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| Adaptive And | learning | Systems Ii (Orlando, Fl, April 12-13 |
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| Adaptive Object Detection Based On Modified Hebbian | learning | |
| Adaptive Target Recognition Using Reinforcement | learning | |
| Algorithm For The | learning | Of Weights In Discrimination Functions Using A Priori Constraints, An |
| Analysis Of The Behaviour Of Genetic Algorithms When | learning | Bayesian Network Structure From Data |
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| Autoassociative | learning | In Relaxation Labeling Networks |
| Autoassociative | learning | In Relaxation Labeling Networks |
| Automated | learning | Of Muscle-Actuated Locomotion Through Control Abstraction |
| Automatic Generation Of Grbf Networks For Visual | learning | |
| Automatic Generation Of Robot Program Code: | learning | From Perceptual Data |
| Automatic | learning | Of Structural Models For Workpiece Recognition Systems |
| Autonomous Mobile Robot Navigation And | learning | |
| Autonomous Navigation Through Case-Based | learning | |
| Autonomous Robot Navigation In Unknown Terrains: Incidental | learning | And Environmental Exploration |
| Bootstrapping Algorithm For | learning | Linear Models Of Object Classes, A |
| Bootstrapping Algorithm For | learning | Linear Models Of Object Classes, A |
| Closed-Loop Object Recognition Using Reinforcement | learning | |
| Closed-Loop Object Recognition Using Reinforcement | learning | |
| Closed-Loop Object Recognition Using Reinforcement | learning | |
| Color Channel Mixing In | learning | From Appearance |
| Color Image Segmentation Using Competitive | learning | |
| Combination Of Two | learning | Algorithms For Automatic Target Recognition |
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| Distributed | learning | Of Texture Classification |
| Divergent Stereo For Robot Navigation: | learning | From Bees |
| Evolutionary | learning | For Orchestration Of A Signal-To-Symbol Mapper |
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| Example-Based | learning | For View-Based Human Face Detection |
| Example-Based | learning | For View-Based Human Face Detection |
| Extensions Of A Theory Of Networks For Approximation And | learning | : Dimensionality Reduction And Clustering |
| From | learning | Objects To Learning Environments: Biological And Computational Neural Systems |
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| General | learning | Algorithm For Robot Vision |
| Generalized Image Matching: Statistical | learning | Of Physically-Based Deformations |
| Generating Image Filters For Target Recognition By Genetic | learning | |
| Generation, Local Receptive Fields And Global Convergence Improve Perceptual | learning | In Connectionist Networks |
| Geometrical | learning | From Multiple Stereo Views Through Monocular Based Feature Grouping |
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| Guest Eds., Joint Special Issue On | learning | In Autonomous Robots |
| Guest Eds., Special Section On | learning | In Computer Vision |
| Hand Segmentation Using | learning | -Based Prediction And Verification For Hand Sign Recognition |
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| Image Understanding Research At Uc Riverside: Integrated Recognition, | learning | And Image Databases |
| Improving Rooftop Detection With Interactive Visual | learning | |
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| Interactive | learning | Of A Multi-Attribute Hash Table Classifier For Fast Object Recognition |
| Interactive | learning | With A Society Of Models |
| Interactive | learning | With A ``Society Of Models'' |
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| learning | And Recognizing Human Dynamics In Video Sequences |
| learning | Automaton Solution To The Stochastic Minimum-Spanning Circle Problem, A |
| learning | Bilinear Models For Two-Factor Problems In Vision |
| learning | Blackboard-Based Scheduling Algorithms For Computer Vision |
| learning | By A Generation Approach To Appearance-Based Object Recognition |
| learning | By Watching: Extracting Reusable Task Knowledge From Visual Observation Of Human Performance |
| learning | Complex Structural Descriptions From Examples |
| learning | Dynamical Models Using Expectation-Maximisation |
| learning | Dynamics Of Complex Motions From Image Sequences |
| learning | Early-Vision Computations |
| learning | Flexible Models From Image Sequences |
