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Research Activities at Florida State Vision Group
Xiuwen Liu
Florida State Vision Group
Department of Computer Science
Florida State University
http://fsvision.cs.fsu.edu
Introduction
An image patch represented by hexadecimals
Introduction - continued
Fundamental problem in computer vision• Given a matrix of numbers representing an image, or a
sequence of images, how to generate a perceptually meaningful description of the matrix?
– An image can be a color image, gray level image, or other format such as remote sensing images
– A two-dimensional matrix represents a signal image
– A three-dimensional matrix represents a sequence of images A video sequence is a 3-D matrix A movie is also a 3-D matrix
Introduction - continued
Why do we want to work on this problem?• It is very interesting theoretically
– It involves many disciplines to develop a computational model for the problem
• It has many practical applications– Internet applications
– Movie-making applications
– Military applications
Introduction - continued
How can we characterize all these images perceptually?
Face Recognition
Given some examples of faces, identify a person under different pose, lighting, and expression conditions
Face Recognition – continued
Faces of the same person under slightly different conditions
Affective Computing
Face Detection
Find all faces in a given picture• Typical faces are available
Appearance-based Object Recognition
Appearance-based object recognition• Recognize objects based on their appearance in images
Columbia object image library• It consists of 7,200 images of 100 objects
• Each object has 72 images from different views
COIL Dataset
3D Recognition Results
Appearance-based 3D object Recognition• We compare our result with SVM and SNoW methods
reported by Yang et al. (Yang et al., 2000)
Methods/Training/test views 36/36 18/54 8/64 4/68
Our method 0.08% 0.67% 4.67% 10.71%
Our method without background 0.00% 0.13% 1.89% 7.96%
SNoW (Yang et al.,2000) 4.19% 7.69% 14.87% 18.54%
Linear SVM (Yang et al.,2000) 3.97% 8.70% 15.20% 21.50%
Nearest Neighbor(Yang et al.,2000) 1.50% 12.46% 20.52% 25.37%
Object Extraction from Remote Sensing Images
An image of Washington, D.C. area
Object Extraction from Remote Sensing Images
Extracted hydrographic regions
Medical Image Analysis
Medical image analysis• Spectral histogram can also be used to characterize
different types of tissues in medical images
• Can be used for automated medical image analysis
Video Sequence Analysis
Motion analysis based on correspondence
Video sequence
Analytical Probability Models for Spectral Representation
Transported generator model (Grenander and Srivastava, 2000)
where • gi’s are selected randomly from some generator space G
• the weigths ai’s are i.i.d. standard normal
• the scales i’s are i.i.d. uniform on the interval [0,L]
• the locations zi’s as samples from a 2D homogenous Poisson process, with a uniform intensity , and
• the parameters are assumed to be independent of each other
Analytical Probability Models - continued
Define
Model u by a scaled -density
Analytical Probability Models - continued
Analytical Probability Models - continued
Analytical Probability Models - continued
3D Model-Based Recognition
Summary
Florida State Vision group offers many interesting research topics/projects• Efficient represent for generic images
• Computational models for object recognition and image classification
• Motion/video sequence analysis and modeling
• They can have significant commercial potentials
• They are challenging
• They are interesting
Contact Information
• Web site at http://fsvision.fsu.edu
http://www.cs.fsu.edu/~liux• Email at [email protected]• Office at MCH 102D• Office hours Mondays and Wednesdays 3:30-5:30PM• Phone 644-0050• Courses CAP5615 – Fall 2001
CAP5630 – Spring 2001