Research Activities at Florida State Vision Group Xiuwen Liu Florida State Vision Group Department of Computer Science Florida State University

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 36/3618/548/644/68 Our method0.08%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 g i ’s are selected randomly from some generator space G the weigths a i ’s are i.i.d. standard normal the scales  i ’s are i.i.d. uniform on the interval [0,L] the locations z i ’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

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 at Office at MCH 102D Office hours Mondays and Wednesdays 3:30-5:30PM Phone Courses CAP5615 – Fall 2001 CAP5630 – Spring 2001