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Books and Book Chapters and Ph.D. Theses

  1. A. Barbu, S.C. Zhu. Monte Carlo Methods. Springer 2020 (Springer, Amazon)
  2. A. Barbu, N. Lay, G.Gramajo. Face Detection with a 3D Model. "Academic Press Library in Signal Processing Volume 6: Image and Video Processing and Analysis and Computer Vision". pp 237-259, 2018. Editors: R. Chellappa and S. Theodoridis.(arxiv, link)
  3. J. Feulner, A. Barbu. Data-Driven Detection and Segmentation of Lymph Nodes. In "Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches". Elsevier 2015. Editor: S. K. Zhou.
  4. L. Ding, A. Barbu. Scalable Subspace Clustering with Application to Motion Segmentation. Current Trends in Bayesian Methodology with Applications, Chapman & Hall/CRC Press. Editors: Dipak K. Dey, Umesh Singh and A. Loganathan. pp 267-286, 2015 (pdf)
  5. A. Barbu. Multi-Path Marginal Space Learning for Object Detection. Academic Press Library in Signal Processing: Volume 4: Image, Video Processing and Analysis, Hardware, Audio, Acoustic and Speech Processing. pp 271-291, 2013 (pdf)
  6. A. Barbu. Cluster sampling and its application to segmentation, stereo and motion (Ph.D. thesis, UCLA 2005) (pdf)
  7. A. Barbu. On the cohomology of GLn(Fp) with Fp coefficients (Ph.D. thesis, OSU 2000) (pdf)

Journal Publications

  1. A. Barbu, H. Mou. The Compact Support Neural Network. Sensors 21 No. 24, 8494, 2021. (arxiv,link)
  2. M. Wang, A. Barbu. Are screening methods useful in feature selection? An empirical study. PLoS One 14, No. 9 (2019) (arxiv,link)
  3. S. Inkoom, J. Sobanjo, A. Barbu, X.Niu. Pavement Crack Rating using Machine Learning Frameworks: Partitioning, Boostrap Forest, Boosted Trees, Naïve Bayes and K - Nearest Neighbors. Journal of Transportation Engineering, Part B: Pavements, 145, No 3, 2019.
  4. S. Inkoom, J. Sobanjo, A. Barbu, X. Niu. Prediction of the Crack Condition of Highway Pavements using Machine Learning Models. Structure and Infrastructure Engineering, 15, No 7, 940-953, 2019.
  5. K. O'Brien, W. Introne, O. Akal, T. Akal, A. Barbu, M. McGowan, M. Merideth, S. Seward, W. Gahl, B. Gochuico. Prolonged Treatment with Open-label Pirfenidone in Hermansky-Pudlak Syndrome Pulmonary Fibrosis. Molecular Genetics and Metabolism 125, No. 1-2, 168-173, September 2018.
