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Hamid Krim

Electrical & Computer Engineering

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Hamid Krim

Works (87)

Thomaz, L. A., Jardim, E., Silva, A. F., Silva, E. A. B., Netto, S. L., & Krim, H. (2018). Anomaly detection in moving-camera video sequences using principal subspace analysis. IEEE Transactions on Circuits and Systems. I, Regular Papers, 65(3), 1003–1015. https://doi.org/10.1109/tcsi.2017.2758379

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Wang, H., Skau, E., Krim, H., & Cervone, G. (2018). Fusing Heterogeneous Data: A Case for Remote Sensing and Social Media. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 56(12), 6956–6968. https://doi.org/10.1109/TGRS.2018.2846199

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Ghanem, S., Krim, H., Clouse, H. S., & Sakla, W. (2018). Metric Driven Classification: A Non-Parametric Approach Based on the Henze-Penrose Test Statistic. IEEE TRANSACTIONS ON IMAGE PROCESSING, 27(12), 5947–5956. https://doi.org/10.1109/TIP.2018.2862352

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Panahi, A., Bian, X., Krim, L., & Dai, L. Y. (2018). Robust subspace clustering by bi-sparsity pursuit: Guarantees and sequential algorithm. In 2018 ieee winter conference on applications of computer vision (wacv 2018) (pp. 1302–1311).

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Thomaz, L. A., Silva, A. F., Silva, E. A. B., Netto, S. L., & Krim, H. (2017). Detection of abandoned objects using robust subspace recovery with intrinsic video alignment. In 2017 ieee international symposium on circuits and systems (iscas) (pp. 599–602).

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Lee, D., & Krim, H. (2017). Determination of a sampling criterion for 3D reconstruction. Journal of Imaging Science and Technology, 61(4). https://doi.org/10.2352/j.imagingsci.technol.2017.61.4.040501

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Lee, D., & Krim, H. (2017). Determination of a sampling criterion for 3D reconstruction. Journal of Imaging Science and Technology, 61(4). https://doi.org/10.2352/j.imagingsci.technol.2017.61.4.040501
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Steenbock, T., Shultz, D. A., Kirk, M. L., & Herrmann, C. (2017). Influence of radical bridges on electron spin coupling. Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment & General Theory, 121(1), 216–225. https://doi.org/10.1021/acs.jpca.6b07270

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Lee, D., & Krim, H. (2017). Sampling Density Criterion for Circular Structured Light 3D Imaging. In PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 6 (pp. 478–483).

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Lee, D., & Krim, H. (2017). System input-output theoretic three-dimensional measurement based on circular-shaped structured light patterns. Optical Engineering, 56(7). https://doi.org/10.1117/1.oe.56.7.073104

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Tang, W., Otero, I. R., Krim, H., & Dai, L. Y. (2016). Analysis dictionary learning for scene classification.

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Shen, X. Y., Krim, H., & Gu, Y. T. (2016). Beyond union of subspaces: Subspace pursuit on grassmann manifold for data representation. In International conference on acoustics speech and signal processing (pp. 4079–4083).

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Krim, H., Gentimis, T., & Chintakunta, H. (2016). Discovering the whole by the coarse: A topological paradigm for data analysis. IEEE Signal Processing Magazine, 33(2), 95–104. https://doi.org/10.1109/msp.2015.2510703

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Mahdizadehaghdam, S., Wang, H., Krim, H., & Dai, L. Y. (2016). Information diffusion of topic propagation in social media. IEEE Transactions on Signal and Information Processing Over Networks, 2(4), 569–581. https://doi.org/10.1109/tsipn.2016.2618324

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Chintakunta, H., Robinson, M., & Krim, H. (2016). Introduction to the special session on topological data Analysis, ICASSP 2016. In International conference on acoustics speech and signal processing (pp. 6410–6414).

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Gamble, J., Chintakunta, H., Wilkerson, A., & Krim, H. (2016). Node dominance: Revealing community and core-periphery structure in social networks. IEEE Transactions on Signal and Information Processing Over Networks, 2(2), 186–199. https://doi.org/10.1109/tsipn.2016.2527923

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Ghanem, S., Skau, E., Krim, H., Clouse, H. S., & Sakla, W. (2016). Non-parametric bounds on the nearest neighbor classification accuracy based on the Henze-Penrose metric. In 2016 ieee international conference on image processing (icip) (pp. 1364–1368).

