Curriculum Vitae Dr. Dimitrios K. Tzovaras



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[J3] D. Tzovaras, N. Grammalidis, M. G. Strintzis, “Joint Three-Dimensional Motion/Disparity Segmentation for Object-Based Stereo Image Sequence Coding” Optical Engineering, Vol.35, No.1, pp137-144, Jan 1996. (12 citations)


  1. Ramaprabha, T., & Sathik, M. M. (2012). A Study on Block Based Vs Object-Based Stereo Image Compression. International Journal, 2(8).

  2. Steinberg, Eran, et al. "Digital image acquisition system with portrait mode." U.S. Patent No. 8,212,897. 3 Jul. 2012.

  3. Steinberg, Eran, et al. "Foreground/background segmentation in digital images with differential exposure calculations." U.S. Patent No. 8,175,385. 8 May 2012.

  4. Multi-stage Branch-and-Bound for Maximum Variance Disparity Clustering, N Thakoor, V Devarajan, J Gao - Pattern Recognition, Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, Publication Date: 8-11 Dec. 2008, On page(s): 1-4, Tampa, FL.

  5. Stereo image displaying based on both physiological and psychological stereoscopy from single image, Chunping Hou, Jiachen Yang *, Zhuoyun Zhang, International Journal of Imaging Systems and Technology, Volume 18 Issue 2-3, Pages 146 – 149, Special Issue: Multimedia Information Retrieval, Published Online: 11 Aug 2008

  6. Embedded planar surface segmentation system for stereo images, Ninad Thakoor · Jean Gao · Sungyong Jung, Machine Vision and Applications, DOI 10.1007/s00138-008-0147-x, 29 April 2008, © Springer-Verlag 2008

  7. Chong, AlbertK , “An inexpensive stereo-image capture tool for motion study”, The Photogrammetric Record, Volume 22, Number 119, September 2007 , pp. 226-237(12)

  8. FOURIER VISION, Segmentation and Velocity Measurement using the Fourier Transform, David Vernon, Kluwer Academic Publishers, copyright 2001

  9. Chun-Jen Tsai and Aggelos K. Katsaggelos, “Dense Disparity Estimation with a Divide-and-Conquer Disparity Space Image Technique”, IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 1, NO. 1, MARCH 1999.

  10. Mel Siegel, “Compression and Interpolation of 3D-Stereoscopic and Multi-View Video”, SPIE Proceedings, 1997.

  11. J. Strom and P. Cosman, “Medical image compression with lossless regions of interest”, Signal Processing, 59:155--171, June 1997.

  12. J. S. McVeigh, ``Efficient Compression Of Arbitrary Multi-View Video Signals’’, Ph.D. in Carnegie Mellon University, Jun. 1996


[J17] D. Tzovaras and M.G. Strintzis, “Use of Nonlinear Principal Component Analysis and Vector Quantization for Image Coding”, IEEE Transactions on Image Processing ,vol.7 ,No. 8, pp. 1218-1223, Aug 1998. (13 citations)


  1. Meyer‐Baese, A., Wildberger, J., Meyer‐Baese, U., & Nilsson, C. L. (2014). Data analysis techniques in phosphoproteomics. Electrophoresis, 35(24), 3452-3462.

  2. Chaabouni, Imen, Wiem Fourati, and Med Salim Bouhlel. "Medical Image Compression Using Improved ISOM." Proceedings Engineering & Technology-Vol 4 (2013): 105-109.

  3. Hume, Andrew. Indexing and behaviour modelling of team sports. Diss. University of Leeds, 2012.

  4. Unaligned training for voice conversion based on a local nonlinear principal component analysis approach, Behrooz Makki, Mona Noori Hosseini, Seyyed Ali Seyyedsalehi, Nasser Sadati, Neural Comput & Applic. DOI 10.1007/s00521-009-0275-x, Received: 17 July 2008 / Accepted: 17 April 2009, _ Springer-Verlag London Limited 2009.

