Research Activities at Florida State Vision Group

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

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)

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 • 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

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

19. Analytical Probability Models - continued

20. Analytical Probability Models - continued

21. 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