Publications 2016

Année 2016:
Revues                                                                                                                                                       
  1. Punithakumar K., Ben Ayed I., Afshin M., Goela A., Islam A., Li S., Boulanger P, Becher H., and Noga M., Detecting left ventricular impaired relaxation in cardiac MRI using moving mesh correspondences, Computer Methods and Programs in Biomedicine, 124: 58–66, 2016. 

     
  2. Miles B., Ben Ayed I., Hojjat S-P, Wang M. H., Li S., Fenster A., and Garvin G. J., Spine labeling in axial magnetic resonance imaging via integral kernels, Computerized Medical Imaging and Graphics, 54: 27–34, 2, 2016. 

     
  3. Mathlouthi Y., Mitiche A., Ben Ayed I., Monocular, Boundary-Preserving Joint Recovery of Scene Flow and Depth, Frontiers in ICT, Computer Image Analysis, 3: 1:14, 2016. 

     
  4. Wong, T., Bigras, P., Duchaîne, V., Roberge, J.-P., Empirical Comparison of Differential Evolution Variants for Industrial Controller Design, International Journal of Computational Intelligence Systems 9(5): 957-970, 2016. 

     
  5. Mohseni, S. A., Wong, T., Duchaîne, V., MCOA: mutated and self-adaptive cuckoo optimization algorithm, Evolutionary Intelligence 9(1): 21-36, 2016. 

     
  6. Faten M'hiri, Luc Duong, Christian Desrosiers, Mohamed Leye, Joaquim Miró, Mohamed Cheriet, A graph-based approach for spatio-temporal segmentation of coronary arteries in X-ray angiographic sequences, Computers in Biology and Medicine, Volume 79, Pages 45–58, December 2016.  

     
  7. Ahmad Chaddad, Christian Desrosiers, Lama Hassan, Camel Tanougast, A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome,The British Journal of Radiology, 2016. 

     
  8. M Zhang, C Desrosiers, Image denoising based on sparse representation and gradient histogram,IET Image Processing 11 (1), 54-63, 2016. 

     
  9. Ahmad Chaddad, Christian Desrosiers, Ahmed Bouridane, Matthew Toews, Lama Hassan, Camel Tanougast, Multi texture analysis of colorectal cancer continuum using multispectral imagery,, PloS one 11 (2), e0149893, 2016. 

     
  10. J.K. Pontes, A.S. Britto Jr., C. Fookes and A.L. Koerich, A Flexible Hierarchical Approach For Facial Age Estimation Based on Multiple Features, Pattern Recognition, 54 (4) 34-51, 2016. 

     
  11. Corriveau, G., Guilbault, R., Tahan, A. and Sabourin, R., "Bayesian Network as an Adaptive Parameter  Setting Approach for Genetic Algorithms", Complex & Intelligent Systems, accepted for publication, February 2016.                                                                

  12. Bernard, S., Châtelain, C., Adam, S.  and Sabourin, R.,  "The Multiclass ROC Front method for cost-sensitive classification Pattern Recognition, Vol 52, April 2016, pp 46-60.                                                                                                                                                                            
  13. Dewan, M.A.A., Granger, E., Marciallis, G.L., Sabourin, R. and Roli, F., "Adaptive Appearance Model Tracking for Still-to-Video Face Recognition," Pattern Recognition, Vol. 40, No. 1, January 2016, pp 129-151.                                                                                                                                           
  14. Diaz, M., Ferrer, M.A., Eskander, G.S. and Sabourin R., "Generation of Duplicated Off-line Signature Images for Verification Systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, April 2016.                                                                                                                                  
  15. Carbonneau, Marc-André, Eric Granger, Alexandre J. Raymond, and Ghyslain Gagnon. "Robust multiple-instance learning ensembles using random subspace instance selection." Pattern Recognition, April 2016.                                                                                                                                 
  16. Roy, A., Cruz, M.O.R., Sabourin, R. and Cavalcanti, G.D.C., "Meta-learning Recommendation of Default Size of Classifier Pool for META-DES," Neurocomputing, August 2016.
 


Chapitres de livre

  1. Schmidt F. R., Gorelick L., Ben Ayed I., Boykov Y., and Brox T., Shape distances for binary image segmentation, Perspectives in Shape Analysis, 137–154, Springer, 2016. 