  5. Vector quantization of images with variable block size , Kazuya Sasazaki, Sato Saga, Junji Maeda and Yukinori Suzuki, Applied Soft Computing, Volume 8, Issue 1, January 2008, Pages 634-645

  6. Intelligent and Adaptive Systems in Medicine, Olivier C.L. Haas and Keith J.Burnham, 2008, Taylor & Francis Group LLC, ISBN No.-13:978-0-7503-0994-3 (Hardcover)

  7. Compression of Medical Images by Using Artificial Neural Networks, Zümray Dokur, Lecture Notes in Computer Science, Volume 4113/2006, Intelligent Computing, pp. 337-344, Friday, September 01, 2006

  8. Image Compression by Vector Quantization with Recurrent Discrete Networks, Domingo L΄opez-Rodr΄ıguez, Enrique M΄erida-Casermeiro, Juan M. Ortiz-de-Lazcano-Lobato, and Ezequiel L΄opez-rubio, Lecture Notes in Computer Science, Volume 4132/2006, Friday, September 01, 2006, pp. 595-605.

  9. Fuzzy Vector Quantization of Images Based on Local Fractal Dimensions, Sasazaki, K.   Ogasawara, H.   Saga, S.   Maeda, J.   Suzuki, Y.  Muroran Inst. of Technol., Muroran;, Fuzzy Systems, 2006 IEEE International Conference on, Publication Date: 2006-09-11, On page(s): 1072-1077

  10. Learning feature representations for an object recognition system, Welke, K.   Oztop, E.   Ude, A.   Dillmann, R.   Cheng, G, Humanoid Robots, 2006 6th IEEE-RAS, International Conference on, Publication Date: 4-6 Dec. 2006, On page(s): 290-295

  11. Image processing with neural networks—a review , M. Egmont-Petersen, D. de Ridder and H. Handels, Pattern Recognition, Volume 35, Issue 10, October 2002, Pages 2279-2301

  12. S. Girard, “A Nonlinear PCA Based on Manifold Approximation”, Computation Stat, Vol. 15, No.2, pp. 145-167, 2000.

  13. Neural net computing for biomedical image processing, Anke Meyer-Baese, Proc. SPIE, Vol. 3722, 414 (1999); doi:10.1117/12.342897, Image Processing

[J57] S.Argyropoulos, D.Tzovaras, D.Ioannidis, and M.G.Strintzis, "A Channel Coding Approach for Human Authentication from Gait Sequences" IEEE Transactions on Information Forensics and Security, vol.4, no.3, pp.428 - 440, Sep 2009 (16 citations)




  1. Cha, B. R., Lee, S. H., Park, S. B., & Ji, G. K. L. Y. K. (2015). Design of Micro-payment to Strengthen Security by 2 Factor Authentication with Mobile & Wearable Devices.

  2. Hoang, T., Choi, D., & Nguyen, T. (2015). Gait authentication on mobile phone using biometric cryptosystem and fuzzy commitment scheme. International Journal of Information Security, 1-12.

  3. Divya, R., & Vijayalakshmi, V. (2015). Analysis of Multimodal Biometric Fusion Based Authentication Techniques for Network Security. International Journal of Security & Its Applications, 9(4).

  4. Ntantogian, C., Malliaros, S., & Xenakis, C. (2015). Gaithashing: A two-factor authentication scheme based on gait features. Computers & Security, 52, 17-32.



  6. Amin, Tahir. Dynamic Descriptors in Human Gait Recognition. Diss. University of Toronto, 2013.

  7. Jung, S., and M. Nixon. "On Using Gait to Enhance Frontal Face Extraction." IEEE Transactions on Information Forensics and Security (Volume:7 , Issue: 6 ), 2012.

  8. Kanade, Sanjay G., Dijana Petrovska-Delacretaz, and Bernadette Dorizzi. "A novel crypto-biometric scheme for establishing secure communication sessions between two clients." Biometrics Special Interest Group (BIOSIG), 2012 BIOSIG-Proceedings of the International Conference of the. IEEE, 2012.

  9. Seetharaman, K., & Ragupathy, R. (2012, January). LDPC and SHA Based Iris Recognition for Smart Card Security. In Proceedings of International Conference on Advances in Computing (pp. 549-559). Springer India.

