Natural Language
* Context Supplied by Text or Language (H3)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Navigation
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Navigation -- Landmarks (H3)
* Navigation Issues (H3)
Nearest Neighbor
* Fast Nearest Neighbor Techniques (H3)
* Nearest Neighbor Classification (H2)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
Network Descriptions
* Basic Comparison of Relational Network Descriptions (H2)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Neural Nets
* OCR, Document Analysis and Character Recognition Systems (H)
* Recognition of Handwritten Musical Notes by a Modified Neocognitron
Neural Networks
* 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Face Recognition Systems Using Neural Networks (H2)
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Graph Matching, Neural Networks, Hopfield Networks (H2)
* Learning in Computer Vision (H1)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Neural Networks Applied to Specific Problems (H3)
* Neural Networks Combinations and Evaluations (H3)
* Neural Networks for Classification and Pattern Recognition (H3)
* Neural Networks for Numbers and Digits (H4)
* Neural Networks for Shapes and Complex Features (H3)
* Neural Networks: General, Survey, Special Issues (H3)
* Neural Networks (H2)
* OCR, Document Analysis and Character Recognition Systems (H)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Automatic Feature Generation for Handwritten Digit Recognition
* Combining Artificial Neural Networks and Symbolic Processing for Autonomous Robot Guidance
* Comparative Study of Three Paradigms for Object Recognition: Bayesian Statistics, Neural Networks, and Expert Systems, A
* Experimental Comparison of Neural and Statistical Nonparametric Algorithms for Supervised Classification of Remote Sensing Images, An
* Fuzzy Hierarchical Data Fusion Networks for Terrain Location Identification Problems
* Handprinted Character-Recognition Based on Spatial Topology Distance Measurement
* Learning Texture-Discrimination Masks
* Multiresolution Recognition of Unconstrained Handwritten Numerals with Wavelet Transform and Multilayer Cluster Neural-Network
* Neural Networks for the Classification of Image Texture
* On the Behavior of Artificial Neural-Network Classifiers in High-Dimensional Spaces
* Online Shape-Recognition with Incremental Training Using Binary Synaptic Weights Algorithm
* simplified neuron model as a principal component analyzer, A
* Use Neural Networks to Determine Matching Order for Recognizing Overlapping Objects
31 for Neural Networks
News Programs
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* News Video Analysis, Cut Detection, Summaries, Indexing (H2)
Noise Removal
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
* Noise Removal, Adaptive, Non-linear Techniques (H3)
* Noise Removal, Denoising (H2)
* Noise Removal, Impulse Noise (H3)
* Noise Removal, Wavelet Techniques (H3)
* Radar, Speckle Analysis and Removal, Speckle Reduction, Despeckle (H2)
7 for Noise Removal
Noise
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Noise Models in Segmentation (H2)
Non-accidentalness
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Perceptual Grouping, Theory (H2)
* What is Perceptual Organization for?
Non-Rigid Motion
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Energy Constraints on Deformable Models: Recovering Shape and Non-rigid Motion
* Volumetric Deformation Analysis Using Mechanics-Based Data Fusion: Applications in Cardiac Motion Recovery
Non-Rigid Registration
* Non-Rigid Image Registration Techniques (H3)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Nonparametric Clustering
* Nonparametric Clustering (H2)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
Nonrigid Motion
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
* Nonrigid, Deformable Motion Analysis and Tracking (H2)
* Nonrigid, Deformable Motion Tracking (H3)
* Reconstruction of dynamic 3-D structures of biological objects using stereo microscopy
Normal Flow
* Optical Flow Field Computations and Use (H)
* What Is Computed by Structure from Motion Algorithms?
Numbers
* Multiple Classifiers Applied to Arabic Numbers (H4)
* Neural Networks for Numbers and Digits (H4)
* OCR, Document Analysis and Character Recognition Systems (H)