- Segmentation Namrata Vaswani, namrata@iastate.edu
- BEST VIEW SELECTION AND COMPRESSION OF MOVING OBJECTS IN IR Namrata Vaswani and Rama Chellappa
- BOUND ON ERRORS IN PARTICLE FILTERING WITH INCORRECT MODEL ASSUMPTIONS AND ITS IMPLICATION FOR CHANGE DETECTION
- IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 9, SEPTEMBER 2010 4595 Modified-CS: Modifying Compressive Sensing for
- General Bayesian Inference I Basic concepts,
- STATISTICAL SHAPE THEORY FOR ACTIVITY MODELING Namrata Vaswani, Amit Roy Chowdhury, Rama Chellappa
- Summarization and Indexing of Human Activity Sequences
- Coherent Detection Ch. 4 in Kay-II.
- 1 Kalman Filter as a causal MMSE estimator Consider the following state space model (signal and observation model).
- Kalman Filter and Extended Kalman Filter Namrata Vaswani, namrata@iastate.edu
- Background and the proposed solution (modified-CS) Exact reconstruction result
- Tracking (Optimal filtering) on Large Dimensional State
- Nonstationary Shape Activities: Dynamic Models for Landmark Shape Change
- Stability of LS-CS-residual and modified-CS for sparse signal sequence reconstruction
- Note: Handouts DO NOT replace the book. In most cases, they only provide a guideline on topics and an intuitive feel.
- Calculus of Variations Namrata Vaswani, namrata@iastate.edu
- IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 55, NO. 3, MARCH 2007 859 Additive Change Detection in Nonlinear Systems
- A Particle Filtering Approach to Abnormality Detection in Nonlinear Systems and its Application to Abnormal Activity
- THE MODIFIED CUSUM ALGORITHM FOR SLOW AND DRASTIC CHANGE DETECTION IN GENERAL HMMS WITH UNKNOWN CHANGE PARAMETERS
- MARKOV CHAIN MONTE CARLO (MCMC) METHODS
- 4108 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 8, AUGUST 2010 LS-CS-Residual (LS-CS): Compressive Sensing on
- IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 56, NO. 10, OCTOBER 2008 4583 Particle Filtering for Large-Dimensional State Spaces
- MONTE CARLO AND MARKOV CHAIN MONTE CARLO METHODS
- Exact Reconstruction Conditions and Error Bounds for Regularized Modified Basis Pursuit
- Modified-CS: Modifying Compressive Sensing for Problems with Partially Known Support
- Statistical Models for Deformable Contour Tracking
- Digital Image Processing Instructor: Namrata Vaswani
- Deform PF-MT: Particle Filter with Mode Tracker for Tracking Non-Affine Contour Deformations
- Stability of Modified-CS over Time for recursive causal sparse reconstruction
- 1 Hidden Markov Model A hidden Markov model (HMM) refers to a set of "hidden" states X0, X1, . . . , Xt, . . . , XT
- SUMMARIZATION AND INDEXING OF HUMAN ACTIVITY SEQUENCES Bi Song*, Namrata Vaswani**, Amit K. Roy-Chowdhury*
- Note: Handouts DO NOT replace the book. In most cases, they only provide a guideline on topics and an intuitive feel.
- Review of Signal Processing This contains a brief review of
- Course Information Instructor: Dr. Namrata Vaswani, Email: namrata AT iastate.edu Office: 3121 Coover Hall
- IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 14, NO. 10, OCTOBER 2005 1603 "Shape Activity": A Continuous-State HMM for
- MODIFIED COMPRESSIVE SENSING FOR REAL-TIME DYNAMIC MR IMAGING Wei Lu and Namrata Vaswani
- Recursive Sparse Recovery Applications in Dynamic Imaging
- Particle Filtered Modified-CS (PaFiMoCS) for tracking signal sequences
- KALMAN FILTERED COMPRESSED SENSING Namrata Vaswani
- ANALYZING LEAST SQUARES AND KALMAN FILTERED COMPRESSED SENSING Namrata Vaswani
- Particle Filtering for Geometric Active Contours with Application to Tracking Moving and Deforming Objects
- Activity Recognition Using the Dynamics of the Configuration of Interacting Namrata Vaswani, Amit Roy Chowdhury, Rama Chellappa
- REAL-TIME DYNAMIC MR IMAGE RECONSTRUCTION USING KALMAN FILTERED COMPRESSED SENSING
- GENERALIZED ELL FOR DETECTING AND TRACKING THROUGH ILLUMINATION MODEL CHANGES
- MODEL-BASED COMPRESSION OF NONSTATIONARY LANDMARK SHAPE SEQUENCES Samarjit Das and Namrata Vaswani
- PARTICLE FILTER WITH EFFICIENT IMPORTANCE SAMPLING AND MODE TRACKING (PF-EIS-MT) AND ITS APPLICATION TO LANDMARK SHAPE TRACKING
- PF-EIS & PF-MT: NEW PARTICLE FILTERING ALGORITHMS FOR MULTIMODAL OBSERVATION LIKELIHOODS AND LARGE DIMENSIONAL STATE SPACES
- PARTICLE FILTERS FOR INFINITE (OR LARGE) DIMENSIONAL STATE SPACES-PART 1 Namrata Vaswani*, Anthony Yezzi**, Yogesh Rathi**, Allen Tannenbaum**
- Change Detection in Partially Observed Nonlinear Dynamic Systems with Unknown Change Parameters
- CLASSIFICATION PROBABILITY ANALYSIS OF PRINCIPAL COMPONENT NULL SPACE ANALYSIS
- Motivation and Problem Formulation Sparse recon. with partially known support
- PF with Efficient Importance Sampling (EIS) and Conditional
- Deformable Contour Tracking & System Identification
- Least Squares & Kalman Filtered Compressed Sensing
- Real-time Dynamic MRI using Kalman Filtered Compressed
- Recursive and Causal Reconstruction of Sparse Signal Sequences
- Background: PCA and Robust PCA Real-time Robust Principal Components' Pursuit
- PARTICLE FILTERS FOR INFINITE (OR LARGE) DIMENSIONAL STATE SPACES-PART 2 Namrata Vaswani
- Modified CUSUM for Slow and Sudden Change Detection with Unknown Parameters
- Time-varying Finite Dimensional Basis for Tracking Contour Deformations Namrata Vaswani*, Anthony Yezzi**, Yogesh Rathi**, Allen Tannenbaum**
- 1370 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 5, MAY 2007 A Generic Framework for Tracking Using
- 1816 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 7, JULY 2006 Principal Components Null Space Analysis
- NonStationary Shape Activities: Tracking & Abnormality Detection
- Detection and Estimation Theory Instructor: Prof. Namrata Vaswani
- A Probability Review A probability review.