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Skau, E., Wohlberg, B., Krim, H., & Dai, L. Y. (2016). Pansharpening via coupled triple factorization dictionary learning. In International conference on acoustics speech and signal processing (pp. 1234–1237).

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Bian, X., Krim, H., Bronstein, A., & Dai, L. Y. (2016). Sparsity and nullity: Paradigms for analysis dictionary learning. Siam Journal on Imaging Sciences, 9(3), 1107–1126. https://doi.org/10.1137/15m1030376

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Liang, W., Wang, H., & Krirn, H. (2016). A behavior-based evaluation of product quality. In International conference on acoustics speech and signal processing (pp. 1916–1920).

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Guan, H., Tang, W., Krim, H., Keiser, J., Rindos, A., & Sazdanovic, R. (2016). A topological collapse for document summarization. In Ieee international workshop on signal processing advances in wireless.

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Bian, X., & Krim, H. (2015). Bi-sparsity pursuit for robust subspace recovery. In 2015 ieee international conference on image processing (icip) (pp. 3535–3539).

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Gamble, J., Chintakunta, H., & Krim, H. (2015). Coordinate-free quantification of coverage in dynamic sensor networks. Signal Processing, 114, 1–18. https://doi.org/10.1016/j.sigpro.2015.02.013

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Gamble, J., Chintakunta, H., & Krim, H. (2015). Emergence of core-periphery structure from local node dominance in social networks. In 2015 23rd european signal processing conference (eusipco) (pp. 1910–1914).

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Krim, H., & Hamza, A. B. (2015). Geometric methods in signal and image analysis. Cambridge, United Kingdom: Cambridge University Press. Retrieved from https://dx.doi.org/10.1017/cbo9781139523967

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Jardim, E., Bian, X., Silva, E. A. B., Netto, S. L., & Krim, H. (2015). On the detection of abandoned objects with a moving camera using robust subspace recovery and sparse representation. In International conference on acoustics speech and signal processing (pp. 1295–1299).

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Ayllon, D., Gil-Pita, R., Rosa-Zurera, M., & Krim, H. (2015). Real-time multiple DOA estimation of speech sources in wireless acoustic sensor networks. In International conference on acoustics speech and signal processing (pp. 2709–2713).

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Bian, X., Krim, H., Bronstein, A., & Dai, L. Y. (2015). Sparse null space basis pursuit and analysis dictionary learning for high-dimensional data analysis. In International conference on acoustics speech and signal processing (pp. 3781–3785).

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Emrani, S., & Krim, H. (2015). Spectral estimation in highly transient data. In 2015 23rd european signal processing conference (eusipco) (pp. 1721–1725).

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Emrani, S., Saponas, T. S., Morris, D., & Krim, H. (2015). A novel framework for pulse pressure wave analysis using persistent homology. IEEE Signal Processing Letters, 22(11). https://doi.org/10.1109/lsp.2015.2441068

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Wang, T., Krim, H., & Viniotis, Y. (2014). Analysis and control of beliefs in social networks. IEEE Transactions on Signal Processing, 62(21), 5552–5564. https://doi.org/10.1109/tsp.2014.2352591

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Wilkerson, A. C., Chintakunta, H., & Krim, H. (2014). Computing persistent features in big data: A distributed dimension reduction approach. In International conference on acoustics speech and signal processing.

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Chintakunta, H., & Krim, H. (2014). Distributed localization of coverage holes using topological persistence. IEEE Transactions on Signal Processing, 62(10), 2531–2541. https://doi.org/10.1109/tsp.2014.2314063

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Emrani, S., Gentimis, T., & Krim, H. (2014). Persistent homology of delay embeddings and its application to wheeze detection. IEEE Signal Processing Letters, 21(4), 459–463. https://doi.org/10.1109/lsp.2014.2305700

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Emrani, S., Chintakunta, H., & Krim, H. (2014). Real time detection of harmonic structure: A case for topological signal analysis. In International conference on acoustics speech and signal processing.

