LADAR
* 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)
* Lasers Sensors for Range (H3)
* LIDAR, LADAR -- Range data (H2)
* Model-Based Automatic Target Recognition (ATR) System for Forward-Looking Groundbased and Airborne Imaging Laser Radars (Lada
Land Mines
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Ground Penetrating Radar, Buried Objects, UXO, Landmines (H3)
* Polarimetric processing of coherent random noise radar data for buried object detection
* Ultrawideband Radar Images of the Surface Disturbance Produced by a Submerged, Mine-Like Object
Landmarks
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Navigation -- Landmarks (H3)
* Qualitative Navigation -- Drop Off (H4)
* Visual Navigation for a Mobile Robot Using Landmarks
Landsat, History
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Landsat Program: Recent History and Prospects, The
Lane Following
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Lane Following, White Line Detection (H3)
Laser Range Finders
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Lasers Sensors for Range (H3)
Laser Scanners
* Buildings from Depth data (H2)
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
Laser
* 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)
* Calibration -- Lidar, Laser Scanner, Depth Sensor, Scanner Error Analysis (H3)
* Shape from Laser Ranging and Structured Light Images (H1)
LDA
* Invariants -- Linear Discriminant Analysis, Fisher Linear Discriminant (H3)
* LDA in Face Recognition (H4)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Learning Models
* Learning Model Descriptions (H3)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
Learning
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Evaluation and Analysis of Learning Techniques (H2)
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection (H)
* Learning -- Conference Listing (H2)
* Learning Face Detection, Neural Nets, SVM (H3)
* Learning for Principal Components, Eigen Representations (H3)
* Learning in Computer Vision (H1)
* Learning Object Descriptions, Object Recognition (H2)
* Learning, General Surveys, Overviews (H2)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* OCR, Document Analysis and Character Recognition Systems (H)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Face Recognition with Learning Machines
* Hidden Tree Markov Models for Document Image Classification
* Learning Structural Descriptions from Examples
* Learning Texture-Discrimination Masks
* Optimizing Learning in Image Retrieval
* RIEVL: Recursive Induction Learning in Hand Gesture Recognition
* Some Experiments in Applying Inductive Inference Principles to Surface Reconstruction
21 for Learning
Least Median
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
* Least Median of Squares Based Robust Analysis of Image Structure
Least Squares
* 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)
* Least Squares Applied to Restoration (H2)
* Sequential Coordinate-Wise Algorithm for the Non-negative Least Squares Problem
Lens Distortions
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Camera Calibration -- Lens Distortion, Aberration, Radial Distortion, Internal Parameters (H2)
* Calibration of Stereo Cameras Using a Non-Linear Distortion Model
* Efficient and Accurate Camera Calibration Technique for 3-D Machine Vision, An
Level Set Methods
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Optical Flow Field Computations and Use (H)
* Image-Processing: Flows under Min/Max Curvature and Mean-Curvature
* Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Science
Level Set
* 2-D Region Segmentation Techniques, Snakes, Active Contours (H)
* Level Set Methods (H2)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* new time dependent model based on level set motion for nonlinear deblurring and noise removal, A
License Plate Recognition
* License Plate Recognition, Extraction, Analysis (H3)
* Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities (H)
LIDAR
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Lasers Sensors for Range (H3)
Lie Groups
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Dominant-Subspace Invariants
Light Source Direction
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Light Source Detection (H2)
* Light Source Direction Computations, Illumination Information, Illuminant (H3)
* Estimation of Illuminant Direction, Albedo, and Shape from Shading
Lightness
* Color Constancy, Retinex (H2)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
Line Adjacency Graph
* 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Run-Length Coding Representations and Operations (H2)
Line Approximation
* Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
* Generation of Straight Line Segments or Curve Partitions (H1)
Line Detection
* Cartography, Aerial Images, Remote Sensing, Buildings, Roads, Terrain, ATR (H)
* Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
* Hueckel -- Basis Functions, Model Fitting for Edge Detection (H2)
* Line Detectors, Direct Detection of Straight Lines (H2)
* Road Following, Road Tracking Systems, Connecting Fragments, Extracting Fragments (H2)
* Road Network Detection, Road Extraction Systems (H1)
* Mathematical Models for Automatic Line Detection
7 for Line Detection
Line Drawings
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Shape from Line Drawings, Junction Labeling (H1)
* Surface and Shape from Contours or Silhouettes (H1)
* Extraction of the Line Drawing of 3-Dimensional Objects by Sequential Illumination from Several Directions
