Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L *a *b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.

This paper furthers the development of the application of Evolutionary Computation, specifically Genetic Algorithms (GAs) to the design of simultaneously transmitted orthogonal waveforms. The goal of the application is to determine a suite of "optimal" waveforms (in the Pareto sense) for a single platform radar system performing multiple radar missions simultaneously. The waveform suite is determined by applying the Strength Pareto Evolutionary Algorithm 2 (SPEA2) developed by Zitzler, Laumanns & Theile [1] to find waveform parameters that successfully realize a set of objectives particular to a variety of radar missions. The objectives to optimize are dictated by the particular missions of interest. The mapping of these objective functions to actual radar performance parameters is used in the SPEA2 algorithm to determine how best to simultaneously perform multiple radar missions such as GMTI, AMTI, SAR etc. using a single radar system in a Pareto optimal sense. Preliminary results are presented for a scaled down multimission multi-objective function scenario.

Hearing-impaired individuals frequently cite intelligibility problems in multi-talker environments. Microphone arrays performing time-delay beamforming address conditions of poor signal-to-noise ratio by spatially filtering incoming sound. Existing beam pattern metrics including peak side lobe level, integrated side lobe level, beamwidth, and planar directivity index fail to quantitatively capture all elements essential for improving speech intelligibility in multi-talker situations. The focal index (FI) was developed to address these deficiencies. Simulations were performed to exemplify the robust nature of the FI and to demonstrate the utility of this metric for driving array parameter selection. Beam patterns were generated and the metrics were calculated and evaluated against the strict unidirectional requirements for the array. Array performance was assessed by human subjects in a speech recognition task that incorporated competing speech from multiple locations. Simulations of array output were presented under conditions differing in array sparsity. The resulting human subject data were used to demonstrate the linear relationship (R(2) > 0.975) between speech-intelligibility-weighted FI (SII-FI) and the signal-to-noise ratio thresholds for 20% and 80% correct responses. Data indicate that the FI and SII-FI are robust singular metrics for determining the effectiveness of the array.

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