Publications 
[Ph.D.] [International Journals] [International Conferences] [Miscellaneous] [Technical Reports]
[Digital Bibliography] [Co-Authors] [Citations]


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Ph.D. 
  1. M. Mignotte. Segmentation d'images sonar par approche Markovienne hierarchique non supervisée et classification d'ombres portées par modèles statistiques.   Defended on the third of july 1998 at Ecole Navale (French Naval academy), Brest, France.

 International Journals
  1. M. Mignotte. MDS-based segmentation model for the fusion of contour and texture cues in natural images. Computer Vision and Image Understanding, accepted in May 2012.
  2. M. Mignotte. A non-stationary MRF model for image segmentation from a soft boundary map. Pattern Analysis and Applications, March 2012.
  3. M. Mignotte. Non-local pairwise energy based model for the HDR image compression problem. Journal of Electronic Imaging, 21(1):013016, January-March 2012.
  4. M. Mignotte. A bi-criteria optimization approach based dimensionality reduction model for the color display of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 50 (2) : 501-513, January 2012.
  5. M. Mignotte. An energy based model for the image edge histogram specification problem. IEEE Transactions on Image Processing, 21 (1) :379-386, January 2012.
  6. M. Mignotte. MDS-based multiresolution nonlinear dimensionality reduction model for color image segmentation. IEEE Transactions on Neural Networks, 22 (3) :447-460, March 2011.
  7. M. Mignotte. A de-texturing and spatially constrained K-means approach for image segmentation. Pattern Recognition Letters, (doi: 10.1016/j.patrec.2010.09.016, online on Sept. 2010), Elsevier, 32(2):359-367, January 2011.
  8. M. Mignotte. A multiresolution Markovian fusion model for the color visualization of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 48 (12) :4236 - 4247, December 2010.
  9. M. Mignotte. Fusion of regularization terms for image restoration. Journal of Electronic Imaging, 19(3):333004- July-September 2010.
  10. M. Mignotte. A label field fusion Bayesian model and its penalized maximum Rand estimator for image segmentation. IEEE Transactions on Image Processing, 19 (6) :1610-1624, June 2010.
  11. S. Benameur, M. Mignotte, J.-P. Soucy, J. Meunier. Image restoration using functional and anatomical information fusion with application to SPECT-MRI images. International Journal of Biomedical Imaging, (doi:10.1155/2009/843160 online on Oct. 2009), Hindawi Publishing Corporation, ID 843160, Volume 2009, 12 pages, October 2009.
  12. P.-M. Jodoin, M. Mignotte. Optical-flow based on an edge-avoidance procedure. Computer Vision and Image Understanding, (doi: 10.1016/j.cviu.2008.12.005, online on Dec. 2008), Elsevier, 113(4):511-531, April 2009.
  13. M. Mignotte. A non-local regularization strategy for image deconvolution. Pattern Recognition Letters, (doi: 10.1016/j.patrec.2008.08.004, online on Aug. 2008), Elsevier, 29(16):2206-2212, December 2008.
  14. M. Mignotte. Segmentation by fusion of histogram-based K-means clusters in different color spaces. IEEE Transactions on Image Processing, 17 (5) :780-787, May 2008.
  15. M. Mignotte, J. Meunier, J.-P. Soucy. DCT-based complexity regularization for EM tomographic reconstruction. IEEE Transactions on Biomedical Engineering, 55 (2) Part 1:801-805, February 2008.
  16. P.-M. Jodoin, M. Mignotte, J. Konrad. Statistical background subtraction methods using spatial cues. IEEE Transactions on Circuits and Systems for Video Technology, 17 (12) :1758-1764, December 2007.
  17. P.-M. Jodoin, M. Mignotte, C. Rosenberger. Segmentation framework based on label field fusion. IEEE Transactions on Image Processing, 16 (10) :2535-2550, October 2007.
  18. M. Mignotte. A post-processing deconvolution step for wavelet-based image denoising methods. IEEE Signal Processing Letters, 14 (9) :621-624, September 2007.
  19. F. Destrempes, M. Mignotte, J.-F. Angers. Localization of shapes using statistical models and stochastic optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (9) :1603-1615, September 2007.
  20. M. Mignotte. Image denoising by averaging of piecewise constant simulations of image partitions. IEEE Transactions on Image Processing, 16 (2) :523-533, February 2007.
