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 ri’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 l, 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
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•
•
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Web site at
http://fsvision.fsu.edu
http://www.cs.fsu.edu/~liux
Email at
liux@cs.fsu.edu
Office at
MCH 102D
Office hours Mondays and Wednesdays 3:30-5:30PM
Phone
644-0050
Courses
CAP5615 – Fall 2001
CAP5630 – Spring 2001