1. 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

2. Introduction  An image patch represented by hexadecimals

3. 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

4. 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

5. Introduction - continued  How can we characterize all these images perceptually?

6. Face Recognition  Given some examples of faces, identify a person under different pose, lighting, and expression conditions

7. Face Recognition – continued  Faces of the same person under slightly different conditions

8. Affective Computing

9. Face Detection  Find all faces in a given picture Typical faces are available

10. 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

11. COIL Dataset

12. 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%

13. Object Extraction from Remote Sensing Images  An image of Washington, D.C. area

14. Object Extraction from Remote Sensing Images  Extracted hydrographic regions

15. 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

16. Video Sequence Analysis  Motion analysis based on correspondence Video sequence

17. 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

18. Analytical Probability Models - continued  Define  Model u by a scaled  -density

19. Analytical Probability Models - continued

22. 3D Model-Based Recognition

23. 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

24. 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