Keywords s

Saliency * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Automatic Detection of Interest Areas of an Image or of a Sequence of Images
* Saliencies and Symmetries: Toward 3D Object Recognition from Large Model Databases

Salient Regions * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Object Extraction, Object Detection for Database Indexing (H4)
* Data and Model-Driven Selection Using Color Regions
* Salient Closed Boundary Extraction with Ratio Contour

Sampling * Image Manipulation -- Sampling, Reduction, Decimation, General Size Changes (H3)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

SAR Image Analysis * Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Localized Radon Transform-Based Detection of Linear Features in Noisy Images

SAR Imagery * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Object Identification From Multiple Images Based on Point Matching Under A General Transformation
* Segmentation of Polarimetric Synthetic Aperture Radar Data

SAR * *Automatic ObjectTarget Recognition VIII
* *Signal Processing, Sensor Fusion, and Target Recognition V
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* ATR -- Model, Object Based Radar and SAR Recognition (H3)
* ATR -- Radar, SAR Applications (H2)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Detection and Analysis of Edges, Lines, Curves, Corners, Hough Transform (H)
* Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Interferometric SAR Analysis, InSAR, IFSAR, ISAR (H2)
* Radar, SAR Analysis (H1)
* Road Extraction in Radar and SAR (H2)
* SAR, Generation, Image Construction, Reconstruction (H2)
* Slope and Shape from Radar and SAR (H2)
* Application of Random Transform Techniques to Wake Detection in Seasat-A SAR Images
* Generation of a Digital Elevation Model-Based on Synthetic-Aperture Radar Airborne Stereoscopic Images: Application to Airsar Images (Hawaii)
* Image-Reconstruction from Fourier-Transform Magnitude with Applications to Synthetic-Aperture Radar Imaging
* Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images
* Performance of a High-Resolution Polarimetric SAR Automatic Target Recogniton System
19 for SAR

Scale-Space Matching * Hierarchical/Scale-Space Contour Matching and Descriptions (H3)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Scale Space * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Detection and Analysis of Edges, Lines, Curves, Corners, Hough Transform (H)
* Filters in Scale Space (H3)
* Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Multiple Resolution Edge Detectors to Improve Performance, Hierarchical (H2)
* Multiresolution, Hierarchical Restoration Techniques (H3)
* Optical Flow Field Computations and Use (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Scale Space -- Lindeberg References (H3)
* Scale Space and Multi-Scale Techniques (H1)
* Scale Space Computation of Features (H2)
* Scale Space for Descriptions (H2)
* Scale Space Theory (H2)
* Abstraction and Scale-Space Events in Image Understanding
* Accurate Edge Detection for Multiple Scale Processing
* Behavior of Edges in Scale Space
* Estimation of Discontinuous Displacement Vector Fields with the Minimum Description Length Criterion
* Fast Algorithms for Estimating Local Image Properties
* Framework for Adaptive Scale Space Tracking Solutions to Problems in Computational Vision, A
* Generic Neighborhood Operators
* Hierarchical Pre-Segmentation without Prior Knowledge
* Histogram Analysis Using A Scale Space Approach
* Lambda-Tau-Space Representation of Images and Generalized Edge Detector
* Reasoning about Edges in Scale Space
* Renormalized Curvature Scale Space and the Evolution Properties of Planar Curves, The
* Representation for Visual Information, A
* Scale Space Aspect Graph, The
* Scale-Based Detection of Corners of Planar Curves
* Scale-Space Properties of the Multiscale Morphological Dilation Erosion
* Segmentation of Multidimensional Images
* Signal Matching Through Scale Space
* Singularities of Contrast Functions in Scale Space
* Structure of Images, The
* Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection, A
* Unified Perspective on Computational Techniques for the Measurement of Visual Motion, A
40 for Scale Space

Scene Analysis, Early papers * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* How to See a Simple World: An Exegesis of Some Computer Programs for Scene Analysis

Seal Recogniton * Chinese Character Seals (H3)
* OCR, Document Analysis and Character Recognition Systems (H)

Search Techniques * Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Machine Vision as State-Space Search

Segmentation * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Binarization -- Threshold selection for documents (H3)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Character Segmentation, Segmentation of Individual Characters (H2)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
* OCR, Document Analysis and Character Recognition Systems (H)
* Other Segmentation Techniques (H1)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Radar, Extraction of Features, Segmentation (H2)
* Snakes, Interactive Region Segmentations (H4)
* Snakes, Region Segmentation Issues (H3)
* Syntactic Methods for Region Segmentation (H2)
* Unsupervised Clustering and Optimal Clusters for Segmentation (H3)
* Adaptive segmentation of MRI data
* Automatic Segmentation and Indexing in a Database of Bird Images
* Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery
* Final Report
* Region Growing in Textured Outdoor Scenes
* Registration and Exploitation of Multi-pass Airborne Synthetic Aperture Radar Images
* Robust Image Segmentation Using Genetic Algorithm with a Fuzzy Measure
22 for Segmentation

Segmentation, 2-D Histogram * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Two-Dimensional Histogram Analysis for Segmentation (H2)
* Clustering Edge Values for Threshold Selection
* Progress Report of Segmentation Using Convergent Evidence

Segmentation, 3-D Data * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Curvature and Features of Surfaces and Range Data (H2)
* Region Techniques for Range and Surfaces (H2)
* Surface Patches, Planes, Descriptions from Range (H2)
* Segmentation Through Symbolic Surface Descriptions

Segmentation, Algorithms * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Algorithms for Image Segmentation