     
 


Conférences

  1. Tang M., Marin D., Ben Ayed I., and Boykov Y., Normalized cut meets MRF, European Conference on Computer Vision (ECCV), LNCS 9906: 748–765, October 2016, Amsterdam, 2016 (Oral). 

     
  2. Roberge, J. P., Rispal, S., Wong, T., Duchaîne, V., Unsupervised feature learning for classifying dynamic tactile events using sparse coding, IEEE International Conference on Robotics and Automation (ICRA), 2016. 

     
  3. Mingli Zhang, Kuldeep Kumar, Christian Desrosiers, A weighted total variation approach for the atlas-based reconstruction of brain MR data,Image Processing (ICIP), IEEE International Conference on, 4329-4333, 2016. 

     
  4. L Trudeau, S Coulombe, C Desrosiers, Sub-partition reuse for fast optimal motion estimation in HEVC successive elimination algorithms,Image Processing (ICIP), IEEE International Conference on, 2016. 

     
  5. A Chaddad, C Desrosiers, L Hassan, M Toews, Multispectral texture analysis of histopathological abnormalities in colorectal tissues,Image Processing (ICIP), IEEE International Conference on, 2016. 

     
  6. M Zhang, C Desrosiers, Robust MRI reconstruction via re-weighted total variation and non-local sparse regression,Multimedia Signal Processing (MMSP), IEEE 18th International Workshop, 2016. 

     
  7. A Chaddad, C Desrosiers, M Toews, Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme,, IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), 2016. 

     
  8. M Zhang, Q Qu, S Nobari, C Desrosiers, LRI: A low rank approach to non-local sparse representation for image interpolation,, International Joint Conference on Neural Networks (IJCNN), 2016. 

     
  9. Ahmad Chaddad, Christian Desrosiers, Matthew Toews, Local discriminative characterization of MRI for Alzheimer's disease,, IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016. 

     
  10. K Kumar, C Desrosiers, A sparse coding approach for the efficient representation and segmentation of white matter fibers,, IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016. 

     
  11. A Chaddad, C Desrosiers, M Toews, Phenotypic characterization of glioblastoma identified through shape descriptors,, SPIE Medical Imaging, 97852M-97852M-7, 2016. 

     
  12. A Chaddad, C Desrosiers, M Toews, GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes,, SPIE Medical Imaging, 978424-978424-7, 2016. 

     
  13. M Zhang, C Desrosiers, Q Qu, F Guo, C Zhang, Medical image super-resolution with non-local embedding sparse representation and improved IBP,, IEEE International Acoustics, Speech and Signal Processing (ICASSP), 2016. 

     
  14. L Trudeau, S Coulombe, C Desrosiers, , C Desrosiers, Methods and systems for determining motion vectors in a motion estimation process of a video encoder,, US Patent App. 15/009,938, 2016. 

     
  15. Kuldeep Kumar, Christian Desrosiers, Ahmad Chaddad, Matthew Toews, Spatially constrained sparse regression for the data-driven discovery of neuroimaging biomarkers,, In 23rd International Conference on Pattern Recognition (ICPR), 2016. 

     
  16. Matthew Toews and William M. Wells III, How Similar are Sibblings? How are Sibblings Similar? Large-Scale Imaging Genetics Using Local Image Features,, International symposium on biomedical engineering (ISBI), 2016. 

     
  17. Jørn Bersvendsen, Matthew Toews, Adriyana Danudibroto, William M. Wells, Stig Urheim, Raúl San José Estépar, Eigil Samset, Robust spatio-temporal registration of 4D cardiac ultrasound sequences,, SPIE Medical Imaging, 2016. 9790-14. (oral) 

     
  18. Mathlouthi Y., Mitiche A., Ben Ayed I., Boundary preserving variational image differentiation, German Conference on Pattern Recognition (GCPR), LNCS 9796: 355–364, Hannover, Germany, September 2016. 

     
  19. Mathlouthi Y., Mitiche A., and Ben Ayed I., Calcul variationnel des dérivées d’une image et application à l’estimation du flot optique et du flot de scène, Reconnaissance de Formes et Intelligence Artificielle (RFIA), 1–7, Clermont-Ferrand, France, June 2016. 

     
  20. M. Senoussaoui, P. Cardinal, N. Dehak and A.L. Koerich, Native Language Detection using the I-Vector Framework, INTERSPEECH 2016, San Francisco, USA, pp.2398-2402, September 2016. 