  21. F. Destrempes, J.-F. Angers, M. Mignotte. Fusion of hidden Markov Random Field models and its Bayesian estimation. IEEE Transactions on Image Processing, 15 (10) :2920-2935, October 2006.
  22. P.-M. Jodoin, M. Mignotte. Markovian segmentation and parameter estimation on graphics hardware. Journal of Electronic Imaging, 15(3):033005-1-15, July-September 2006.
  23. M. Mignotte. A Segmentation-based regularization term for image deconvolution. IEEE Transactions on Image Processing, 15 (7) :1973-1984, July 2006.
  24. S. Benameur, M. Mignotte, H. Labelle, J.A. De Guise. A hierarchical statistical modeling approach for the unsupervised 3D biplanar reconstruction of the scoliotic spine. IEEE Transactions on Biomedical Engineering, 52 (12) :2041-2057, December 2005.
  25. S. Benameur, M. Mignotte, F. Destrempes, J.A. De Guise. Three-Dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models. IEEE Transactions on Biomedical Engineering, 52 (10) :1713-1728, October 2005.
  26. F. Destrempes, M. Mignotte, J.-F. Angers. A stochastic method for Bayesian estimation of Hidden Markov Random Field models with application to a color model. IEEE Transactions on Image Processing, 14 (8) :1096-1124, August 2005.
  27. J.-F. Laliberté, J. Meunier, M. Mignotte, J.P. Soucy. Detection of abnormal diffuse perfusion in SPECT using a normal brain atlas. NeuroImage, 23(2):561-8, October 2004.
  28. F. Destrempes, M. Mignotte. A statistical model for contours in images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (5) :626-638, May 2004.
  29. M. Mignotte. Nonparametric multiscale energy-based model and its application in some imagery problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (2) :184-197, February 2004.
  30. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. 3D/2D Registration and segmentation of scoliotic vertebrae using statistical models. Computerized Medical Imaging and Graphics, 27(5):321-327, September 2003.
  31. M. Mignotte, J. Meunier, J.-P. Soucy, C. Janicki. Comparison of deconvolution techniques using a distribution mixture parameter estimation : application in SPECT imagery. Journal of Electronic Imaging, 11(1):11-25, January 2002.
  32.  M. Mignotte, J. Meunier, J.-C. Tardif. Endocardial boundary estimation and tracking in echocardiographic images using deformable templates and Markov Random Fields. Pattern Analysis and Applications, 4(4):256-271, November 2001.
  33.  M. Mignotte, J. Meunier. A multiscale optimization approach for the dynamic contour-based boundary detection issue. Computerized Medical Imaging and Graphics, 25(3):265-275, May 2001.
  34. K.C. Yao, M. Mignotte, C. Collet, P. Galerne, G. Burel. Unsupervised segmentation using a Self-Organizing Map and a noise model estimation in sonar imagery. Pattern Recognition, 33(9):1575-1584, September 2000.
  35. M. Mignotte, C. Collet, P. Perez, P. Bouthemy. Markov Random Field and fuzzy logic modeling in sonar imagery: application to the classification of underwater floor. Computer Vision and Image Understanding, 79(1):4-24, July 2000.
  36. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Sonar image segmentation using an unsupervised hierarchical MRF model.  IEEE Transactions on Image Processing, 9 (7) :1216-1231, July 2000.
  37. M. Mignotte, J. Meunier. Three-dimensional blind deconvolution of SPECT images. IEEE Transactions on Biomedical Engineering, 47 (2) :274-281, February 2000.
  38. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Hybrid genetic optimization and statistical model-based approach for the classification of shadow shapes in sonar imagery.  IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (2):129-141, February 2000.
  39. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Three-class Markovian segmentation of high resolution sonar images.  Computer Vision and Image Understanding, 76(3):191-204, December 1999.
  40. C. Collet, P. Thourel, M. Mignotte, P. Pérez, P. Bouthemy. Une nouvelle approche en traitement d'images sonar haute résolution : la segmentation markovienne hiérarchique multimodèle. Traitement du Signal, 15(3):231-250, September 1998.

