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A Neural Network Model for Solving the Feature Correspondence Problem

A. Aboudib, V. Gripon and G. Coppin, "A Neural Network Model for Solving the Feature Correspondence Problem," in Lecture Notes in Computer Science, Volume 9887, pp. 439--446, September 2016.

Finding correspondences between image features is a fundamental question in computer vision. Many models in literature have proposed to view this as a graph matching problem whose solution can be approximated using optimization principles. In this paper, we propose a different treatment of this problem from a neural network perspective. We present a new model for matching features inspired by the architecture of a recently introduced neural network. We show that by using popular neural network principles like max-pooling, k-winners-take-all and iterative processing, we obtain a better accuracy at matching features in cluttered environments. The proposed solution is accompanied by an experimental evaluation and is compared to state-of-the-art models.

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Bibtex
@article{AboGriCop20169,
  author = {Ala Aboudib and Vincent Gripon and Gilles
Coppin},
  title = {A Neural Network Model for Solving the
Feature Correspondence Problem},
  journal = {Lecture Notes in Computer Science},
  year = {2016},
  volume = {9887},
  pages = {439--446},
  month = {September},
}




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