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 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