 International Conferences
  1. S. Benameur, M. Mignotte, F. Lavoie. An homomorphic filtering and expectation maximization approach for the point spread function estimation in ultrasound imaging. SPIE conference on Medical Imaging, SPIE'12, Paper 8295A-28, Volume 8295A, San Francisco, CA, USA, January 2012.
  2. N. Widynski, M. Mignotte. A contrario edge detection with edgelets. 2nd IEEE International Conference on Signal & Image Processing Applications, ICSIPA 2011, DOI:10.1109/ICSIPA.2011.6144087, pages 421-426, Kuala Lumpur, Malaysia, November 2011.
  3. C. Rougier, E. Auvinet, J. Meunier, M. Mignotte, J.A. de Guise. Depth energy image for gait symmetry quantification. 33rd International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'11, DOI: 10.1109/IEMBS.2011.6091272, pages 5136-5139, Boston, MA, USA, August 2011.
  4. C. Rougier, E. Auvinet, J. Rousseau, M. Mignotte, J. Meunier. Fall detection from depth map video sequences. 9th International Conference on Smart Homes and Health Telematics, ICOST'11, Lecture Notes in Computer Science, Volume 6719/2011, DOI: 10.1007/978-3-642-21535-3_16, pages 121-128, Montréal, Canada, June 2011.
  5. S. Benameur, M. Mignotte, F. Lavoie. Unsupervised segmentation of ultrasound images by fusion of spatio-frequential textural features. SPIE conference on Medical Imaging, SPIE'11, volume 7962, pages 7962-116, Lake Buena Vista, Florida, USA, February 2011.
  6. R. Hedjam, M. Mignotte. A hierarchical graph-based Markovian clustering approach for the unsupervised segmentation of textured color images. 16th IEEE International Conference on Image Processing, ICIP'09, pages 1365-1368, Cairo, Egypt, November 2009.
  7. S. Benameur, M. Mignotte, J.-P. Soucy, J. Meunier. SPECT image restoration via recursive inverse filtering constrained by a probabilistic MRI atlas. 15th IEEE International Conference on Image Processing, ICIP'08, pages 2984-2987, San Diego, CA, USA, October 2008.
  8. P.-M. Jodoin, M. Mignotte. Optical-flow based on an edge-avoidance procedure. 13th IEEE International Conference on Image Processing, ICIP'06, pages 1253-1256, Atlanta, GA, USA, October 2006.
  9. P.-M. Jodoin, M. Mignotte, J. Konrad. Light and fast statistical motion detection method based on ergodic model. 13th IEEE International Conference on Image Processing, ICIP'06, pages 1053-1056, Atlanta, GA, USA, October 2006.
  10. S. Benameur, M. Mignotte, J. Meunier, J.-P. Soucy. An edge-preserving anatomical-based regularization term for the NAS-RIF restoration of SPECT image. 13th IEEE International Conference on Image Processing, ICIP'06, pages 1177-1180, Atlanta, GA, USA, October 2006.
  11. P.-M. Jodoin, C. Rosenberger, M. Mignotte. Detecting half-occlusion with a fast region-based fusion. 17th British Machine Vision Conference, BMVC'06, volume I, pages 417-426, Edinburgh, United Kingdom (UK), September 2006.
  12. P.-M. Jodoin, M. Mignotte, J. Konrad. Background subtraction framework based on local spatial distributions. 3rd International Conference on Image Analysis and Recognition, ICIAR'06, Lecture Notes in Computer Science, volume LNCS 4141-I, pages 370-380, Povos de Varzim, Portugal, September 2006.
  13. P.-M. Jodoin, M. Mignotte. Motion segmentation using a K-nearest neighbor-based fusion procedure of spatial and temporal label cues. 2nd International Conference on Image Analysis and Recognition, ICIAR'05, Lecture Notes in Computer Science, volume LNCS 3656, pages 778-785, Toronto, Canada, September 2005.
  14. P.-M. Jodoin, M. Mignotte, J.-F. St-Amour. Markovian energy-based computer vision algorithms on graphics hardware. 13th International Conference on Image Analysis and Processing, ICIAP'05, Lecture Notes in Computer Science, volume LNCS 3617, Image Analysis and Processing, pages 592-603, Cagliary, Italy, September 2005.
  15. M. Mignotte. An adaptive segmentation-based regularization term for image restoration. 12th IEEE International Conference on Image Processing, ICIP'05, volume 1, pages 901-904, Genova, Italy, September 2005.
  16. P.-M. Jodoin, J.-F. St-Amour, M. Mignotte. Unsupervised Markovian segmentation on graphics hardware. 3rd International Conference on Advances in Pattern Recognition, ICAPR'05, Lecture Notes in Computer Science, volume LNCS 3686, Pattern Recognition and Data Mining, Proceedings Part 2, pages 444-454, Bath, United Kingdom (UK), August 2005.
