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Selected Journal Papers (* corresponding author)

  1. C.C. Hsu, C.-H. Lin, C.-H. Kao, and Y.-C. Lin, “DCSN: Deep Compressed Sensing Network for Efficient Hyperspectral Data Transmission of Miniaturized Satellite,” IEEE Transactions on Geoscience and Remote Sensing, 2020. (IF: 5.85, Rank 29/1409=2% in Electrical and Electronic Engineering), Dec. 2020.
  2. C.C. Hsu, Y. X. Zhuang, and C. Y. Lee, “Deep Fake Image Detection Based on Pairwise Learning,” Appl Sci-Basel, vol. 10, no. 1, p. 370, Jan 2020. (IF: 2.47, Rank 41/275 in General Engineering)
  3. C.C. Hsu, C. W. Lin, W. T. Su, and G. Cheung, “SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination,” IEEE Trans Image Process, vol. 28, no. 12, pp. 6225-6236, Dec 2019. (IF:9.34, Rank: 5/334=1% in Computer Graphics and Computer-Aided Design Software).
  4. C.C. Hsu and C.-W. Lin, “Cnn-based joint clustering and representation learning with feature drift compensation for large-scale image data,” IEEE Transactions on Multimedia, vol. 20, no. 2, pp. 421-429, 2017. (IF:6.051, Rank: 2/195=1% in Media Technology).
  5. C.C. Hsu, L. Kang, and C. Lin, “Temporally Coherent Super-Resolution of Textured Video via Dynamic Texture Synthesis,” IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 919 – 931, 2015. (IF:9.34, Rank: 5/334=1% in Computer Graphics and Computer-Aided Design Software).
  6. W. Kang, C.C. Hsu, B. Q. Zhuang, C. W. Lin, and C. H. Yeh, “Learning-Based Joint Super-Resolution and Deblocking for a Highly Compressed Image,” Ieee Transactions on Multimedia, vol. 17, no. 7, pp. 921-934, Jul 2015. (IF:6.051, Rank: 2/195=1% in Media Technology).
  7. C.C. Hsu, C. W. Lin, Y. M. Fang, and W. S. Lin, “Objective Quality Assessment for Image Retargeting Based on Perceptual Geometric Distortion and Information Loss,” Ieee Journal of Selected Topics in Signal Processing, vol. 8, no. 3, pp. 377-389, Jun 2014. (IF:6.688, Rank: 7/543=1% in Signal Processing).

Book Chapters

  1. H.T. Chang and C.C. Hsu, “Fractal-based Secured multiple-image distribution and compression,” in Handbook of Research on Secure Multimedia Distribution, Chapter 27, Edited by Dr. Shiguo Lian and Dr. Yan Zhang, ISBN: 978-1-60566-262-6, 2008.