     
  21. M.J. Cossetin, J.C. Nievola and A.L. Koerich, Facial Expression Recognition Using a Pairwise Feature Selection and Classification Approach, IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada, pp.5149-5155, July 2016. 

     
  22. Ali A, Dehak N, Cardinal P, Khuranam S, Yella SH, Bell P and Renals S, Automatic Dialect Detection in Arabic Broadcast Speech, INTERSPEECH 2016, San Francisco, USA, pp.2398-2402, September 2016. 

     
  23. Boucher P, Plusquellec P, Dufour P, Dehak N, Cardinal P and Dumouchel P., PHYSIOSTRESS: A Multimodal Corpus of Data on Acute Stress and Physiological Activation, LREC 2016 Workshop on Multimedia Corpora: Computer vision and language processing, 2016. 

     
  24. Farshad Nourbakhsh and Eric Granger, "Learning of Graph Compressed Dictionaries for Sparse Representation Classification", 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2016                                                                  
  25. Roghayeh Soleymani , Eric Granger and Giorgio Fumera, "Classifier Ensembles with Trajectory Under-Sampling for Face Re-Identification", 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2016                                                                     
  26. Hafemann, L.G., Sabourin, R. and Oliveira L. S., "Writer-independent Feature Learning for Offline Signature Verification using Deep Convolutional Neural Networks," The International Conference on Neural Networks (IJCNN'2016), Vancouver, Canada, July 24-29, 2016. [Code and Pre-trained models]                                                                                                                                                                                            
  27. Brun, A.L., Britto Jr., A.S., Oliveira, L.S., Enembreck, F., and Sabourin, R., "Contribution of Data Complexity Features on Dynamic Classifier Selection," The International Conference on Neural Networks  (IJCNN'2016), Vancouver, Canada, July 24-29, 2016.                                                                                                                                                                                                                                  
  28. Lévesque, J.C., Gagné, C. and Sabourin, R., "Bayesian Hyperparameter Optimization for Ensemble Learning," 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), New York City, NY, USA, June 25 – 29, 2016.                                                                                                                                 
  29. Bertolini D., Oliveira L.S. and Sabourin R., "Multi-script Writer Identification using Dissimilarity," 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 4-8 December 2016.                                                                                                                                
  30. Hafemann, L.G., Sabourin, R. and Oliveira L.S., "Analyzing features learned for Offline Signature Verification using Deep CNNs," 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 4-8 December 2016. [Code and Pre-trained models]                                                                                                                                  
  31. Diaz, M., Ferrer, M.A. and Sabourin, R., "Approaching the Intra-Class Variability in Multi-Script Static Signature Evaluation," 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 4-8 December 2016. 
                                  
  32. Roy, A., Cruz, M.O.R., Sabourin, R. and Cavalcanti, G.D.C., "Meta-Regression based Pool Size Prediction Scheme for Dynamic Selection of Classifiers," 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 4-8 December 2016.                                                                                                                               
  33. Dubos, C., Bernard, S., Adam, S. and Sabourin, R., "ROC-based cost-sensitive classification with a reject option," 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 4-8 December 2016.

  34. Almeida, P.R.L., Britto Jr, A.S., Oliveira, L.S. and Sabourin, R., "Handling Concept Drifts Using Dynamic Selection of Classifiers," 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2016), San Jose, CA, USA, 6-8 November 2016.
     
  35. Roghayeh Soleymani, Eric Granger, Giorgio Fumera, "Loss Factors for Learning Boosting Ensembles from Imbalanced Data," 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 4-8 December 2016.                                                                                                                           
  36. Marc-Andre Carbonneau, Eric Granger and Ghyslain Gagnon, "Witness identification in multiple instance learning using random subspaces," 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, 4-8 December 2016.                                                                                                                           
  37. Farshad Nourbakhsh, Eric Granger and Giorgio Fumera, "An Extended Sparse Classification Framework for Domain Adaptation in Video Surveillance," Workshop on Human Identification for Surveillance (HIS): Methods & Applications, The 13th Asian Conference on Computer Vision (ACCV’16), Taipei, Taiwan, Nov 20-24, 2016.

  38. Paulo R. Lisboa de Almeida, Luiz S. Oliveira, Alceu de Souza Britto Jr. and Robert Sabourin, "Handling Concept Drifts Using Dynamic Selection of Classifiers", IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2016.
 


Rapports techniques

  1. Dolz J., Desrosiers C., Ben Ayed I., 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study, arXiv 1612.03925.