  6. J. Anaya, A. Barbu. RENOIR - A Dataset for Real Low-Light Image Noise Reduction. Journal of Visual Comm. and Image Rep. 51, No. 2, 144-154, 2018(arxiv, link, data)
  7. A. Gupta, A. Barbu. Parameterized Principal Component Analysis. Pattern Recognition 78, No. 6, 215–227, 2018(arxiv, link)
  8. A. Barbu, Y. She, L. Ding, G. Gramajo. Feature Selection with Annealing for Computer Vision and Big Data Learning. IEEE PAMI, 39, No. 2, 272-286, 2017. (arxiv, link)
  9. A. Barbu, L. Lu, H. Roth, A. Seff, R. Summers. An Analysis of Robust Cost Functions for Deep CNN in Computer-Aided Diagnosis. Computer Methods in Biomechanics and Biomedical Engineering, 2016. (pdf)
  10. A. Barbu, T.F. Wu, Y. N. Wu. Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee. Electronic Journal of Statistics 8, No. 2, 3004-3030, 2014 (pdf, arxiv)
  11. E. Coyle, R. Roberts, E. Collins, A. Barbu. Synthetic Data Generation for Classification via Uni-Modal Cluster Interpolation. Autonomous Robots, 37, No. 1, 27-45, 2014.(pdf)
  12. A. Barbu. Hierarchical Object Parsing from Structured Noisy Point Clouds. IEEE PAMI, 35, No. 7, 1649-1659, 2013. (pdf, arxiv, slides)
  13. K. Zhang, E. Collins, A. Barbu. An Efficient Stochastic Clustering Auction for Heterogeneous Robotic Collaborative Teams. Journal of Intelligent and Robotic Systems 72, 541-558, 2013 (pdf)
  14. A. Barbu, N. Lay. An Introduction to Artificial Prediction Markets for Classification. Journal of Machine Learning Research, 13, 2177-2204, 2012. (pdf)
  15. A. Barbu, M. Suehling, X. Xu, D. Liu, S. K. Zhou, D. Comaniciu. Automatic Detection and Segmentation of Lymph Nodes from CT Data. IEEE Trans Medical Imaging, 31, No. 2, 240-250, 2012.(pdf)
  16. F. Bunea, A. Tsybakov, M. Wegkamp and A.Barbu. SPADES and mixture models. Annals of Statistics 38, No. 4, 2525-2558, 2010. (pdf)
  17. F. Bunea and A.Barbu. Dimension reduction and variable selection in case control studies via regularized likelihood optimization. Electronic Journal of Statistics, 3, 2009. (pdf)
  18. A. Barbu. Training an Active Random Field for Real-Time Image Denoising. IEEE Trans. Image Processing, 18, November 2009. (pdf)
  19. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering and D. Comaniciu. Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features. IEEE Trans Medical Imaging, November 2008. (pdf)
  20. A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang for Image Analysis. J. Comp. Graph. Stat. 16, No 4, 2007 (pdf)
  21. A. Barbu, S.C. Zhu. Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities, PAMI, 27, August 2005 (pdf)
  22. C.V. Ciobanu, A. Barbu, R.M. Briggs. Interactions of carbon atoms and dimer vacancies on the Si(001) surface. Journal of Engineering Materials and Technology -ASME 127, 462 (2005) (pdf) 
  23. A. Barbu. On the range of non-vanishing p-torsion cohomology for GLn(Fp), Journal of Algebra, 278, pp 456-472, August 2004 (pdf, link)
  24. A. Barbu. On a conjecture of Ash, Journal of Algebra, 251, pp 178-184, May 2002 (pdf, link)
  25. A. Barbu. The ring generated by the elements of degree 2 in H*(Un(Fp),Z ), Journal of Algebra, 237, pp 247-261, March 2001 (pdf, link)

Conference Publications

  1. H. Huang, A. Barbu. Predicting Lane Change Decision Making with Compact Support. IEEE Intelligent Vehicles Symposium, 2021. (pdf)
  2. Y.Guo, A. Barbu. A study of local optima for learning feature interactions using neural networks. IJCNN 2021 (arxiv)
  3. Y.Guo, Y. She, A. Barbu. Training Efficient Network Architecture and Weights via Direct Sparsity Control. IJCNN 2021 (arxiv)