Segmentation, Binarization * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* OCR, Document Analysis and Character Recognition Systems (H)
* Segmentation by Thresholding, Quantization, or Relaxation (H2)
* Block Segmentation and Text Extraction in Mixed Text/Image Documents
* Evaluation of Binarization Methods for Document Images
* Goal-Directed Evaluation of Binarization Methods
* Gray Level Thresholding in Badly Illuminated Images
* IBM 1975 Optical Page Reader, Part II: Video Thresholding, The
* Iterative Thresholding Algorithm for Image Segmentation, An
* Picture Thresholding Using an Iterative Selection Method
* Segmentation of Document Images
* Spatial Thresholding Method for Image Segmentation, A
* Threshold Selection Based on a Simple Image Statistic
13 for Segmentation, Binarization

Segmentation, Blobs * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Detecting and Extracting Compact Textured Regions Using Pyramids
* Detection and Segmentation of Blobs in Infrared Images, The
* Maximum Likelihood Estimation of Markov-Process Blob Boundaries in Noisy Images
* Multi-scale Region Detector, A
* Using Pyramids to Define Local Thresholds for Blob Detection

Segmentation, Clustering * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Clustering for Region Segmentation (H2)
* On Threshold Selection Using Clustering Criteria

Segmentation, Color * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Color Applied to Segmentation (H1)
* Color Segmentation, Healey (H2)
* Complete Segmentation Systems Based on Ohlander Technique (H2)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Application of Color Information to Visual Perception
* Color Image Analysis with an Intrinsic Reflection Model
* Color Image Segmentation Using Markov Random Fields
* Compression of Color Image via the Technique of Surface Fitting
* Computational Techniques in the Visual Segmentation of Static Scenes
* Hierarchical Color Image Region Segmentation for Content-Based Image Retrieval System
* Iterative Segmentation Method Based on a Contextual Color and Shape Criterion, An
* Multiresolution Color Image Segmentation
* Physics-Based Segmentation of Complex Objects Using Multiple Hypotheses of Image-Formation
* Production System for Region Analysis, A
* Recursive Clustering Technique for Color Picture Segmentation, A
* Relaxation Methods in Multispectral Pixel Classification
* Scene Segmentation by Cluster Detection in Color Space
19 for Segmentation, Color

Segmentation, Combined with Edges * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Combining Region and Edge Based Techniques (H1)

Segmentation, Comparison * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Comparison and Evaluation of Different Techniques (H1)

Segmentation, Criteria * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Image Segmentation Techniques

Segmentation, Curves * Basic Algorithms to Partition Curves, The Early Days (H2)
* Basic Algorithms to Partition Curves (H2)
* Detection and Analysis of Edges, Lines, Curves, Corners, Hough Transform (H)

Segmentation, Divide and Conquer * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Boundary Detection of Radiographic Images by a Threshold Method

Segmentation, Edges * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Active Contours, Snakes or Deformable Curves (H2)
* Combining Region and Edge Based Techniques (H1)
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Edge and Region Analysis for Digital Image Data
* Finding Object Boundaries Using Guided Gradient Ascent
* Integrating Region Growing and Edge Detection
* Locating Cultural Regions in Aerial Imagery using Geometric Cues
* New Method for Image Segmentation, A
* Parallel Color Algorithm for Segmenting Images of 3-D Scenes, A
* Scene Analysis Using Regions
* Segmentation of Fingerprint Images Using the Directional Image
* Segmentation System Based on Thresholding, A
14 for Segmentation, Edges

Segmentation, Evaluation * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Comparison and Evaluation of Different Techniques (H1)
* Comparative Performance Study of Several Global Thresholding Techniques for Segmentation, A
* Comparison of Some Segmentation Algorithms for Cytology, A
* Error Measures for Scene Segmentation
* Low Level Image Segmentation: An Expert System
* MOOSE Users' Manual Implementation Guide Evaluation
* Multiresolution Color Image Segmentation
* On the Evaluation of Scene Analysis Algorithms
* Phoenix Image Segmentation System: Description and Evaluation, The
* Segmentation of FLIR Images: A Comparative Study
* Survey on Evaluation Methods for Image Segmentation, A
12 for Segmentation, Evaluation

Segmentation, Expert system * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Low Level Image Segmentation: An Expert System
* Rule-Based Image Segmentation: A Dynamic Control Strategy Approach

Segmentation, Facet Model * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Edge and Region Analysis for Digital Image Data
* Experiments in Segmentation Using a Facet Model Region Grower
* Generalized Sloped Facet Models Useful in Multispectral Image Analysis

Segmentation, Grouping * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Perceptual Grouping, Perceptual Organization Techniques (H1)
* Perceptual Organization for Segmentation and Description

Segmentation, Guided * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Fua and Leclerc Guided Segmentation Papers (H2)
* Techniques for Model Guided Segmentation (H1)

Segmentation, Histogram * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Complete Segmentation Systems Based on Ohlander Technique (H2)
* Complete Systems Derived from the Univ. Massachusetts Work (H2)
* Histogram Analysis for Threshold Selection and Segmentation (H2)
* Two-Dimensional Histogram Analysis for Segmentation (H2)
* Clustering Edge Values for Threshold Selection
* Comparison of Some Segmentation Algorithms for Cytology, A
* Image Segmentation by a Parallel, Non-Parametric Histogram Based Clustering Algorithm
* Iterative Thresholding Algorithm for Image Segmentation, An
* Object Enhancement and Extraction
* Picture Segmentation Using a Recursive Region Splitting Method
* Probabilistic Information Fusion for Multi-Modal Image Segmentation
* Progress Report of Segmentation Using Convergent Evidence
* Scene Segmentation by Cluster Detection in Color Space
14 for Segmentation, Histogram