  10. Multi-biometrics Based Crypto-biometric Session Key Generation and Sharing Protocol, Sanjay Kanade, Dijana Petrovska-Delacrétaz, and Bernadette Dorizzi, MM&Sec’11, September 29–30, 2011, Buffalo, New York, USA. Copyright 2011 ACM 978-1-4503-0806-9/11/09

  11. Kose, N., Vipperla, R., & Evans, N. TABULA RASA Trusted Biometrics under Spoofing Attacks.

  12. Jung, S., & Nixon, M. (2011). On Using Gait to Enhance Frontal Face Extraction.

  13. Noto, S., Correia, P. L., & Soares, L. D. (2011, April). Analysis of error correcting codes for the secure storage of biometric templates. In EUROCON-International Conference on Computer as a Tool (EUROCON), 2011 IEEE (pp. 1-4). IEEE.

  14. Automatic Gait Recognition Using Kernel Principal Component Analysis, Xiang-tao Chen; Zhi-hui Fan; Hui Wang; Zhe-qing Li, Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on Issue Date: 23-25 April 2010, On page(s): 1 – 4, Location: Wuhan, Print ISBN: 978-1-4244-5315-3

  15. Template Protection for Biometric Gait Data, Claudia Nickel1, Xuebing Zhou2, Christoph Busch1, BIOSIG 2010, Darmstadt, Germany, 9-10 September 2010

  16. Lu, J., & Tan, Y. P. (2010). Gait-based human age estimation. Information Forensics and Security, IEEE Transactions on, 5(4), 761-770.

  17. Kanade, S., Petrovska-Delacrétaz, D., & Dorizzi, B. (2010, September). Generating and sharing biometrics based session keys for secure cryptographic applications. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on (pp. 1-7). IEEE.

[J52] A. Mademlis, P.Daras, A. Axenopoulos, D. Tzovaras and M.G.Strintzis, "Combining Topological and Geometrical Features for Global and Partial 3D Shape Retrieval", IEEE Trans on Multimedia, vol. 10 No. 5, pp.819-831, Aug 2008. (26 citations)




  1. Leng, B., Zeng, J., Yao, M., & Xiong, Z. (2015). 3D Object Retrieval With Multitopic Model Combining Relevance Feedback and LDA Model. Image Processing, IEEE Transactions on, 24(1), 94-105.

  2. Son, H., Kim, C., & Kim, C. (2015). 3D reconstruction of as-built industrial instrumentation models from laser-scan data and a 3D CAD database based on prior knowledge. Automation in Construction, 49, 193-200.

  3. Liu, Z., Bu, S., & Han, J. (2015). Locality-constrained sparse patch coding for 3d shape retrieval. Neurocomputing, 151, 583-592.

  4. Tabia, H., & Laga, H. (2015). Covariance-based Descriptors for efficient 3D shape matching, retrieval and classification.

  5. LEI, H., LI, Y., CHEN, H., LIN, S., ZHENG, G., & LUO, X. (2015). A novel sketch-based 3D model retrieval method by integrating skeleton graph and contour feature. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 9(4), JAMDSM0049-JAMDSM0049.

  6. Bu, S., Liu, Z., Han, J., Wu, J., & Ji, R. (2014). Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition. Multimedia, IEEE Transactions on, 16(8), 2154-2167.

  7. Chen, J. Y., Lin, C. H., Hsu, P. C., & Chen, C. H. (2014). Point cloud encoding for 3D building model retrieval. Multimedia, IEEE Transactions on, 16(2), 337-345.

  8. Zeng, J., Leng, B., & Xiong, Z. (2014). 3-D object retrieval using topic model. Multimedia Tools and Applications, 1-23.

  9. Shih, J. L., & Tsai, C. C. (2014). A 3D model retrieval system based on the multi-resolution frequency descriptors. Journal of Information Technology and Applications (資訊科技與應用期刊), 8(2), 24-32.

  10. Liu, Z., Bu, S., Han, J., & Wu, J. (2014, January). Sparse patch coding for 3D model retrieval. In MultiMedia Modeling (pp. 116-127). Springer International Publishing.

  11. Wang, Z., Tian, L., & Duan, W. (2014). Annotation and retrieval system of CAD models based on functional semantics. Chinese Journal of Mechanical Engineering, 27(6), 1112-1124.

