SoC2007 project Feature Matching

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* Standalone program for the feature matching part, which at the end should accept the features found by the automatic feature detection task. Preliminary studies can be done using the existing SIFT and SURF detector/descriptors.
 
* Standalone program for the feature matching part, which at the end should accept the features found by the automatic feature detection task. Preliminary studies can be done using the existing SIFT and SURF detector/descriptors.
 
* Integration into hugin and a standalone executable similar to Autopano-sift or Autopano  
 
* Integration into hugin and a standalone executable similar to Autopano-sift or Autopano  
 
==Project schedule==
 
 
  
 
==Required knowledge or interest==
 
==Required knowledge or interest==

Revision as of 18:55, 23 March 2007

Contents

Goal

This is a Google Summer of Code project. Its goal is to develop a robust and efficient matching method of local image features. The image features detection is treated by another project Feature Detection.

Tasks

  • Implementation of the matching step, including geometry based outlier pruning (for example using RANSAC) and nearest neighbour matching, possibly using a fast algorithm such as cover trees. For the panoramic imaging use case, several heuristics could be used to improve the matching behaviour, including using the EXIF timestamps, or previously known approximate orientation of the images. The source code must be licensed under the GPL license.

Expected result

A desired result of the projects would be:

  • Standalone program for the feature matching part, which at the end should accept the features found by the automatic feature detection task. Preliminary studies can be done using the existing SIFT and SURF detector/descriptors.
  • Integration into hugin and a standalone executable similar to Autopano-sift or Autopano

Required knowledge or interest

  • signal or image processing background
  • C or C++ development skills.

Resources

Mentor

  • Pablo d'Angelo
  •  ?

Students planning to apply

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