Segmentation, Knowledge * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Fua and Leclerc Guided Segmentation Papers (H2)
* Techniques for Model Guided Segmentation (H1)
* Decision Theory and Artificial Intelligence: I. A Semantics Based Region Analyzer
* IGS: A Paradigm for Integrating Image Segmentation and Interpretation
* Integrating Non-Semantic Knowledge into Image Segmentation Processes
* Integration Scheme for Image Segmentation and Labeling Based on Markov Random-Field Model, An
* Region Segmentation: Signal vs. Semantics
8 for Segmentation, Knowledge

Segmentation, Learning * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Genetic Learning for Adaptive Image Segmentation
* Unsupervised Image Segmentation Using A Distributed Genetic Algorithm

Segmentation, MDL * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Fast Algorithm for MDL-Based Multi-Band Image Segmentation, A

Segmentation, Model Based * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Fua and Leclerc Guided Segmentation Papers (H2)
* Techniques for Model Guided Segmentation (H1)
* Scene Analysis Using a Semantic Base for Region Growing
* Structural Analysis of Complex Aerial Photographs, A
* Visual Identification of People by Computer
8 for Segmentation, Model Bas

Segmentation, Motion * Extract Moving Objects from Sequences (H2)
* Image Segmentation from Motion Information (H2)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

Segmentation, MRF * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* MRF Models for Segmentation (H2)
* Color Image Segmentation Using Markov Random Fields
* Computational Approach to Boundary Detection, A
* Results Using Random Field Models for the Segmentation of Color Images

Segmentation, Multi-Level * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Multi-level Segmentation and Smoothing Methods (H2)
* Boundary Detection of Radiographic Images by a Threshold Method
* Image Surface Approximation with Irregular Samples
* Threshold Selection Using Quadtrees

Segmentation, Ohlander * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Complete Segmentation Systems Based on Ohlander Technique (H2)
* Picture Segmentation Using a Recursive Region Splitting Method

Segmentation, Quantization * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Optimal Multiple Threshold Scheme for Image Segmentation, An

Segmentation, Range * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Curvature and Features of Surfaces and Range Data (H2)
* Region Techniques for Range and Surfaces (H2)
* Surface Patches, Planes, Descriptions from Range (H2)
* Range Segmentation Using Visibility Constraints
* Segmentation Through Symbolic Surface Descriptions
* Use of Range and Reflectance Data to Find Planar Surface Regions
9 for Segmentation, Range

Segmentation, Recursive * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Recursive Region Extraction

Segmentation, Region Growing * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Segmentation by Region Growing Techniques (H1)
* MIR: An Approach to Robust Clustering-Application to Range Image Segmentation
* Parallel Technique for Signal-Level Perceptual Organization, A
* Region Coloring Technique for Scene Analysis, A
* Scene Analysis Using Regions
* Texture Information-Directed Region Growing Algorithm for Image Segmentation and Region Classification, A
8 for Segmentation, Region Growing

Segmentation, Region Merging * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Segmentation by Region Growing Techniques (H1)
* Segmentation by Split and Merge Techniques (H1)
* Segmenting Images Using Localized Histograms and Region Merging

Segmentation, Region Splitting * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Segmentation by Split and Merge Techniques (H1)
* Picture Segmentation Using a Recursive Region Splitting Method

Segmentation, Relaxation * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Segmentation by Thresholding, Quantization, or Relaxation (H2)
* Segmentation of Images Using a Relaxation Technique

Segmentation, Rule-Based * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Angy: A Rule-Based Expert System for Identifying and Isolating Coronary Vessels in Digital Angiograms

Segmentation, Shadows * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Combining Color and Geometry for the Active, Visual Recognition of Shadows
* Shadow Identification

Segmentation, Shapes * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Using Generic Geometric Models for Intelligent Shape Extraction

Segmentation, Split and Merge * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Segmentation by Split and Merge Techniques (H1)
* Parallel Color Algorithm for Segmenting Images of 3-D Scenes, A
* Picture Segmentation by a Tree Traversal Algorithm
* Segmentation by Texture Using a Co-Occurrence Matrix and a Split-and-Merge Algorithm

Segmentation, Surfaces * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Segmentation Through Symbolic Surface Descriptions

Segmentation, Survey * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Segmentation, Survey and General Topics (H1)
* Comparison of Some Segmentation Algorithms for Cytology, A
* Computational Techniques in the Visual Segmentation of Static Scenes
* Image Segmentation
* Region Growing: Childhood and Adolescence
* Survey of Threshold Selection Techniques, A
* survey on Image Segmentation, A
8 for Segmentation, Survey

Segmentation, Systems * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Other Complete Systems (H2)
* Picture Segmentation Using a Recursive Region Splitting Method
* Recursive Region Segmentation by Analysis of Histograms
* Region Growing in Textured Outdoor Scenes
* Segmenting Images Using Localized Histograms and Region Merging

Segmentation, Texture * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Fractal Texture Segmentation (H2)
* Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
* MRF Models for Segmentation (H2)
* Texture Based Segmentation Techniques (H1)
* Texture Boundaries and Edges (H2)
* Texture Segmentation Using Filters (H2)
* Texture Segmentation, Surveys, Review and General (H2)
* Boundary Finding Algorithm and Its Applications, A
* Equal Probability Quantizing and Texture Analysis of Radiographic Images
* Image Segmentation by Unifying Region and Boundary Information
* Markov Random Field Approach to Data Fusion and Colour Segmentation, A
* Multiresolution Texture Segmentation Approach with Application to Diagnostic Ultrasound Images, A
* Region Coloring Technique for Scene Analysis, A
* Texture Segmentation and Shape in the Same Image
* Texture Segmentation Using 2-D Gabor Elementary Functions
* Unsupervised Texture Segmentation of Images Using Tuned Matched Gabor Filters
* Unsupervised Texture Segmentation Using Gabor Filters
20 for Segmentation, Texture

Segmentation, Three Classes * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Object Enhancement and Extraction

