Academic literature on the topic 'Tensor driven diffusion'
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Journal articles on the topic "Tensor driven diffusion"
Cover, Giovana, Mariana Pereira, Mariana Bento, Simone Appenzeller, and Leticia Rittner. "Data-Driven Corpus Callosum Parcellation Method Through Diffusion Tensor Imaging." IEEE Access 5 (2017): 22421–32. http://dx.doi.org/10.1109/access.2017.2761701.
Full textKallehauge, Jesper Foldsted, Trine Lignell Guldberg, Anja Harbøll, Morten Bjørn Jensen, and Slávka Lukacova. "RTHP-09. DIFFUSION TENSOR IMAGING DRIVEN GROWTH MODELING FOR TARGET DEFINITION IN GLIOBLASTOMA." Neuro-Oncology 19, suppl_6 (November 2017): vi221. http://dx.doi.org/10.1093/neuonc/nox168.894.
Full textJensen, Morten B., Trine L. Guldberg, Anja Harbøll, Slávka Lukacova, and Jesper F. Kallehauge. "Diffusion tensor magnetic resonance imaging driven growth modeling for radiotherapy target definition in glioblastoma." Acta Oncologica 56, no. 11 (September 12, 2017): 1639–43. http://dx.doi.org/10.1080/0284186x.2017.1374559.
Full textDrblíková, Olga, Angela Handlovičová, and Karol Mikula. "Error estimates of the finite volume scheme for the nonlinear tensor-driven anisotropic diffusion." Applied Numerical Mathematics 59, no. 10 (October 2009): 2548–70. http://dx.doi.org/10.1016/j.apnum.2009.05.010.
Full textJia, Xiao-xiong, Yang Yu, Xiao-dong Wang, Hui Ma, Qing-hua Zhang, Xue-yin Huang, and He-chun Xia. "fMRI-Driven DTT Assessment of Corticospinal Tracts Prior to Cortex Resection." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 40, no. 4 (July 2013): 558–63. http://dx.doi.org/10.1017/s0317167100014669.
Full textKim, Jeongchul, Youngkyoo Jung, Richard Barcus, Jocelyne H. Bachevalier, Mar M. Sanchez, Michael A. Nader, and Christopher T. Whitlow. "Rhesus Macaque Brain Developmental Trajectory: A Longitudinal Analysis Using Tensor-Based Structural Morphometry and Diffusion Tensor Imaging." Cerebral Cortex 30, no. 8 (April 2, 2020): 4325–35. http://dx.doi.org/10.1093/cercor/bhaa015.
Full textEngelbrecht, N. Eugene. "On the Effects of Pickup Ion-driven Waves on the Diffusion Tensor of Low-energy Electrons in the Heliosphere." Astrophysical Journal 849, no. 1 (October 26, 2017): L15. http://dx.doi.org/10.3847/2041-8213/aa9372.
Full textHoh, N. J., and R. N. Zia. "Force-induced diffusion in suspensions of hydrodynamically interacting colloids." Journal of Fluid Mechanics 795 (April 20, 2016): 739–83. http://dx.doi.org/10.1017/jfm.2016.209.
Full textKoch, Donald L., and John F. Brady. "Dispersion in fixed beds." Journal of Fluid Mechanics 154 (May 1985): 399–427. http://dx.doi.org/10.1017/s0022112085001598.
Full textEmsell, L., C. Chaddock, N. Forde, W. Van Hecke, G. J. Barker, A. Leemans, S. Sunaert, et al. "White matter microstructural abnormalities in families multiply affected with bipolar I disorder: a diffusion tensor tractography study." Psychological Medicine 44, no. 10 (November 26, 2013): 2139–50. http://dx.doi.org/10.1017/s0033291713002845.
Full textDissertations / Theses on the topic "Tensor driven diffusion"
Larsson, Karl. "Scale-Space Methods as a Means of Fingerprint Image Enhancement." Thesis, Linköping University, Department of Science and Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2282.
Full textThe usage of automatic fingerprint identification systems as a means of identification and/or verification have increased substantially during the last couple of years. It is well known that small deviations may occur within a fingerprint over time, a problem referred to as template ageing. This problem, and other reasons for deviations between two images of the same fingerprint, complicates the identification/verification process, since distinct features may appear somewhat different in the two images that are matched. Commonly used to try and minimise this type of problem are different kinds of fingerprint image enhancement algorithms. This thesis tests different methods within the scale-space framework and evaluate their performance as fingerprint image enhancement methods.
The methods tested within this thesis ranges from linear scale-space filtering, where no prior information about the images is known, to scalar and tensor driven diffusion where analysis of the images precedes and controls the diffusion process.
The linear scale-space approach is shown to improve correlation values, which was anticipated since the image structure is flattened at coarser scales. There is however no increase in the number of accurate matches, since inaccurate features also tends to get higher correlation value at large scales.
