J. Sklansky

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The use of Markov Random Fields as models of texture

M. Hassner, J. Sklansky, 1980 .

Segmentation of people in motion

J. Sklansky, A. Shio, 1991, Proceedings of the IEEE Workshop on Visual Motion.

Robust Classifiers by Mixed Adaptation

J. Sklansky, D. Gutfinger, 1991, IEEE Trans. Pattern Anal. Mach. Intell..

On the Hough Technique for Curve Detection

Jack Sklansky, J. Sklansky, 1978, IEEE Transactions on Computers.

Learning systems for automatic control

J. Sklansky, J. Sklansky, 1966 .

Tumor detection in radiographs.

J. Sklansky, D. Ballard, 1973, Computers and biomedical research, an international journal.

The Use of Markov Random Fields as Models of Texture

M. Hassner, J. Sklansky, 1981 .

An adaptive nonparametric linear classifier

J. Sklansky, G. Wassel, 1976, Proceedings of the IEEE.

Automated design of multiple-class piecewise linear classifiers

J. Sklansky, Youngtae Park, 1989 .

Computing the skeleton of coronary arteries in cineangiograms.

J. Sklansky, T. V. Nguyen, 1986, Computers and biomedical research, an international journal.

Fast polygonal approximation of digitized curves

Jack Sklansky, Victor Gonzalez, J. Sklansky, 1980, Pattern Recognit..

Estimating the 3D skeletons and transverse areas of coronary arteries from biplane angiograms.

J. Sklansky, K. Kitamura, J. Tobis, 1988, IEEE transactions on medical imaging.

Pattern classifiers and trainable machines : with 117 illustrations

J. Sklansky, G. Wassel, 1981 .

Finding circles by an array of accumulators

Jack Sklansky, Caolyn Kimme, Dana Balard, 1975, Commun. ACM.

Locally Trained Piecewise Linear Classifiers

Jack Sklansky, Leo Michelotti, J. Sklansky, 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Skeleton generation fromx, y boundary sequences

J. Sklansky, B. A. Shapiro, Jim Pisa, 1981 .

A Ladder-Structured Decision Tree for Recognizing Tumors in Chest Radiographs

Dana H. Ballard, Jack Sklansky, J. Sklansky, 1976, IEEE Transactions on Computers.

Finding the rib cage in chest radiographs

Harry Wechsler, Jack Sklansky, J. Sklansky, 1977, Pattern Recognit..

Automated design of linear tree classifiers

Jack Sklansky, Youngtae Park, J. Sklansky, 1990, Pattern Recognit..

Efficient two-dimensional filters using B -spline functions

J. Sklansky, P. Sankar, L. Ferrari, 1986 .

A note on genetic algorithms for large-scale feature selection

Jack Sklansky, Wojciech W. Siedlecki, J. Sklansky, 1989, Pattern Recognition Letters.

On Automatic Feature Selection

Jack Sklansky, Wojciech W. Siedlecki, J. Sklansky, 1988, Int. J. Pattern Recognit. Artif. Intell..

On filling cellular concavities

J. Sklansky, 1975 .

Measuring Concavity on a Rectangular Mosaic

Jack Sklansky, J. Sklansky, 1972, IEEE Transactions on Computers.

A stopping rule for threshold learning

H. R. Ramanujam, J. Sklansky, 1973 .

Computer-aided, case-based diagnosis of mammographic regions of interest containing microcalcifications.

Eric Y. Tao, J Sklansky, J. Sklansky, 2000, Academic radiology.

Computational discovery for nonlinear classifiers

J. Sklansky, 1992, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics.

Linear Classifiers for Nonseparable Classes

J. Sklansky, G. Wassel, 1981 .

Loss Functions and Stochastic Approximation

J. Sklansky, G. Wassel, 1981 .

Linearly Separable Classes

J. Sklansky, G. Wassel, 1981 .

Correspondence: Adaptation, learning, self-repair, and feedback

J. Sklansky, J. Sklansky, 1964 .

Continuous-State Models

J. Sklansky, G. Wassel, 1981 .

Technical Note: A new TLD‐phantom measurement system for determining dose distribution levels in the right and left breast from spiral CT chest imaging

Jeffery L. Hall, J. Sklansky, E. Surprenant, 2002, Journal of applied clinical medical physics.

Database-Aided Diagnosis in Digital Mammography

J. Sklansky, 2001 .

Works Title Estimating the 3 D skeletons and transverse areas of coronary arteries from biplane

J. Sklansky, K. Kitamura, J. Tobis, 2004 .

On Automatic Feature Selection

Jack Sklansky, Wojciech W. Siedlecki, J. Sklansky, 1988, Int. J. Pattern Recognit. Artif. Intell..

Pattern Classifiers and Trainable Machines

Jack Sklansky, Gustav N. Wassel, J. Sklansky, 1981 .

Biomedical Image Analysis

Jack Sklansky, J. Sklansky, 1980 .

Circularly coded x-ray tomography.

J. Sklansky, S. Kawata, 1984, Applied optics.

Computer-aided analysis of radiographs

Dana H. Ballard, Jack Sklansky, Morton Nadler, 1972, CDC 1972.

Constrained Genetic Optimization via Dynamic Reward-penalty Balancing and its Use in Pattern Recognition

Jack Sklansky, Wojciech W. Siedlecki, J. Sklansky, 1993, Handbook of Pattern Recognition and Computer Vision.

Comparison of algorithms that select features for pattern classifiers

Mineichi Kudo, Jack Sklansky, J. Sklansky, 2000, Pattern Recognit..