Segmentation, Thresholds * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Complete Segmentation Systems Based on Ohlander Technique (H2)
* Fuzzy Threshold Segmentation (H2)
* Global - Threshold Based Segmentation Techniques (H1)
* Histogram Analysis for Threshold Selection and Segmentation (H2)
* Other Threshold Selection Systems (H2)
* Segmentation by Thresholding, Quantization, or Relaxation (H2)
* Two-Dimensional Histogram Analysis for Segmentation (H2)
* Fast Pyramidal Algorithms for Image Thresholding
* Segmentation of Blood Smears by Hierarchical Thresholding
* Segmentation System Based on Thresholding, A
* Survey of Threshold Selection Techniques, A
* Survey of Thresholding Techniques, A
13 for Segmentation, Thresholds

Segmentation, Unimodal * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Fast Thresholding Selection Procedure for Multimodal and Unimodal Histograms, A
* Minimum Error Thresholding
* Segmentation of Images Having Unimodal Distributions

Segmentation, Watershed * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Watershed Algorithms, Watershed Segmentation (H2)

Segmented Descriptions * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Structured Description of Complex Objects

Seismic Processing * Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Sesimic Analysis, Geological Analysis (H3)

Sensor Fusion * *Sensor Fusion II: Human and Machine Strategies
* *Signal Processing, Sensor Fusion, and Target Recognition V
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* ATR -- Multiple Sensors, Multiple Algorithms, Fusion (H2)
* Books, Collections, Overviews, General, and Surveys (H)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Image and Sensor Fusion -- General (H2)
* Image and Sensor Fusion -- IR and Thermal (H3)
* Image and Sensor Fusion -- Review and Survey Articles, Evaluations (H3)
* Information Fusion, Sensor Fusion (H3)
* Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Shape Computations from Multiple Sensors (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Target Tracking, Multiple Sensors, Multiple Cameras (H3)
* Analysis of Thermal Infrared and Visual Images for Industrial Inspection Tasks
* Architecture for Sensor Fusion in a Mobile Robot, An
* Automatic MR-PET Registration Algorithm
* Data Fusion for Sensory Information Processing Systems
* Data Fusion in Robotics and Machine Intelligence
* Distributed Fusion Architectures and Algorithms for Target Tracking
* Epipolar Parametrization, The
* Integrated Modelling of Thermal and Visual Image Generation
* Integration, Coordination and Control of Multi-Sensor Robot Systems
* Modeling and Fusion of Radar and Imaging Sensor Data for Target Tracking
* Multiple Sensor Integration/Fusion through Image Processing: A Review
* Multisensor Integration: Experiments in Integrating Thermal and Visual Sensors
* On the Positioning of Multisensor Imagery for Exploitation and Target Recognition
* Planning Sensing Strategies in Robot Work Cell with Multi-Sensor Capabilities
* Robotic Object Recognition Using Vision and Touch
* Sensor Fusion in Certainty Grids for Mobile Robots
* Statistical cue integration in DAG deformable models
* Three-Dimensional Shape Construction and Recognition by Fusing Intensity and Structured Lighting
* Unified Modeling of Nonhomogeneous 3D Objects for Thermal and Visual Image Synthesis
36 for Sensor Fusion

Sensor Fusion, Thermal, Visible * Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* New Method for Acquiring Time-Sequential Range Images by Integrating Stereo Pairs of Thermal and Intensity Images, A

Sensor Path Planning * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Planning Sensor Position, View Selection, View Planning (H3)

Sensor Planning * 3D Modelling from Range Imagery: An Incremental Method with a Planning Component
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Camera Position, Sensor Position for Model Generation (H3)
* Planning Optimal Sensor Positions (H4)
* Planning Sensor Position, View Selection, View Planning (H3)

Sensors * Complex Log Mapping, Algorithms and Sensors (H2)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Sensors for Machine Vision (H1)
* Computational Sensors
* Image capture: simulation of sensor responses from hyperspectral images

Sensors, Lasers * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Lasers Sensors for Range (H3)

Sensors, Log Polar Map * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Complex Log Mapping, Algorithms and Sensors (H2)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Connectivity Graphs in Space-Variant Active Vision
* Disparity Estimation on Log-Polar Images and Vergence Control

Sensors, Optical * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Optical Sensors for Machine Vision (H2)

Sensors, Range * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Range Sensors for Machine Vision (H2)
* Stereo Sensors for Range (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Sensors, Sonar * Acoustic, Sonar Sensors for Range (H3)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)

Sensors, Space Variant * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Miniaturized Space-Variant Active Vision System: Cortex-I, A

Sensors, Touch * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Integrating Vision and Touch for Object Recognition Tasks

Separable Filters * Detection and Analysis of Edges, Lines, Curves, Corners, Hough Transform (H)
* Fast Computation of Unbiased Intensity Derivatives in Images Using Separable Filters

Separable Templates * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Simplified Algorithm for Approximate Separable Decomposition of Morphological Templates, A

Sequence Database * Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Pattern Detection in Biomolecules Using Synthesized Random Sequence

Sequences * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Extract Moving Objects from Sequences (H2)
* General Spatio-Temporal Analysis (H2)
* Image Segmentation from Motion Information (H2)
* Integration over a Sequence (H2)
* Long Sequence Matching and Motion (H2)
* Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Motion Detection, Analysis of Motion Detectors (H2)
* Motion Estimates Using 5 or More Frames (H2)
* Region, Target Tracking (H2)
* Shape from Specularities -- Multiple Images (H2)
* Spatio-Temporal Analysis -- Many Frames (H1)
* Spatio-Temporal Filtering (H2)
* Tracking of Moving Objects and Matching in Sequences (H1)
15 for Sequences

