@conference {
	title = {Weak Continuity with Structural Constraints},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1996},
	month = {06/1996},
	abstract = {Nonlinear regression and nonlinear regularization are two powerful approaches to segmentation and nonlinear filtering. In this correspondence, we propose a hybrid approach that effectively combines the best of both worlds and can be efficiently implemented via the Viterbi algorithm.},
	address = {Corfu, Greece},
	booktitle = {8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing}
}
@conference {
	title = {Computer-Aided Selection of Window and Level for Filmless Radiology},
	author = {Sidiropoulos, N D., Baras, John S., Siegel, E., Protopapas, Z., S. M. Pomerantz, Reiner, B I.},
	year = {1996},
	month = {06/1996},
	address = {Denver, Colorado},
	booktitle = {SCAR \&lsquo;96},
	pages = {pp. 251-256}
}
@conference {
	title = {On Morphological Openings and Closings of Signals in Shaped Noise},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1995},
	month = {10/1995},
	abstract = {In recent work, we have shown that morphological Openings and closingS can be viewed as consistent MAP estimators of morphologically Smooth binary image signals immersed in i.i.d. union (clutter) noise, or suffering from i.i.d. random dropouts. We revisit this viewpoint under a different set of assumptions, which allows the explicit incorporation of geometric and morphological constraints into the noise model, i.e., the noise may now exhibit geometric Structure; surprisingly, it turns out that this affects neither the optimality nor the consistency of these filters.},
	address = {Washington DC},
	booktitle = {The IEEE 1995 International Conference on Image Processing},
	pages = {pp. 518-521}
}
@conference {
	title = {New Results on Optimality and Consistency of Morphological Openings and Closings},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1995},
	month = {07/1995},
	abstract = {It has been recently shown that morphological openings and closings can be viewed as consistent MAP estimators of morphologically smooth signals in i.i.d. noise. We revisit this viewpoint under a different set of assumptions, which allows the explicit incorporation of geometric and morphological constraints into the noise model, i.e., the noise may now exhibit geometric structure; surprisingly, it turns out that this affects neither the optimality nor the consistency of these filters.},
	address = {San Diego, CA},
	booktitle = {The Morphological Image Processing and Random Image Modeling},
	pages = {pp. 118-122}
}
@conference {
	title = {The Combinatorial Basis of Uniformly Bounded Discrete Random Set Theory},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1995},
	month = {03/1995},
	abstract = {We provide a purely combinatorial view of the essential structure of uniformly bounded DRS theory without any reference to discrete probability and highlight the two fundamental functionals arising out of this exposition. The key tool is Moebius inversion. We conclude that the study of uniformly bounded DRS theory is the study of Incidence functions on Boolean algebras of finite rank. Some useful results arise as a by-product of this investigation.},
	address = {Baltimore, MD},
	booktitle = {The 29th Annual Conference on Information Sciences and Systems},
	pages = {pp. 463-467},
	organization = {Johns Hopkins University}
}
@conference {
	title = {Optimal Filtering of Digital Binary Images Corrupted by Union/Intersection Noise},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1992},
	month = {12/1992},
	abstract = {We model digital binary image data as realizations of a uniformly bounded discrete random set (or discrete random set, for short), a mathematical object which can be directly defined on a finite lattice. We consider the problem of estimating realizations of discrete random sets distorted by a degradation process which can be described by a union/intersection noise model. We start by providing some theoretical justification of the popularity of certain Morphological filters, namely Morphological openings, closings, unions of openings, and intersections of closings. In particular, we prove that if the signal is {\textquotedblleft}smooth{\textquotedblright}, then these filters are optimal (in the sense of providing the MAP estimate of the signal) under reasonable worst-case statistical scenarios. Then we consider a class of filters which arises quite naturally from the set-theoretic analysis of optimal filters. We call this the class of mask filters. We consider both fixed and adaptive mask filters~and derive explicit formulas for the optimal mask filter under quite general assumptions on the signal and the degradation process.},
	address = {Tucson, Arizona},
	booktitle = { The 31st IEEE Conference on Decision and Control},
	pages = {pp. 3287-3292},
	organization = {Westin La Paloma}
}
@conference {
	title = {Optimal Morphological Filters for Discrete Random Sets under a  Union or Intersection Noise},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1992},
	month = {11/1992},
	abstract = {We consider the problem of optimal binary image restoration under a union or intersection noise model. Union noise is well suited to model random clutter (obscuration), whereas intersection noise is a good model for random sampling. Our approach is random set-theoretic, i.e. digital images are viewed as realizations of a uniformly bounded discrete random set. First we provide statistical proofs of some "folk theorems{\textquotedblright} of Morphological filtering. In particular, we prove that, under some reasonable worst-case statistical scenarios, Morphological openings, closings, unions of openings, and intersections of closings, can be viewed as MAP estimators of the signal based on the noisy observation. Then we propose a "generic{\textquotedblright} procedure for the design of optimal Morphological filters for independent union or intersection noise.},
	address = {Boston, Massachusetts},
	booktitle = {The SPIE Conference on Visual Communication \& Image Processing},
	pages = {pp. 402-413}
}
@conference {
	title = {Discrete Random Sets: An Inverse Problem, Plus Tools for the Statistical Inference of the Discrete Boolean Model},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1992},
	month = {07/1992},
	abstract = {We consider digital binary images as a realization~of a bounded discrete random set, a mathematical object which can be defined directly on a finite lattice. In this setting, we show that it is possible to move between two equivalent probabilistic model specifications. We formulate a restricted version of the discrete-case analog of a Boolean random set model, obtain it{\textquoteright}~probability mass function. and employ some methods of Morphological image analysis to derive tools for its statistical inference.},