  4. B. R. Bartoldson, A. S. Morcos, A. Barbu, G. Erlebacher. The Generalization-Stability Tradeoff in Neural Network Pruning. Neural Information Processing Systems (NeurIPS), 2020,(arxiv)
  5. D. Li, A. Barbu. Training a Steerable CNN for Guidewire Detection. CVPR 2020, (pdf)
  6. G. Dawer, Y.Guo, A. Barbu. Generating Compact Tree Ensembles via Annealing. IJCNN 2020 (arxiv)
  7. G. Dawer, Y.Guo, S. Liu, A. Barbu. Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks. IJCNN 2020 (arxiv)
  8. O. Akal, A. Barbu. Learning Chan-Vese. ICIP 2019, Taipei, Taiwan (pdf)
  9. D. Li, A. Barbu. Training a CNN for Guidewire Detection. ICIP 2019, Taipei, Taiwan (pdf)
  10. O. Akal, A. Barbu, T. Mukherjee, K. George, J. Paquet, E. L. Pasiliao. A Distributed Sensing Approach for Single Platform Image-based Localization. International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, 2018
  11. H. Mou, A. Barbu. Accurate Dictionary Learning with Direct Sparsity Control. ICIP 2018, Athens, Greece (pdf)
  12. N. Lay, Y. Tsehay, R. Cheng, S. Gaur, A. Barbu, L. Lu, B. Turkbey, P. Choyke , P. Pinto, R. Summers. A Decomposable Model for Prostate Cancer Detection in Multi-Parametric MRI. MICCAI, 2018, Granada, Spain (pdf)
  13. A. Barbu. A Directed Graph Approach to Active Contours. ICIP 2017 (pdf)
  14. D. Barbu, A. Barbu. Traditional and Nontraditional Undergraduate Enrollments across All Sectors. Association for Institutional Research Annual Conference, Washington DC, 2017
  15. D. Barbu, A. Barbu. Do Macroeconomic and Financial Aid Indicators Impact Graduate Enrollments? AIR Conference, New Orleans, May 2016
  16. D. Barbu, A. Barbu. Do Macroeconomic and Financial Aid Indicators Impact Graduate Enrollments? Florida AIR Conference, St Petersburg, FL, January 2016
  17. A. Barbu, L. Lu, H. Roth, A. Seff, R.M. Summers. An Analysis of Robust Cost Functions for CNN in Computer-Aided Diagnosis. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2016. (pdf, link)
  18. A. Seff, L. Lu, A. Barbu, H. Roth, H.C. Shin, R. Summers. Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection. MICCAI 2015 (pdf)
  19. A. Meyer-Baese, A. Barbu, M. Lobbes, S. Hoffmann, B. Burgeth, A. Kleefeld, U. Meyer-Baese. Computer-aided diagnosis of breast MRI with high accuracy optical flow estimation. International Society for Optics and Photonics Conference (SPIE). 2015
  20. A. Meyer-Baese, D. Fratte, A. Barbu, K. Pinker-Domenig. Dynamical complex network theory applied to the therapeutics of brain malignancies. SPIE 2015
  21. A. Barbu, N. Lay. Artificial prediction markets for lymph node detection. EHB 2013 (pdf)
  22. A. Barbu, M. Pavlovskaia, S.C. Zhu. Rates for Inductive Learning of Compositional Models. AAAI Workshop Replearn 2013. (pdf)
  23. L. Ding, A. Barbu, A. Meyer-Baese. Learning a Quality-Based Ranking for Feature Point Trajectories. ACCV 2012 (pdf)
  24. L. Ding, A. Barbu, A. Meyer-Baese. Motion Segmentation by Velocity Clustering with Estimation of Subspace Dimension. ACCV Workshop DTCE 2012 (pdf)
  25. K. Zhang, E. Collins, A. Barbu. An Efficient Stochastic Clustering Auction for Heterogeneous Robot Teams. Int. Conf. on Robotics and Automation (ICRA) 2012. (pdf)
  26. W. Wu, T. Chen, A. Barbu, P. Wang, N. Strobel, S. Zhou, D. Comaniciu. Learning-based Hypothesis Fusion for Robust Catheter Tracking in 2D X-ray Fluoroscopy. CVPR 2011 (pdf)
  27. K. Zhang, E. Collins, A. Barbu. Efficient Stochastic Clustering Auctions for Agent-Based Collaborative Systems. Workshop on Agent Technology in Robotics and Automation, the 2011 International Conference on Robotics and Automation (ICRA), Shanghai, China, May 9-13, 2011.