A Formalism for Program Translation

Jack Sklansky, M. Finkelstein, J. Sklansky, 1968, JACM.

A note on genetic algorithms for large-scale feature selection

Jack Sklansky, Wojciech W. Siedlecki, J. Sklansky, 1989, Pattern Recognit. Lett..

Image Segmentation and Feature Extraction

Jack Sklansky, J. Sklansky, 1978, IEEE Transactions on Systems, Man, and Cybernetics.

Thresholded Convolutions Operations

Jack Sklansky, J. Sklansky, 1970, JACM.

Multiple-order derivatives for detecting local image characteristics

Jack Sklansky, Masayuki Hashimoto, J. Sklansky, 1987 .

Finding the convex hull of a simple polygon

Jack Sklansky, J. Sklansky, 1982, Pattern Recognit. Lett..

A Gestalt-Guided Heuristic Boundary Follower for X-Ray Images of Lung Nodules

Jack Sklansky, Pathamadi V. Sankar, J. Sklansky, 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Extracting nonrigid moving objects by temporal edges

S. Suzuki, J. Sklansky, J. Sklansky, 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

Genetic Selection and Neural Modeling of Piecewise-Linear Classifiers

Jack Sklansky, Mark Vriesenga, J. Sklansky, 1996, Int. J. Pattern Recognit. Artif. Intell..

A neural network that explains as well as predicts financial market behavior

Jack Sklansky, Chester Ornes, J. Sklansky, 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

R70-16 Unate Cellular Logic

Jack Sklansky, J. Sklansky, 1970, IEEE Transactions on Computers.

Two-Resolution Detection of Lung Tumors in Chest Radiographs

J. Sklansky, D. Petković, J. Sklansky, 1984 .

Parallel Detection of Concavities in Cellular Blobs

Luigi P. Cordella, Stefano Levialdi, Jack Sklansky, 1976, IEEE Transactions on Computers.

Recognition of convex blobs

Jack Sklansky, J. Sklansky, 1970, Pattern Recognit..

Motion-compensated temporal filtering of B-mode echocardiograms

Robert A. Close, Jack Sklansky, Dietrich Meyer-Ebrecht, 1997, Medical Imaging.

The Delay Method of Analyzing Markov Chains

J. Sklansky, K. R. Kaplan, J. Sklansky, 1968 .

The relative neighborhood graph for mixed feature variables

Manabu Ichino, Jack Sklansky, J. Sklansky, 1985, Pattern Recognit..

An overview of mapping techniques for exploratory pattern analysis

Jack Sklansky, Kinga Siedlecka, Wojciech W. Siedlecki, 1988, Pattern Recognit..

Conditional-Sum Addition Logic

Jack Sklansky, J. Sklansky, 1960, IRE Trans. Electron. Comput..

Reconstructing the 3-D medial axes of coronary arteries in single-view cineangiograms

Jack Sklansky, Thinh V. Nguyen, J. Sklansky, 1994, IEEE Trans. Medical Imaging.

Training a One-Dimensional Classifier to Minimize the Probability of Error

Jack Sklansky, Gustav N. Wassel, J. Sklansky, 1972, IEEE Trans. Syst. Man Cybern..

Controlling illumination color to enhance object discriminability

Glenn Healey, Kalman Peleg, Jack Sklansky, 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

The Dynamics of Time-Varying Threshold Learning

Jack Sklansky, Neil J. Bershad, J. Sklansky, 1969, Inf. Control..

Automatic detection of microcalcifications in digital mammography

Jack Sklansky, Eric Y. Tao, Chester J. Ornes, 1998, Medical Imaging.

Minimum-Perimeter Polygons of Digitized Silhouettes

Jack Sklansky, Robert L. Chazin, Bruce J. Hansen, 1972, IEEE Transactions on Computers.

A note on duhamel integrals and running average filters

Jack Sklansky, Leonard A. Ferrari, J. Sklansky, 1985, Comput. Vis. Graph. Image Process..

A Parallel Mechanism for Describing Silhouettes

Paul J. Nahin, Jack Sklansky, J. Sklansky, 1972, IEEE Transactions on Computers.

Threshold learning and Brownian motion (Corresp.)

Jack Sklansky, Neil J. Bershad, J. Sklansky, 1971, IEEE Trans. Inf. Theory.

Traininga One-DimensionalClassifier to MinimizetheProbability of Error

Jack Sklansky, J. Sklansky, 1972 .

Scan-Along Polygonal Approximation for Data Compression of Electrocardiograms

J. Sklansky, G. Hostetter, Soon-Bum Shin, 1983, IEEE Transactions on Biomedical Engineering.

Flexible Mask Subtraction For Digital Angiography

Luong Van Tran, Jack Sklansky, J. Sklansky, 1988, Defense, Security, and Sensing.

The Detection and Segmentation of Blobs in Infrared Images

Jack Sklansky, Lewis G. Minor, J. Sklansky, 1981, IEEE Transactions on Systems, Man, and Cybernetics.

Classifier-Independent Feature Selection For Two-Stage Feature Selection

Mineichi Kudo, Jack Sklansky, J. Sklansky, 1998, SSPR/SPR.

A Theory of Nonuniformly Digitized Binary Pictures

Dennis F. Kibler, Jack Sklansky, J. Sklansky, 1976, IEEE Transactions on Systems, Man, and Cybernetics.

Boundary Detection in Medical Radiographs

J. Sklansky, J. Sklansky, 1976 .