Jack Sklansky

Brian Lofy, Jack Sklansky: Segmenting multisensor aerial images in class-scale space. Pattern Recognition 34(9): 1825-1839 (2001)

Mineichi Kudo, Jack Sklansky: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33(1): 25-41 (2000)

Mineichi Kudo, Petr Somol, Pavel Pudil, Masaru Shimbo, Jack Sklansky: Comparison of Classifier-Specific Feature Selection Algorithms. SSPR/SPR 2000: 677-686

Chester Ornes, Jack Sklansky: A visual neural classifier. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(4): 620-625 (1998)

Jack Sklansky, E. Y. Tao, Chester Ornes, A. C. Disher: A Neurodatabase System for Mammographic Screening. CBMS 1998: 78-83

Mineichi Kudo, Jack Sklansky: Classifier-Independent Feature Selection For Two-Stage Feature Selection. SSPR/SPR 1998: 548-554

Chester Ornes, Jack Sklansky: A neural network that visualizes what it classifies. Pattern Recognition Letters 18(11-13): 1301-1306 (1997)

Jack Sklansky, Mark Vriesenga: Genetic Selection and Neural Modeling of Piecewise-Linear Classifiers. IJPRAI 10(5): 587-612 (1996)

Bing-Bing Chai, Tong Huang, Xinhua Zhuang, Yunxin Zhao, Jack Sklansky: Piecewise linear classifiers using binary tree structure and genetic algorithm. Pattern Recognition 29(11): 1905-1917 (1996)

Leon Bobrowski, Jack Sklansky: Linear classifiers by window training. IEEE Transactions on Systems, Man, and Cybernetics 25(1): 1-9 (1995)

Satoshi Suzuki, Naonori Ueda, Jack Sklansky: Graph-Based Thinning for Binary Images. IJPRAI 7(5): 1009-1030 (1993)

R. Agarwal, Jack Sklansky: Estimating Optical Flow for Large Interframe Displacements. CAIP 1993: 403-411

D. Petkovic, J. Wilder, Masakazu Ejiri, Robert M. Haralick, Ramesh Jain, Peter A. Ruetz, Jack Sklansky, C. W. Swonger: Machine vision in the 1990s: Applications and how to get there. Mach. Vis. Appl. 4(2): 113-126 (1991)

Dan Gutfinger, Jack Sklansky: Robust Classifiers by Mixed Adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 13(6): 552-567 (1991)

Youngtae Park, Jack Sklansky: Automated design of linear tree classifiers. Pattern Recognition 23(12): 1393-1412 (1990)

Dan Gutfinger, R. Nishimura, H. Doi, Jack Sklansky: Robust curve detection by temporal geodesics. ICCV 1990: 752-756

Wojciech W. Siedlecki, Jack Sklansky: A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters 10(5): 335-347 (1989)

Wojciech W. Siedlecki, Jack Sklansky: Constrained Genetic Optimization via Dynarnic Reward-Penalty Balancing and Its Use in Pattern Recognition. ICGA 1989: 141-150

Ramesh Jain, André Oosterlinck, Jorge Sanz, Jack Sklansky, Masahiko Yachida: An editorial welcome. Mach. Vis. Appl. 1(1): 1 (1988)

Wojciech W. Siedlecki, Kinga Siedlecka, Jack Sklansky: An overview of mapping techniques for exploratory pattern analysis. Pattern Recognition 21(5): 411-429 (1988)

Wojciech W. Siedlecki, Kinga Siedlecka, Jack Sklansky: Experiments on mapping techniques for exploratory pattern analysis. Pattern Recognition 21(5): 431-438 (1988)

Leonard A. Ferrari, P. V. Sankar, S. Shinnaka, Jack Sklansky: Recursive Algorithms for Implementing Digital Image Filters. IEEE Trans. Pattern Anal. Mach. Intell. 9(3): 461-466 (1987)

Leonard A. Ferrari, Jack Sklansky: A note on duhamel integrals and running average filters. Computer Vision, Graphics, and Image Processing 29(3): 358-360 (1985)

Manabu Ichino, Jack Sklansky: The relative neighborhood graph for mixed feature variables. Pattern Recognition 18(2): 161-167 (1985)

Leonard A. Ferrari, Jack Sklansky: A fast recursive algorithm for binary-valued two-dimensional filters. Computer Vision, Graphics, and Image Processing 26(3): 292-302 (1984)

Leonard A. Ferrari, P. V. Sankar, Jack Sklansky: Minimal rectangular partitions of digitized blobs. Computer Vision, Graphics, and Image Processing 28(1): 58-71 (1984)

Leonard A. Ferrari, Jack Sklansky: A fast recursive algorithm for binary valued two-dimensional filters. Computer Vision, Graphics, and Image Processing 22(2): 310 (1983)

Pathamadi V. Sankar, Jack Sklansky: A Gestalt-Guided Heuristic Boundary Follower for X-Ray Images of Lung Nodules. IEEE Trans. Pattern Anal. Mach. Intell. 4(3): 326-331 (1982)

Chul E. Kim, Jack Sklansky: Digital and cellular convexity. Pattern Recognition 15(5): 359-367 (1982)

Jack Sklansky, Victor Gonzalez: Fast polygonal approximation of digitized curves. Pattern Recognition 12(5): 327-331 (1980)

Jack Sklansky: On the Hough Technique for Curve Detection. IEEE Trans. Computers 27(10): 923-926 (1978)

Harry Wechsler, Jack Sklansky: Finding the rib cage in chest radiographs. Pattern Recognition 9(1): 21-30 (1977)

Jack Sklansky, Luigi P. Cordella, Stefano Levialdi: Parallel Detection of Concavities in Cellular Blobs. IEEE Trans. Computers 25(2): 187-196 (1976)

Dana H. Ballard, Jack Sklansky: A Ladder-Structured Decision Tree for Recognizing Tumors in Chest Radiographs. IEEE Trans. Computers 25(5): 503-513 (1976)

Caolyn Kimme, Dana Balard, Jack Sklansky: Finding Circles by an Array of Accumulators. Commun. ACM 18(2): 120-122 (1975)

Harry Wechsler, Jack Sklansky: Automatic Detection Of Rib Contours in Chest Radiographs. IJCAI 1975: 688-694

Jack Sklansky, P. J. Najin: A Parallel Mechanism for Recognizing Silhouettes. IFIP Congress (1) 1971: 224-228

Jack Sklansky: Thresholded Convolutions Operations. J. ACM 17(1): 161-165 (1970)

Jack Sklansky: Recognition of convex blobs. Pattern Recognition 2(1): 3-10 (1970)

Jack Sklansky, Neil J. Bershad: The Dynamics of Time-Varying Threshold Learning. Information and Control 15(6): 455-486 (1969)

Jack Sklansky: Recognizing Convex Blobs. IJCAI 1969: 107-116

Jack Sklansky, M. Finkelstein: A Formalism for Program Translation. J. ACM 15(2): 165-175 (1968)