Shading and Stereo * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Surface Reconstruction from Disparate Shading: An Integration of Shape-from-Shading and Stereopsis

Shading from Shape * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Determining Reflectance Parameters Using Range and Brightness Images

Shadow Analysis * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Three-Dimensional Information from Shadows (H1)
* Buildings Detection and Description from Monocular Aerial Images
* Classification of Edges for Object Detection in Aerial Images
* Detection of Buildings from Monocular Views of Aerial Scenes Using Perceptual Grouping and Shadows

Shadow Detection * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Cloud Detection, Shadow Detection and Extraction (H2)
* Shadows and Motion, Detection and Extraction (H3)
* Remote Sensing and Cast Shadows in Mountainous Terrain

Shape Decomposition * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Parts of Planar Shapes

Shape Features * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Computation of Shape Features of Two Dimensional Objects (H1)

Shape from Contours * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Multiple Contours and Images (H2)
* Polyhedral Shape from Contours (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Surface and Shape from Contours or Silhouettes (H1)
* Surfaces from Contours -- Ulupinar (H2)
* Multimodal 3D Shape Recovery from Texture, Silhouette and Shadow Information
* Recovery of Superquadrics from 3D Information
9 for Shape from Contours

Shape from Focus * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Depth from Focus, Changing Camera Parameters (H1)

Shape from Grid Patterns * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Grid Patterns (H2)

Shape from Light Stripes * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Light Stripes (H2)

Shape from Monocular Cues * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Three-Dimensional Reconstruction from Different Views (H1)

Shape from Motion * 3-D Object Reconstruction Using Stereo and Motion
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Structure, Depth, and Shape from Motion (H1)

Shape from Multiple Cues * Integration of Different Shape-from-X Cues (H1)
* Shape from Two or More Properties (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Shape from Multiple Light Sources * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Shape from Multiple Light Sources, Photometric Stereo (H1)

Shape from Optical Flow * Optical Flow Field Computations and Use (H)
* Surface Reconstruction from Optical Flow (H1)

Shape from Panorama * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Shape and Stereo from Panoramic Views (H1)

Shape from Perspective * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Shape and Structure from Perspective Effects, Vanishing Points (H1)
* Modeling and Using Physical Constraints in Scene Analysis

Shape from Polarization * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Polarization Effects (H2)

Shape from Radar * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Slope and Shape from Radar and SAR (H2)
* Surface Deformation From SAR, InSAR, IFSAR, Interferometry (H3)

Shape from Shading * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Shape from Shading -- Horn (H2)
* Shape from Shading, General Techniques (H2)
* Shape from Shading, Local Techniques (H3)
* Shape from Shading, Planes, Planar Faces (H3)
* Shape from Shading (H1)
* Computer Analysis of Visual Properties of Curved Objects
* Integrating Vision Modules: Stereo, Shading, Grouping, and Line Labeling
* Physically-Based Adaptive Preconditioning for Early Vision
* Recovery of Superquadrics from 3D Information
* Symmetric Shape-from-Shading Using Self-Ratio Image
16 for Shape from Shading

Shape from Shading, Analysis * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Analysis of Shape from Shading (H2)
* Analysis of Shape from Shading Techniques

Shape from Shading, Color * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Shape Using Color Images (H2)

Shape from Shading, Multiple Images * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Shape from Multiple Light Sources, Photometric Stereo (H1)

Shape from Shadows * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Three-Dimensional Information from Shadows (H1)
* Classification of Edges for Object Detection in Aerial Images
* Methods for Exploiting the Relationship between Buildings and Their Shadows in Aerial Imagery
* Modeling and Using Physical Constraints in Scene Analysis
* Multimodal 3D Shape Recovery from Texture, Silhouette and Shadow Information
* Obtaining 3-D from Shadows in Aerial Images
* Use of Shadows for Extracting Buildings in Aerial Images
10 for Shape from Shadows

Shape from Silhouettes * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Acquiring 3-D Models from a Sequence of Contours

Shape from Single Image * Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Computer Vision Based on a Hypothesization and Verification Scheme by Parallel Relaxation

Shape from Slices * Shape Descriptions Computed from a Set of Slices (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Shape from Sonar * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Flat Surface Reconstruction Using Minimal Sonar Readings

Shape from Specularity * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Shape from Specularities -- Multiple Images (H2)
* Shape from Specularities (H1)
* Separation of diffuse and specular components of surface reflection by use of polarization and statistical analysis of images

Shape from Structured Light * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Other Patterns (H2)
* Shape from Laser Ranging and Structured Light Images (H1)

Shape from Texture * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Shape from Texture (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Texture for Surface Orientation (H2)
* Analyzing Oriented Textures Through Phase Portraits
* Computer Identification of Visual Surfaces
* Multimodal 3D Shape Recovery from Texture, Silhouette and Shadow Information
* Shape from Texture Using Markov Random Field Models and Stereo-Windows
9 for Shape from Texture

Shape from Zoom * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Dense Reconstruction by Zooming

Shape Measure * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* MDL, Minimum Description Length for Shape Measure (H4)

Shape Representation * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Distortion of Steroscopic Visual Space
* Flexible Search-Based Approach for Morphological Shape Decomposition

Shape Signature * Region or Contour Invariants, Signatures, Metrics for Matching (H3)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Shape, 2D * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Computing Area, Diameter and Perimeter (H3)
* General Shape Computation and Representation (H2)
* Shape Computation Surveys, Comparisons (H3)
* Shape Measures (H3)

Shape, Survey * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Algorithms for Shape Analysis of Contours and Waveforms
* Review of Algorithms for Shape Analysis, A

Shape, Three-Dimensional - Evaluation * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Error Analysis for Surface Orientation from Vanishing Points, An
* Surface Classification: Hypothesis Testing and Parameter Estimation

