A structure feature for some image processing applications based on spiral functions
Computer Vision, Graphics, and Image Processing
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Saliency, Scale and Image Description
International Journal of Computer Vision
Digital Picture Processing
International Journal of Computer Vision
A Framework for Low Level Feature Extraction
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Comparison of Edge Detectors: A Methodology and Initial Study
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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
Wide-Baseline Stereo Matching with Line Segments
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
A Comparison of Affine Region Detectors
International Journal of Computer Vision
ICML '06 Proceedings of the 23rd international conference on Machine learning
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Invariant Feature Detectors: A Survey
Local Invariant Feature Detectors: A Survey
Image description with features that summarize
Computer Vision and Image Understanding
Evaluating the Suitability of Feature Detectors for Automatic Image Orientation Systems
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Estimating the entropy of a signal with applications
IEEE Transactions on Signal Processing
Measuring the coverage of interest point detectors
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
Context-aware features and robust image representations
Journal of Visual Communication and Image Representation
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We develop a qualitative measure for the completeness and complementarity of sets of local features in terms of covering relevant image information. The idea is to interpret feature detection and description as image coding, and relate it to classical coding schemes like JPEG. Given an image, we derive a feature density from a set of local features, and measure its distance to an entropy density computed from the power spectrum of local image patches over scale. Our measure is meant to be complementary to existing ones: After task usefulness of a set of detectors has been determined regarding robustness and sparseness of the features, the scheme can be used for comparing their completeness and assessing effects of combining multiple detectors. The approach has several advantages over a simple comparison of image coverage: It favors response on structured image parts, penalizes features in purely homogeneous areas, and accounts for features appearing at the same location on different scales. Combinations of complementary features tend to converge towards the entropy, while an increased amount of random features does not. We analyse the complementarity of popular feature detectors over different image categories and investigate the completeness of combinations. The derived entropy distribution leads to a new scale and rotation invariant window detector, which uses a fractal image model to take pixel correlations into account. The results of our empirical investigations reflect the theoretical concepts of the detectors.