Chen Chen — Research

I. Human Action/Gesture Recognition and Application

16. C. Chen, M. Liu, B. Zhang, J. Han, J. Jiang and H. Liu, “3D Action Recognition Using Multi-temporal Depth Motion Maps and Fisher Vector,” 25th International Joint Conference on Artificial Intelligence (IJCAI-16), New York City, NY, July 2016, accepted.

15. S. Liu, C. Chen, and N. Kehtarnavaz, “A Computationally Efficient Denoising and Hole-filling Method for Depth Image Enhancement,” SPIE Conference on Real-Time Image and Video Processing, Brussels, Belgium, April 2016, accepted.

14. C. Chen, R. Jafari, and N. Kehtarnavaz, “Fusion of Depth, Skeleton, and Inertial Data for Human Action Recognition,” the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016, accepted.

13. C. Chen, B. Zhang, Z. Hou, J. Jiang, M. Liu, and Y. Yang, “Action Recognition from Depth Sequences Using Weighted Fusion of 2D and 3D Auto-Correlation of Gradients Features,” Multimedia Tools and Applications, 2016, to appear. (doi: 10.1007/s11042-016-3284-7) (2014 Impact Factor: 1.346)

12. C. Chen, R. Jafari, and N. Kehtarnavaz, “A Survey of Depth and Inertial Sensor Fusion for Human Action Recognition,” Multimedia Tools and Applications, 2015, to appear. (doi: 10.1007/s11042-015-3177-1) (2014 Impact Factor: 1.346)

11. C. Chen, R. Jafari, and N. Kehtarnavaz, “A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion,” IEEE Sensors Journal, vol. 16, no. 3, pp. 773-781, February 2016. (doi: 10.1109/JSEN.2015.2487358) (2014 Impact Factor: 1.762)

10. C. Chen, Zhenjie Hou, Baochang Zhang, Junjun Jiang, and Yun Yang, “Gradient Local Auto-Correlations and Extreme Learning Machine for Depth-Based Activity Recognition,” 11th International Symposium on Visual Computing (ISVC'15), Las Vegas, December 14-16, 2015, pp. 613-623.

9. Y. Yang, B. Zhang, L. Yang, C. Chen, and W. Yang, “Action Recognition Using Completed Local Binary Patterns and Multiple-class Boosting Classifier,” ACPR, November 3-6, 2015, Kuala Lumpur, Malaysia.

8. C. Chen, R. Jafari, and N. Kehtarnavaz, “UTD-MHAD: A Multimodal Human Dataset for Human Action Recognition Utilizing a Depth Camera and a Wearable Inertial Sensor,” IEEE International Conference on Image Processing (ICIP), Quebec city, Canada, September 2015. [UTD Multimodal Human Action Dataset Website]

7. C. Chen, R. Jafari, and N. Kehtarnavaz, “Action Recognition from Depth Sequences Using Depth Motion Maps-based Local Binary Patterns,” in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2015), Waikoloa Beach, HI, January, 2015, pp. 1092-1099. (Oral and poster presentation) [BibTeX] [Matlab code]

6. C. Chen, R. Jafari, and N. Kehtarnavaz, “Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors,” IEEE Transactions on Human-Machine Systems, vol. 45, no. 1, pp. 51-61, February 2015. (2014 Impact Factor: 1.982)

5. K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, “Multi-HMM classification for hand gesture recognition using two differing modality sensors,” in Proceedings of the 10th IEEE Dallas Circuits and Systems Conference (DCAS'14), Richardson, TX, October 2014, pp. 1-4. (Oral presentation) [Video Demo]

4. K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, “Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition,” IEEE Sensors Journal, vol. 14, no. 6, pp. 1898-1903, June 2014. [BibTeX] (2014 Impact Factor: 1.762)

3. C. Chen, K. Liu, and N. Kehtarnavaz, “Real-Time Human Action Recognition Based on Depth Motion Maps,” Journal of Real-Time Image Processing, 2013. [Code] [Project Website] (2014 Impact Factor: 2.020)

2. C. Chen, K. Liu, R. Jafari, and N. Kehtarnavaz, “Home-based Senior Fitness Test Measurement System Using Collaborative Inertial and Depth Sensors,” in Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'14), Chicago, IL, August 2014, pp. 4135-4138. (Oral presentation) [BibTeX] [Video Demo]

1. C. Chen, N. Kehtarnavaz, and R. Jafari, “A Medication Adherence Monitoring System for Pill Bottles Based on a Wearable Inertial Sensor,” in Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'14), Chicago, IL, August 2014, pp. 4983-4986. (Poster presentation) [BibTeX]

II. Hyperspectral Image Classification

7. J. Jiang, C. Chen, X. Song, and Z. Cai, “Hyperspectral Image Classification Using Set-to-Set Distance,” the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016, accepted.

6. C. Chen, J. Jiang, B. Zhang, W. Yang, and J. Guo, “Hyperspectral Image Classification Using Gradient Local Auto-Correlations,” ACPR, November 3-6, 2015, Kuala Lumpur, Malaysia.