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Yi, S., & Krim, H. (2014). Subspace learning of dynamics on a shape manifold: A generative modeling approach. IEEE Transactions on Image Processing, 23(11), 4907–4919. https://doi.org/10.1109/tip.2014.2358200

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Wilkerson, A. C., Moore, T. J., Swami, A., & Krim, H. (2013). Simplifying the homology of networks via strong collapses. In International conference on acoustics speech and signal processing (pp. 5258–5262).

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Emrani, S., & Krim, H. (2013). Wheeze detection and location using spectro-temporal analysis of lung sounds. In 29th southern biomedical engineering conference (sbec 2013) (pp. 37–38).

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Wang, T., Krim, H., & Viniotis, Y. (2013). A Generalized markov graph model: Application to social network analysis. IEEE Journal of Selected Topics in Signal Processing, 7(2), 318–332. https://doi.org/10.1109/jstsp.2013.2246767

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Yi, S., Krim, H., & Norris, L. K. (2012). Human activity as a manifold-valued random process. IEEE Transactions on Image Processing, 21(8), 3416–3428. https://doi.org/10.1109/tip.2012.2197008

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Yi, S., Krim, H., & Norris, L. K. (2012). Human activity modeling as Brownian motion on shape manifold. In Scale space and variational methods in computer vision (Vol. 6667, pp. 628–639).

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Lee, D., & Krim, H. (2012). A sampling theorem for a 2D surface. In Scale space and variational methods in computer vision (Vol. 6667, pp. 556–567).

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Miao, S., & Krim, H. (2011). Robustness and expression independence in 3D face recognition. In 2011 ieee workshop on signal processing systems (sips) (pp. 289–292).

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Clouse, H. S., Krim, H., Sakla, W., & Mendoza-Schrock, O. (2011). Vehicle tracking through the exploitation of remote sensing and LWIR polarization science. In Polarization science and remote sensing v (Vol. 8160).

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Yi, S., Krim, H., & Norris, L. K. (2011). A invertible dimension reduction of curves on a manifold.

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Clouse, H. S., Krim, H., & Mendoza-Schrock, O. (2011). A scaled, performance driven evaluation of the layered sensing framework utilizing polarimetric infrared imagery. In Evolutionary and bio-inspired computation: theory and applications v (Vol. 8059).

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Miao, S., & Krim, H. (2010). 3D face recognition based on evolution of ISO-geodesic distance curves. In International conference on acoustics speech and signal processing (pp. 1134–1137).

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Lee, D., & Krim, H. (2010). 3D surface reconstruction using structured circular light patterns. In Advanced concepts for intelligent vision systems, pt i (Vol. 6474, pp. 279–289).

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Feng, S. O., Kogan, I., & Krim, H. (2010). Classification of curves in 2D and 3D via affine integral signatures. Acta Applicandae Mathematicae, 109(3), 903–937. https://doi.org/10.1007/s10440-008-9353-9

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Yang, Z., Lichtenwalner, D., Morris, A., Krim, J., & Kingon, A. I. (2010). Contact degradation in hot/cold operation of direct contact micro-switches. Journal of Micromechanics and Microengineering, 20(10). https://doi.org/10.1088/0960-1317/20/10/105028

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Poliannikov, O. V., Zhizhina, E., & Krim, H. (2010). Global optimization by adapted diffusion. IEEE Transactions on Signal Processing, 58(12), 6119–6125. https://doi.org/10.1109/tsp.2010.2071867

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Chen, P. F., Krim, H., & Mendoza, O. L. (2010). Multiphase joint segmentation-registration and object tracking for layered images. IEEE Transactions on Image Processing, 19(7), 1706–1719. https://doi.org/10.1109/tip.2010.2045164

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Baloch, S., & Krim, H. (2010). Object recognition through topo-geometric shape models using error-tolerant subgraph isomorphisms. IEEE Transactions on Image Processing, 19(5), 1191–1200. https://doi.org/10.1109/tip.2009.2039372

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Aouada, D., & Krim, H. (2010). Squigraphs for fine and compact modeling of 3-D shapes. IEEE Transactions on Image Processing, 19(2), 306–321. https://doi.org/10.1109/TIP.2009.2034693

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Aouada, D., & Krim, H. (2009). Novel similarity invariant for space curves using turning angles and its application to object recognition. In International conference on acoustics speech and signal processing (pp. 1277–1280).

