Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering

School of Electrical Electrical and Computer Engineering, Purdue University

email: bouman@purdue.edu; phone: (765) 494-0340

MSEE 320, 465 Northwestern Avenue, West Lafayette IN 47907-2035

Short Biography and Vita

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Publications and Presentations

All Publications

Integrated Imaging Plenary, Electronic Imaging 2014

Integrated Imaging Seminar (at RIT): link to RIT seminar

Katie Bouman (i.e., my daughter) in the news

OK, if you haven't seen her in the news, then just Google "Katie Bouman".

First talk given at Caltech on the Computational Imaging methods used to form the first Black Hole image;

Subsequent lectures at Stanford and MIT

Here are two well written editorials in QRIUS and Telegraph.

The original CVPR paper introducing the phase estimation (and also regularized inversion methods) used in forward modeling for all reconstruction algorithms.

TED Talk: What does a black hole look like?; and interview;

BBC News interview; BBC News interview on facebook; Radio Program; Pallab Ghosh interview on BBC;

Seeing around corners: Wired Magazine; Seeing around corners: Boston Globe;

What's Happening in Computational Imaging

Computational Imaging Twitter blog;

2020 Computational Imaging Meeting;

Veo/MBIR

Veo/MBIR Announcement ; Veo/MBIR Publications; Like a Jigsaw Puzzle (Optik & Photonik, vol. 8, no. 1, April 2013

OpenMBIR software for EM and synchrotron reconstruction

My MBIR Tomography Software

MURI: Managing the Mosaic of Microstructure

TIMBIR Featured at Advanced Photon Source

Mild Controversy

Firsts long before Compressed Sensing:

First paper on total variation (TV) regularized reconstruction. See equation (6). Sauer and Bouman, TNS, Aug. 1992.

Very early or first paper on sparse view reconstruction using regularized inversion. See Figure 17. Sauer and Bouman, TSP, Feb. 1992.

First paper on sparse view reconstruction using total variation (TV) regularization. See Figure 14. Sauer and Bouman, TIP, July. 1993.

Follow Linda: The Facebook Page; The Final Chapter; The (Redacted) Conclusion, and Spending $1 Million to Get Rid of Katehi.

What's New?

The New IEEE Transactions on Computational Imaging (TCI) Home page; Xplore page; Submit a paper;

Cool videos: What is Signal Processing? and Signal Processing and Machine Learning

Beautiful essay: Shannon and the history of "information"

Integrated Imaging Cluster at Purdue

Integrated Imaging Web Page; and Integrated Imaging Seminar Series

Teaching

Course Lectures: YouTube video playlist

ECE301: Signals and Systems: Class web page

ECE438: Digital Signal Processing with Applications: Class web page and Laboratory

ECE637: Digital Image Processing I: Class web page; and video lectures

ECE641: Model-Based Imaging: Class web page; video lectures; and Model-Based Image Processing Textbook

An Unsupervised Algorithm for Modeling Gaussian Mixtures based on the EM algorithm and the MDL order estimation criteria. This program clusters feature vectors to produce a Gaussian mixture model. It also estimates the number of clusters directly from the data.

Tomography software

This directory contains two software packages for model-based iterative reconstruction (MBIR) tomography.
It includes code for STEM/TEM recon of bright and darkfield data,
and 3-D/4-D synchrotron recon code.
It also contains some old lagancy code packages.

A python implementation of an algorithm for dynamic 2D sampling. This algorithm can be used in applications such as microscopy to select the most informative next pixel location based on previous measurements.

Plug-and-Play Priors for Interpolation and Reconstruction

This links to software packages for easy-to-use Plug-and-Play implementations.
Simple examples with data are giving for sparse interpolation and tomographic reconstruction.

Multi-UAV Target Tracking Datasets

This links to a ground-truthed dataset that contains HD video of multiple fixed-winged GPUs.
The data was given to us courtesy of Tim Chung, Mathias Kolsch, and Oleg Yakimenko of the Navel Postgraduate School,
and then it was manually ground-truthed by Jin Li and Dong Hye Ye using the VATIC package.

SMAP segmentation software

Robust and computationally efficient software for segmenting
images using a Bayesian multiscale framework.
It uses the Cluster software above as a basis for characterizing
the region classes.
The package includes TEST IMAGES from associated publications.

Sparse Matrix Transform (SMT) code and publications

The SMT is a generalization of the FFT and orthonormal wavelet transform
that can be used for covariance estimation and fast matrix-vector product computation.
It is particularly well suited for non-stationary random processes
and time(space)-varying systems analysis.

Color Quantization Software

This directory contains a software packages
for performing color quantization of color images using
the binary splitting method.

Document Descreening

Color document descreening softwared known as Resolution Synthesis based Descreening
as described in the IEEE TIP March 2007 paper.

Document Text Segmentation for MRC Coding

This software uses a combination of block-based segmentation
known as cost-optimized segmentation (COS)
and MRF based classification
known as connected-components classification (CCC) to detect and segment text from raster document images.

YCxCz Fidelity Metrics Software

This is a Matlab package for computing two fidelity metrics based on the YCxCz color transformation.
One quality metric is based on a non-linear transformation to an Lab-like space,
and the other transform is based on a linearized Lab space.
Both metrics incoporate a modulation transfer function for the human visual system.

Clustered Component Analysis (CCA)

An Algorithm for Estimating Component Directions in Data.
This matlab coding is designed to automatically estimate the
number and directions of distinct non-orthogonal components
in multivariate data.
It can be used in a manner similar to independent components analysis (ICA),
but it is based on an explict signal and noise model,
and it performs maximum likihood (ML) parameter estimation using the
expectation maximization (EM) algorithm.
Its algorithmic structure is similar to that used in the Cluster algorithm above.

RegressionTree

Tree-Structured Predictor Program C source code that implements
an algorithm for nonlinear prediction using a tree-structured predictor.
The code consists of two basic programs/algorithms. The first algorithm
estimates the order (size) and parameters of a tree using a growing (splitting)
and cross-validation pruning strategy. The second program/algorithm then
applies the estimated tree parameters to efficiently compute nonlinear
estimates from vector inputs.

Browse

Software for browsing and searching large image databases
efficiently. This is a robust and useful application which runs on win95/98,
NT, and unix. It also includes C++ source code, so it can be modified for
other applications.

MatrixSourceCoding

This directory contains software
that implements an algorithm for fast space-varying convolution
using the matrix source coding technique.

Raster Document Test Images

A 400 dpi and 600 dpi version of a mixed raster document
for use in testing document compression algorithms.

Image read and write C-code

Easy to use C-subroutines for reading and writing TIFF and
JPEG images.