A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Determination of the Attitude of 3D Objects from a Single Perspective View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solution of the simultaneous pose and correspondence problem using Gaussian error model
Computer Vision and Image Understanding
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polyhedral Object Detection and Pose Estimation for Augmented Reality Applications
CA '02 Proceedings of the Computer Animation
Real-Time 3D Object Recognition for Automatic Tracker Initialization
ISAR '01 Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR'01)
A real-time tracker for markerless augmented reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Fully Automated and Stable Registration for Augmented Reality Applications
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Robust Visual Tracking for Non-Instrumented Augmented Reality
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Sensor Fusion and Occlusion Refinement for Tablet-Based AR
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Automated Initialization for Marker-Less Tracking: A Sensor Fusion Approach
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Scene Modelling, Recognition and Tracking with Invariant Image Features
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
A Head Tracking Method Using Bird's-Eye View Camera and Gyroscope
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Adaptive Line Tracking with Multiple Hypotheses for Augmented Reality
ISMAR '05 Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality
A Hybrid and Linear Registration Method Utilizing Inclination Constraint
ISMAR '05 Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality
Online camera pose estimation in partially known and dynamic scenes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
A mobile markerless AR system for maintenance and repair
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Going out: robust model-based tracking for outdoor augmented reality
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Supporting outdoor mixed reality applications for architecture and cultural heritage
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Model-Based 3d object localization using occluding contours
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Augmented Reality: Handheld Augmented Reality involving gravity measurements
Computers and Graphics
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We propose a hybrid camera pose estimation method using an inclination sensor value and correspondence-free line segments. In this method, possible azimuths of the camera pose are hypothesized by a voting method under an inclination constraint. Then some camera positions for each possible azimuth are calculated based on the detected line segments that affirmatively voted for the azimuth. Finally, the most consistent one is selected as the camera pose out of the multiple sets of the camera positions and azimuths. Unlike many other tracking methods, our method does not use past information but rather estimates the camera pose using only present information. This feature is useful for an initialization measure of registration in augmented reality (AR) systems. This paper describes the details of the method and shows its effectiveness with experiments in which the method is actually used in an AR application.