5. W. Li, C. Chen, H. Su, and Q. Du, “Local Binary Patterns for Spatial-Spectral Classification of Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 7, pp. 3681-3693, July 2015. (doi: 10.1109/TGRS.2014.2381602) [BibTeX] (2014 Impact Factor: 3.514)

4. H. Su, Y. Sheng, P. Du, C. Chen, and K. Liu, “Hyperspectral Image Classification Based on Volumetric Texture and Dimensionality Reduction,” Frontiers of Earth Science, November 2014. (doi: 10.1007/s11707-014-0473-4) [BibTeX] (2014 Impact Factor: 0.883)

3. H. Su, B. Yong, P. Du, H. Liu, C. Chen, and K. Liu, “Dynamic Classifier Selection Using Spectral-Spatial Information for Hyperspectral Image Classification,” Journal of Applied Remote Sensing, vol. 8, no. 1, pp. 085095, August 2014. (doi: 10.1117/1.JRS.8.085095) [BibTeX] (2014 Impact Factor: 1.183)

2. C. Chen, W. Li, H. Su, and K. Liu, “Spectral-Spatial Classification of Hyperspectral Image based on Kernel Extreme Learning Machine,” Remote Sensing, vol. 6, no. 6, pp. 5795-5814, June 2014. [BibTeX] (2014 Impact Factor: 3.180)

1. C. Chen, W. Li, E. W. Tramel, M. Cui, S. Prasad, and J. E. Fowler, “Spectral-Spatial Preprocessing Using Multihypothesis Prediction for Noise-Robust Hyperspectral Image Classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 4, pp. 1047-1059, April 2014. [MATLAB Code] [BibTeX] (A simple but effective preprocessing algorithm for noise-robust hyperspectral image classification!) (2014 Impact Factor: 3.026)

III. Image Scene Classification

3. J. Zou, W. Li, C. Chen, and Q. Du, “Scene Classification Using Local and Global Features with Collaborative Representation Fusion,” Information Sciences, vol. 348, pp. 209-226, June 2016. (doi: 10.1016/j.ins.2016.02.021) (2014 Impact Factor: 4.038)

2. C. Chen, L. Zhou, J. Guo, W. Li, H. Su, and F. Guo, “Gabor-Filtering-Based Completed Local Binary Patterns for Land-Use Scene Classification,” in Multimedia Big Data and Hyperspectral Imaging (BigMM-HSI) Workshop, the First IEEE International Conference on Multimedia Big Data, Beijing, China, April 2015, pp. 324-329.

1. C. Chen, B. Zhang, H. Su, W. Li, and L. Wang, “Land-Use Scene Classification Using Multi-Scale Completed Local Binary Patterns,” Signal, Image and Video Processing, July 2015, DOI: 10.1007/s11760-015-0804-2. (2014 Impact Factor: 1.430)

IV. Image Super-Resolution

5. J. Jiang, C. Chen, K. Huang, Z. Cai, and R. Hu, “Noise Robust Position-Patch based Face Super-Resolution via Tikhonov Regularized Neighbor Representation,” Information Sciences, 2016, major revision. (2014 Impact Factor: 4.038)

4. J. Jiang, C. Chen, J. Fu, and R. Hu, “SRLSR: A Face Image Super-Resolution Algorithm using Smooth Regression with Local Structure Prior,” IEEE Transactions on Multimedia, 2016, minor revision. (2014 Impact Factor: 2.303)

3. J. Jiang, Z. Wang, C. Chen, and T. Lu, “L1-L1 Norms for Face Super-Resolution with Mixed Gaussian-Impulse Noise,” the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016, accepted.

2. Z. Zhu, F. Guo, H. Yu, and C. Chen, “Fast Single Image Super-Resolution via Self-Example Learning and Sparse Representation,” IEEE Transactions on Multimedia, vol. 16, no. 8, pp. 2178-2190, December 2014. [BibTeX] (2014 Impact Factor: 2.303)

1. C. Chen, and J. E. Fowler, “Single-Image Super-Resolution Using Multihypothesis Prediction,” in Proceedings of the 46th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2012, pp. 608-612. [Code]

V. Compressed-Sensing of Images and Videos

4. J. Zhang, Q. Xiang, Y. Yin, C. Chen, and X. Luo, “Adaptive Compressed Sensing for Wireless Image Sensor Networks,” Multimedia Tools and Applications, 2016, to appear. (2014 Impact Factor: 1.346)

3. C. Chen, W. Li, E. W. Tramel, and J. E. Fowler, “Reconstruction of Hyperspectral Imagery from Random Projections Using Multihypothesis Prediction,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 365-374, January 2014. [Code] [Project Website] (2014 Impact Factor: 3.514)

2. C. Chen, E. W. Tramel, and J. E. Fowler, “Compressed-Sensing Recovery of Images and Video Using Multihypothesis Predictions,” in Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2011, pp. 1193-1198. [Code] [Project Website]

1. C. Chen, “Multihypothesis Prediction for Compressed Sensing and Super-Resolution of Images,” Master's thesis, Mississippi State University, 2012. [BibTeX]