Selected Conference Papers 

  1. G.L. Chen, C.C. Hsu, and M.H. Wu, “Adaptive Distribution Learning with Statistical Hypothesis Testing for COVID-19 CT Scan Classification,” in Proc. of IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Oct. 2021.
  2. C.C. Hsu, C, Lee, C. Lin, K.M. Hung, Y.L. Lin, X.Y. Wang, “Efficient-ROD: Efficient Radar Object Detection based on Densely Connected Residual Network,” in Proc. of ACM International Conference on Multimedia Retrieval (ICMR), 21-24 Aug., 2021.
  3. C.-C. Hsu, W.-H. Tseng, H.-T. Yang, C.-H. Lin, and C.-H. Kao, “Rethinking Relation between Model Stacking and Recurrent Neural Networks for Social Media Prediction,” in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 4585-4589.
  4. C.-C. Hsu, W.-H. Tseng, and H.-T. Yang, “Learning to Predict Risky Driving Behaviors for Autonomous Driving,” in 2020 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-Taiwan), 2020: IEEE, pp. 1-2.
  5. Y.-X. Zhuang and C.-C. Hsu, “Detecting Generated Image Based on a Coupled Network with Two-Step Pairwise Learning,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019: IEEE, pp. 3212-3216. (Best Student Papwe Award)
  6. C.-C. Hsu, L.-W. Kang, C.-Y. Lee, J.-Y. Lee, Z.-X. Zhang, and S.-M. Wu, “Popularity prediction of social media based on multi-modal feature mining,” in Proceedings of the 27th ACM International Conference on Multimedia, 2019, pp. 2687-2691.
  7. Lugmayr et al., “Aim 2019 challenge on real-world image super-resolution: Methods and results,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019: IEEE, pp. 3575-3583.
  8. C.-C. Hsu, C.-H. Hung, C.-Y. Jian, and Y.-X. Zhuang, “Stronger Baseline for Vehicle Re-Identification in the Wild,” in 2019 IEEE Visual Communications and Image Processing (VCIP), 2019: IEEE, pp. 1-4.
  9. C.-C. Hsu and C.-H. Lin, “Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution,” in IEEE International Conference on Computer Vision Workshop (ICCVW), 2019.
  10. W.-T. Su, C.-C. Hsu, Z. Huang, C.-W. Lin, and G. Cheung, “Joint pairwise learning and image clustering based on a siamese cnn,” in 2018 25th IEEE International Conference on Image Processing (ICIP), 2018: IEEE, pp. 1992-1996.
  11. C.-C. Hsu et al., “An iterative refinement approach for social media headline prediction,” in Proceedings of the 26th ACM international conference on Multimedia, 2018, pp. 2008-2012.
  12. C.-C. Hsu, C.-Y. Lee, and Y.-X. Zhuang, “Learning to detect fake face images in the wild,” in 2018 International Symposium on Computer, Consumer and Control (IS3C), 2018: IEEE, pp. 388-391.
  13. C.-C. Hsu et al., “Social media prediction based on residual learning and random forest,” in Proceedings of the 25th ACM international conference on Multimedia, 2017, pp. 1865-1870. (Oral & Best Grand Challenge Paper Award)
  14. C.-C. Hsu and C.-W. Lin, “Objective quality assessment for video retargeting based on spatio-temporal distortion analysis,” in 2017 IEEE Visual Communications and Image Processing (VCIP), 2017: IEEE, pp. 1-4.
  15. C.-C. Hsu and C.-W. Lin, “Unsupervised convolutional neural networks for large-scale image clustering,” in 2017 IEEE International Conference on Image Processing (ICIP), 2017: IEEE, pp. 390-394.
  16. W.-T. Su, C.-C. Hsu, C.-W. Lin, and W. Lin, “Supervised-learning based face hallucination for enhancing face recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016: IEEE, pp. 1751-1755.
  17. C.-C. Hsu, L.-W. Kang, and C.-W. Lin, “Video super-resolution via dynamic texture synthesis,” in 2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP), 2014: IEEE, pp. 1-6.
  18. L.-W. Kang, B. Zhuang, C.-C. Hsu, C.-W. Lin, and C.-H. Yeh, “Self-learning-based low-quality single image super-resolution,” in IEEE Workshop Multimedia Signal Processing (MMSP), Sardinia, Italy, Sept. 2013, 2013. (Top 10% Paper Award)
  19. C.-C. Hsu, C.-W. Lin, Y. Fang, and W. Lin, “Objective quality assessment for image retargeting based on perceptual distortion and information loss,” in 2013 Visual Communications and Image Processing (VCIP), 2013: IEEE, pp. 1-6.
  20. C.-C. Hsu and C.-W. Lin, “Image super-resolution via feature-based affine transform,” in 2011 IEEE 13th International Workshop on Multimedia Signal Processing, 2011: IEEE, pp. 1-5.
  21. C.-C. Hsu, C.-W. Lin, C.-T. Hsu, H.-Y. M. Liao, and J.-Y. Yu, “Face hallucination using Bayesian global estimation and local basis selection,” in 2010 IEEE International Workshop on Multimedia Signal Processing, 2010: IEEE, pp. 449-453.
  22. C.-C. Hsu, T.-Y. Hung, C.-W. Lin, and C.-T. Hsu, “Video forgery detection using correlation of noise residue,” in 2008 IEEE 10th workshop on multimedia signal processing, 2008: IEEE, pp. 170-174.

Patents

  1. C. Hsu and T.C. Lee, “System and method for diagnosing gastrointestinal neoplasm,” US Patent, US11011275B2, 5/18/2021.
  2. 童曉儒, 許志仲, 張文誠, “影像辨識方法”, Taiwan Patent, #I689872, 4/1 /2020.
  3. 許志仲與張軒韶,”影像辨識系統及其操作方法,” Taiwan Patent #201329874, 2013.
  4. 許志仲與林嘉文,”利用仿射轉換建立影像資料庫之方法,” Taiwan Patent #201317938, 2013.
  5. Chih-Chung Hsu and Chia-Wen Lin, “Method and system for example-based face hallucination,” USA patent # 8,488,913, 2013.
  6. Chih-Chung Hsu and Chia-Wen Lin, “Super-resolution method and system for human face based on sample,” China patent #CN102298775 B, 2013.
  7. 林嘉文與許志仲,”以樣本為基礎之人臉超解析度方法與系統,” Taiwan Patent #201145181, 2011.
  8. 張廷政, 張軒庭, 與許志仲, “應用於網際網路上之分散式並聯加密方法,” Taiwan Patent #094122766, 2005.
  9. 許志仲, 王焜潔, 與張廷政, “應用於網際網路上之分散式加密方法,” Taiwan Patent #093135605, 2004.