The nonlinear isotropic scale-space (scalar dependent diffusion), or the edge- preservation, approach is proven to be an ill fit method for fingerprint image enhancement. This is due to the fact that the analysis of edges may be unreliable, since edge structure is often distorted in fingerprints affected by the template ageing problem.
The nonlinear anisotropic scale-space (tensor dependent diffusion), or coherence-enhancing, method does not give any overall improvements of the number of accurate matches. It is however shown that for a certain type of template ageing problem, where the deviating structure does not significantly affect the ridge orientation, the nonlinear anisotropic diffusion is able to accurately match correlation pairs that resulted in a false match before they were enhanced.
Book chapters on the topic "Tensor driven diffusion"
Welk, Martin, Joachim Weickert, and Gabriele Steidl. "From Tensor-Driven Diffusion to Anisotropic Wavelet Shrinkage." In Computer Vision – ECCV 2006, 391–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744023_31.
Full textGrigorescu, Irina, Alena Uus, Daan Christiaens, Lucilio Cordero-Grande, Jana Hutter, A. David Edwards, Joseph V. Hajnal, Marc Modat, and Maria Deprez. "Diffusion Tensor Driven Image Registration: A Deep Learning Approach." In Biomedical Image Registration, 131–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50120-4_13.
Full textPeter, Pascal, Joachim Weickert, Axel Munk, Tatyana Krivobokova, and Housen Li. "Justifying Tensor-Driven Diffusion from Structure-Adaptive Statistics of Natural Images." In Lecture Notes in Computer Science, 263–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14612-6_20.
Full textJerome, Neil Peter, Anna Caroli, and Alexandra Ljimani. "Renal Diffusion-Weighted Imaging (DWI) for Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), and Diffusion Tensor Imaging (DTI): Basic Concepts." In Methods in Molecular Biology, 187–204. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_11.
Full textFlorack, Luc, Rick Sengers, Stephan Meesters, Lars Smolders, and Andrea Fuster. "Riemann-DTI Geodesic Tractography Revisited." In Mathematics and Visualization, 225–43. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_11.
Full textExner, H. E., and P. Bross. "Material Transport Rate and Stress Distribution During Grain Boundary Diffusion Driven by Surface Tension." In Sintering Key Papers, 101–10. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0741-6_7.
Full textConference papers on the topic "Tensor driven diffusion"
Becattini, G., L. S. Mattos, and D. G. Caldwell. "Diffusion tensor driven contour closing for cell microinjection targeting." In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iembs.2010.5627608.
Full textJin, Yu, Joseph F. JaJa, Rong Chen, and Edward H. Herskovits. "A data-driven approach to extract connectivity structures from diffusion tensor imaging data." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363843.
Full textHu, Yong, Tin Yan Chan, Xiang Li, KC Mak, Keith DK Luk, and Shu-Qiang Wang. "Identify myelopathic cervical spinal cord using diffusion tensor image: A data-driven approach." In 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, 2015. http://dx.doi.org/10.1109/icdsp.2015.7251933.
Full textHai-Yun Wang and Kai-Kuang Ma. "Accurate optical flow estimation in noisy sequences by robust tensor-driven anisotropic diffusion." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530636.
Full textChen, Yunmei, and Stacey Chastain. "Anisotropic diffusion driven by diffusion tensors." In International Symposium on Optical Science and Technology, edited by David C. Wilson, Hemant D. Tagare, Fred L. Bookstein, Francoise J. Preteux, and Edward R. Dougherty. SPIE, 2000. http://dx.doi.org/10.1117/12.402435.
Full textKim, Y., Y. J. Chao, M. J. Pechersky, and M. J. Morgan. "Investigation of Hydrogen Transport Related Fracture in Stainless Steels." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-13823.
Full textAlmutairi, Zeyad, Carolyn Ren, and David Johnson. "Effects of Hydrophobic Recovery of Plasma Treated PDMS Microchannels on Surface Tension Driven Flow." In ASME 2010 8th International Conference on Nanochannels, Microchannels, and Minichannels collocated with 3rd Joint US-European Fluids Engineering Summer Meeting. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31243.
Full textRao, Qinyang, and Barclay G. Jones. "Study of Mechanism of Initial Crud Formation on Fuel Cladding in Subcooled Boiling Region in PWR." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22558.
Full textRahman, M. A., and M. Z. Saghir. "Thermodiffusion, Marangoni and Gravity Driven Convections in a Liquid Layer Overlying a Porous Layer: Lateral Heating Conditions." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66129.
Full textWei, P. S., C. L. Lin, and C. N. Ting. "Scaling Thermocapillary Surface Velocity in Weld Pool." In ASME/JSME 2011 8th Thermal Engineering Joint Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajtec2011-44068.
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