| learning | Generic Prior Models For Visual Computation |
| learning | Geometric Hashing Functions For Model Based Object Recognition |
| learning | Grouping Strategies For 2d And 3d Object Recognition |
| learning | Hand/Eye Coordination By An Active Observer |
| learning | Hierarchical Representations Of Objects |
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| learning | Indexing Functions For 3-D Model-Based Object Recognition |
| learning | Integrated Online Indexing For Image Databases |
| learning | Knowledge-Directed Visual Strategies |
| learning | Metric-Topological Maps For Indoor Mobile Robot Navigation |
| learning | Motion From Images |
| learning | Motion Trajectories Via Self-Organization |
| learning | Object Models From Visual Observation And Background Knowledge |
| learning | Object Recognition Models From Images |
| learning | Of Patterns And Picture Languages |
| learning | Of Recognizable Picture Languages |
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| learning | Parameterized Models Of Image Motion |
| learning | Recognition And Segmentation Of 3-D Objects From 2-D Images |
| learning | Recognition And Segmentation Using The Cresceptron |
| learning | Relational Structures: Applications To Computer Vision |
| learning | Rules For 3d Object Recognition |
| learning | Shape Classes |
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| learning | Shape From Shading By A Multilayer Network |
| learning | Structural And Corruption Information From Samples For Markov Random Field Binary Image Reconstruction |
| learning | Structural Descriptions Of Patterns: A New Technique For Conditional Clustering And Rule Generation |
| learning | Structural Descriptions Of Shape |
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| learning | Techniques Applied To Multi-Font Character Recognition |
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| learning | The Distribution Of Object Trajectories For Event Recognition |
| learning | The Human Face Concept From Black And White Images |
| learning | The Parameters Of A Hidden Markov Random Field Image Model: A Simple Example |
| learning | To Detect Rooftops In Aerial Images |
| learning | To Detect Salient Objects In Natural Scenes Using Visual Attention |
| learning | To Fixate On 3d Targets With Uncalibrated Active Cameras |
| learning | To Form Large Groups Of Salient Image Features |
| learning | To Identify And Track Faces In Image Sequences |
| learning | To Navigate On A Graph |
| learning | To Recognise Talking Faces |
| learning | To Recognize 3d Objects Using Sparse Depth And Intensity Information |
| learning | To Recognize Faces From Examples |
| learning | To Recognize Generic Visual Categories Using A Hybrid Structural Approach |
| learning | To Recognize Objects Using Feature Indexed Hypotheses |
| learning | To See |
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| learning | To Track The Visual Motion Of Contours |
| learning | Two-Dimensional Shapes Using Wavelet Local Extrema |
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| Gray- | level | 2d Feature Detector Using Circular Statistics, A |
| Gray- | level | Corner Detector Using Fuzzy Logic, A |
| Gray- | level | Image Thresholding Based On Fisher Linear Projection Of Two-Dimensional Histogram |
| Gray- | level | Threshold Selection Method Based On Maximum Entropy Principle, A |
| Gray- | level | s Can Improve The Performance Of Binary Image Digitizers |
| Grey | level | Corner Detection: A Generalization And A Robust Real Time Implementation |
| Grey- | level | Thresholding Of Images Using A Correlation Criterion |
| Hardware Structure For The Automatic Selection Of Multi- | level | Thresholds In Digital Images, A |
| Hcl: A Language For Low- | level | Image Analysis |
| Heuristics For Intermediate | level | Road Finding Algorithms |
| Hierarchical Inference Scheme For High- | level | Image Understanding |
| High | level | 3d Structures From A Single View |
| High | level | Language For Parallel Image Processing, A |
| High | level | Language For Pyramidal Architectures, A |
| High- | level | Motion Processing|Computational, Neurobiological, And Psychophysical Perspectives, Mit Press, Cambridge |
| High- | level | Perception, Representation, And Analogy: A Critique Of Artificial Intelligence Methodology |
| High- | level | Surface Descriptions From Composite Range Images |
| High- | level | Vision--Object Recognition And Visual Cognition, Mit Press, Cambridge |
| High-Order Moment Computation Of Gray- | level | Images |
