Vincent Gripon's Homepage

A Neural Network Model for Solving the Feature Correspondence Problem

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.

@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},
}