  17. A. Id-Oumohmed, M. Mignotte, J.-Y. Nie. Semantic-based cross-media image retrieval. 3rd International Conference on Advances in Pattern Recognition, ICAPR'05, Lecture Notes in Computer Science, volume LNCS 3686, Pattern Recognition and Data Mining, Proceedings Part 2, pages 414-423, Bath, United Kingdom (UK), August 2005.
  18. S. Benameur, M. Mignotte, F. Destrempes, J. De Guise. Estimation of mixture of probabilistic PCA with stochastic EM for the 3D biplanar reconstruction of scoliotic rib cage. 11th IEEE International Conference on Image Processing, ICIP'04, volume 5, pages 2949-2952, Singapore, October 2004.
  19. P-M. Jodoin, M. Mignotte. An energy-based framework using global spatial constraints for the stereo correspondence problem. 11th IEEE International Conference on Image Processing, ICIP'04, volume 5, pages 3001-3004, Singapore, October 2004.
  20. P-M. Jodoin, M. Mignotte. Unsupervised motion detection using a Markovian temporal model with global spatial constraints. 11th IEEE International Conference on Image Processing, ICIP'04, volume 4, pages 2591-2594, Singapore, October 2004.
  21. C. Alvarez, A. Id-Oumohmed, M. Mignotte, J.-Y. Nie. Toward cross-language and cross-media image retrieval. 5th Workshop on Cross Langage Evaluation Forum, CLEF 2004, lecture notes in Computer science, volume 3491, Multilingual information access for text, speech and images, pages 676-688, Bath, United Kingdom, September 2004.
  22. S. Benameur, M. Mignotte, H. Labelle, J. De Guise. 3D biplanar reconstruction of scoliotic rib cage using statistical models. 5th Meeting of the International Research Society of Spinal Deformities Meeting, IRSSD'04, pages 135-138, British Columbia, Canada, June 2004.
  23. S. Benameur, M. Mignotte, H. Labelle, J. De Guise. Three-dimensional statistical shape models for automatic 3D biplanar reconstruction of scoliotic spine. 5th Meeting of the International Research Society of Spinal Deformities Meeting, IRSSD'04, pages 223-226, British Columbia, Canada, June 2004.
  24. F. Destrempes, M. Mignotte. Unsupervised texture segmentation using a statistical wavelet-based hierarchical multi data model. 10th IEEE International Conference on Image Processing, ICIP'03, volume 2, pages 1053-1056, Barcelona, Spain, September 2003.
  25. M. Mignotte. Unsupervised statistical sketching for Non-Photorealistic Rendering models. 10th IEEE International Conference on Image Processing, ICIP'03, volume 3, pages 573-577, Barcelona, Spain, September 2003.
  26. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. A hierarchical statistical modeling approach for the unsupervised 3D reconstruction of the scoliotic spine. 10th IEEE International Conference on Image Processing, ICIP'03, volume 1, pages 561-564, Barcelona, Spain, September 2003.
  27. J.F. Laliberte, J. Meunier, M. Mignotte, J. Soucy. Detection of abnormal diffuse perfusion in SPECT using a normal brain atlas. SPIE Conference on Medical Imaging, SPIE'03, volume 5032-04, pages, San Diego, CA, USA, February 2003.
  28. F. Destrempes, M. Mignotte. Unsupervised statistical method for edgel clustering with application to shape localization. 3rd Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP'02, Space Application Centre (ISRO),pages 411-416, Ahmedabad, India, December 2002.
  29. F. Destrempes, M. Mignotte. Unsupervised detection of contours using a statistical model. 9th IEEE International Conference on Image Processing, ICIP'02, volume 2, pages 757-760, Rochester, New-York, USA, September 2002.
  30. M. Mignotte. A new and simple shape descriptor based on a non-parametric multiscale model. 9th IEEE International Conference on Image Processing, ICIP'02, volume 1, pages 445-448, Rochester, New-York, USA, September 2002.
  31. F. Destrempes, M. Mignotte. Unsupervised localization of shapes using statistical models. 4th IASTED International Conference on Signal and Image Processing, ICSIP'02, pages 60-65, Kaua'i Marriott, Hawaii, USA, August 2002.
  32. F. Destrempes, M. Mignotte. Unsupervised detection and semi-automatic extraction of contours using a statistical model and dynamic programming. 4th IASTED International Conference on Signal and Image Processing, ICSIP'02, pages 66-71, Kaua'i Marriott, Hawaii, USA, August 2002.