Sharpening * Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Sharpening, Unsharp Masking (H2)

Shot Boundary * Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Video Analysis, Cut Detection, Scene Segmentation, Shot Detection, Shot Boundary (H3)

Shot Detection * Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Video Analysis, Cut Detection, Scene Segmentation, Shot Detection, Shot Boundary (H3)

Sign Detection * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Road Signs, Traffic Signs, Objects along the Road, Inspections (H3)

Sign Language * Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Sign Language, ASL Recognition (H3)

Signature Recognition * OCR, Document Analysis and Character Recognition Systems (H)
* Off Line Signature Analysis (H4)
* On-Line Signatures, Online Signatures (H3)
* Signature Recognition, Surveys, Analysis, Comparisons (H4)

Signatures * OCR, Document Analysis and Character Recognition Systems (H)
* Off Line Signature Analysis (H4)
* On-Line Signatures, Online Signatures (H3)
* Signature Recognition, Surveys, Analysis, Comparisons (H4)

Silhouette * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Surface and Shape from Contours or Silhouettes (H1)

Similarity Measures * General Similarity Measures for Database Indexing (H3)
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Region or Contour Invariants, Signatures, Metrics for Matching (H3)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)

Simulated Annealing * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Boltzmann Machine, Simulated Annealing, and Related Topics (H3)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Depth and Image Recovery Using a MRF Model
* Registering Multiview Range Data to Create 3D Computer Objects
* Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
8 for Simulated Annealing

Simulations * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Simulation and Graphical Interface for Programming, Operation, and Interactive Control of Sensor-based Robots

Singular Points * Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Fingerprint Features, Singular Points (H2)

Singular Value Decomposition * Factorization Approach to Motion (H2)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)

Skeletal * Medical Applications -- Skeleton, Bone (H2)
* Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)

Skeletonization * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Skeletons and Axial Descriptions - Medial Axis Transform (MAT) etc. (H

Skeletons * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Distance Transforms, Functions and Skeletons (H2)
* Fast, Parallel, Multiresolution Techniques for the Computation of Skeletons (H2)
* General Three-Dimensional Symmetries (H3)
* OCR, Document Analysis and Character Recognition Systems (H)
* Processing of Skeletons (H2)
* Skeletons in Three Dimensions (H2)
* Use of Skeletons for Recognition and Representation (H2)
* Direct Extraction of Topographic Features for Gray Scale Character Recognition
10 for Skeletons

Skew Correction * Detection and Analysis of Edges, Lines, Curves, Corners, Hough Transform (H)
* Detection and Correction of Document Skew, and other Distortions (H2)
* OCR, Document Analysis and Character Recognition Systems (H)
* Estimation of Skew Angle in Text-Image Analysis by SLIDE: Subspace-Based Line Detection
* Preprocessing Techniques for Cursive Script Word Recognition
* Reading Chess

Skew Detection * Detection and Correction of Document Skew, and other Distortions (H2)
* OCR, Document Analysis and Character Recognition Systems (H)
* Robust and Fast Skew Detection Algorithm for Generic Documents, A

Skew Symmetry * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Mapping Image Properties into Shape Constraints: Skewed Symmetry, Affine-Transformable Patterns, and the Shape-from-Texture Paradigm
* Recovery of the Three-Dimensional Shape of and Object from a Single View

SLAM * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* SLAM: Simultaneous Location and Mapping or Matching (H3)

Slant Normalization * OCR, Document Analysis and Character Recognition Systems (H)
* Recognition of Handprinted Thai Characters Using Loop Structures

Slices * Shape Descriptions Computed from a Set of Slices (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Smoothing * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Diffusion Process for Enhancement, Restoration and Smoothing (H2)
* Implementation of Convolution and Smoothing Techniques (H3)
* Multi-level Segmentation and Smoothing Methods (H2)
* Smoothing Techniques (H2)
* Image Smoothing by Local Use of Global Information
* Region Extraction by Averaging and Thresholding
9 for Smoothing

Smoothing, Adaptive * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Smoothing Techniques (H2)

Smoothing, Contours * Detection and Analysis of Edges, Lines, Curves, Corners, Hough Transform (H)
* Optimal Local Weighted Averaging Methods in Contour Smoothing

Smoothness * Optical Flow Field -- Smoothness (H1)
* Optical Flow Field Computations and Use (H)

Snakes * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Active Contours, Snakes or Deformable Curves (H2)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Geodesic Active Contours (H3)
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Snakes, Algorithms for Computation (H3)
* Snakes, Applications (H3)
* Snakes, General Techniques and Descriptions (H3)
* Snakes, Global Fits, Fitting Specific Models (H3)
* Snakes, Interactive Region Segmentations (H4)
* Snakes, Motion Tracking (H2)
* Snakes, Region Segmentation Issues (H3)
* Snakes, Restricted Curves, Splines, etc. (H2)
* Affine-Invariant Contour Tracking with Automatic Control of Spatiotemporal Scale
* Automatic Road Extraction Based on Multi-Scale Modeling, Context, and Snakes
* Building and Using Flexible Models Incorporating Grey-Level Information
* Deformable Templates for Face Recognition
* Real-Time Lip Tracking for Audio-Visual Speech Recognition Applications
* Semiautomatic Linear Feature-Extraction by Dynamic-Programming and LSB-Snakes
* Three Dimensional Movement Analysis of Dynamic Line Images
* Visual Image Retrieval by Elastic Matching of User Sketches
25 for Snakes

Snakes, 3-D * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Active Volumes, Deformable Solids, 3-D Snakes, etc. (H1)

Snakes, Active Contours * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Simulating the Grassfire Transform Using an Active Contour Model

