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Fingerprint Dive into the research topics where Chang-Su Kim is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 1 Similar Profiles
Pixels Engineering & Materials Science
Color Engineering & Materials Science
Image coding Engineering & Materials Science
Textures Engineering & Materials Science
Mean square error Engineering & Materials Science
Motion estimation Engineering & Materials Science
Motion compensation Engineering & Materials Science
Code division multiple access Engineering & Materials Science

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Research Output 1995 2019

  • 3026 Citations
  • 28 h-Index
  • 164 Conference contribution
  • 84 Article
  • 1 Chapter
  • 1 Paper
1 Citation (Scopus)

Change Detection in High Resolution Satellite Images Using an Ensemble of Convolutional Neural Networks

Lim, K., Jin, D. & Kim, C-S., 2019 Mar 4, 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 509-515 7 p. 8659603. (2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Satellites
Neural networks
Color
Supervised learning
Earth (planet)
1 Citation (Scopus)

Object tracking under large motion: Combining coarse-to-fine search with superpixels

Kim, C., Song, D., Kim, C-S. & Park, S. K., 2019 Apr 1, In : Information Sciences. 480, p. 194-210 17 p.

Research output: Contribution to journalArticle

Object Tracking
Motion
Sampling
Particle Filtering
Target
3 Citations (Scopus)

Comparison of objective functions in CNN-based prostate magnetic resonance image segmentation

Mun, J., Jang, W. D., Sung, D. J. & Kim, C-S., 2018 Feb 20, 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, Vol. 2017-September. p. 3859-3863 5 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Magnetic resonance
Image segmentation
Neural networks
Entropy
Hamming distance

Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement

Heo, M., Lee, J., Kim, K. R., Kim, H. U. & Kim, C-S., 2018 Jan 1, Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. Ferrari, V., Sminchisescu, C., Weiss, Y. & Hebert, M. (eds.). Springer Verlag, p. 39-55 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11208 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Depth Estimation
Masking
Strip
Refinement
Neural Networks
3 Citations (Scopus)

Multiscale Feature Extractors for Stereo Matching Cost Computation

Kim, K. R., Koh, Y. J. & Kim, C-S., 2018 May 17, In : IEEE Access. 6, p. 27971-27983 13 p.

Research output: Contribution to journalArticle

Costs
Computational complexity
Agglomeration
Neural networks
Experiments