Sklain, C.[Cheryl]
* 1989: Multilevel Vision Based Spatial Reasoning for Robotic Tasks
Sklair, C.
* 1991: Employing Sensor Repositioning to Refine Spatial Reasoning in an Industrial Robotic Environment
Sklair, C.W.
* 1991: Perspective View of Three Points, The
Sklansky, J.
* 1969: Recognition of Convex Blobs
* 1970: Recognition of Convex Blobs
* 1970: Thresholded Convolutions Operations
* 1972: Measuring Concavity on a Rectangular Mosaic
* 1972: Minimum Perimeter Polygons of Digitized Silhouettes
* 1972: Parallel Mechanism for Describing Silhouettes, A
* 1974: Parallel Detection of Concavities in Cellular Blobs
* 1975: Automatic Detection of Rib Contours in Chest Radiographs
* 1975: Finding Circles by an Array of Accumulators
* 1975: On Filling Cellular Concavities
* 1976: Ladder-Structured Decision Tree for Recognizing Tumors in Chest Radiographs, A
* 1976: Parallel Detection of Concavities in Cellular Blobs
* 1976: Theory of Nonuniformly Digitized Binary Pictures, A
* 1977: Finding the Rib Cage in Chest Radiographs
* 1978: Image Segmentation and Feature Extraction
* 1978: Markov Random Field Models of Digitized Image Texture
* 1978: Markov Random Fields as Models of Digitized Image Texture
* 1978: On the Hough Technique for Curve Detection
* 1979: Skeletons from Sequential Boundary Data
* 1980: Fast polygonal approximation of digitized curves
* 1980: Minimal Rectangular Partitions of Digitized Blobs
* 1980: Piecewise Linear Detection of Boundaries in Chest Radiographs
* 1980: Use of Markov Random Fields as Models of Texture, The
* 1981: Detection and Segmentation of Blobs in Infrared Images, The
* 1981: Detection and Segmentation of Blobs in Infrared Images, The
* 1981: Skeleton Generation from x,y Boundary Sequences
* 1982: Detecting the Edges of Lung Tumors by Classification Techniques
* 1982: Digital and Cellular Convexity
* 1982: Finding the Convex Hull of a Simple Polygon
* 1982: Gestalt-Guided Boundary Follower for X-Ray Images of Lung Nodules, A
* 1983: Edge Detection by Estimation of Multiple-Order Derivatives
* 1984: Fast Recursive Algorithm for Binary-Valued Two-Dimensional Filters, A
* 1984: Minimal Rectangular Partitions of Digitized Blobs
* 1984: Reconstructing a Network of Three-Dimensional Curves from a Small Number of Projections
* 1985: Note on Duhamel Integrals and Running Average Filters, A
* 1985: relative neighborhood graph for mixed feature variables, The
* 1986: Biomedical Image Analysis
* 1986: Efficient Two-Dimensional Filters Using B-Spline Functions
* 1987: Multiple-Order Derivatives for Detecting Local Image Characteristics
* 1987: Recursive Algorithms for Implementing Digital Image Filters
* 1988: Automated design of piecewise-linear classifiers of multiple-class data
* 1988: Biplane analysis of atheromatous coronary arteries
* 1988: Experiments on Mapping Techniques for Exploratory Pattern Analysis
* 1988: On Automatic Feature Selection
* 1988: Overview of Mapping Techniques for Exploratory Pattern Analysis, An
* 1989: Note on Genetic Algorithms for Large-Scale Feature Selection, A
* 1990: Automated design of linear tree classifiers
* 1990: Fast tree classifiers
* 1990: Robust Curve Detection by Temporal Geodesics
* 1991: Robust classifiers by mixed adaptation
* 1991: Segmentation of People in Motion
* 1992: Controlling Illumination color to Enhance Object Discriminability
* 1992: Estimating Optical Flow from Clustered Trajectories in Velocity-Time
* 1992: Extracting Nonrigid Moving Objects By Temporal Edges
* 1992: Interpolation of cinematic sequences
* 1992: Multiresolution method for reconstructing the cross sections of coronary arteries from biplane angiograms
* 1993: Estimating optical flow for large interframe displacements
* 1993: Graph-Based Thinning for Binary Images
* 1994: Linear classifiers by window training and basis exchange
* 1995: Colored Illumination for Enhancing Discriminability in Machine Vision
* 1996: Neural Modeling of Piecewise Linear Classifiers
* 1996: Piecewise-Linear Classifiers Using Binary-Tree Structure and Genetic Algorithm
* 1997: Large-Scale Feature Selection
* 1997: Neural-Network That Visualizes What It Classifies, A
* 1998: Visual Neural Classifier, A
* 1998: Visual Neural Network that Learns Perceptual Relationships, A
* 2000: Comparison of algorithms that select features for pattern classifiers
* 2001: Segmenting multisensor aerial images in class-scale space
Includes: Sklansky, J. Sklansky, J.[Jack]
68 for Sklansky, J.