Society, Asia * *Asian Federation of Computer Vision

Society, Australia * *Australian Pattern Recognition Society

Society, Austria * *Austrian Association for Pattern Recognition

Society, Belgium * *AVS: Scientific Research Community Audio-Visual Systems

Society, Canada * *Canadian Image Processing and Pattern Recognition Society
* *Canadian Institute of Geomatics

Society, Europe * *European Computer Vision Network

Society, Finland * *Pattern Recognition Society of Finland

Society, France * *French Association for Pattern Recognition and Interpretation

Society, Germany * *German Association for Pattern Recognition, The
* *German Society for Photogrammetry, Remote Sensing and Geoinformation

Society, Image Analysis * *MPEG Industry Forum
* Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)

Society, India * *Indian Unit for Pattern Recognition and Artificial Intelligence

Society, International * *IAPR: International Association for Pattern Recognition
* *International Federation of Information Processing
* *ISIF: International Society of Information Fusion
* *ISPRS: International Society for Photogrammetry and Remote Sensing

Society, Italy * *Italian Association for Pattern Recognition

Society, Japan * *IEICE
* *Information Processing Society of Japan: Computer Vision and Image Media

Society, New Zealand * *National group of New Zealand in Image and Vision Computing

Society, Norway * *Norwegian Society for Image Processing and Pattern Recognition

Society, Spain * *Spanish Association of Pattern Recognition and Image Analysis

Society, Sweden * *Swedish Society for Automated Image Analysis

Society, The Netherlands * *Nederlandse Vereniging voor Patroonherkenning en Beeldverwerking

Society, UK * *BMVA: British Machine Vision Association
* *UKIVA: The UK Industrial Vision Association

Society, US * *ASPRS: American Society for Photogrammetry and Remote Sensing
* *IEEE Computer Society: TC PAMI
* *IEEE Signal Processing Society
* *Optical Society of America
* *SPIE: The International Society for Optical Engineering

Sonar Range Sensors * Acoustic, Sonar Sensors for Range (H3)
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Three-Dimensional Ultrasonic Vision for Robotic Applications

Sonar * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* ATR -- Sonar (H2)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Hybrid Genetic Optimization and Statistical Model-Based Approach for the Classification of Shadow Shapes in Sonar Imagery
* Mobile Robot Localization Using Sonar

Space Application * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Affine Visual Servoing for Robot Relative Positioning and Landmark-Based Docking

Space Carving * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Multiple Views, Space Carving (H2)

Space Variant * Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Space Varying Restoration, Adaptive Restoration (H2)

Space * Applied Restoration and Hardware (H2)
* Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)

Sparse Data * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Generation from Sparse Data (H2)
* Reconstruction from Sparse Data (H2)

Spatial Indexing * Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Speeding Up Construction of Quadtrees for Spatial Indexing

Spatial Reasoning * General Spatial Reasoning and Geometric Reasoning Issues (H1)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Qualitative Spatial Reasoning from the Observers Point-of-View: Towards a Generalization of Symbolic Projection

Spatial Relations * General Spatial Reasoning and Geometric Reasoning Issues (H1)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)

Spatio-Temporal Analysis * General Spatio-Temporal Analysis (H2)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Spatio-Temporal Analysis -- Many Frames (H1)
* Acquisition of 3D Structure of Selectable Quality from Image Streams
* On Integrating Depth from Motion and Stereo Approaches

Spatio-Temporal Filtering * Optical Flow Field Computations and Use (H)
* Detection of Independent Motion Using Directional Motion Estimation

Spatio-Temporal Filters * Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Spatio-Temporal Filtering (H2)
* On the Use of Trajectory Information to Assist Stereopsis in a Dynamic Environment

Speckle * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Radar, Speckle Analysis and Removal, Speckle Reduction, Despeckle (H2)

SPECT * Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
* Single Photon emission computed tomography -- SPECT (H2)

Spectral Reflectance * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Reflectance Computations, Albedo (H2)
* Surface Roughness, Rough Surfaces (H3)

Spectral * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Spectral and Rank Order Approaches to Texture Analysis

Speech * Combined Audio Visual Recognition (H3)
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* New Unsorted Entries, and Other Miscellaneous Papers (H)
* Speaker Verification, Speaker Identification (H3)
* Speech, Grammar Based Analysis, Language Issues (H3)

Spheres * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Representations from Spheres (H1)

Spline * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Spline Based Models, B-Splines (H2)

Splines * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Detection and Analysis of Edges, Lines, Curves, Corners, Hough Transform (H)
* Snakes, Restricted Curves, Splines, etc. (H2)
* Splines, General Papers (H2)
* Implicitization, Inversion, and Intersection of Planar Rational Cubic Curves

Split and Merge * 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Segmentation by Split and Merge Techniques (H1)

Sports * Human Activities, Sports, Planned Activities (H4)
* Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)

Spots * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Feature, Object, Blob Detection and Spot Detection Systems (H2)

Sprites * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Face Animation, Video Face Synthesis (H3)

SRI Cartography * Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* SRI General Cartography Systems (H1)

Stabilization * Image Montage, Mosaic Generation, Super Resolution and Stabilization (H1)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Stabilization, Alignment (H2)

Statistical Learning * Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Statistical Learning, Clustering, Learning Feature Values (H2)

Steerable Filter * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Steerable Filters (H3)
* Design of Multi-Dimensional Derivative Filters
* Optimally Rotation-Equivariant Directional Derivative Kernels
* Steerable Pyramid: A Flexible Architecture for Multi-Scale Derivative Computation, The

Steerable Filters * Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Deformable Kernels for Early Vision

Steganography * Halftone Images, Compressed Images: Image Hiding, Data Hiding, Steganography (H3)
* Image Hiding, Data Hiding, Steganography (H2)
* OCR, Document Analysis and Character Recognition Systems (H)

