop scheme, loop subdivision surfaces, mesh generation, mesh simplification, quadric error metric, smoothness, surface fitting, surface reconstruction, Tensile stress},
    pages = {477--480},
    file = {{IEEE} Xplore Abstract Record:E:\Zotero\storage\EQUKRR3U\abs_all.html:text/html;{IEEE} Xplore Full Text PDF:E:\Zotero\storage\GPHFTKG2\Jeong et al. - 2002 - Subdivision surface simplification.pdf:application/pdf}
    }

 

  • [PDF] W. Jeong, K. Kahler, J. Haber, and H. Seidel, “Automatic generation of subdivision surface head models from point cloud data,” in In graphics interface 2002 conf. proc, 2002, p. 181–188.
    [Bibtex]
    @inproceedings{jeong_automatic_2002,
    title = {Automatic Generation of Subdivision Surface Head Models from Point Cloud Data},
    booktitle = {In Graphics Interface 2002 Conf. Proc},
    author = {Jeong, Won-Ki and Kahler, Kolja and Haber, Jörg and Seidel, Hans-peter},
    year = {2002},
    pages = {181–188},
    }

 

  • [PDF] [DOI] W. Jeong and C. Kim, “Direct reconstruction of displaced subdivision surface from unorganized points,” in Ninth pacific conference on computer graphics and applications, 2001. proceedings, 2001, pp. 160-168.
    [Bibtex]
    @inproceedings{jeong_direct_2001,
    title = {Direct reconstruction of displaced subdivision surface from unorganized points},
    doi = {10.1109/PCCGA.2001.962869},
    booktitle = {Ninth Pacific Conference on Computer Graphics and Applications, 2001. Proceedings},
    author = {Jeong, Won-Ki and Kim, Chang-Hun},
    year = {2001},
    keywords = {Clouds, compact mesh size, computational geometry, computer graphics, Computer science, direct reconstruction, displaced subdivision surface, displacement map, explicit polygonal mesh, initial coarse control mesh, Laser modes, mesh generation, mesh reconstruction algorithm, multiresolution modeling, parametric domain surface, piecewise regular connectivity, Probes, Reconstruction algorithms, Sampling methods, smooth domain surface, surface detail sampling scheme, surface fitting, surface reconstruction, unorganized points, valid sampling triangle},
    pages = {160--168},
    }

 


Book Chapters

  • W. Jeong, H. Pfister, and M. Fatica, “Chapter 46: medical image processing using GPU-accelerated itk image filters.” .
    [Bibtex]
    @inbook{jeong_chap46_2011,
    author = {Jeong, Won-Ki and Pfister, Hanspeter and Fatica, Massimiliano},
    title = {Chapter 46: Medical Image Processing using {GPU}-Accelerated ITK image filters},
    }
  • Nvidia GPU computing gems emerald edition, W. W. Hwu, Ed., Morgan-Kaufmann, 2011.
    [Bibtex]
    @book{{GPU}gem_2011,
    editor = {Wen-mei W. Hwu},
    title = {NVIDIA {GPU} Computing Gems Emerald Edition},
    publisher = {Morgan-Kaufmann},
    year = {2011}
    }

 

  • W. Jeong, H. Pfister, J. Beyer, and M. Hadwiger, “Chapter49: GPU-accelerated brain connectivity reconstruction and visualization in large-scale electron micrographs.” .
    [Bibtex]
    @inbook{jeong_chap46_2011,
    author = {Jeong, Won-Ki and Pfister, Hanspeter and Beyer, Johanna and Hadwiger, Markus},
    title = {Chapter49: {GPU}-accelerated Brain Connectivity Reconstruction and Visualization in Large-Scale Electron Micrographs},
    }
  • Nvidia GPU computing gems emerald edition, W. W. Hwu, Ed., Morgan-Kaufmann, 2011.
    [Bibtex]
    @book{{GPU}gem_2011,
    editor = {Wen-mei W. Hwu},
    title = {NVIDIA {GPU} Computing Gems Emerald Edition},
    publisher = {Morgan-Kaufmann},
    year = {2011}
    }

 


Patents

  • W. Jeong and R. T. Whitaker, Fast iterative method for processing hamilton-jacobi equations, , 2009.
    [Bibtex]
    @book{JEONG:2009:biblatex,
    author = {Jeong, Won-Ki and Whitaker, Ross T.},
    title = {Fast Iterative Method For Processing Hamilton-Jacobi Equations},
    year = {2009},
    month = {05},
    day = {07},
    number = {WO 2009/059045 A2},
    type = {Patent Application},
    version = {A2},
    location = {WO},
    url = {http://www.patentlens.net/patentlens/patent/WO_2009_059045_A2/en/},
    filing_num = {US2008/081855},
    yearfiled = {2008},
    monthfiled = {10},
    dayfiled = {30},
    pat_refs = {},
    IPC_class = {G06G 7/32},
    US_class = {},
    }