A heretofore unsolved challenge is the completely automatic and accurate estimation of road boundaries in aerial images when the roads may be partially or completely locally occluded and clutter may be prevalent. We introduce a roadfinder that is effective in meeting this challenge. The roadfinder begins with one or more seeds on each long road, and then accurately estimates the remaining boundaries, which can be found completely automatically by the algorithm described by Barzohar and Cooper (see IEEE PAMI, vol.18, no.7, p.407-21, 1996). The algorithm is robust to missing boundary edges on one side of the road and on both sides of the road simultaneously. (These arise from shadows and occlusion by trees, poles, small structures, etc.) It is also robust to clutter within the road caused by cars or trucks, and to clutter resulting from intersecting or close parallel roads. The algorithm is based on simple clutter and occlusion models and a combined multihypothesis generalized Kalman filter (MGKF).

A novel algorithm to estimate the coefficient of q AR (autoregressive) processes from a coarsely quantized signal is presented. The input signal to the quantizer is the superposition of qAR processes and noise. In a related problem a modified version of the above algorithm is used to estimate the frequencies of closely quantized data obtained from q sinusoids embedded in noise. The proposed algorithm can accommodate a nonuniform m-level quantizer, as well as the special case of 1-b quantizer. The proposed estimator is based on the maximum likelihood (ML) criterion and is realized here by judiciously combining the expectation-maximization algorithm of A.D. Dempster et al. (1977) and the Gaussian fit scheme. Simulations reveal that one can accurately estimate the coefficients of several AR processes, or the frequencies of several sinusoids by using 1-b quantized data at low signal to noise ratios and moderate number of observations.>

The authors present a novel approach to the problem of detecting the number of sources impinging on a passive sensor array. The approach is applicable also to the case of fully correlated sources appearing, for example, in specular multipath propagation. Two slightly different detection criteria, both based on the minimum description length (MDL) principle and both requiring the estimation of the locations of the sources, are presented. The first is tailored to the detection problem per se, while the other is tailored to the combined detection-estimation problem. The authors prove the consistency of the two criteria and demonstrate their performance by simulation results.>

We present a novel and efficient algorithm for computing the maximum likelihood estimator of the locations of multiple sources in passive sensor arrays. The algorithm is equally well applicable to the case of fully correlated signals appearing, for example, in multipath propagation problems. Simulation results that demonstrate the performance of the algorithm and a detailed analysis of the uniqueness of the solution are included.

Tree encoding and sequential decoding are considered for noisy channels that respond a random number of times to each input. Such channels appear in mathematical models of certain speech recognition systems. The decoding error probability and the channel capacity are bounded by extension of the methods of Jelinek and Zigangirov to noisy multilevel channels with input-dependent insertions. Certain analytical difficulties peculiar to the channels in question are indicated.