  33. M. Mignotte. Bayesian rendering with non-parametric multiscale prior model. 16th IEEE International Conference on Pattern Recognition, ICPR'02, volume 1, pages 247-250, Québec City Convention Center, Québec, Canada, August 2002.
  34. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. 3D biplanar statistical reconstruction of scoliotic models. 4th Meeting of the International Research Society of Spinal Deformities, IRSSD, included in the book: Research into spinal Deformities, IRSSD'02, pages 67-71, Astir Palace Resort, Athens, Greece, May 2002.
  35. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. 3D Biplanar reconstruction of scoliotic vertebrae using statistical models. 20th IEEE International Conference on Computer Vision and Pattern Recognition, CVPR'01, volume 2, pages 577-582, Kauai Marriott, Hawaii, USA, December 2001.
  36. M. Mignotte, J. Meunier, J.-P. Soucy, C. Janicki. Segmentation and classification of brain SPECT images using 3D Markov Random Field and density mixture estimations. 5th World Multi-Conference on Systemics, Cybernetics and Informatics, SCI'01, Concepts and Applications of Systemics and Informatics, volume X, pages 239-244, Orlando, Floride, USA, July 2001.
  37. M. Mignotte, J. Meunier. Unsupervised restoration of brain SPECT volumes. 13th Conference on Vision Interfaces, VI'00, pages 55-60, Montréal, Québec, Canada, May 2000.
  38. M. Mignotte, J. Meunier. An unsupervised multiscale approach for the dynamic contour-based boundary detection issue in ultrasound imagery. 3rd International Conference on Computer Vision, Pattern Recognition and Image Processing, CVPRIP'00, volume 2, pages 366-369, Atlantic City, New Jersey, USA, March 2000.
  39.  M. Mignotte, J. Meunier. Blind deconvolution of human brain SPECT images using a distribution mixture estimation. SPIE Conference on Medical Imaging, SPIE'00, volume 3979-2, pages 1370-1377, San Diego, CA, USA, February 2000.
  40.  M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Markov Random Field model and fuzzy formalism-based data modeling for the sea-bed classification in sonar imagery. SPIE Conference on  Mathematical Modeling, Bayesian Estimation and Inverse Problems, SPIE'00, volume 3816-29, pages 229-240, Denver, Colorado, USA, July 1999.
  41. M. Mignotte, J. Meunier. Deformable template and distribution mixture-based data modeling for the endocardial contour tracking in an echographic sequence. 18th IEEE International Conference on Computer Vision and Pattern Recognition, CVPR'99, volume 1, pages 225-230, Fort Collins, Colorado, USA, June 1999.
  42. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Bayesian inference and optimization strategies for some detection and classification problems in sonar imagery.  SPIE Conference on Non Linear Image Processing, SPIE'99, Electronic Imaging'99, volume 3646, pages 14-27, San Jose, California, USA, January 1999.
  43. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Statistical model and genetic optimization: application to pattern detection in sonar images. 23rd IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'98, volume 5, pages 2741-2745, Seattle, Washington, USA, May 1998.
  44. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Unsupervised hierarchical Markovian segmentation of sonar images.  4th IEEE International Conference on Image Processing, ICIP'97, On CD-ROM, Santa-Barbara, California, USA, October 1997.
  45. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Unsupervised segmentation applied on sonar images.  Workshop on Energy Minimisation Methods in Computer Vision and Pattern Recognition, EMMCVPR'97, Lecture Notes in Computer Science (Springer-Verlag), volume LNCS 1223, pages 491-506, Venice, Italy, May1997.
  46. M. Mignotte, C. Collet, P. Pérez, P. Bouthemy. Unsupervised Markovian segmentation of sonar images.  22nd IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'97, volume 4, pages 2781-2785, Munchen, Germany, May 1997.
  47. F. Schmitt, M. Mignotte, C. Collet, P. Thourel. Estimation of noise parameters on sonar images. SPIE conference on Signal and Image Processing, SPIE'96, volume 2823, pages 1-12, Denver, Colorado, USA, August 1996.