  28. K. Zhang, E. Collins, A. Barbu. A Novel Stochastic Clustering Auction for Task Allocation in Multi-Robot Teams. IROS 2010. (pdf)
  29. A. Barbu, M. Suehling, X. Xu, D. Liu, S. K. Zhou, D. Comaniciu. Automatic Detection and Segmentation of Axillary Lymph Nodes. MICCAI 2010. (pdf)
  30. N. Lay, A. Barbu. Supervised Aggregation of Classifiers using Artificial Prediction Markets. ICML 2010 (pdf)
  31. A. Barbu. Learning Real-Time MRF Inference for Image Denoising. CVPR 2009 (pdf)
  32. A. Barbu, R. Ionasec. Boosting Cross-Modality Image Registration. URBAN 2009 (pdf)
  33. A. Meyer-Baese, S. Lespinats, F. Steinbrucker, A. Saalbach, T. Schlossbauer, A. Barbu. Visual exploratory analysis of DCE-MRI data in breast cancer based on novel nonlinear dimensional data reduction techniques. SPIE Defense and Security, 2009
  34. S. Seifert, A. Barbu, S. Zhou, D. Liu, J. Feulner, M. Huber, M. Suehling, A. Cavallaro, D. Comaniciu. Hierarchical parsing and semantic navigation of full body CT data. SPIE Medical Imaging, 2009 (pdf)
  35. L. Lu, A. Barbu, J. Liang, L. Bogoni, M. Salganicoff and D. Comaniciu. Simultaneous Detection and Registration for Ileo-Cecal Valve Detection in 3D CT Colonography. ECCV 2008 (pdf)
  36. L. Lu, A. Barbu, M. Wolf, J. Liang, M. Salganicoff, D. Comaniciu. Accurate Polyp Segmentation for 3D CT Colonography Using Multi-Staged Probabilistic Binary Learning and Compositional Model. CVPR 2008.(pdf)
  37. R. Socher, A. Barbu, D. Comaniciu. A Learning Based Hierarchical Model for Vessel Segmentation. IEEE International Symposium on Biomedical Imaging, 2008. (pdf)
  38. Y. Zheng, B. Georgescu, A. Barbu, M. Scheuering and D. Comaniciu. Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes, SPIE Medical Imaging, 2008.
  39. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, D. Comaniciu. Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features. ICCV 2007 (pdf)
  40. S Lakare, M Wolf, L Bogoni, A. Barbu, M Dundar, L Lu, M Salganicoff. Evaluation of a Learning-based Component for Suppression of False Positives Located on the Ileo Cecal Valve or Rectal Tube, RSNA 2007
  41. A. Barbu, V. Athitsos, B. Georgescu, S. Boehm, P. Durlak, D. Comaniciu. Hierarchical Learning of Curves: Application to Guidewire Localization in Fluoroscopy. CVPR 2007 (pdf)
  42. S. Lakare, A. Barbu, M. Dundar, M. Wolf, L. Bogoni, D. Comaniciu. Learning-based Component for Suppression Rectal Tube False Positives: Evaluation of Performance on 780 CTC Cases, RSNA 2006 (ppt)
  43. A. Barbu, L. Bogoni, D. Comaniciu. Hierarchical Part-Based Detection of 3D flexible tubes:Application to CT Colonoscopy, MICCAI 2006 (pdf)
  44. Z. Tu, X.S. Zhou, A. Barbu, L. Bogoni, D. Comaniciu. Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment, CVPR 2006 (pdf)
  45. A. Barbu, S.C. Zhu. Incorporating visual knowledge representation in stereo reconstruction, ICCV 2005 (pdf)
  46. A. Barbu, S.C. Zhu. Multigrid and Multi-level Swendsen-Wang Cuts for Hierarchic Graph Partition, CVPR 2004 (pdf)
  47. A. Barbu, A.L. Yuille. Motion Estimation by Swendsen-Wang Cuts, CVPR 2004 (pdf)
  48. A. Barbu, S.C. Zhu. On the relationship between image and motion segmentation, SCVMA workshop, ECCV 2004 (pdf)
  49. A. Barbu, S.C. Zhu. Graph Partition By Swendsen-Wang Cuts, ICCV 2003  (pdf)