Difference between revisions of "Historical:SoC2007 project Feature Descriptor"
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Erik Krause (talk | contribs) m (Erik Krause moved page SoC2007 project Feature Descriptor to Historical:SoC2007 project Feature Descriptor) |
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| + | See [[SoC 2007 overview]] for usage hints and a page list. | ||
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= Robust local feature detector and descriptor = | = Robust local feature detector and descriptor = | ||
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A detector and descriptor that takes into account the approximately known distortions will have a much higher matching rate, especially when fisheye or wide angle images are used. | A detector and descriptor that takes into account the approximately known distortions will have a much higher matching rate, especially when fisheye or wide angle images are used. | ||
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* Implementation of the feature detector and descriptor, and a suitable test suite to verify the correctness of the implementation. | * Implementation of the feature detector and descriptor, and a suitable test suite to verify the correctness of the implementation. | ||
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A desired result of the projects would be: | A desired result of the projects would be: | ||
* C or C++ library that implements the detection and description steps. | * C or C++ library that implements the detection and description steps. | ||
| − | * | + | * An executable for extracting features from image file. |
| − | * Integration of | + | * Test suite to evaluate descriptor on a large amount of images. |
| + | * Integration of the library into [[hugin]] | ||
| + | |||
| + | == Project Schedule == | ||
| + | * 0w April 9 - Application selection | ||
| + | |||
| + | * Interim Period: Learn more about the project, the literature that is being used to develop it, and build a testing set with ground truth ~200 images. This can be done asking for pictures to the mailing list. | ||
| + | |||
| + | * 0w May 28 - Begin coding for the project | ||
| + | |||
| + | * 1w June 4 - Create all the evaluation software, (mainly available from the website of Krystian Mikolajczyk) | ||
| + | |||
| + | * 3w June 18 - Implement of the algorithm chosen for the detector of feature points on the image. | ||
| + | |||
| + | * July 9: Students upload code to code.google.com/hosting; mentors begin mid-term evaluations | ||
| + | |||
| + | * 7w July 23 - Implementation of the descriptor to match the feature points detected. | ||
| + | |||
| + | * 10w August 13 - Benchmark and code optimization. | ||
| − | + | * 11w August 20 - Final report (Google Deadline for all student work),students upload code to code.google.com/hosting; mentors begin final evaluations; students begin final program evaluations. | |
== Required/available ressources == | == Required/available ressources == | ||
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* [http://en.wikipedia.org/wiki/Scale-invariant_feature_transform Wikipedia article on SIFT], contains several good references | * [http://en.wikipedia.org/wiki/Scale-invariant_feature_transform Wikipedia article on SIFT], contains several good references | ||
* [http://homes.esat.kuleuven.be/%7Etuytelaa/ECCV06tutorial.html Tutorial on local invariant features] | * [http://homes.esat.kuleuven.be/%7Etuytelaa/ECCV06tutorial.html Tutorial on local invariant features] | ||
| − | * [http://www.mis.informatik.tu-darmstadt.de/Education/Courses/cv/index.html Lecture notes] on feature detection/matching | + | * [http://www.mis.informatik.tu-darmstadt.de/Education/Courses/cv/index.html Lecture notes] on feature detection/matching |
| + | |||
| + | === Software === | ||
* Framework for [http://www.robots.ox.ac.uk/~vgg/research/affine/ testing descriptors] | * Framework for [http://www.robots.ox.ac.uk/~vgg/research/affine/ testing descriptors] | ||
| − | + | Mentor: Pablo d'Angelo, Herbert Bay, ? | |
| + | License: LGPL | ||
| − | + | == Students planning to apply == | |
| + | |||
| + | * [[User:pedro|Pedro Alonso Ferrer]] | ||
| − | + | [[Category:Community:Project]] | |
Latest revision as of 18:40, 5 June 2020
See SoC 2007 overview for usage hints and a page list.
Robust local feature detector and descriptor
Goal: Robust matching of features between multiple images using a Hessian-based detector and a suitable descriptor. A detector and descriptor that takes into account the approximately known distortions will have a much higher matching rate, especially when fisheye or wide angle images are used.
- Implementation of the feature detector and descriptor, and a suitable test suite to verify the correctness of the implementation.
Deliverables
A desired result of the projects would be:
- C or C++ library that implements the detection and description steps.
- An executable for extracting features from image file.
- Test suite to evaluate descriptor on a large amount of images.
- Integration of the library into hugin
Project Schedule
- 0w April 9 - Application selection
- Interim Period: Learn more about the project, the literature that is being used to develop it, and build a testing set with ground truth ~200 images. This can be done asking for pictures to the mailing list.
- 0w May 28 - Begin coding for the project
- 1w June 4 - Create all the evaluation software, (mainly available from the website of Krystian Mikolajczyk)
- 3w June 18 - Implement of the algorithm chosen for the detector of feature points on the image.
- July 9: Students upload code to code.google.com/hosting; mentors begin mid-term evaluations
- 7w July 23 - Implementation of the descriptor to match the feature points detected.
- 10w August 13 - Benchmark and code optimization.
- 11w August 20 - Final report (Google Deadline for all student work),students upload code to code.google.com/hosting; mentors begin final evaluations; students begin final program evaluations.
Required/available ressources
Required knowledge or interest in:
- signal or image processing background
- C or C++ development skills.
- Matlab or octave
Ressources
Literature
Literature about feature detection
- Wikipedia article on SIFT, contains several good references
- Tutorial on local invariant features
- Lecture notes on feature detection/matching
Software
- Framework for testing descriptors
Mentor: Pablo d'Angelo, Herbert Bay, ?
License: LGPL