| Higher | level | Operations Using Processor Arrays |
| Hybrid Architecture For A High Performance And Physical[Ly] Small Low- | level | Image Processing System, A |
| Icondensation: Unifying Low- | level | And High-Level Tracking In A Stochastic Framework |
| Icondensation: Unifying Low- | level | And High-Level Tracking In A Stochastic Framework |
| Identifying High | level | Features Of Texture Perception |
| Image Characterizations Based On Joint Gray | level | -Run Length Distributions |
| Image Reconstruction Using High- | level | Constraints |
| Improved Method For Computing Gray | level | Cooccurrence Matrix Based Texture Measures, An |
| Incorporation Of Gray- | level | Imprecision In Representation And Processing Of Digital Images |
| Integrating Low | level | And High Level Computer Vision |
| Integrating Low | level | And High Level Computer Vision |
| Integrating Low- | level | Features Computation With Inductive Learning Techniques For Texture Recognition |
| Inter/Intraframe Coding Of Color Tv Signals For Transmission At The Third | level | Of The Digital Hierarchy |
| Interactive Computer Graphics-Functional, Procedural, And Device- | level | Methods, Addison-Wesley, Reading |
| Intermediate- | level | Image Processing (Gers, France, May 21-24, 1985), Academic Press, Orlando |
| Intrinsic Constraints In Space-Time Filtering: A New Approach To Representing Uncertainty In Low- | level | Vision |
| Invariant Architectures For Low- | level | Vision |
| level | Compression-Based Image Representation And Its Applications |
| level | Crossing Curvature And The Laplacian |
| level | Crossings And The Panum Area |
| level | Line(S) Based Disocclusion |
| level | Set And Fast Marching Methods In Image Processing And Computer Vision |
| level | -Set Approach To 3d Reconstruction From Range Data, A |
| level | s Of Knowledge For Object Extraction |
| level | s Of Modeling Of Mechanisms Of Visually Guided Behavior |
| Local Characteristics Of Binary Images And Their Application To The Automatic Control Of Low- | level | Robot Vision |
| Locally Ordered Gray | level | s As An Aid To Corner Detection |
| Low And Intermediate | level | Image Processing On Sympatix, A Simd Parallel Computer |
| Low | level | Image Analysis On An Mimd Architecture |
| Low | level | Image Processing Operators On Fpga: Implementation Examples And Performance Evaluation |
| Low | level | Image Segmentation: An Expert System |
| Low | level | Information Fusion: Multisensor Scene Segmentation Using Learning Automata |
| Low | level | Learning For A Mobile Robot: Environment Model Acquisition |
| Low | level | Motion Events: Trajectory Discontinuities |
| Low | level | Vision As The Opportunist Scheduling Of Incremental Edge And Region Detection Processes |
| Low- | level | Image Analysis Tasks On Fine-Grained Tree-Structured Simd Machines |
| Low- | level | Image Processing By Max-Min Filters |
| Low- | level | Processing Techniques In Geophysical Image Interpretation |
| Low- | level | Segmentation Of Aerial Images With Fuzzy Clustering |
| Low- | level | Segmentation Of Multispectral Images Via Agglomerative Clustering In Uniform Neighborhoods |
| Managing The | level | Of Detail In 3d Shape Reconstruction And Representation |
| Matching The Resolution | level | To Salient Image Features |
| Measuring The Effectiveness Of Task- | level | Parallelism For High-Level Vision |
| Measuring The Effectiveness Of Task- | level | Parallelism For High-Level Vision |
| Medium | level | Scene Representation Using A Vlsi Smart Hexagonal Sensor With Multiresolution Edge Extraction Capability And Scale Space Integration Processing |
| Message Based Control Of Parallel High | level | Depth And Intensity Matching |
| Metrics For The Strength Of Low- | level | Motion Perception |
| Mid- | level | Vision: New Directions In Vision And Video |
| Mimd | level | Of The System Sy Mp A Ti Simulation And Performances Expected, The |
| Model-Based Strategies For High- | level | Robot Vision |
| Modelling Grey | level | Surfaces Using Three-Dimensional Point Distribution Models |
| Morphological Scheme For Mean Curvature Motion And Applications To Anisotropic Diffusion And Motion Of | level | Sets, A |
| Morphological Scheme For Mean Curvature Motion And Applications To Anisotropic Diffusion And Motion Of | level | Sets, A |
| Moving Object Extraction Method Robust Against Illumination | level | Changes For A Pedestrian Counting System, A |
| Multi-Channel Autofocusing Scheme For Gray- | level | Shape Scale Detection, A |
