Creating full view panoramic image mosaics and environment maps
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting
International Journal of Computer Vision
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Visual Correspondence Using Energy Minimization and Mutual Information
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Two-View Geometry Estimation Unaffected by a Dominant Plane
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An Enhanced Correlation-Based Method for Stereo Correspondence with Sub-Pixel Accuracy
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Randomized RANSAC with Sequential Probability Ratio Test
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Hallucinating multiple occluded face images of different resolutions
Pattern Recognition Letters
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Homography-based partitioning of curved surface for stereo correspondence establishment
Pattern Recognition Letters
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose Priors for Simultaneously Solving Alignment and Correspondence
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A consensus sampling technique for fast and robust model fitting
Pattern Recognition
Super-resolution of human face image using canonical correlation analysis
Pattern Recognition
Multiple camera people detection and tracking using support integration
Pattern Recognition Letters
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
Distributed multi-camera visual mapping using topological maps of planar regions
Pattern Recognition
Fast and accurate global motion compensation
Pattern Recognition
Subspace estimation using projection based m-estimators over grassmann manifolds
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
| Hi-index | 0.01 |
Accurate homography estimation is a classical problem with high industrial value and has been investigated extensively. Most previous homography estimation methods used either appearance similarity or keypoint correspondences to find their best estimation. In this paper, a novel algorithm is proposed which integrates the advantages of the pixel-based and the feature-based homography estimation approaches. We elegantly combined the probability models of appearance similarity and keypoint correspondences in a Maximum Likelihood framework, which is named as Homography Estimation based on Appearance Similarity and Keypoint correspondences (HEASK). In the model of keypoint correspondences, the distribution of inlier location error is represented by a Laplacian distribution, which outperforms the previous Gaussian distribution in characterizing heavy-tailed distributions. And in the model of appearance similarity, the enhanced correlation coefficient (ECC) is adopted for describing image similarity, and the distribution of ECC is studied and parametrically formulated using a truncated exponential distribution. The proposed model is solved based on an improved framework of random sample consensus (RANSAC). Several simulations summarize the performance of the proposed approach in objective quality measurement, subjective visual quality, and computation time. The experimental results demonstrate that the proposed approach can achieve more accurate homography estimation under different image transformation degrees and with different ratios of inlier keypoint correspondences as compared to the state-of-the-art works.