Misc.
  1. J.-P. Soucy, S. Benameur, M. Mignotte, J. Meunier. SPECT restoration by a new approach to the NAS-RIF algorithm using MRI-based anatomical constraints. Society for Neuroscience, San Diego, CA, USA, November 2007.
  2. S. Benameur, M. Mignotte, F. Destrempes, J. de Guise. Reconstruction stéréographique de la cage thoracique à l'aide de modèles statistiques. 34e Réunion annuelle de la Société de la scoliose du Québec, Octobre 2004.
  3. J.-P. Soucy, J-F. Laliberté, J. Meunier, M. Mignotte. Further work on detecting diffuse abnormalities in SPECT cerebral blood flow studies. 10th Annual Meeting of the Organization for Human Brain Mapping, HBM'04, Imaging Techniques, Poster and Abstract No. TH-389, Budapest, Hungary, June 2004.
  4. S. Benameur, M. Mignotte, H. Labelle, J. De Guise. Reconstruction 3D statistique hiérarchique biplanaire du rachis scoliotique. 6ième Congrès Annuel des Étudiants et Stagiaires du CRCHUM, Hôpital Notre-Dame, Montréal, Québec, Canada, Décembre 2003.
  5. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. Reconstruction 3D des vertèbres scoliotiques par modélisation statistique. Congres des stagiaires de recherche en santé de la faculté de médecine, du CHUM et des centres hospitaliers et instituts, Palais des Congrès de Montréal. Médicine Sciences, Supplement 2, Vol. 19, page 7, Université de Montréal, Janvier 2003.
  6. J. De Guise, S. Benameur, M. Mignotte, S. Parent, W. Skalli, H. Labelle. Reconstruction 3D biplanaire détaillée des vertèbres scoliotiques par modélisation statistique. 32e Réunion annuelle de la Société de la scoliose du Québec,page 12, Octobre 2002.
  7. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. Laboratoire de recherche en imagerie et orthopédie CHUM, Montréal. Reconstruction 3D biplanaire des vertèbres scoliotiques par modèle statistique. Prix du Public, 5th édition du Concours Innovation Recherche 2002 de l'Association québécoise des fabricants de l'industrie médicale (AQFIM), http://www.concours-aqfim.org/, Poster, Avril 2002. ( )
  8. J.-P. Soucy, J.-F. Laliberté, J. Meunier, M. Mignotte. A Cerebral blood flow atlas of the normal brain: Application to automated recognition of abnormal perfusion in SPECT Soc. Neurosci. Abstract No. 28, 2002.
  9. J.-P. Soucy, M. Mignotte, C. Janicki, J. Meunier. A three-dimensional blind deconvolution technique for improving contrast recovery in SPECT brain blood flow studies. 7th Annual Meeting of the Organization for Human Brain Mapping, Conference on Functional Mapping of the Human Brain, HBM'01, NeuroImage 12, #5, Abstract No. 9923, page S254, Brighton, England, June 2001.
  10. J.-P. Soucy, M. Mignotte, C. Janicki, J. Meunier. Comparison of 10 contrast recovery iterative deconvolution techniques for SPECT cerebral blood flow (CBF) imaging. Soc. Neurosci. Abstract No. 27 ,Part. 1, page 1480, June 2001.
  11. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. Detailed 3D reconstruction of scoliotic vertebrae. Spine Biomechanics Research Day, Sainte-Justine Hospital, Montréal, Québec, Canada, Abstract, June 2001.
  12. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. Reconstruction 3D biplanaire des vertèbres scoliotiques. Mini Symposium, Imagerie du système musculo-squelettique, LIO, Hôpital Notre-Dame, Montréal, Québec, Canada, Abstract, March 2001.
  13. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. Reconstruction 3D détaillée des vertèbres scoliotiques par modèle statistique à partir d´images radiographiques biplanaires. Journée de la recherche du POES 2001, Hôpital Maisonneuv-Rosemont, Montréal, Québec, Canada, Abstract, May 2001.
  14. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, J. De Guise. Reconstruction 3D détaillée des vertèbres scoliotiques. 3ième Congrès Annuel des Étudiants et Stagiaires du CRCHUM, Hôpital Notre-Dame, Montréal, Québec, Canada, Abstract, December 2000.

