An improved version of the original parametric formulation of the generalized spectral subtraction method is presented in this study. The original formulation uses parameters that minimize the mean-square error (MSE) between the estimated and true speech spectral amplitudes. However, the MSE does not take into account any perceptual measure. We propose two new short-time spectral amplitude esti...

In this paper, the fuzzy coding histogram representation for an image region is proposed for visual tracking. At the initial frame, we use the fuzzy clustering technology on all pixels within the initial detection box to get the cluster prototypes, which are used to construct the fuzzy codebook. During the tracking period, the candidate image region is also represented by a fuzzy coding histogr...

In this paper, we propose a robust approach for tracking targets in forward looking infrared (FLIR) imagery taken from an airborne moving platform. First, the targets are detected using fuzzy clustering, edge fusion and local texture energy. The position and the size of the detected targets are then used to initialize the tracking algorithm. For each detected target, intensity and local standar...

A speech enhancement system which utilizes an optimal (in the minimum mean square error sense) short-time spectral amplitude estimetor is described. The derivation of the optimal estimator is based on modeling speech es a quasi-periodic signal, and on applying spectral decomposition. The optimal spectral amplitude estimator and a recently developed vector spectral subtraction amplitude estimato...

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gapwere estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the...

The echo effect due to late reverberation can severely degrade speech quality and intelligibility. Prior attempts to reduce this degradation in the modulation domain used time-invariant filtering. In this paper, we show that performing minimum mean squared error spectral estimation in the modulation domain (MDMMSE) can significantly reduce the severity of audible reverberation and enhance the l...

K-means is a widely used iterative clustering algorithm. There has been considerable work on improving k-means in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose two heuristics to overcome this bottleneck and speed up k-means. Our first heuristic predicts...

T HE THEORY developed in the statistical literature for the method of k-means can be applied to the study of optimal k-level vector quantizers. In this paper, I describe some of this theory, including a consistency theorem (Section II) and a central lim it theorem (Section IV) for k-means cluster centers. These results help to explain the behavior of optimal vector quantizers constructed from l...

We present a novel unsupervised algorithm for quickly finding clusters in multidimensional data. It does not make the assumption of isotropy, instead taking full advantage of the anisotropic Gaussian kernel, to adapt to local data shape and scale. We employ some little-used properties of the multivariate Gaussian distribution to represent the data, and also give, as a corollary of the theory we...

Spectral matching algorithms can be used for the identification of unknown spectra based on a measure of similarity with one or more known spectra. Two popular spectral matching algorithms use different error metrics and constraints to determine the existence of a spectral match. Multiple endmember spectral mixture analysis (MESMA) is a linear mixing model that uses a root mean square error (RM...