Computational geometry: an introduction
Computational geometry: an introduction
Biological Cybernetics
A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
The Topological Structure of Scale-Space Images
Journal of Mathematical Imaging and Vision
Saliency, Scale and Image Description
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
On the Representation and Matching of Qualitative Shape at Multiple Scales
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Pseudo-Metric for Weighted Point Sets
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
What Do Features Tell about Images?
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Earth Mover's Distance under Transformation Sets
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
SMI '04 Proceedings of the Shape Modeling International 2004
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Object Recognition as Many-to-Many Feature Matching
International Journal of Computer Vision
The representation and matching of categorical shape
Computer Vision and Image Understanding
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Many-to-many matching of scale-space feature hierarchies using metric embedding
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Content based image retrieval using multiscale top points a feasibility study
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Image reconstruction from multiscale critical points
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Feature vector similarity based on local structure
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Many-to-many graph matching via metric embedding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Top-points as interest points for image matching
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Stability of top-points in scale space
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
A model for smooth viewing and navigation of large 2D information spaces
IEEE Transactions on Visualization and Computer Graphics
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Object categorization using bone graphs
Computer Vision and Image Understanding
International Journal of Computer Vision
Complexity of computing distances between geometric trees
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Hi-index | 0.00 |
In previous work, singular points (or top points) in the scale space representation of generic images have proven valuable for image matching. In this paper, we propose a novel construction that encodes the scale space description of top points in the form of a directed acyclic graph. This representation allows us to utilize coarse-to-fine graph matching algorithms for comparing images represented in terms of top point configurations instead of using solely the top points and their features in a point matching algorithm, as was done previously. The nodes of the graph represent the critical paths together with their top points. The edge set captures the neighborhood distribution of vertices in scale space, and is constructed through a hierarchical tessellation of scale space using a Delaunay triangulation of the top points. We present a coarse-to-fine many-to-many matching algorithm for comparing such graph-based representations. The algorithm is based on a metric-tree representation of labeled graphs and their low-distortion embeddings into normed vector spaces via spherical encoding. This is a two-step transformation that reduces the matching problem to that of computing a distribution-based distance measure between two such embeddings. To evaluate the quality of our representation, four sets of experiments are performed. First, the stability of this representation under Gaussian noise of increasing magnitude is examined. Second, a series of recognition experiments is run on a face database. Third, a set of clutter and occlusion experiments is performed to measure the robustness of the algorithm. Fourth, the algorithm is compared to a leading interest point-based framework in an object recognition experiment.