Technical Reports
  1. M. Mignotte. Statistical sketching for Non-Photorealistic Rendering models and animations. Technical Report, DIRO, Montreal University, Canada, Québec, January 2004.
  2. S. Benameur, M. Mignotte, J-P Soucy, J. Meunier. SPECT study restoration using anatomical and geometrical constraints extracted from MRI images. Technical Report 1310. Computer Science and Operations Research department (DIRO), University of Montreal, Montreal, Québec, Canada, October 2000.
  3. M. Mignotte. Bayesian inference and/or optimization strategies for some detection, traking, and deconvolution problems in medical images. INRIA postdoctoral  Report, DIRO, Montreal University, Canada, Québec, September 1999.
  4.  M. Mignotte. Classification d'ombres portées en imagerie sonar par modèle statistique et algorithme génétique hybride. Technical Report No. 28, GTS, Ecole Navale, Brest, France, October 1998.
  5.  M. Mignotte. Segmentation trois classes et modélisation du bruit par lois de Weibull en imagerie sonar. Technical Report No. 23, GTS, Ecole Navale, Brest, France, June 1997.
  6.  M. Mignotte. Segmentation d'images sonar : Modèle hiérarchique proposé. Technical Report No. 14, GTS, Ecole Navale, Brest, France, December 1996.
  7.  M. Mignotte. Modèle markovien: Estimation des paramètres. Technical Report No. 7, GTS, Ecole Navale, Brest, France, March 1996.
  8.  M. Mignotte. Etude de differentes techniques de corrélation et d'un filtre en treillis pour le suivi de formes dans une séquence d'images.  DEA Report, ITMI,  Grenoble, France, July 1993.
  9.  M. Mignotte. Logiciel de traitement de spectre induit par laser impulsionnel. M.Sc. Report, LOE and Université de Toulon et du Var, France, June 1992.


