Publications

Journals

  1. D. Saire and A. Ramírez Rivera, “Global and Local Features through Gaussian Mixture Models on Image Semantic Segmentation,” IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3192605.
  2. S. Robles, J. Gómez, A. Ramírez Rivera, N. Padilla, and D. Dujovne, “A Deep Learning Approach to Halo Merger Tree Construction,” Monthly Notices of the Royal Astronomical Society, 2022, doi: 10.1093/mnras/stac1569.
  3. J. Hernández Albarracín and A. Ramírez Rivera, “Video Reenactment as Inductive Bias for Content-Motion Disentanglement,” IEEE Transactions on Image Processing, 2022, doi: 10.1109/TIP.2022.3153140.
  4. D. Saire and A. Ramírez Rivera, “Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task,” IEEE Access, vol. 9, pp. 80654–80670, 2021, doi: 10.1109/ACCESS.2021.3085218.
  5. M. Rodríguez Santander, J. Hernández Albarracín, and A. Ramírez Rivera, “On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study,” Experts Systems with Applications, 2021, doi: 10.1016/j.eswa.2021.114991.
  6. M. T. B. Iqbal, B. Ryu, A. Ramírez Rivera, F. Makhmudkhujaev, O. Chae, and S. H. Bae, “Facial Expression Recognition with Active Local Shape Pattern and Learned-Size Block Representations,” IEEE Transactions on Affective Computing, 2020, doi: 10.1109/TAFFC.2020.2995432.
  7. R. Quispe, D. Ttito, A. Ramírez Rivera, and H. Pedrini, “Multi-Stream Networks and Ground-Truth Generation for Crowd Counting,” International Journal of Electrical and Computer Engineering Systems, vol. 11, no. 1, pp. 25–33, 2020.
  8. A. Ramírez Rivera, A. Khan, I. E. I. Bekkouch, and T. Sheikh, “Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation,” IEEE Transactions on Neural Networks and Learning Systems, 2020, doi: 10.1109/TNNLS.2020.3027667.
  9. B. Ryu, A. Ramírez Rivera, J. Kim, and O. Chae, “Local Directional Ternary Pattern for Facial Expression Recognition,” IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 6006–6018, 2017, doi: 10.1109/TIP.2017.2726010.
  10. A. Ramírez Rivera, J. Rojas Castillo, and O. Chae, “Local Directional Texture Pattern Image Descriptor,” Pattern Recognition Letters, vol. 51, no. 0, pp. 94–100, 2015, doi: 10.1016/j.patrec.2014.08.012. [Online ] . Available at: http://www.sciencedirect.com/science/article/pii/S0167865514002724
  11. A. Ramírez Rivera and O. Chae, “Spatiotemporal Directional Number Transitional Graph for Dynamic Texture Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 10, pp. 2146–2152, 2015, doi: 10.1109/TPAMI.2015.2392774.
  12. A. Ramírez Rivera, M. Murshed, J. Kim, and O. Chae, “Background Modeling Through Statistical Edge-Segment Distributions,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 8, pp. 1375–1387, Aug. 2013, doi: 10.1109/TCSVT.2013.2242551.
  13. J. Kim, M. Murshed, A. Ramírez Rivera, and O. Chae, “Background Modelling Using Edge-Segment Distributions,” International Journal of Advanced Robotic Systems, Feb. 2013, doi: 10.5772/54185.
  14. A. Ramírez Rivera, J. Rojas Castillo, and O. Chae, “Local Directional Number Pattern for Face Analysis: Face and Expression Recognition,” IEEE Transactions on Image Processing, vol. 22, no. 5, pp. 1740–1752, 2013, doi: 10.1109/TIP.2012.2235848.
  15. J. Rojas Castillo, A. Ramírez Rivera, and O. Chae, “Robust Facial Recognition Based on Local Gaussian Structural Pattern,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 12, pp. 8399–8413, Dec. 2012.
  16. A. Ramírez Rivera, B. Ryu, and O. Chae, “Content-Aware Dark Image Enhancement through Channel Division,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 3967–3980, Sep. 2012, doi: 10.1109/TIP.2012.2198667.
  17. M. Murshed, A. Ramírez Rivera, J. Kim, and O. Chae, “Statistical Binary Edge Frequency Accumulation Model for Moving Object Detection,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 7(B), pp. 4943–4957, Jul. 2012.
  18. M. Murshed, A. Ramírez Rivera, and O. Chae, “Moving Edge Segment Matching for the Detection of Moving Object,” Lecture Notes in Computer Science, vol. 6753, pp. 274–283, Jun. 2011.