- 1 MMSE estimation 1. Define Bayesian MSE
- Importance Sampling and Particle Filtering Namrata Vaswani, namrata@iastate.edu
- Introduction to Detection Theory Ch. 3 in Kay-II.
- Edge Detection Discrete approx. of a derivative
- Optical Flow Namrata Vaswani, namrata@iastate.edu
- Least Squares Estimation Namrata Vaswani, namrata@iastate.edu
- Registration and Landmark Shape Analysis Namrata Vaswani, namrata@iastate.edu
- 1 Topics from Chapter 4 Sections 4.1, 4.2, 4.5, 4.6
- 1 Hypothesis Testing Simple Hypothesis Testing: H0: = 0, H1: = 1
- Course Information: EE 528 (Digital Image Processing) Instructor: Dr. Namrata Vaswani, Email: namrata AT iastate.edu Office: 3121 Coover Hall
- Compressive Sensing Based on Candes and Tao's work
- Pattern Recognition 36 (2003) 20692081 www.elsevier.com/locate/patcog
- NonStationary "Shape Activities" Namrata Vaswani Rama Chellappa
- Closed-Loop Tracking and Change Detection in Multi-Activity Sequences , Namrata Vaswani 2
- Note: Handouts DO NOT replace the book. In most cases, they only provide a guideline on topics and an intuitive feel.
- KF-CS: Compressive Sensing on Kalman Filtered Residual Namrata Vaswani
- Research Summary Namrata Vaswani, ECE Dept, Iowa State University, Ames, IA
- LINEAR MODELS Polynomial Curve Fitting Example. Continuous signal x(t)
- (A Quick) Probability Review Go over handouts 25 in EE 420x notes.
- Kalman Filter Application toKalman Filter Application to Electrical ImpedanceElectrical Impedance
- Abnormal "Shape Activity" Detection and Tracking Namrata Vaswani
- Edge Detection & Boundary EE 528 Digital Image Processing
- Maximum Likelihood from Incomplete Data via the EM Algorithm A. P. Dempster; N. M. Laird; D. B. Rubin
- IEEE TRANSACTIONS ON ACOUSTICS. SPEECH. AND SIGNAL PROCESSING. VOL. 3X. NO. 6. JUNE IYYO 1039 Stochastic and Deterministic Networks for Texture
- Note: Handouts DO NOT replace the book. In most cases, they only provide a guideline on topics and an intuitive feel. The math details will be covered in class, so
- KALMAN FILTERED COMPRESSED SENSING Namrata Vaswani
- Particle Filtering and Change Namrata Vaswani
- Cramer-Rao Bound (CRB) and Minimum Variance Unbiased (MVU) Estimation
- A Linear Classifier for Gaussian Class Conditional Distributions with Unequal Covariance Matrices
- Particle Filtering for Large Dimensional Problems with
- Make-up MidTerm, Fall 06 (Out of 30) 5 questions, 6 points for each question
- Introduction EE 520: Image Analysis &
- 1 Topics from Chapter 4 Sections 4.1, 4.2, 4.5, 4.6
- Note: Handouts DO NOT replace the book. In most cases, they only provide a guideline on topics and an intuitive feel.
- Motivation and Applications: Why Should I Study Probability? As stated by Laplace, "Probability is common sense reduced to
- EE 424 #2: Time-domain Representation of Discrete-time Signals
- 1 Hypothesis Testing Simple Hypothesis Testing: H0: = 0, H1: = 1
- Recursive Sparse Recovery in Large but Correlated Chenlu Qiu and Namrata Vaswani
- EE 424 #1: Sampling and Reconstruction January 13, 2011
- An Introduction to Probabilistic Graphical Models
- Note: Handouts DO NOT replace the book. In most cases, they only provide a guideline on topics and an intuitive feel.
- Real-time Principal Components' Pursuit Chenlu Qiu, Namrata Vaswani
- Course Information Instructor: Dr. Namrata Vaswani, Email: namrata AT iastate.edu Office: 3121 Coover Hall
- General Bayesian Inference I Basic concepts,
- 1 MMSE estimation 1. Define Bayesian MSE
- Sparse Reconstruction / Compressive Sensing Namrata Vaswani
- 1 Hidden Markov Model A hidden Markov model (HMM) refers to a set of "hidden" states X0, X1, . . . , Xt, . . . , XT
- Real-time Principal Components' Pursuit Chenlu Qiu, Namrata Vaswani
- Note: Handouts DO NOT replace the book. In most cases, they only provide a guideline on topics and an intuitive feel.
- 1 Kalman Filter as a causal MMSE estimator Consider the following state space model (signal and observation model).
