this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the- ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 could potentially model the brain as a generafive model in such a way that feedback serves to disambiguate and 'explain away' the earlier representa- tion. The Helmholtz machine 4, 5 was an excellent step towards approximating this proposal, with feedback implementing priors. Its development, however, was rather limited, dealing only with binary images. Moreover, its feedback mechanisms were engaged only during the learning of the feedforward connections but not during perceptual inference, though the Gibbs sampling process for inference can potentially be interpreted as top-down feedback disambiguating low level representations? Rao and Ballard's predictive coding/Kalman filter model 6 did integrate generafive feedback in the perceptual inference process, but it was primarily a linear model and thus severely limited in practical utility. The data-driven Markov Chain Monte Carlo approach of Zhu and colleagues 7, 8 might be the most successful recent application of this proposal in solving real and difficult computer vision problems using generafive models, though its connection to the visual cortex has not been explored. Here, we bring in a powerful and widely applicable paradigm from artificial intelligence and computer vision to propose some new ideas about the algorithms of visual cortical process- ing and the nature of representations in the visual cortex. We will review some of our and others' neurophysiological experimental data to lend support to these ideas

Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and statistical problems in domains that involve a large number of variables. In this paper, we

A new method for target tracking of multiple points on an object by using particle filter with its novel importance function is proposed. The assumptions are such that the number of points is fixed and known, and the association between points of object and observed points are unknown. There exists missing and clutter in observation process where which observation corresponds to them are also unknown. The main difficulty of this problem is the formidable number of combinations in the association. The novel importance function using an idea of soft gating makes the problem tractable in a proper framework of particle filter. Simulation experiment illustrates the performance of the method. 1.

The safety of the flights, and in particular separation assurance, is one of the main tasks of Air Traffic Control. Conflict resolution refers to the process used by air traffic controllers to prevent loss of separation. Conflict resolution involves issuing instructions to aircraft to avoid loss of safe separation between them and, at the same time, direct them to their destinations. Conflict resolution requires decision making in the face of the considerable levels of uncertainty inherent in the motion of aircraft. We present a framework for conflict resolution which allows one to take into account such levels of uncertainty through the use of a stochastic simulator. The conflict resolution task is posed as the problem of optimizing an expected value criterion. Optimization of the expected value resolution criterion is carried out through an iterative procedure based on Monte Carlo Markov Chain. Simulation examples inspired by current air traffic control practice in terminal maneuvering areas and approach sectors illustrate the proposed conflict resolution strategy. 1

Nonlinear multimodal filtering problems are usually addressed via Monte Carlo algorithms. These algorithms involve sampling procedures that are similar to proportional selection...

Abstract: A new method for 3D reconstruction from dynamic stereo image by using state estimation with particle filter is proposed. Associations of feature points between two images and 3D position of the feature points are estimated simultaneously by particle filter. It is due to the applicability of particle filter for nonlinear and non-Gaussian state space model including unknown associations, non-Gaussian distributions appeared here, and nonlinearlity of projection. We assume that there are missing and error detection of feature points through the image processing for feature extraction. The novelty of our method is simultaneous estimation of the unknown associations and the 3D positions of feature points while most conventional methods are using 2 step estimation, i.e., firstly estimate the associations then secondly calculate the 3D positions depending on the estimated associations. Further improvement of estimation called Rao-blackwellization, which is a method for variance reduction of the estimate, is used at the implementation of particle filter with extended Kalman filter for nonlinear part of the model. Simulation experiment illustrates the efficiency of the method. At the concluding part, we mention about a possibility of this method to provide a basis for multiple sensor fusion problem in dynamic situation by extending the model into a general form.

Tracking of fingers image on omnidirection camera has been investigated which aims at developing of human friendly interface using fingers ’ gesture. Where the way of using the omnidirection camera is different from a normal usage in which the user holds the camera with his/her fingers and move them. The interface can use the motion of the fingers to establish a communication between the user and computer system in human friendly way. To achieve this, it is necessary for the system to track the fingers in the dynamic image of omnidirection camera. We have employed CONDENSATION algorithm for the tracking. The algorithm uses pre-defined shape of finger called template, and estimates the parameters of affine transformation to adjust the template to the image. The estimation is performed with many number of particles where each particle has an realization of the affine parameters. Applying three steps called prediction, observation, and selection, we have updated particles which approximates a posteriori distribution of the affine parameters given the image sequence up to current time. Real image experiments show the performance of the tracking method. KEY WORDS Omnidirection camera, dynamic image, finger image, tracking, CONDENSATION algorithm. 1

Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative examples, together with new, more general, methods for solving the algebraic equations and with a new algorithm for parameter identification. 1

In this paper, a solution to the TENET nonlinear filtering challenge is presented.

A perceptual system encompasses an important perspective of the interface between human and an interacting system. In this context, an approach is presented to establish a natural and effective interaction between an operator and a measurement system. The system output is acting as a social agent, based on sensory data and is presenting impressions as facial expressions as well as conclusions in form of natural language based identification. The proposed method has been evaluated in an experimental perceptual odour system set up using an artificial olfactory system.