Parallel processing in speech recognition is described, which is carried out at each frame on time axis. We have already proposed a parallel processing algorithm for HMM (Hidden Markov Model)-based speech recognition using Markov Random Fields (MRF). The parallel processing is realized by modeling the hidden state sequence by an MRF and using the Iterated Conditional Modes (ICM) algorithm to estimate the optimal state sequence given an observation sequence and model parameters. However this parallel processing with the ICM algorithm is applicable only to the standard HMM but not to the improved HMM like the linear predictive HMM which takes into account the correlations between nearby observation vectors. In this paper we propose a parallel processing algorithm applicable to the correlation-considered HMM, where a new deterministic relaxation algorithm called the Generalized ICM (GICM) algorithm is used instead of the ICM algorithm for estimation of the optimal state sequence. Speaker independent isolated word recognition experiments show the effectiveness of the proposed parallel processing using the GICM algorithm.

This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of multispectral images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability (LAP) of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region image. Experiments show that the use of LAPs is essential to perform a good image segmentation.

We consider an asymptotically sparsely encoded associative memory. Patterns are encoded by n-dimensional vectors of 1 and 1 generated randomly by a sequence of biased Bernoulli trials and stored in the network according to Hebbian rule. Using a heuristic argument we derive the following capacities:c(n)ne/4k log n'C(n)ne/4k(1e)log n'where, 0e1 controls the degree of sparsity of the encoding scheme and k is a constant. Here c(n) is the capacity of the network such that any stored pattern is a fixed point with high probability, whereas C(n) is the capacity of the network such that all stored patterns are fixed points with high probability. The main contribution of this technical paper is a theoretical verification of the above results using the Poisson limit theorems of exchangeable events.

The problem of restoring binary (black and white) images degraded by color-dependent flip-flap noises is considered. The real image is modeled by a Markov Random Field (MRF). The Iterated Conditional Modes (ICM) algorithm is adopted. It is shown that under certain conditions the ICM algorithm is insensitive to the MRF image model and noise parameters. Using this property, we propose a parameter-free restoration algorithm which does not require the estimations of the image model and noise parameters and thus can be implemented fully in parallel. The effectiveness of the proposed algorithm is shown through applying the algorithm to degraded hand-drawn and synthetic images.