  12. Ding, K., Wang, W., & Liu, Y. (2014). 3D model retrieval using Bag-of-View-Words. Multimedia Tools and Applications, 72(3), 2701-2722.

  13. Tabia, Hedi, et al. "A parts-based approach for automatic 3D shape categorization using belief functions." ACM Transactions on Intelligent Systems and Technology (TIST) 4.2 (2013): 33.

  14. Ding, Ke, Wei Wang, and Yunhui Liu. "3D model retrieval using Bag-of-View-Words." Multimedia Tools and Applications (2013): 1-22.

  15. Ding, Ke, and Yunhui Liu. "A probabilistic 3d model retrieval system using sphere image." Computer Vision–ACCV 2012. Springer Berlin Heidelberg, 2013. 536-547.

  16. Bae, Myungsoo, Jinwook Kim, and Young J. Kim. "User-guided volumetric approximation using swept sphere volumes for physically based animation."Computer Animation and Virtual Worlds (2012).

  17. Shih, Jau-Ling. "Combination of Interior and Exterior Shape Descriptors for 3D Model." Journal of Information Technology and Applications 6.1 (2012): 31.

  18. Semantic-Oriented 3D Model Classification and Retrieval Using, Gaussian Processes, Boyong GAO 1,2, Sanyuan ZHANG 1,†, Xiang PAN, Journal of Computational Information Systems 7:4 (2011) 1029-1037

  19. A 3D Model Retrieval Approach Based on the Combination of PCA Plane Projections, Jau-Ling Shih, Chang-Hsing Lee, Chao-Hung Chuang, Journal of Information Technology and Applications, Vol 5, No 2 (2011)

  20. Stuetzle, C., Franklin, W. R., Cutler, B., Kamalzare, M., Chen, Z., & Zimmie, T. (2011). Measuring terrain distances through extracted channel networks. SIGSPATIAL Special, 3(3), 21-26.

  21. Shih, J. L., Lee, C. H., Chou, C. H., & Chang, H. Y. (2010, November). A 3D Model Retrieval System Based on the Cylindrical Projection Descriptor. In Broadband, Wireless Computing, Communication and Applications (BWCCA), 2010 International Conference on (pp. 550-555). IEEE.

  22. Gong, B., Liu, J., Wang, X., & Tang, X. (2010). Learning Semantic Signatures for 3D Object Retrieval.

  23. 2009 Seventh International Workshop on Content-Based Multimedia Indexing, C Käs, H Nicolas, KWC Domain - computer.org Shih, J. L., Lee, C. H., & Chuang, C. H. (2009). A 3D Model Retrieval System Based On The Derivative Radial Distance. In Proceedings of The 22th IPPR Conference On Computer Vision, Graphics and Image Processing (CVGIP).

[C77] P. Daras, D. Zarpalas, D. Tzovaras and M.G. Strintzis, “Shape Matching Using the 3D Radon Transform”, 3D Data Processing, Visualization & Transmission (3DPVT 2004), Thessaloniki, Greece, Sept 6-9, 2004. (15 citations)




  1. Yuan, C., Wu, B., Li, X., Hu, W., Maybank, S., & Wang, F. (2015). Fusing {\ mathcal {R}} Features and Local Features with Context-Aware Kernels for Action Recognition. International Journal of Computer Vision, 1-21.

  2. Alquran, H., Shaheen, E., O'Connor, J. M., & Mahd, M. (2014, March). Enhancement of 3D modeling and classification of microcalcifications in breast computed tomography (BCT). In SPIE Medical Imaging (pp. 903436-903436). International Society for Optics and Photonics.

  3. Jribi, M., & Ghorbel, F. (2014). A Stable and Invariant Three-polar Surface Representation: Application to 3D Face Description.

  4. Jribi, Majdi, and Faouzi Ghorbel. "A robust and isotropic curved surface representation for 3D faces description." Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on. IEEE, 2013.

  5. Ghorbel, Faouzi, and Majdi Jribi. "A robust invariant bipolar representation for R 3 surfaces: applied to the face description." annals of telecommunications-annales des télécommunications (2013): 1-12.

