Michael K. Schneider

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Recent experiences in urban operations highlight the need for automated methods to help execute the data collection and association functions in the intelligence process. Technology development in this area has been challenged by the heterogeneity of data to be processed and by the diversity of potential applications. Here, we report lessons learned during(More)
This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for(More)
This work presents a method for segmenting images based on gradients in the intensity function. Past approaches have centered on formulating the problem in the context of variational calculus as the minimization of a functional involving the image intensity and edge functions. Computational methods for nding the minima of such variational problems are prone(More)
Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to robustly, accurately, and repeatably quantify morphological measures from such data is aided by the ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal(More)
We present verifiable sufficient conditions for determining optimal policies for finite horizon, discrete time Markov decision problems (MDPs) with terminal reward. In particular, a control policy is optimal for the MDP if (i) it is optimal at the terminal time, (ii) immediate decisions can be deferred to future times, and (iii) the probability transition(More)
This paper describes an approach to automatic nuggetization and implemented system employed in GALE Distillation evaluation to measure the information content of text returned in response to an open-ended question. The system identifies nuggets, or atomic units of information, categorizes them according to their semantic type, and selects different types of(More)
Computing the linear least-squares estimate of a high-dimensional random quantity given noisy data requires solving a large system of linear equations. In many situations, one can solve this system efficiently using the conjugate gradient (CG) algorithm. Computing the estimation error variances is a more intricate task. It is difficult because the error(More)