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Chen, P. F., Steen, R. G., Yezzi, A., & Krim, H. (2009). brain MRi T-1-map and T-1-weighted image segmentation in a variational framework. In International conference on acoustics speech and signal processing (pp. 417–420).

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Yi, S., Labate, D., Easley, G. R., & Krim, H. (2009). A shearlet approach to edge analysis and detection. IEEE Transactions on Image Processing, 18(5), 929–941. https://doi.org/10.1109/TIP.2009.2013082

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Aouada, D., Dreisigmeyer, D. W., & Krim, H. (2008). Geometric modeling of rigid and non-rigid 3D shapes using the global geodesic function. Pattern Recognition, 935–942. https://doi.org/10.1109/cvprw.2008.4563075

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El Ouafdi, A. F., Ziou, D., & Krim, H. (2008). A smart stochastic approach for manifolds smoothing. Computer Graphics Forum, 27(5), 1357–1364. https://doi.org/10.1111/j.1467-8659.2008.01275.x

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Baloch, S. H., & Krim, H. (2007). Flexible skew-symmetric shape model for shape representation, classification, and sampling. IEEE Transactions on Image Processing, 16(2), 317–328. https://doi.org/10.1109/TIP.2006.888348

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Ben Hamza, A., & Krim, H. (2006). Geodesic matching of triangulated surfaces. IEEE Transactions on Image Processing, 15(8), 2249–2258. https://doi.org/10.1109/TIP.2006.875250

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Statistics and analysis of shapes. (2006). Boston: Birkhauser. Retrieved from https://dx.doi.org/10.1007/0-8176-4481-4

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Unal, G., Krim, H., & Yezzi, A. (2005). Fast incorporation of optical flow into active polygons. IEEE Transactions on Image Processing, 14(6), 745–759. https://doi.org/10.1109/TIP.2005.847286

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Poliannikov, O. V., & Krim, H. (2005). Identification of a discrete planar symmetric shape from a single noisy view. IEEE Transactions on Image Processing, 14(12), 2051–2059. https://doi.org/10.1109/TIP.2005.859387

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Unal, G., Yezzi, A., & Krim, H. (2005). Information-theoretic active polygons for unsupervised texture segmentation. International Journal of Computer Vision, 62(3), 199–220. https://doi.org/10.1007/s11263-005-4880-6

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Ben Hamza, A., He, Y., Krim, H., & Willsky, A. (2005). A multiscale approach to pixel-level image fusion. Integrated Computer-Aided Engineering, 12(2), 135–146. https://doi.org/10.3233/ica-2005-12201

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Krim, J., Abdelmaksoud, M., Borovsky, B., & Winder, S. M. (2004). Scanning tunneling microscope-quartz crystal microbalance studies of "real world" and model lubricants. In Y. Braiman ... et al. (Eds.) (Ed.), Dynamics and friction of submicrometer confining systems (Vol. 882). Washington, D.C.: American Chemical Society.

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Baloch, S. H., Krim, H., & Genton, M. G. (2004). Shape representation with flexible skew-symmetric distributions. In M. G. Genton (Ed.) (Ed.), Skew-elliptical distibutions and their applications: A journey beyond normality. Boca Raton, FL: Chapman & Hall/CRC.

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Bao, Y. F., & Krim, H. (2004). Smart nonlinear diffusion: A probabilistic approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1), 63–72. https://doi.org/10.1109/TPAMI.2004.1261079

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Ben Hamza, A., Krim, H., & Zerubia, J. (2004). A nonlinear entropic variational model for image filtering. EURASIP Journal on Applied Signal Processing, 2004(16), 2408–2422. https://doi.org/10.1155/S1110865704407197

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Karacali, B., & Krim, H. (2003). Fast minimization of structural risk by nearest neighbor rule. IEEE Transactions on Neural Networks, 14(1), 127–137. https://doi.org/10.1109/TNN.2002.804315

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Ben Hamza, A., & Krim, H. (2003). Geodesic object representation and recognition. In B. Hamza & H. Krim (Eds.) (Ed.), Discrete geometry for computer imagery: 11th International Conference, DGCI 2003, Naples, Italy, November 19-21, 2003 (Vol. 2886, pp. 378–387). Berlin; New York: Springer.

