Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
SMD: A Locally Stable Monotonic Change Invariant Feature Descriptor
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Spiders as robust point descriptors
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
The farthest point strategy for progressive image sampling
IEEE Transactions on Image Processing
Indoor calibration using segment chains
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Towards feature-based situation assessment for airport apron video surveillance
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
| Hi-index | 0.00 |
In order to achieve a complete image description, we introduce no-feature-features (NF-features) representing object regions where regular interest point detectors do not detect features. As these regions are usually non-textured, stable re-localization in different images with conventional methods is not possible. Therefore, a technique is presented which re-localizes once-detected NF-features using correspondences of regular features. Furthermore, a distinctive NF descriptor for nontextured regions is derived which has invariance towards affine transformations and changes in illumination. For the matching of NF descriptors, an approach is introduced that is based on local image statistics. NF-features can be used complementary to all kinds of regular feature detection and description approaches that focus on textured regions, i.e. points, blobs or contours. Using SIFT, MSER, Hessian-Affine or SURF as regular detectors, we demonstrate that our approach is not only suitable for the description of non-textured areas but that precision and recall of the NF-features is significantly superior to those of regular features. In experiments with high variation of the perspective or image perturbation, at unchanged precision we achieve NF recall rates which are better by more than a factor of two compared to recall rates of regular features.