  6. Zhao, Xiangjun, and Mei Lu. "3D Object Retrieval Based on PSO-K-Modes Method." Journal of Software 8.4 (2013): 963-970.

  7. Yuan, Chunfeng, et al. "3D R Transform on Spatio-Temporal Interest Points for Action Recognition."

  8. Ghorbel, Faouzi, and Majdi Jribi. "A robust invariant bipolar representation for R 3 surfaces: applied to the face description." Annals of Telecommunications(2012): 1-12.

  9. Bustos, Benjamin, and Ivan Sipiran. "3D Shape Matching for Retrieval and Recognition." 3D Imaging, Analysis and Applications (2012): 265-308.

  10. Park, D. J. (2011). Video event detection framework on large-scale video data.

  11. Ravichandran, L. (2011). Waveform Mapping and Time-Frequency Processing of Biological Sequences and Structures (Doctoral dissertation, ARIZONA STATE UNIVERSITY).

  12. PITIKAKIS, M. (2010). A SEMANTIC BASED APPROACH FOR KNOWLEDGE MANAGEMENT, DISCOVERY AND SERVICE COMPOSITION APPLIED TO 3D SCIENTIFIC OBJECTS.

  13. ”3D model retrieval using probability density-based shape descriptors”, CB Akgül, B Sankur, Y Yemez, F Schmitt IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 31, Issue: 6, pp: 1117-1133, June 2009

  14. J Niu, Z Li, G Salvendy, “Mathematical Methods for Shape Analysis and form Comparison in 3D Anthropometry: A Literature Review”, Book chapter, SPRINGER: Lecture notes in computer science, pp. 161-170, Volume 4561/2007, Aug. 2007

  15. Liu Wei and He Yuanjun, “Representation and retrieval of 3D CAD models in parts library”, SPRINGER: The International Journal of Advanced Manufacturing Technology, DOI: 10.1007/s00170-006-0914-7, Jan 2007

[J23] G. A. Triantafyllidis, D. Tzovaras and M. G. Strintzis, "Occlusion and Visible Background and Foreground areas in Stereo: A Bayesian Approach", IEEE Trans. on Circuits and Systems for Video Technology, Special Issue on 3D Video Technology, Vol. 10, No. 4, pp. 563-576, June 2000. (10 citations)




  1. Verstockt, S., Van Hoecke, S., De Potter, P., Lambert, P., Hollemeersch, C., Sette, B., ... & Van de Walle, R. (2014). Multi-modal time-of-flight based fire detection. Multimedia Tools and Applications, 69(2), 313-338.

  2. Choi, Wook, Vladimir Rubtsov, and Chang-Jin Kim. "Miniature Flipping Disk Device for Size Measurement of Objects Through Endoscope."Microelectromechanical Systems, Journal of 21.4 (2012): 926-933.

  3. Pei, Wei, et al. "Study on the 3D non-contact measurement method of hyaloid and closed container." Systems and Informatics (ICSAI), 2012 International Conference on. IEEE, 2012.

  4. Verstockt, Steven, et al. "Multi-modal time-of-flight based fire detection."Multimedia Tools and Applications (2012): 1-26.

  5. Zhu, Y. Y., Pei, W., Xu, Z., & Wang, C. X. (2011, October). Micro stereo occlusion correction algorithm. In Image and Signal Processing (CISP), 2011 4th International Congress on (Vol. 3, pp. 1433-1437). IEEE.

  6. Ghazouani, H., Zapata, R., & Tagina, M. Fuzzy Sets Based Improvement of a Stereo Matching Algorithm with Balanced Correlation Window and Occlusion Detection. In International Conference on Image Processing, Computer Vision, Pattern Recognition, IPCV (Vol. 10, pp. 12-15).

  7. A robust foreground segmentation method by temporal averaging multiple video frames
    Hongxing Guo   Yaling Dou   Ting Tian   Jingli Zhou   Shengsheng Yu,   Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on, Publication Date: 7-9 July 2008, On page(s): 878-882

  8. Moving object detection using region tracking, Eun Young Song · Ju-Jang Lee, Artif Life Robotics (2004) 8:20–28 © ISAROB 2004, DOI 10.1007/s10015-004-0282-z, Volume 8, Number 1 / September, 2004

  9. Qiuming Luo, Jingli Zhou, Shengsheng Yu, Degui Xiao, "Stereo matching and occlusion detection with integrity and illusion sensitivity", Pattern Recognition Letters, Vol. 24, Issue 9-10, pp. 1143 - 1149, June 2003.

