Model-based image matching using location
Model-based image matching using location
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Advances in neural information processing systems 2
A common framework for image segmentation
International Journal of Computer Vision
On the Verification of Hypothesized Matches in Model-Based Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quasi-random sequences and their discrepancies
SIAM Journal on Scientific Computing
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Optimal geometric model matching under full 3D perspective
Computer Vision and Image Understanding
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Pose Clustering Using a Randomized Algorithm
International Journal of Computer Vision
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Robust Affine Structure Matching for 3D Object Recognition
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
Indexing without Invariants in 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Globally Convergent Pose Estimation from Video Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Linear Solution of Exterior Orientation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Robot Vision
View Variation of Point-Set and Line-Segment Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial-Time Object Recognition in the Presence of Clutter, Occlusion, and Uncertainty
ECCV '92 Proceedings of the Second European Conference on Computer Vision
ForeSight: fast object recognition using geometric hashing with edge-triple features
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Registration of Cad-Models to Images by Iterative Inverse Perspective Matching
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
An algorithm for projective point matching in the presence of spurious points
Pattern Recognition
Human Motion Tracking with a Kinematic Parameterization of Extremal Contours
International Journal of Computer Vision
Robust Optimal Pose Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A Comparison of Iterative 2D-3D Pose Estimation Methods for Real-Time Applications
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Robust and efficient feature tracking for indoor navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Study on an indoor tracking system with infrared projected markers for large-area applications
Proceedings of the 8th International Conference on Virtual Reality Continuum and its Applications in Industry
CAD-based recognition of 3D objects in monocular images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object recognition using point uncertainty regions as pose uncertainty regions
Image and Vision Computing
Distributed consensus on camera pose
IEEE Transactions on Image Processing
Computers and Electrical Engineering
Combining geometric and appearance priors for robust homography estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Probabilistic 3D object recognition based on multiple interpretations generation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Landmark detection for autonomous spacecraft landing on mars
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
3D markerless motion tracking in real-time using a single camera
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
A vision-based navigation facility for planetary entry descent landing
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Random model variation for universal feature tracking
Proceedings of the 18th ACM symposium on Virtual reality software and technology
Accurate single image multi-modal camera pose estimation
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Reconstructing detailed dynamic face geometry from monocular video
ACM Transactions on Graphics (TOG)
Occlusion-aware multi-view reconstruction of articulated objects for manipulation
Robotics and Autonomous Systems
Hi-index | 0.00 |
The problem of pose estimation arises in many areas of computer vision, including object recognition, object tracking, site inspection and updating, and autonomous navigation when scene models are available. We present a new algorithm, called SoftPOSIT, for determining the pose of a 3D object from a single 2D image when correspondences between object points and image points are not known. The algorithm combines the iterative softassign algorithm (Gold and Rangarajan, 1996; Gold et al., 1998) for computing correspondences and the iterative POSIT algorithm (DeMenthon and Davis, 1995) for computing object pose under a full-perspective camera model. Our algorithm, unlike most previous algorithms for pose determination, does not have to hypothesize small sets of matches and then verify the remaining image points. Instead, all possible matches are treated identically throughout the search for an optimal pose. The performance of the algorithm is extensively evaluated in Monte Carlo simulations on synthetic data under a variety of levels of clutter, occlusion, and image noise. These tests show that the algorithm performs well in a variety of difficult scenarios, and empirical evidence suggests that the algorithm has an asymptotic run-time complexity that is better than previous methods by a factor of the number of image points. The algorithm is being applied to a number of practical autonomous vehicle navigation problems including the registration of 3D architectural models of a city to images, and the docking of small robots onto larger robots.