| Multi-Channel-Based Approach For Extracting Significant Scales On Gray- | level | Images, A |
| Multi- | level | 3-D Rotational Invariant Classification |
| Multi- | level | 3d Reconstruction With Visibility Constraints |
| Multi- | level | Based Stereo Line Matching With Structural Information Using Dynamic Programming |
| Multi- | level | Contour Segmentation Using Multiple Segmentation Primitives |
| Multi- | level | Dynamic Programming Method For Line Segment Matching In Axial Motion Stereo, A |
| Multi- | level | Dynamic Programming Method For Stereo Line Matching, A |
| Multi- | level | Geometric Reasoning System For Vision, A |
| Multi- | level | Perception Approach To Reading Cursive Script, A |
| Multi- | level | Perception Approach To Reading Cursive Script, A |
| Multi- | level | Shape Representation Using Global Deformations And Locally Adaptive Finite Elements |
| Multi | level | Relaxation In Low-Level Computer Vision |
| Multiple Object Tracking System With Three | level | Continuous Processes |
| Multiple- | level | Heterogeneous Architecture For Image Understanding, A |
| Multipurpose Low- | level | Visual Processing |
| Neurophysiology Of High- | level | Vision-Collected Tutorial Essays, Erlbaum, Hillsdale, The |
| Neurophysiology Of High- | level | Vision-Collected Tutorial Essays, Erlbaum, Hillsdale, The |
| New Automatic Multi- | level | Thresholding Technique For Segmentation Of Thermal Images |
| New Dynamic Approach For Finding The Contour Of Bi- | level | Images, A |
| New Efficient Representations Of Photographic Images With Restricted Number Of Gray | level | s |
| New Gray | level | Based Hough Transform For Region Extraction: An Application To Irs Images, A |
| New Method For Gray- | level | Picture Thresholding Using The Entropy Of The Histogram, A |
| Non Supervised Segmentation Using Multi- | level | Markov Random Fields |
| Note On Grey | level | -Intensity Transformation: Effect On Hvs Thresholding, A |
| Novel Scale-Spectrum Space Method For Representing Gray- | level | Shape, The |
| Number Of Simultaneous Colors Versus Gray | level | s: A Quantitative Relationship |
| O(N) Iterative Solution To The Poisson Equation In Low- | level | Vision Problems, An |
| Object Detection Based On Gray | level | Cooccurrence |
| Object Location Strategy Using Shape And Grey- | level | Models, An |
| On Detection And Representation Of Multiscale Low- | level | Image Structure |
| On | level | s Of Detail In Terrains |
| On Surface Curvature Computation From | level | Set Contours |
| On The Application Of Massively Parallel Simd Tree Machines To Certain Intermediate- | level | Vision Tasks |
| On The Choice Of The First | level | On Graph Pyramids |
| On The Hough Transform Of Multi- | level | Pictures |
| On The Orthogonal Expansion Of Images For Low | level | Recognition. Fourier-Bessel Representation. |
| On The Use Of | level | Curves In Image Analsis |
| Parallel Algorithms For Low | level | Vision On The Homogeneous Multiprocessor |
| Parallel Bit- | level | Pipelined Vlsi Processing Unit For The Histogramming Operation |
| Parallel Split- | level | Relaxation |
| Parallel Split- | level | Relaxation |
| Parallel Technique For Signal- | level | Perceptual Organization, A |
| Parametrisable Skeletonization Of Binary And Multi- | level | Images |
| Pattern Recognition In Gray- | level | Images By Fourier Analysis |
| Performance Prediction For Parallel Reconfigurable Low- | level | Image Processing |
| Picquery: A High | level | Query Language For Pictorial Database Management |
| Pilot | level | Of A Hierarchical Controller For An Unmanned Mobile Robot |
| Pipelined Processor For Low- | level | Vision, A |
| Planning Collision-Free Trajectories In Time-Varying Environments: A Two- | level | Hierarchy |
| Predicting Expected Gray | level | Statistics Of Opened Signals |
| Principles Of Information Structure Common To Six | level | s Of The Human Cognitive System |
| Probabilistic Reasoning In High- | level | Vision |
| Programming Intermediate | level | Vision Tasks On Parallel Machines |
| Pyramid Implementation Of Optimal-Step Conjugate-Search Algorithms For Some Low- | level | Vision Problems |
| Quantification And Abstraction: Low | level | Tokens For Object Extraction |
| Quantitative Evaluation Of Similar Images With Quasi-Gray | level | s |
| Real-Time 2d Feature Detection With Low- | level | Image Processing Algorithms On Smart Ccd/Cmos Image Sensors |
| Realistic Landscape Modelling With High | level | Of Detail |
| Reasoning About Nonlinear Inequality Constraints: A Multi- | level | Approach |
