Roberto Cristi

Bayes Smoothing Algorithms for Segmentation of Binary Images Modeled by Markov Random Fields

A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modeled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm yields the a posteriori distribution of the scene value at each pixel, given the total noisy image, in a recursive way. The a posteriori distribution together with a criterion of optimality then determine a Bayes estimate of the scene. The algorithm presented is an extension of a 1-D Bayes smoothing algorithm to 2-D and it gives the optimum Bayes estimate for the scene value at each pixel. Computational concerns in 2-D, however, necessitate certain simplifying assumptions on the model and approximations on the implementation of the algorithm. In particular, the scene (noiseless image) is modeled as a Markov mesh random field, a special class of Markov random fields, and the Bayes smoothing algorithm is applied on overlapping strips (horizontal/vertical) of the image consisting of several rows (columns). It is assumed that the signal (scene values) vector sequence along the strip is a vector Markov chain. Since signal correlation in one of the dimensions is not fully used along the edges of the strip, estimates are generated only along the middle sections of the strips. The overlapping strips are chosen such that the union of the middle sections of the strips gives the whole image. The Bayes smoothing algorithm presented here is valid for scene random fields consisting of multilevel (discrete) or continuous random variables.

Global stability of adaptive pole placement algorithms

This paper presents direct and indirect adaptive control schemes for assigning the closed-loop poles of a single-input, single-output system in both the continuous- and discrete-time cases. The resulting closed-loop system is shown to be globally stable when driven by an external reference signal consisting of a sum of sinusoids. In particular, persistent excitation of the potentially unbounded closed-loop input-output data, and hence convergence of a sequential least-squares identification algorithm is proved. The results are applicable to standard sequential least squares, and least squares with covariance reset.

Fuzzy logic data correlation approach in multisensor-multitarget tracking systems

Abstract In this paper, a fuzzy logic data correlation approach for multisensor–multitarget tracking is proposed. This fuzzy correlation approach is developed based on fuzzy clustering means algorithm. The proposed approach is applied to a two- and a four-dimensional multisensor–multitarget tracking system using Monte Carlo simulations. Fuzzy system performance evaluation is presented to demonstrate the efficiency of the new approach. The computational complexity of this approach is also analyzed and compared to that of conventional fuzzy logic data association methods. Considerable improvement in terms of computational complexity and performance is achieved.

Multirate, multiresolution, recursive Kalman filter

Abstract An approach to the decomposition of a signal into orthogonal components at different resolution levels is presented in this paper. It is shown that a signal generated by the standard state-space stochastic model can be decomposed into innovations at the different sampling frequencies associated to different levels of resolution. The main result is that these innovations are all uncorrelated with each other. A multiresolution multirate (MRMR) Kalman filter is then introduced which allows multiple MRMR observations to be combined in an optimal fashion.

Application of the Gibbs distribution to image segmentation

This paper presents a new statistical approach to image segmentation. Making use of Gibbs distribution models of Markov random fields a dynamic programming based segmentation algorithm is developed. The algorithm is described in detail and examples are given.

Acoustic motion estimation and control for an unmanned underwater vehicle in a structured environment

Abstract The problem of identification and navigation, guidance and control in unmanned underwater vehicles (UUVs) is addressed in this paper. A task-function-based guidance system and an acoustic motion estimation module have been integrated with a conventional UUV autopilot within a two-layered hierarchical architecture for closed-loop control. Basic techniques to estimate the robot dynamics using the sensors mounted on the vehicle have been investigated. The proposed identification techniques and navigation, guidance and control (NGC) system have been tested on Roby2, a UUV developed at the Istituto Automazione Navale of the Italian C.N.R. The experimental set-up, as well as the modalities and results, are discussed.

Extraction of polyphase radar modulation parameters using a wigner-ville distribution - radon transform

Often used in low probability of intercept continuous waveform (CW) emitters, polyphase modulations can have extremely long code lengths (large processing gain), good sidelobe performance and robust Doppler tolerance. This paper presents an efficient algorithm to autonomously extract the polyphase radar modulation parameters from an intercepted waveform using a Wigner-Ville Radon transform. Results show that our method results in a small relative error in the extracted parameters for signal-to-noise ratios as low as - 6dB.

Multirate filtering and estimation: the multirate Wiener filter

In this paper we address the problem of estimating a random process from two observed signals at different sampling rates. In particular, we consider the case where one of the observed signals is sampled at half the rate of the other. The optimal filter for this problem is derived as a linear filter with periodically varying coefficients. We provide quantitative expressions for the reduction in mean-square error due to added observations at the lower sampling rate.

On the Steady State Performance of Frequency Domain LMS Algorithms

The use of the fast Fourier transform (FFT) in the implementation of the least mean square (LMS) algorithm in the frequency Manuscript received May 24, 1990; revised May 4, 1992. A. Feuer is with the Department of Electrical Engineering, TechnionIsrael Institute of Technology, Haifa 32000, Israel. R. Cristi is with the Department of Electrical and Computer Engineering, Naval Postgraduate School, Monterey, CA 93943. IEEE Log Number 9203376. 1053-587X/93

Quaternion feedback regulator for large angle maneuvers of underactuated spacecraft

03.00