TSI report for the period 2005-2009 - Département Traitement du ...

13. Image Processing and Interpretation (TII) 13.3. References
The last axis deals with SAR data regularization. It is a recent research axis based on the
development of two families of approaches: Markovian methods coupled with graph-cut optimization
and non-local means. General contributions have been brought: first a fast graph-cut based
algorithm for optimization of vectorial data have been developed [2664]; secondly, a probabilitic
patch-based method has been proposed, which is able to deal with any kind of noise. These
works have been applied to the regularization of amplitude data and interferometric data [2663],
specially in the frame of a CNES project and a collaboration of Naples University [2669].
Other specific themes of SAR imagery have been developed. On change detection a collaboration
has started with CEA in 2008. In the frame of a collaboration with Télécom Sud Paris in
2007, a classification coupling Fisher distributions and triplet Markov fields has been proposed.
Improvements of previous works on road detection have been done in the frame of a collaboration
with University of Pavie [2691] [2681]. A PhD on SAR data compression in relation with DGA has
been led. Micro-Doppler have also been studied in a collaboration with ONERA [2672].
In general, the team has developed an expertise on TerraSAR-X data through its participation
to different projects, and specially on urban area processing [2671]. Moreover, its competence in
coherent imagery (in particular on temporal approach [3069]) is used for sonar imagery (project
with Telecom Bretagne) and in ultrasound imagery (PhD with SuperSonic Imagine).
13.3 References
13.3.1 ACL: Articles in ISI-Indexed Journals
[2634] R. Abdelfattah and J. M. Nicolas. Interferometric SAR coherence magnitude estimation using second kind statistics.
IEEE Transactions on Geoscience and Remote Sensing, 44(7 part 2):1942– 1953, July 2006.
[2635] C. B. Akgul, B. Sankur, Y. Yemez, and F. Schmitt. Density-based 3d shape descriptors. JASP - EURASIP Journal
on Applied Signal Processing, 2007(Article ID 32503):1–16, 2007.
[2636] C. B. Akgül, B. Sankur, Y. Yemez, and F. Schmitt. 3d model retrieval using probability density-based shape
descriptors. IEEE Pattern Analysis and Machine Intelligence, 31(6):1117–1133, June 2009.
[2637] E. Angelini, T. Song, B. Mensh, and A. Laine. Brain MRI segmentation with multiphase minimal partitioning: A
comparative study. International Journal of Biomedical Imaging, 2007.
[2638] E. D. Angelini, O. Clatz, E. Mandonnet, E. Konukoglu, L. Capelle, and H. Duffau. Glioma dynamics and computational
models: A review of segmentation, registration and in silico growth algorithms and their clinical validations.
Current Medical Imaging Review, 3(4):262–276, March 2007.
[2639] E. D. Angelini, S. Homma, G. Pearson, J. W. Holmes, and A. F. Laine. Segmentation of real-time three-dimensional
ultrasound for quantification of ventricular function: a clinical study on right and left ventricles. Ultrasound in
Medicine and Biology, 31(9):1143–1158, September 2005.
[2640] A. Bhattacharya, M. Roux, M. Maître, I. Jermyn, X. Descombes, and J. Zerubia. Computing Statistics from
Man-Made Structures on the Earth’s Surface for Indexing Satellite Images. International Journal of Simulation
Modelling, 6(2):73–83, June 2007.
[2641] L. Bibin, J. Anquez, E. D. Angelini, and I. Bloch. Hybrid 3D modeling of mother and fetus from medical imaging
for dosimetry studies. International Journal of Computer Assisted Radiology and Surgery, 2009.
[2642] I. Bloch. Fuzzy Spatial Relationships for Image Processing and Interpretation: A Review. Image and Vision
Computing, 23(2):89–110, February 2005.
[2643] I. Bloch. Spatial Reasoning under Imprecision using Fuzzy Set Theory, Formal Logics and Mathematical Morphology.
International Journal of Approximate Reasoning, 41(2):77–95, February 2006.
[2644] I. Bloch. Defining Belief Functions using Mathematical Morphology – Application to Image Fusion under Imprecision.
International Journal of Approximate Reasoning, 48:437–465, 2008.
[2645] I. Bloch. Fuzzy Skeleton by Influence Zones - Application to Interpolation between Fuzzy Sets. Fuzzy Sets and
Systems, 159:1973–1990, 2008.
[2646] I. Bloch. Duality vs. Adjunction for Fuzzy Mathematical Morphology and General Form of Fuzzy Erosions and
Dilations. Fuzzy Sets and Systems, 160:1858–1867, 2009.
[2647] I. Bloch, O. Colliot, O. Camara, and T. Géraud. Fusion of Spatial Relationships for Guiding Recognition. Example
of Brain Structure Recognition in 3D MRI. Pattern Recognition Letters, 26(4):449–457, March 2005.
[2648] I. Bloch, O. Colliot, and R. Cesar. On the Ternary Spatial Relation Between. IEEE Transactions on Systems,
Man, and Cybernetics SMC-B, 36(2):312–327, April 2006.
[2649] I. Bloch, N. Milisavljevic, and M. Acheroy. Multisensor Data Fusion for Spaceborne and Airborne Reduction of
Mine Suspected Areas. International Journal of Advanced Robotics Systems, 4(2):173–186, June 2007.
[2650] I. Bloch, J. Pescatore, and L. Garnero. A New Characterization of Simple Elements in a Tetrahedral Mesh.
Graphical Models, 67(4):260–284, July 2005.
[2651] C. Bordenave, Y. Gousseau, and F. Roueff. The dead leaves model: an example of a general tessellation.
Advances in Applied Probability, 38(1):31–46, March 2006.
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