Line Drawings, Analysis
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Line Drawing Analysis (H2)
Line Drawings, Shape
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Shape from Line Drawing (H2)
Line Labeling
* 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings (H)
* Line Labeling Techniques (H2)
* Shape from Line Drawings, Junction Labeling (H1)
Line Labels
* 3-D Object Description and Computation Techniques, Surfaces, Deformable, View Generation, Video Conferencing (H)
* Extracting a Valid Boundary Representation from a Segmented Range Image
Line Matching
* 2-D Lines with 2-D Structure (H2)
* 2-D Lines with 3-D Structure (H2)
* 3-D Lines with 3-D Structure (H2)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
Line Segments
* 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
* Generation of Straight Line Segments or Curve Partitions (H1)
* Bayesian Method for Fitting Parametric and Nonparametric Models to Noisy Data, A
Line Vectorization
* Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
* Line Vectorization, Document Analysis (H2)
Linear Constraints
* Optical Flow Field Computations and Use (H)
* Performance of Camera Translation Direction Estimators from Optical-flow: Analysis, Comparison, and Theoretical Limits, The
Linear Discriminant Analysis
* Discriminant Analysis (H3)
* Invariants -- Linear Discriminant Analysis, Fisher Linear Discriminant (H3)
* LDA in Face Recognition (H4)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms (H)
Linear Features
* Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
* Extended Linear Features - Beyond Segments (H2)
* Line Vectorization, Document Analysis (H2)
Linear Prediction
* Linear Prediction Techniques (H3)
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
Linear Programming
* Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants (H)
* Linear Programming Approach fo the Weighted Graph Matching Problem, A
Lines
* 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Digital Geometry -- Lines, Curves and Contours (H3)
Lines, Colinear
* Colinear Line Segments (Collinear) (H
* Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
Lines, General
* Edge Detection and Analysis, Lines, Segments, Curves, Corners, Hough Transform (H)
* General Systems for Lines and Curves (H2)
Linkoping Univ.
* *Linkoping University
Lipreading
* Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics (H)
* Lipreading, Lip Reading, Lip Tracking (H2)
Localization
* Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following (H)
* Localization -- Where is the robot, Where is the camera (H3)
Log-Polar Sensor
* Optical Flow Field Computations and Use (H)
* On the Estimation of Depth from Motion Using an Anthropomorphic Visual Sensor
Log-Polar
* 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)
* Optical Flow Field Computations and Use (H)
* Binocular Fusion Revisited Utilizing a Log-Polar Tessellation
* Direct Computation of the Focus of Expansion from Velocity Field Measurements
* Disparity Estimation on Log-Polar Images and Vergence Control
7 for Log-Polar
Log Mapping
* 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture (H)
* Complex Log Mapping, Algorithms and Sensors (H2)
* Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar (H)
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Optical Flow Field Computations and Use (H)
* Complex Logarithmic Mapping and the Focus of Expansion
* Direct Computation of the Focus of Expansion
* Direct Estimation of Time-to-Impact from Optical Flow
* Geometric invariance in space-variant vision systems: The exponential chirp transform
* Motion Stereo Using Ego-Motion Complex Logarithmic Mapping
* On the Advantage of Polar and Log-Polar Mapping for Direct Estimation of Time-to-Impact from Optical Flow
* Position, Rotation and Scale Invarient Optical Correlation
12 for Log Mapping
Log Polar Mapping
* Motion Analysis --Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion (H)
* Segmentation of Frame Sequence Obtained by a Moving Observer
Logo Recognition
* OCR, Document Analysis and Character Recognition Systems (H)
* Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces (H)
* Analysis of Compressed Document Images for Dominant Skew, Multiple Skew, and Logotype Detection
* Content-Based Retrieval for Trademark Registration
* Content-Based Trademark Retrieval System Using Visually Salient Features
* Neural Based Architecture for Spot-Noisy Logo Recognition, A
* None
* Retrieval of Images from Image Databases: Trademarks, The
* Shape-Based Retrieval: A Case-Study with Trademark Image Databases
* Trademark Shape-Recognition Using Closed Contours
* Trademark Shapes Description by String-Matching Techniques
* Using Negative Shape Features for Logo Similarity Matching
12 for Logo Recognition
Lossless Compression
* Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
* Lossless Coding, Lossless Compression, Transmission (H2)
* Lossless Compression of AVIRIS Images by Vector Quantization, The
Low Bit Rate Coding
* Computing Very Low Bitrate, 3-D and Object Based Coding (H4)
* High Rate Compression, Low Bit Rate Compression, Region Based Coding (H2)
* Image Processing, Restoration, Enhancement, Filters, Image and Video Coding (H)
* Motion Compensation, Block, Region, Object, and Low Bit Rate Coding (H2)
* Motion Compensation, Low Bit Rate, Survey, Evaluations (H4)
* Very Low Bitrate, 3-D and Object Based Coding (H4)
Lungs
* Lungs, and Lung Cancer Image Analysis (H2)
* Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models (H)
* Pulmonary Nodules (H3)