Won-Dong Jang

1.6k total citations · 1 hit paper
29 papers, 1.0k citations indexed

About

Won-Dong Jang is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Artificial Intelligence. According to data from OpenAlex, Won-Dong Jang has authored 29 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 5 papers in Biophysics and 4 papers in Artificial Intelligence. Recurrent topics in Won-Dong Jang's work include Visual Attention and Saliency Detection (12 papers), Advanced Image and Video Retrieval Techniques (12 papers) and Video Surveillance and Tracking Methods (9 papers). Won-Dong Jang is often cited by papers focused on Visual Attention and Saliency Detection (12 papers), Advanced Image and Video Retrieval Techniques (12 papers) and Video Surveillance and Tracking Methods (9 papers). Won-Dong Jang collaborates with scholars based in South Korea, United States and United Kingdom. Won-Dong Jang's co-authors include Chang‐Su Kim, Jae-Young Sim, Jin Hwan Kim, Chulwoo Lee, Hanspeter Pfister, Donglai Wei, Zudi Lin, Yun Fu, Yulun Zhang and Peter K. Sorger and has published in prestigious journals such as IEEE Access, Human Reproduction and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Won-Dong Jang

27 papers receiving 984 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Optimized contrast enhancement for real-time image and video dehazing

2013 360 citations Jin Hwan Kim, Won-Dong Jang et al. profile →

Peers

Countries citing papers authored by Won-Dong Jang

Since Specialization

Citations

This map shows the geographic impact of Won-Dong Jang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Won-Dong Jang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Won-Dong Jang more than expected).

Fields of papers citing papers by Won-Dong Jang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Won-Dong Jang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Won-Dong Jang. The network helps show where Won-Dong Jang may publish in the future.

Co-authorship network of co-authors of Won-Dong Jang

This figure shows the co-authorship network connecting the top 25 collaborators of Won-Dong Jang. A scholar is included among the top collaborators of Won-Dong Jang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Won-Dong Jang. Won-Dong Jang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	BlastAssist: a deep learning pipeline to measure interpretable features of human embryos 2024 ·Human Reproduction ·(unknown), Brian Leahy, Won-Dong Jang, Donglai Wei, Yael Kalma, (unknown), (unknown), (unknown), Foad Azem, (unknown), Colm P. Kelleher, (unknown), Hanspeter Pfister, Daniel Needleman, Dalit Ben‐Yosef	8
2	Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation 2023 ·Won-Dong Jang, Donglai Wei, Xingxuan Zhang, Brian Leahy, (unknown), James Tompkin, Dalit Ben‐Yosef, Daniel Needleman, Hanspeter Pfister	6
3	UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues 2022 ·Communications Biology ·Clarence Yapp, Edward Novikov, Won-Dong Jang, Tuulia Vallius, Yuan Chen, Marcelo Cicconet, Zoltan Maliga, Connor A. Jacobson, Donglai Wei, Sandro Santagata, Hanspeter Pfister, Peter K. Sorger	20
4	Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution 2021 ·2021 IEEE/CVF International Conference on Computer Vision (ICCV) ·(unknown), Yulun Zhang, Donglai Wei, Won-Dong Jang, Zudi Lin, Yun Fu, Hanspeter Pfister	69
5	Two Stream Active Query Suggestion for Active Learning in Connectomics 2020 ·Lecture notes in computer science ·Zudi Lin, Donglai Wei, Won-Dong Jang, (unknown), Xupeng Chen, Xueying Wang, Richard Schalek, Daniel R. Berger, (unknown), Lee Kamentsky, Adi Peleg, Daniel Haehn, Thouis R. Jones, Toufiq Parag, Jeff W. Lichtman, Hanspeter Pfister	10
6	Channel Embedding for Informative Protein Identification from Highly Multiplexed Images 2020 ·Lecture notes in computer science ·(unknown), Won-Dong Jang, Denis Schapiro, Donglai Wei, James Tompkin, Peter K. Sorger, Hanspeter Pfister	2
7	Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data 2019 ·IEEE Transactions on Visualization and Computer Graphics ·Robert Krueger, Johanna Beyer, Won-Dong Jang, Nam Wook Kim, Artem Sokolov, Peter K. Sorger, Hanspeter Pfister	35
8	Interactive Image Segmentation via Backpropagating Refinement Scheme 2019 ·Won-Dong Jang, Chang‐Su Kim	140
9	Tracking-by-Segmentation Using Superpixel-Wise Neural Network 2018 ·IEEE Access ·Seho Lee, Won-Dong Jang, Chang‐Su Kim	10
10	Background subtraction using encoder-decoder structured convolutional neural network 2017 ·(unknown), Won-Dong Jang, Chang‐Su Kim	43
11	Temporal Superpixels Based on Proximity-Weighted Patch Matching 2017 ·(unknown), Won-Dong Jang, Chang‐Su Kim	7
12	Contour-Constrained Superpixels for Image and Video Processing 2017 ·(unknown), Won-Dong Jang, Chang‐Su Kim	16
13	RGB-D image segmentation based on multiple random walkers 2016 ·Seho Lee, Won-Dong Jang, Byung Kwan Park, Chang‐Su Kim	4
14	POD: Discovering Primary Objects in Videos Based on Evolutionary Refinement of Object Recurrence, Background, and Primary Object Models 2016 ·Yeong Jun Koh, Won-Dong Jang, Chang‐Su Kim	12
15	Near-duplicate video clustering using multiple complementary video signatures 2015 ·(unknown), Kyung-Rae Kim, Won-Dong Jang, Chang‐Su Kim	1
16	Multiple random walkers and their application to image cosegmentation 2015 ·Scholarworks@UNIST (Ulsan National Institute of Science and Technology) ·Chulwoo Lee, Won-Dong Jang, Jae-Young Sim, Chang‐Su Kim	61
17	Automatic Video Genre Classification Using Multiple SVM Votes 2014 ·Scholarworks@UNIST (Ulsan National Institute of Science and Technology) ·Won-Dong Jang, Chulwoo Lee, Jae-Young Sim, Chang‐Su Kim	2
18	FDQM: Fast Quality Metric for Depth Maps Without View Synthesis 2014 ·IEEE Transactions on Circuits and Systems for Video Technology ·Won-Dong Jang, Taeyoung Chung, Jae-Young Sim, Chang‐Su Kim	7
19	Efficient depth video coding based on view synthesis distortion estimation 2012 ·Tae‐Young Chung, Won-Dong Jang, Chang‐Su Kim	8
20	Temporally x real-time video dehazing 2012 ·Scholarworks@UNIST (Ulsan National Institute of Science and Technology) ·Jin Hwan Kim, Won-Dong Jang, Yongsup Park, (unknown), Jae-Young Sim, Chang‐Su Kim	11

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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