Fine-grained detection and localization of objects in images Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/dv13zw987

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Object recognition is a fundamental problem in computer vision. Recognition is required by many applications. This thesis presents a distance based approach to recognize objects. We are interested in objects that belong to very similar classes, where each class has large variations. This problem is called fine-grained object recognition. Given a set of training images our approach identifies a sparse number of image patches in the training set which cover the most parts of the target object in the test image. We use Hungarian algorithm to match the image patches, based on a linear combination of appearance and geometric image features. We also specify a voting scheme for each possible location of the target object in the test image. The location which is close to the training image center is more likely to be the object center in the test image. Our results on a set of the challenging benchmark datasets are promising. This suggests that our approach is suitable to effectively address fine-grained object recognition.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2013-06-17T18:27:22Z (GMT) No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) YaofeiFeng2013.pdf: 828815 bytes, checksum: 9e9df32139ac72900458f886469e63b1 (MD5)
  • description.provenance : Made available in DSpace on 2013-06-20T20:32:49Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) YaofeiFeng2013.pdf: 828815 bytes, checksum: 9e9df32139ac72900458f886469e63b1 (MD5) Previous issue date: 2013-05-31
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-06-20T20:32:48Z (GMT) No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) YaofeiFeng2013.pdf: 828815 bytes, checksum: 9e9df32139ac72900458f886469e63b1 (MD5)
  • description.provenance : Submitted by Yaofei Feng (fengy@onid.orst.edu) on 2013-06-12T17:26:37Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: bb87e2fb4674c76d0d2e9ed07fbb9c86 (MD5) YaofeiFeng2013.pdf: 828815 bytes, checksum: 9e9df32139ac72900458f886469e63b1 (MD5)

Relationships

In Administrative Set:
Last modified: 08/09/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items