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Co-Authors
    Patrick Bouthemy ........... Research Director at Inria, Rennes, France
    Patrick Pérez ................. Research Director at Inria, Rennes, France

    Jean Francois Angers ..... Professor at DMS, Université de Montréal, Canada
    Jean Meunier ................. Professor at DIRO, Université de Montréal, Canada
    Jian-Yun Nie .................. Professor at DIRO, Université de Montréal, Canada
    Christophe Collet ............ Professor at ULP (ENSPS), Strasbourg, France
    Jacques De Guise .......... Professor at ETS, Research Director at LIO, Montréal, Canada
    Wafa Skalli .................... Professor at ENSAM, Paris, France
    Gilles Burel .................... Professor at UBO, Brest, France
    Janusz Konrad, .............. Professor at Boston University, USA
    Christophe Rosenberger .. Professor at Ensicaen, Caen, France

    Jean Paul Soucy ............ Professor of medecine, Montréal University, Canada
    Hubert Labelle ................ Professor of surgery, St Just. Hosp., Montréal, Canada
    Jean Claude Tardif ......... Medical Doctor at ICM, Montréal, Canada

    Koffie Clement Yao ........ MdC at Ecole Navale, Brest, France
    Francoise Schmitt .......... MdC at Ecole Navale, Brest, France
    Christian Janicki ............ Searcher at CHUM, Montréal, Canada

    Pascal Galerne .............. Ph.D.
    Pierre Thourel ................ Captain at French Naval, Brest, France
    Stephane Parent ............ Ph.D. student in medecine, Montréal, Canada
    Carmen Alvarez ............. M.Sc. student at DIRO
    J.-F. St-Amour ............... M.Sc. student at DIRO

    Said Benameur .............. Ph.D. student under my co-supervision, Montréal, Canada
    Francois Destrempes ..... Ph.D. student under my supervision, Montréal, Canada
    Pierre-Marc Jodoin ......... Ph.D. student under my supervision, Montréal, Canada
    Jean Francois Laliberté ... M.Sc. student under my co-supervision, Montréal, Canada
    Ahmed Id Oumohmed ..... M.Sc. student under my supervision, Montréal, Canada

Co-Authors



Citations

Citations In Digital Libraries



H-Index Measure
The so-called h-index measure has been recently proposed by J. E. Hirsch in order to both measure publication activity and citation impact of a searcher

According to the definition by Jorge E. Hirsch, a scientist has index h if h is the largest number of his/her N papers having received at least h citations each. This index has immediately found interest in the public and received positive reception both in the scientific community and the scientometrics literature. Let us note that this measure must be used to compare searchers of a same research field (e.g., image processing is different than bioinformatics or learning technologies or distributed multimedia systems etc.) and that this measure also allows (to a certain extent) to measure scientific output of departments and labs.
  • My Google scholar h-number is presently h-number= 18 (this simply means that h-number papers of mine got at least h-number citations according to the Web engine search Google scholar).
  • My Google scholar g-number is presently g-number= 31 (this means that g-number is the largest number such that my top g articles received on average at least g citations according to Google scholar).

You can find this h/g numbers either :
  • by using this script
  • by using the Google scholar link (close to each of my significant papers)
  • In order to quickly get a rough estimation of this h-number (with some possible biases such as homonyms, forgotten references, etc.)




Impact Factor

Academic Journal

Journal Impact Factor [2006] [2008] [html]
Eigenfactor
Journal Citation Report
JCR Thomson Reuters



Vision Conference

Conference Impact Factor