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Florida State Vision Group

  
Florida State Vision Group was founded by Dr. Xiuwen Liu at the Florida State University with the support from the Department and the University. The ultimate goal of our research is to build a vision system that is capable of performing detection and recognition of a large number of objects in real-time under a typical unconstrained environment. The current goal of our group's research is to build a machine vision system that provides useful functionalities for variety of computer vision applications in real time; in speech recognition technology terms, we are seeking a system that is equivalent to a large vocabulary, speeker independent speech recognition real-time system. Florida State Vision Group

Our Approach

Our research activities are centered around efficient representations and inference architectures for computer vision applications. As a concrete goal for the following few years, we want to build a system that is capable of detecting and recognizing 30,000 different objects in real-time. Here 30,000 is an estimate of the human's capacity for basic-level visual categorization given by Biederman [1]. In order to make progress toward our goal of building a machine system, our group recently has focused on the following problems. or real-world problems.

The main research areas of the Florida State Vision Group at the Florida State University are broad areas of artificial intelligence, focusing on computer vision and perception modeling. Our group believes that the traditional way of from-specific-to-generic methodology does not work because a system specially designed under certain assumptions will fail when those assumptions are not met. Also as shown by many perceptual phenomena, visual perception is nonlinear. This makes many of the tools very critical for linear systems not applicable.

It is widely accepted that a successful vision system must have a feedback loop between low-level modules, such as filtering, and high-level modules such as recognition. Our current research projects build on a spectral histogram, which can be viewed as a bottom-up feature. However, this bottom-up feature has some distinctive properties and can be used very effectively for classification, segmentation, and even recognition. We view that this generic feature is necessary because without it the system would have to search a very gigantic solution space, which would be very inefficient.

Justifications and Philosophical Arguments

It has been a dream of many ambitious scientists to make a machine which can "see" robustly and flexibly in a natural environment as we human beings do. Toward realizing the dream, our approach is based on our belief that a feasible vision system can only be achieved by efficient approximations of representations and inference architectures as the computational complexity is a key requirement. There are several reasons that support our belief.
  1. The computational capacity of the universe is estimated to 10^{120} operations, known as the Landauer-Lloyd limit [2]. Given this, any algorithm that requires computation beyond this limit is a phantom.
  2. To certain extend, computer vision is not a theoretical problem. For example, one can give an optimal solution based on a look-up table; any intelligent agent can be modeled this way [3], which, of course, does not lead to any .
  3. Most effective computer vision techniques must be unique to vision applications; this is just another way of stating the well known "no free lunch theorem" (p. 456, [4]) and "ugly duckling theorem" (p. 461, [4]).

Reference

[1] I. Biederman, ``Recognition-by-Components: A theory of human image understanding,'' Psychological Review, vol. 94, pp. 115-147, 1987.
[2] S. Lloyd, "Computational capacity of the universe," Physical Review Letters, vol. 88, no. 23, pp. 237901-1 - 237901-4, 2002.
[3] S. Russell and P. Norvig, "Artificial intelligence: A modern approach," 2nd edition, Prentice Hall, 2003.
[4] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, 2001.
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Last modified oon June 10, 2002