  10. Predrag Steric, "Verdeckungsanalyse in Stereobildern anhand des Vergleiches eines kooperativen und eines bayes schen Verfahrens", Proseminar aus Informatik Grundlagen wissenschaftlichen Arbeitens, Technische Universität Wien.

[J27] I. Kompatsiaris, D.Tzovaras and M.G.Strintzis: "Hierarchical Representation and Coding of Surfaces using 3D Polygon Meshes", IEEE Trans. on Image Processing, vol 10, no. 8, pp.1133-1151, August 2001 (9 citations)




  1. Antoine, Lennox Bertrand. "Collaborative environments in a graphical information system." U.S. Patent No. 8,199,156. 12 Jun. 2012.

  2. Mateï, Basarab, and Sylvain Meignen. "Analysis of a class of nonlinear and non-separable multiscale representations." Numerical Algorithms (2012): 1-28.

  3. Xuli Shi, Zhaoyang Zhang, Liquan Shen, Yu Lu and Suxing Liu, “Multiresolution segmentation of video objects in the compression domain” Optical Engineering, Volume 46, Issue 9, September 2007

  4. Jose A. Garcia, Rosa Rodriguez-Sánchez, and Joaquín Fdez-Valdivia, “Optimal exploratory effort to build knowledge for video transmission”, Optical Engineering, Volume 46, Issue 4, April 2007

  5. Zhi Liu, Zhaoyang Zhang, Liquan Shen, “Moving object segmentation in the H.264 compressed domain”, Optical Engineering, Volume 46, Issue 1, January 2007

  6. Munteanu, A., Cernea, D. C., Alecu, A., Cornelis, J., & Schelkens, P. (2010). Scalable L-infinite coding of Meshes. Visualization and Computer Graphics, IEEE Transactions on, 16(3), 513-528.

  7. Antoine, L. (2009). U.S. Patent No. 7,535,473. Washington, DC: U.S. Patent and Trademark Office.

  8. Gérot, C., Matei, B., & Meignen, S. (2010). A New Formalism for Nonlinear and Non-Separable Multi-scale Representation.

  9. Matei, B., & Meignen, S. A New Formalism for Non-Linear and Non-Separable Multiscale Representation. submitted for publication.

[C26] D. Tzovaras and M. G. Strintzis, ''Disparity Estimation Using Rate-Distortion Theory for Stereo Image Sequence Coding,'' in the IEEE 1997 Conference on Digital Signal Processing, Santorini, Greece, Jul. 1997 (9 citations)




  1. Zhao, T., Zhao, W., Hoffman, B. D., Nowlin, W. C., & Hui, H. (2015). U.S. Patent No. 8,971,597. Washington, DC: U.S. Patent and Trademark Office.

  2. Zhao, W., Wu, C., Hirvonen, D., Hasser, C. J., Miller, B. E., Mohr, C. J., ... & Hoffman, B. D. (2014). U.S. Patent No. 8,830,224. Washington, DC: U.S. Patent and Trademark Office.



  4. Bensalma, R., & Larabi, M. C. (2010, September). Stereo image coding based on binocular energy modeling. In Image Processing (ICIP), 2010 17th IEEE International Conference on (pp. 2989-2992). IEEE.

  5. Bensalma, Rafik, and M-C. Larabi. "Optimizing the disparity map by the integration of HVS binocular properties for efficient coding of stereoscopic images." 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), 2010. IEEE, 2010.

  6. Bensalma, R., & LARABI, C. (2010, August). Using Binocular Energy Modeling for Stereoscopic Color Image Coding. In EUSIPCO (pp. 120-124).

