Mathematical Sciences

School of Natural Sciences & Mathematics

Image and Signal Analysis Journal Club

Fall 2017 – Spring 2018

This seminar is organized by Drs. Cao and Lou. If you have questions about this seminar please contact Dr. Yan Cao. The journal club meets on Tuesdays at 10:00-11:00am in FO 2.610F in Spring 2018. The members include both graduate and undergraduate students. Each week one of the students presents a paper in the field of image and signal analysis of his/her interest. The chosen paper will be announced prior to the meeting. The goal of the journal club is to introduce graduate and undergraduate students to image and signal analysis research and create an open venue for friendly but lively scientific discussion.

Fall 2016 – Spring 2017 Archive

Date

Speaker

Title

Paper

Apr. 17 Russell Hart Colorful Image Colorization

Colorful Image Colorization, R. Zhang, P. Isola and A.A. Efros, arXiv:1603.08511v5, 2016.

Apr. 10 Rajendra Khatri A Fast Learning Algorithm for Deep Belief Nets

A Fast Learning Algorithm for Deep Belief Nets, G.E. Hinton, S. Osindero and Y. Teh, Neural Computation 18, pp. 1527–1554, 2006.

Apr. 3 Mohammadmedhi
Akhavan
Richardson-Lucy method iterative deconvolution and their applications

Richardson–Lucy Algorithm With Total Variation
Regularization for 3D Confocal Microscope Deconvolution, N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J. Olivo-Marin and J. Zerubia, Microscopy Research and Technique, 69, pp. 260-266, 2006.

Mar. 27 Jun Zhang Fast linearized ALMs for image denoising problems based on fourth-order PDEs

A Fast Augmented Lagrangian Method for Euler’s Elastica Models, Y. Duan, Y. Wang and J. Hahn, Numer. Math. Theor. Meth. Appl., 6(1), pp 47-71, 2013.

A Fast Algorithm for a Mean Curvature Based Image Denoising Model Using Augmented Lagrangian Method, W. Zhu, X. Tai and T. Chan, Global Optimization Methods, LNCS 8293, pp. 104–118, 2014.

Mar. 20 Mujibur Chowdhury Total bounded variation-based Poissonian
images recovery by split Bregman iteration
Total bounded variation-based Poissonian images recovery by split Bregman iteration, X. Liu and L. Huang, Mathematical Methods in the Applied Sciences, 35, pp 520-529, 2012.
Mar. 6 Varun Balaraju Long Short-Therm Memory Networks Long Short-Therm Memory, S. Hochreiter and J. Schmidhuber, Neural Computation, 9(8), pp. 1735-1780, 1997.
Feb. 27 Yaghoub Rahimi Necessary and Sufficient Conditions for Sparse Recovery Unified sufficient conditions for uniform recovery of sparse signals via nonconvex minimizations, H. Tran and C. Webster, arXiv:1710.07348.
Feb. 20 Siyuan Wang Clustering Algorithms

Topics: k-means, fuzzy c-means and mixture models.

Feb. 13 Sijie Shen Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data, Y. Chena, S. L. Bressler, M. Ding, Journal of Neuroscience Methods, vol. 150, pp 228–237, 2006.  
Feb. 6 Tian Jiang Deep Residual Learning for Image Recognition Deep Residual Learning for Image Recognition, K. He, X. Zhang, S. Ren, J. Sun, In CVPR 2016.
Jan. 23   Organization Meeting  
Dec. 5 Rajendra Khatri Diffeomorphic Nonlinear Transformations: A  local Parametric Approach for Image Registration Diffeomorphic Nonlinear Transformations: A  local Parametric Approach for Image Registration,  R. Narayanan, J.A. Fessler, H. Park, and C.R. Meyer, Information Processing in Medical Imaging, LNCS, volume 3565, pp 174-185, 2005.
Nov. 27 Md Mujibur  Chowdhury Total Generalized Variation Total Generalized Variation, K. Bredies, K. Kunisch, and T. Pock, SIAM J. Imaging Sciences, Vol. 3, No. 3, pp. 492–526, 2010.
Nov. 7 Mohammadmedhi
Akhavan
One Dimensional Bar-code Deblurring  
Oct. 31 Siyuan Wang Linear Filtering

https://web.stanford.edu/class/ee368/Handouts/Lectures/2016_Autumn/9-LinearProcessingFiltering_16x9.pdf

Oct. 24 Russell Hart Deblurring Text Images Using a Convolutional Neural Network in Tensorflow

https://www.robots.ox.ac.uk/~vgg/practicals/cnn-reg/

https://www.tensorflow.org/get_started/mnist/pros

Oct. 17 Mengqi Hu Compressive Sensing Imaging for General Synthetic Aperture Radar Echo Model Based on Maxwell’s Equations

Compressive sensing for a general SAR imaging model based on Maxwell’s equations, B. Sun, H. Gu, M. Hu, Z. Qiao, Proc. of SPIE Vol. 9484, 2015.

Compressive sensing imaging for general synthetic aperture radar echo model based on Maxwell’s equations, B. Sun, Y. Cao, J. Chen, C. Li, Z. Qiao, EURASIP Journal on Advances in Signal Processing 2014, 2014:153.

Oct. 10 Brendan Caseria and Alsadig Ali Automatic Extraction of Cell Nuclei from Pathological Images Automatic extraction of cell nuclei from H&E-stained histopathological images, F. Yi, J. Huang, Y. Xie, G. Xiao, Journal of Medical Imaging, 4(2), 2017.
Oct. 3 Noor Abbas Morphological Image Processing Practical Image and Video Processing Using MATLAB, Chapter 13, O. Marques, Hoboken, N.J. : J. Wiley & Sons/IEEE Press, 2011.
September 26 Tian Jiang Deep Learning Deep learning, Y. LeCun, Y. Bengio, G. Hinton, Nature, Vol 521, 436-444, 2015.
September 19 Sijie Shen Causality Detection Detecting Causality in Complex Ecosystems, G. Sugihara, R. May, H. Ye, C. Hsieh,  E. Deyle, M. Fogarty, S. Munch, Science, vol 338, 496-500, 2012.
September 12 Yaghoub Rahimi Logistic Regression
September 5   Organization meeting