| Recognition Of Handprinted Characters In The First | level | Of Jis Chinese Characters |
| Reconstruction Of Two-Dimensional Signals From | level | Crossings |
| Recovering A Boundary- | level | Structural Description From Dynamic Stereo |
| Recovering A Boundary- | level | Structural Description From Dynamic Stereo |
| Region- | level | Graph Labeling Approach To Motion-Based Segmentation, A |
| Relaxation Algorithms For Map Estimation Of Gray- | level | Images With Multiplicative Noise |
| Representations In High- | level | Vision: Reassessing The Inverse Optics Paradigm |
| Rgf Pandemonium: A Low- | level | Representational Model For Images, The |
| Scale And Segmentation Of Grey- | level | Images Using Maximum Gradient Paths |
| Scilaim: A Multi- | level | Interactive Image Processing Environment |
| Segmentation Of Deformable Templates With | level | Sets Characterized By Particle Systems |
| Self-Learning Capabilities Of Vap For Low- | level | Vision |
| Semantically-Based Multi- | level | Edge Detection System, A |
| Set Of Invariant Features For Three-Dimensional Gray- | level | Objects By Harmonic Analysis |
| Shape From Shading: | level | Set Propagation And Viscosity Solutions |
| Shape In Picture: Mathematical Description Of Shape In Grey- | level | Images (Proceedings Of The Nato Advanced Research Workshop ``Shape In Picture'', Driebergen, The Netherlands, September 7-11, 1992), Springer |
| Shape Modeling With Front Propagation: A | level | Set Approach |
| Simd Machine For Low- | level | Vision, An |
| Skeletonization Via Distance Maps And | level | Sets |
| Sliding Memory Plane Array Processor For Low | level | Vision, A |
| Some Computational Aspects Of Low- | level | Computer Vision |
| Sorting Jordan Sequences In Linear Time Using | level | -Linked Search Trees |
| Source Coding Bounds Using Quantizer Reproduction | level | s |
| Sparse Groups: A Polynomial Middle- | level | Approach For Object Recognition |
| Stereo By Two- | level | Dynamic Programming |
| Susan|A New Approach To Low | level | Image Processing |
| System- | level | Design Of Specialized Vlsi Hardware For Computing Relative Orientation |
| Task- | level | Planning Of Pick-And-Place Robot Motions |
| Task- | level | Tour Plan Generation For Mobile Robots |
| Theoretical Aspects Of Gray- | level | Morphology |
| Three- | level | Checkerboard Pattern (Tcp) Projection Method For Curved Surface Measurement, A |
| Tracking | level | Sets By Level Sets: A Method For Solving The Shape From Shading Problem |
| Tracking | level | Sets By Level Sets: A Method For Solving The Shape From Shading Problem |
| Transformation Of Gray | level | And Color Images |
| Two-Phase Area- | level | Line/Edge Detector, A |
| Unified Approach To Segmentation Of Grey- | level | And Dot-Pattern Images, A |
| Unified Approach To The Change Of Resolution: Space And Gray- | level | , A |
| Unsupervised Segmentation Of Gray | level | Markov Model Textures With Hierarchical Self Organizing Maps |
| Use Of A Priori Descriptions In A High- | level | Language And Management Of The Uncertainty In A Scene Recognition System |
| Use Of Geometric And Grey- | level | Models For Industrial Inspection, The |
| Use Of High- | level | Knowledge For Enhanced Entry Of Engineering Drawings, The |
| Using Domain Knowledge In Low- | level | Visual Processing To Interpret Handwritten Music: An Experiment |
| Using Grey- | level | Models To Improve Active Shape Model Search |
| Using Stability Of Interpretation As Verification For Low- | level | Processing: An Example From Egomotion And Optic Flow |
| Using Vision Data In An Object- | level | Robot Language-Rapt |
| Variational Principles, Surface Evolution, Pde'S, | level | Set Methods, And The Stereo Problem |
| Visual Constraint Recognition System: Analyzing The Role Of Reasoning In High | level | Vision, The |
| Visual Dynamic Scene Understanding Exploiting High- | level | Spatio-Temporal Models |
| Wavelet Transformation For Gray- | level | Corner Detection |
| Wavelet-Based Multiresolution Edge Detection Utilizing Gray | level | Edge Maps |
| Workshop On High- | level | Vision With Multicomputers, Rome, Italy |
| Writing Retargetable Parallel Programs For Low And High | level | Vision Using A Global Address Space |
| [Flexibly] (Flexibility) Coupled Hypercube Multiprocessor For High | level | Vision, A |
| ``Complexity | level | '' Analysis Of Immediate Vision, A |
| ``Complexity | level | '' Analysis Of Vision, A |
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