  7. PROPERTIES FOR EFFICIENT CODING OF STEREOSCOPIC IMAGES, Rafik BENSALMA, Mohamed-Chaker LARABI, 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON), 2010 , Issue Date: 7-9 June 2010 , On page(s): 1 – 4, Location: Tampere, E-ISBN: 978-1-4244-6378-7

  8. Bensalma, R., & Larabi, C. (2009, November). A Stereo Image Coding using Wavelet based quadtree and binocular rivalry. In Signal-Image Technology & Internet-Based Systems (SITIS), 2009 Fifth International Conference on (pp. 152-159). IEEE.

  9. Bensalma, R., & Larabi, M. C. (2008, February). Toward a stereoscopic encoder/decoder for digital cinema. In Electronic Imaging 2008 (pp. 68031Q-68031Q). International Society for Optics and Photonics.

  10. Nath, S. K. (2004). Wavelet-based Scalable Coding of Still and Time-varying Stereoscopic Imagery (Doctoral dissertation, School of Information Technology and Engineering (SITE), Faculty of Graduate and Postdoctoral Studies, University of Ottawa).

  11. Zeger, K. (2003). Residual image coding for stereo image compression. Optical Engineering, 42(1), 182-189.

  12. Frajka, T. (2003). Image Coding Subject to Constraints (Doctoral dissertation, University of California, San Diego).

[C55] I. Kolonias, D. Tzovaras, S. Malassiotis and M. G. Strintzis, "Fast Content-Based Search of VRML Models based on Shape Descriptions", International Conference of Image Processing, ICIP' 01, Thessaloniki, Greece, Oct. 2001 (9 citations)




  1. Yu, F., Luo, H., Lu, Z., & Wang, P. (2010). 3D Model Feature Extraction. Three-Dimensional Model Analysis and Processing, 161-235.

  2. Yang, Y.; Lin , H.; Zhang , Y., "Content-Based 3-D Model Retrieval: A Survey," Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on , vol.37, no.6, pp.1081-1098, Nov. 2007

  3. GUO J ian f eng , GUXin jian , Q I Guo-ning , WANG Sheng- f a , MA J un, "In components storehouse based on virtual reality modelling language model graph application service", Computer Integration Manufacture System, vol. 3, pp.472-477, 2007

  4. N. Vajramushti, I. A. Kakadiaris, and T. Theoharis, G, “Efficient 3D Object Retrieval Using Depth Images”, Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval, New York, pp. 189-196, 2004.

  5. P. Min, “A 3D Model Search Engine”, A Dissertation Presented to the Faculty of Princeton University in Candidacy for the Degree of Doctor of Philosophy, Jan. 2004.

  6. Y.-T. Shen, X. P. Tian, M. Ouhyoung, “3D Model Search Engine Based on Lightfield Descriptors”, EUROGRAPHICS Interactive Demos, Granada, Spain, Sep. 2003.

  7. D.-Y. Chen, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung, “On Visual Similarity Based 3D Model Retrieval”, Computer Graphics Forum, vol. 22, no. 3, pp. 223-232(10), Sep. 2003.

  8. D. Y. Chen and M. Ouhyoung, “A 3D Model Alignment and Retrieval System”, Proc. of International Computer Symposium, Workshop on Multimedia Technologies, Vol. 2, pp. 1436-1443, Hualien, Taiwan, Dec. 2002.

  9. D. Y. Chen and M. Ouhyoung, “A 3D Object Retrieval System Based on Multi-Resolution Reeb Graph”, in Proc. of Computer Graphics Workshop, pp.16, Tainan, Taiwan, June 2002.

[BC1] Ioannis Tsampoulatidis, Dimitrios Tzovaras, and Michael G. Strintzis, Ontology-Based E-government Thematic Services Based on Topic Maps, R. Meersman et al. (Eds.): OTM Workshops 2004, LNCS 3292, pp. 569–580, 2004. © Springer-Verlag Berlin Heidelberg 2004 (9 citations)




  1. Tsai, Hsine-Jen, et al. "Expanding the Disaster Management Knowledge Space through Spatial Mediation." System Science (HICSS), 2012 45th Hawaii International Conference on. IEEE, 2012.