Stereo and Motion * Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Motion Using Stereo Pairs or Depth -- Features (H1)
* Motion Using Stereo Pairs or Depth (H1)

Stereo * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Stereoscopic Viewing (H1)
* Computational and Biological Models of Stereo Vision
* Depth Perception for Robots
* Detection and Description of Buildings from Multiple Aerial Images
* Distortion of Steroscopic Visual Space
* Integrated Stereo-Based Approach to Automatic Vehicle Guidance, An
* Integrating Vision Modules: Stereo, Shading, Grouping, and Line Labeling
* Structure from Stereo: A Review
12 for Stereo

Stereo, Analysis * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Stereo Vision for Planetary Rovers: Stochastic Modeling to Near Real-Time Implementation

Stereo, Boundaries * Stereo Analysis - Boundaries of Curved Surfaces (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Camera Calibration * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Camera Calibration -- Stereo (H2)

Stereo, Coding * Coding, Stereo, Disparity Maps, 3-D Shapes, Mesh (H2)
* Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)

Stereo, Combined Area and Local * Stereo Analysis: Regions, Combine Area and Edge (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Computation * Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Accurate Building Structure Recovery from High Resolution Aerial Imagery

Stereo, Edges * Edge Based Stereo Analysis: Scan Line Oriented (H2)
* Stereo Analysis: Regions, Combine Area and Edge (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Epipolar * Edge Based Stereo Analysis: Scan Line Oriented (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Computer Determination of Depth Maps

Stereo, Evaluation * Error Analysis, Performance Analysis of Computation Methods (H1)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Models of Errors and Mistakes in Machine Perception, Part 1. First Results for Computer Vision Range Measurements
* Quantization Error in Stereo Imaging
* Quantization Errors in Stereo Triangulation
* Recovering 3D Information from Complex Aerial Imagery
* Robust Parameter Estimation in Computer Vision
* Some Accuracy and Resolution Aspects of Computer Vision Distance Measurements
* Stereo Error Detection, Correction, and Evaluation
10 for Stereo, Evaluation

Stereo, Fusion * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Gaze Control -- Binocular System, Vergence (H3)

Stereo, Gradient Approach * Stereo Analysis - Gradient Based (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Grimson * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Computational Experiments with a Feature Based Stereo Algorithm

Stereo, Line Segments * Line Segment Based Stereo Analysis (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Many views * Multiple Cameras or Views, Multi-Baseline (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Marr * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Cooperative Computation of Stereo Disparity
* Theory of Human Stereo Vision, A

Stereo, Matching * Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Matching Issues for Stereo (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Iterative Relaxational Stereo Matching Based on Adaptive Support Between Disparities

Stereo, Motion * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Optical Flow Field Computations and Use (H)
* Computation of Cloud-base Height from Paired Whole-Sky Imaging Cameras, The
* Integrated Stereo-Based Approach to Automatic Vehicle Guidance, An

Stereo, Multiple Resolution * Stereo Systems: Multiple Resolutions, Hierarchical (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Other * Other Stereo Work (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Photogrammetry * Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Close Range Photogrammetry: Principles, Techniques and Applications

Stereo, Point Matching * Stereo Analysis: Point Matching (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Real time * Stereo: Real Time Systems (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Reconstruction * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Reconstructions, Applied to Stereo Imagery (H2)

Stereo, Regions * Stereo Analysis: Regions, Combine Area and Edge (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Refinement of Disparity Estimates Through the Fusion of Monocular Image Segmentations

Stereo, Scan Line * Edge Based Stereo Analysis: Scan Line Oriented (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Surface Models * Stereo and Surface Models (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, System * Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Stereo Vision for Planetary Rovers: Stochastic Modeling to Near Real-Time Implementation
* Stereoscopic Navigation System, A

Stereo, Theory * General Stereo Discussion: Human and Computer (H2)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Theory of Human Stereo Vision, A

Stereo, Trinocular * Stereo Using Three Views, Trinocular Stereo (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
* Trinocular Stereo Vision for Robotics
* Trinocular Stereo: A Real-Time Algorithm and its Evaluation

Stereo, Two views * Stereo Analysis, Two Views (H1)
* Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)

Stereo, Uncalibrated * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Weakly Calibrated Cameras (H1)

Stereo, Weakly Calibrated * Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular (H)
* Weakly Calibrated Cameras (H1)

Streaming Video * Filters, Image Processing, Restoration, Enhancement, Image and Video Coding (H)
* Streaming Video, Video Streaming, Transmission Issues (H4)
* Transmission Issues, ATM Networks (H3)
* Transmission Issues, Wireless Systems, Mobile Systems (H3)

String Matching * Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* String Matching, Syntatic Matching (H3)
* String Matching (H2)

Stroke Based * Chinese Characters, Using Stroke and Radical Analysis (H3)
* OCR, Document Analysis and Character Recognition Systems (H)

Structural Decomposition * Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Periodic Lines: Definition, Cascades, and Application to Granulometries

Structural Descriptions * Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Organizing Large Structural Modelbases

Structural Features * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Defect Detection in Random Color Textures

Structural Texture * 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Structural Methods for Texture Description (H1)
* Distorted Shape Recognition Using Attributed Grammars and Error-Correcting Techniques
* Finding Anomalies in an Arbitrary Image

Structure from Motion * 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Optical Flow Field Computations and Use (H)
* Surface Reconstruction from Optical Flow (H1)
* Relative Affine Structure: Canonical Model for 3D from 2D Geometry and Applications
* What Is Computed by Structure from Motion Algorithms?

Structured Light * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Other Patterns (H2)
* Shape from Laser Ranging and Structured Light Images (H1)

Structured Light, Grid * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Grid Patterns (H2)

Structured Light, Stripes * 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Light Stripes (H2)
* Motion Analysis