Conferences

  1. T. Silva and A. Ramírez Rivera, “Representation Learning via Consistent Assignment of Views to Clusters,” in ACM/SIGAPP Symposium on Applied Computing (SAC), 2022, doi: 10.1145/3477314.3507267.
  2. T. Silva and A. Ramírez Rivera, “Consistent Assignment for Representation Learning,” in Energy-based Models Workshop (ICLRW), 2021.
  3. A. Khusainova, A. Khan, A. Ramírez Rivera, and V. Romanov, “Hierarchical Transformer for Multilingual Machine Translation,” in VarDial—Workshop on NLP for Similar Languages, Varieties and Dialects, 2021.
  4. G. Nikolentzos, M. Thomas, A. Ramírez Rivera, and M. Vazirgiannis, “Image Classification using Graph-based Representations and Graph Neural Networks,” in International Conference Complex Networks and their Applications, 2020.
  5. M. V. S. Silva, L. Bittencourt, and A. Ramírez Rivera, “Towards Federated Learning in Edge Computing for Real-Time Traffic Estimation in Smart Cities,” in Workshop of Urban Computation (CoUrb), 2020.
  6. B. Kim, A. Ramírez Rivera, O. Chae, and J. Kim, “Background Modeling through Spatiotemporal Edge Feature and Color,” in International Symposium on Visual Computing (ISVC), 2019.
  7. S. Robles, J. Gómez, A. Ramírez Rivera, J. González, N. Padilla, and D. Dujovne, “A Halo Merger Tree Generation and Evaluation Framework,” in Workshop on Theoretical Physics for Deep Learning (ICMLW), 2019.
  8. D. Saire and A. Ramírez Rivera, “Graph Learning Network: A Structure Learning Algorithm,” in Workshop on Learning and Reasoning with Graph-Structured Data (ICMLW), 2019.
  9. D. Ttito, R. Quispe, A. Ramírez Rivera, and H. Pedrini, “Where are the People? A Multi-Stream Convolutional Neural Network for Crowd Counting via Density Map from Complex Images,” in International Conference on Systems, Signals and Image Processing (IWSSIP), 2019.
  10. A. Khusainova, A. Khan, and A. Ramírez Rivera, “SART—Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation,” in International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), 2019.
  11. P. Zhdanov, A. Khan, A. Ramírez Rivera, and A. Khattak, “Improving Human Action Recognition through Hierarchical Neural Network Classifiers,” in International Joint Conference on Neural Networks (IJCNN), 2018.
  12. J. Arias Figueroa and A. Ramírez Rivera, “Is Simple Better?: Revisiting Simple Generative Models for Unsupervised Clustering,” in Second workshop on Bayesian Deep Learning (NIPS 2017), 2017.
  13. J. Arias Figueroa and A. Ramírez Rivera, “Learning to Cluster with Auxiliary Tasks: A Semi-Supervised Approach,” in 31th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2017, 2017, pp. 1–8.
  14. A. Dobrenkii, R. Kuleev, A. Khan, A. Ramírez Rivera, and A. Khattak, “Large Residual Multiple View 3D CNN for False Positive Reduction in Pulmonary Nodule Detection,” in Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE International Conference on, 2017.
  15. M. Gusarev, R. Kuleev, A. Khan, A. Ramírez Rivera, and A. Khattak, “Deep Learning Models for Bone Suppression in Chest Radiographs,” in Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE International Conference on, 2017.
  16. J. Kim, A. Ramírez Rivera, B. Kim, K. Roy, and O. Chae, “Background Modeling using Adaptive Properties of Hybrid Features,” in Advanced Video and Signal-Based Surveillance (AVSS), IEEE International Conference on, 2017.
  17. S. Hong, J. Kim, A. Ramírez Rivera, G. Song, and O. Chae, “Edge Shape Pattern for Background Modeling based on Hybrid Local Codes,” in Advanced Video and Signal-Based Surveillance (AVSS), IEEE International Conference on, 2016.
  18. J. Kim, A. Ramírez Rivera, B. Ryu, and O. Chae, “Simultaneous foreground detection and classification with hybrid features,” in Computer Vision (ICCV), IEEE International Conference on, 2015, pp. 3307–3315.
  19. J. Kim, A. Ramírez Rivera, B. Ryu, K. Ahn, and O. Chae, “Unattended object detection based on edge-segment distributions,” in Advanced Video and Signal Based Surveillance (AVSS), IEEE International Conference on, 2014, pp. 283–288, doi: 10.1109/AVSS.2014.6918682.
  20. J. Kim, A. Ramírez Rivera, G. Song, B. Ryu, and O. Chae, “Edge-segment-based Background Modeling: Non-parametric online background update,” in Advanced Video and Signal Based Surveillance (AVSS), IEEE International Conference on, 2013, pp. 214–219, doi: 10.1109/AVSS.2013.6636642.
  21. A. Ramírez Rivera, J. Rojas Castillo, and O. Chae, “Local Gaussian Directional Pattern for Face Recognition,” in International Conference on Pattern Recognition (ICPR), 2012, pp. 1000–1003.
  22. A. Ramírez Rivera, J. Rojas Castillo, and O. Chae, “Recognition of Face Expressions Using Local Principal Texture Pattern,” in International Conference on Image Processing (ICIP), 2012, pp. 2609–2612.
  23. J. Rojas Castillo, A. Ramírez Rivera, and O. Chae, “Facial Expression Recognition Based on Local Sign Directional Pattern,” in International Conference on Image Processing (ICIP), 2012, pp. 2613–2616.
  24. J. Kim, A. Ramírez Rivera, M. Park, and O. Chae, “Scene Modeling using Edge Segment Distributions,” in International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), 2012.
  25. A. Ramírez Rivera, M. Murshed, and O. Chae, “Object Detection through Edge Behavior Modeling,” in Advanced Video and Signal-Based Surveillance (AVSS), IEEE International Conference on, 2011, pp. 273–278.
  26. M. Murshed, A. Ramírez Rivera, and O. Chae, “Statistical Background Modeling: An Edge Segment based Moving Object Detection Approach,” in Advanced Video and Signal Based Surveillance (AVSS), IEEE International Conference on, 2010, pp. 300–306.

Book chapters