  2. Managing Knowledge for Organization-Wide Ad Hoc Committees, Alok Sharma Les Miller Sree Nilakanta, Proceedings of the 44th Hawaii International Conference on System Sciences – 2011

  3. Deliyska, B., & Ilieva, R. (2011). Ontology-Based Model of E-Governance. Annual of Section “Informatics”, Union of Bulgarian Scientists, 103-119.

  4. Sharma, A. (2010). Enabling knowledge management of organizational memory for groups through shared topic maps

  5. Enabling Knowledge Management of Organizational Memory for Groups Through Shared Topic Maps, Les Miller_ Meghana Rao, Sree Nilakanta, International Conference on Information Resources Management (CONF-IRM), CONF-IRM 2008 Proceedings

  6. Based on Automatic Ontology Mapping, Donglin Chen1, Guihua Nie2, Pingfeng Liu3, The Sixth Wuhan International Conference on E-Business, e-Business Track.

  7. Key challenges and policy implications for governments and regulators in a networkcentric healthcare, D Von Lubitz, N Wickramasinghe - Electronic Government, an International Journal, 2006 – Inderscience, Page 1. Electronic Government, Vol. 3, No. 2, 2006 Copyright © 2006
    Inderscience Enterprises Ltd. 204 Key challenges and policy.

  8. E-payment: Cheque 21, PE Walsh - International Journal of Electronic Finance, 2006 - Inderscience
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  9. The contribution of e-government to the modernisation of the Hellenic taxation system, P Tahinakis, J Mylonakis, N Protogeros - Electronic Government, an International Journal, 2006 - Inderscience
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  1. Real-time deformation simulation of hand-object interaction, Feng, Miao;   Li, Jiting, Robotics, Automation and Mechatronics (RAM), 2011 IEEE Conference on, Issue Date: 17-19 Sept. 2011, On page(s): 154 – 157, Location: Qingdao, China, ISSN: 2158-2181

  2. Designing Digital Technologies and Learning Activities for Different Geometries, Keith Jones, Kate Mackrell and Ian Stevenson, New ICMI Study Series, Volume 13, Mathematics Education and Technology-Rethinking the Terrain, pages 47-60, October 2009

  3. A Study of Multi-Representation of Geometry Problem Solving with Virtual Manipulatives and Whiteboard System, Wu-Yuin Hwang, Jia-Han Su, Yueh-Min Huang and Jian-Jie Dong, Educational Technology & Society, vol. 12, no. 3, pp. 229-247, 2009

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  5. A. Widmer and Y. Hu, “Integration of the Senses of Vision and Touch in Perceiving Object Softness”, Canadian Conference on Electrical and Computer Engineering, CCECE, November 2007.

  6. Jia-Han Su, “A Study of Manipulative Web3D Virtual Classroom System and its Effects on Geometry Problem Solving”, Master’s Thesis, Department of Network Learning Technology, National Central University, Taiwan, R.O.C, July 2006.

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  8. L. Ni, M. Krzeminski, K. Tuer, “Application of haptic, visual and audio integration in astronomy education”, Proceedings of the 2006 IEEE International Workshop on Haptic Audio Visual Environments and Their Applications, HAVE 2006, pp. 152-156.

[C109] A. Mademlis, A. Axenopoulos, P.Daras, D. Tzovaras and M.G.Strintzis, : "3D Content-based Search based on 3D Krawtchouk moments", 3D Data Processing, Visualization & Transmission (3DPVT 2006), University of North Carolina, Chapel Hill, USA, June 2006. (9 citations)




  1. Uppalapati, S., Femiani, J. C., Razdan, A., & Gary, K. (2009, April). 3D VQI: 3D visual query interface. In Information Technology: New Generations, 2009. ITNG'09. Sixth International Conference on (pp. 1347-1354). IEEE.

  2. Plantb, C., Ngo, D., Rettera, F., Schlossbauerc, O. Z. T., Lobbesd, M., Lockwoode, M., & Meyer-Bäseb, A. (2012, May). Computer-aided diagnosis of small lesions and non-masses in breast MRI. In Proc. of SPIE Vol (Vol. 8367, pp. 83670A-1).

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  8. “Moments and Moment Invariants in Pattern Recognition”, J Flusser, B Zitova, T Suk, Book, 2009

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