	address = {San Diego, California},
	booktitle = {SPIE Conference on Image Algebra and Morphological Image Processing III},
	pages = {pp. 32-43}
}
@conference {
	title = {Optimal Mask Filtering of Discrete Random Sets under a Union/Intersection Noise Model},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1992},
	month = {03/1992},
	abstract = {We consider the problem of estimating realizations of highly nonstationary Discrete Random Sets, distorted by a degradation process which can be described by a union/intersection model. We start by presenting some important structural results concerning the probabilistic description of Discrete Random Sets defined on a finite lattice. Then we propose a family of filters which can be viewed as lattice operators, and, for each degradation model, derive the optimal filter by means of minimizing a suitable fidelity criterion.},
	address = {Princeton, New Jersey},
	booktitle = {Twenty-Sixth Annual Conference on Information Sciences and Systems},
	pages = {pp. 313-318},
	organization = {Princeton University}
}
@conference {
	title = {Two-Dimensional Signal Deconvolution: Design Issues Related to a Novel Multisensor-based Approach},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1991},
	month = {07/1991},
	abstract = {We employ recent results of analysis in several complex variables to come up with a set of compactly supported approximate deconvolution kernels for the reconstruction of a two dimensional signal based on multiple linearly degraded versions of the signal with a family of kernels that satisfy suitable technical conditions. We discuss the question of convergence of the proposed deconvolution kernels, present simulation results that demonstrate the gain in bandwidth, and propose two data parallel grid layouts for the off-line computation of the deconvolution kernels.},
	address = {San Diego, California},
	booktitle = {SPIE 1991 International Symposium on Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision},
	pages = {pp. 356-366}
}
@conference {
	title = {Two-Dimensional Signal Deconvolution Using Multiple Sensors},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1991},
	month = {03/1991},
	abstract = {We consider the two dimensional Analytic Bezout Equation (ABE) and investigate the properties of a particular solution, based on certain conditions imposed on the convolution kernels. We propose the use of approximate deconvolution kernels, prove compactness of their support, and discuss various design problems pertaining to the question of convergence of the proposed deconvolution kernels. We point out the fact that these approximate deconvolution kernels exhibit a strong asymmetry in the FT domain, and propose solutions to this problem. Simulation results are presented in order to demonstrate the gain in bandwidth, the asymmetry of the FT of the overall system, and the effectiveness of the proposed solutions to compensate for this asymmetry.},
	address = {Baltimore, MD},
	booktitle = {The 1991 Conference on Information Sciences and Systems},
	pages = {pp. 594-599},
	organization = {Johns Hopkins University}
}
@article {
	title = {Weak Continuity with Structural Constraints},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1997},
	month = {12/1997},
	abstract = {Nonlinear regression and nonlinear regularization are two powerful approaches to segmentation and nonlinear filtering. In this correspondence, we propose a hybrid approach that effectively combines the best of both worlds and can be efficiently implemented via the Viterbi algorithm.},
	pages = {pp.  3096-3104},
	volume = {Vol. 45},
	number = {No. 12},
	journal = {IEEE Transactions on Signal Processing}
}
@article {
	title = {Further results on MAP Optimality and Strong Consistency of Certain Classes of Morphological Filters},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1996},
	month = {05/1996},
	abstract = {Morphological openings and closings can be viewed as consistent MAP estimators of smooth random binary image signals immersed in i.i.d. clutter, or suffering from i.i.d. random dropouts. We revisit this viewpoint under much more general assumptions and show that, quite surprisingly, the above interpretation is still valid.},
	pages = {pp. 762-764},
	volume = {Vol. 5},
	number = {No. 5},
	journal = {IEEE Transactions on Image Processing}
}
@article {
	title = {Binary Images Corrupted by Union/Intersection Noise},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1995},
	month = {01/1995},
	abstract = {This paper derives risk sensitive optimality results for finite-dimensional controllers. The controllers can be conveniently derived for {\textquoteright}linearized{\textquoteright} (approximate) models (applied to nonlinear stochastic systems). Performance indices for which the controllers are optimal for the nonlinear plants are revealed. That is, inverse risk-sensitive optimal control results for nonlinear stochastic systems with finite dimensional linear controllers are generated. It is instructive to see from these results that as the nonlinear plants approach linearity, the risk sensitive finite dimensional controllers designed using linearized plant models and risk sensitive indices with quadratic cost kernels, are optimal for a risk sensitive cost index which approaches one with a quadratic cost kernel. Also even far from plant linearity, as the linearized model noise variance becomes suitably large, the index optimized is dominated by terms which can have an interesting and practical interpretation.},
	pages = {pp. 223-230},
	journal = {Systems and Control Letters, 26}
}
@article {
	title = {Algebraic Analysis of the Generating Functional for Discrete Random Sets, and Statistical Inference for Intensity in the Discrete Boolean Random Set Model},
	author = {Sidiropoulos, N D., Baras, John S., Berenstein, Carlos A.},
	year = {1994},
	month = {06/1994},
	abstract = {We consider binary digital images as realizations fo a uniformly bounded discrete random set, a mathematical object that can be defined directly on a finite lattice. In this setting, we show that it is possible to move between two equivalent probabilistic model specifications. We formulate a restricted version of the discrete-case analog of a Boolean random-set model obtained its probability mass function, and use some methods of morphological image analysis to derive tools for its statistical stability},
	pages = {pp.  273-290},
	volume = {Vol. 4},
	number = {Issue 3},
	journal = {Journal of Mathematical Imaging and Vision}
}