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Ben Hamza, A., & Krim, H. (2003). Image registration and segmentation by maximizing the Jensen-Renyi divergence. In Energy minimization methods in computer vision and pattern recognition (Vol. 2683, pp. 147–163). Berlin; New York: Springer.

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Zhang, J., Zhang, X., Krim, H., & Walter, G. G. (2003). Object representation and recognition in shape spaces. Pattern Recognition, 36(5), 1143–1154. https://doi.org/10.1016/S0031-3203(02)00226-1

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Benazza-Benyahia, A., Pesquet, J. C., & Krim, H. (2003). A nonlinear diffusion-based three-band filter bank. IEEE Signal Processing Letters, 10(12), 360–363. https://doi.org/10.1109/LSP.2003.818864

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He, Y., Hamza, A. B., & Krim, H. (2003). A generalized divergence measure for robust image registration. IEEE Transactions on Signal Processing, 51(5), 1211–1220. https://doi.org/10.1109/TSP.2003.810305

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Hero, A. O., & Krim, H. (2002). Mathematical methods in imaging. IEEE Signal Processing Magazine, 19(5), 13–14. https://doi.org/10.1109/MSP.2002.1028348

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He, Y., & Krim, H. (2002). Multiscale signal enhancement: Beyond the normality and independence assumption. IEEE Transactions on Image Processing, 11(4), 423–433. https://doi.org/10.1109/TIP.2002.999676

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Unal, G., Krim, H., & Yezzi, A. (2002). Stochastic differential equations and geometric flows. IEEE Transactions on Image Processing, 11(12), 1405–1416. https://doi.org/10.1109/TIP.2002.804568

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Hamza, A. B., Krim, H., & Unal, G. B. (2002). Unifying probabilistic and variational estimation. IEEE Signal Processing Magazine, 19(5), 37–47. https://doi.org/10.1109/MSP.2002.1028351

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Ben Hamza, A., & Krim, H. (2001). Image denoising: A nonlinear robust statistical approach. IEEE Transactions on Signal Processing, 49(12), 3045–3054. https://doi.org/10.1109/78.969512

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Ben Hamza, A., & Krim, H. (2001). A variational approach to maximum a posteriori estimation for image denoising. Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence, 2134, 19–33. https://doi.org/10.1007/3-540-44745-8_2

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Pollak, I., Willsky, A. S., & Krim, H. (2000). Image segmentation and edge enhancement with stabilized inverse diffusion equations. IEEE Transactions on Image Processing, 9(2), 256–266. https://doi.org/10.1109/83.821738

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Poliannikov, O. V., Bao, Y., & Krim, H. (1999). Levy processes for image modeling. In Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, June 14-16, 1999, Caesarea, Israel (pp. 233–236). Los Alamitos, CA: IEEE Computer Society.

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Krim, H., & Schick, I. C. (1999). Minimax description length for signal denoising and optimized representation. IEEE Transactions on Information Theory, 45(3), 898–908. https://doi.org/10.1109/18.761331

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Krim, H., Willinger, W., Juditski, A., & Tse, D. N. C. (1999). Multiscale statistical signal analysis and its applications - Introduction. IEEE Transactions on Information Theory, 45(3), 825–827. https://doi.org/10.1109/TIT.1999.761320

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Krim, H., Tucker, D., Mallat, S., & Donoho, D. (1999). On denoising and best signal representation. IEEE Transactions on Information Theory, 45(7), 2225–2238. https://doi.org/10.1109/18.796365

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Chen, T. H., Hero, A., Djuric, P. M., Messer, H., Goldberg, J., Thomson, D. J., … Krolik, J. (1998). Highlights of statistical signal and array processing. IEEE Signal Processing Magazine, 15(5), 21–64. https://doi.org/10.1109/79.708539

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