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Proceedings ArticleDOI

A modified Hausdorff distance for object matching

M.-P. Dubuisson, +1 more
- Vol. 1, pp 566-568
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TLDR
Based on experiments on synthetic images containing various levels of noise, the authors determined that one of these distance measures, called the modified Hausdorff distance (MHD) has the best performance for object matching.
Abstract
The purpose of object matching is to decide the similarity between two objects. This paper introduces 24 possible distance measures based on the Hausdorff distance between two point sets. These measures can be used to match two sets of edge points extracted from any two objects. Based on experiments on synthetic images containing various levels of noise, the authors determined that one of these distance measures, called the modified Hausdorff distance (MHD) has the best performance for object matching. The advantages of MHD ever other distances are also demonstrated on several edge snaps of objects extracted from real images.

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Citations
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DissertationDOI

Data-driven simulations of wildfire spread at regional scales

Cong Zhang
TL;DR: Zhang et al. as discussed by the authors developed a grid-based spatialized parameter estimation approach where the estimation targets are the spatially-varying input model parameters and proposed an efficient and robust method to compute the discrepancy between the observed and simulated fire fronts, which is based on a front shape similarity measure inspired from image processing theory.
Dissertation

Potential of kilometric-resolution meteorological forecasts for snowpack modelling in mountainous terrain

Louis Quéno
TL;DR: In this paper, the authors assessed the potential of forecasts from the numerical weather prediction model AROME at 2.5 km horizontal resolution to drive the detailed snowpack model Crocus, and showed how the cloud microphysics scheme of AROME associated with Crocus enables to better predict ice formation on top of the snowpack due to freezing precipitation in the Pyrenees.
Proceedings ArticleDOI

Predicting Missing and Spurious Protein-Protein Interactions Using Graph Embeddings on GO Annotation Graph

TL;DR: A novel method that employs graph embeddings to learn vector representations from constructed Gene Ontology (GO) annotation graphs and preserves properties of both local and global structural information of the GO annotation graph is proposed.
Journal ArticleDOI

3D object retrieval with graph-based collaborative feature learning

TL;DR: A novel view-based 3D object retrieval framework is presented, which is deployed over a graph-based collaborative learning scheme to intelligently fuse multiple features, and a hypergraph based collaborative feature learning scheme is introduced to fuse complement descriptors from both the contour and the interior region of3D object effectively.
Proceedings ArticleDOI

An Improved Least Trimmed Square Hausdorff Distance Finger Vein Recognition

TL;DR: The improved Least Trimmed Square Hausdorff Distance algorithm is used to achieve finger vein recognition by introducing the optimal weights and the results show that the proposed algorithm has a significant improvement in the objective indicators such as matching speed and accuracy.
References
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Journal ArticleDOI

Comparing images using the Hausdorff distance

TL;DR: Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented and it is shown that the method extends naturally to the problem of comparing a portion of a model against an image.
Proceedings ArticleDOI

2D matching of 3D moving objects in color outdoor scenes

TL;DR: An object matching system which is able to extract objects of interest from outdoor scenes and match them to obtain a reliable estimate of the average travel time in a road network is described.