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[70230] Artykuł:

Efficiency Investigation of BoF, SVT and Pyramid Match Algorithms in Practical Recognition Applications

Czasopismo: Proceedings of the 4th International Conference on Mathematics and Computers in Sciences and in Industry (MCSI 2017)   Tom: 4, Strony: 171-178
ISBN:  978-1-5386-2820-1
Wydawca:  IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Opublikowano: Sierpień 2017
Liczba arkuszy wydawniczych:  2.00
 
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Remigiusz Baran orcid logo WEAiIKatedra Informatyki, Elektroniki i Elektrotechniki *Takzaliczony do "N"Automatyka, elektronika, elektrotechnika i technologie kosmiczne10015.0015.00  

Grupa MNiSW:  Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science)
Punkty MNiSW: 15
Klasyfikacja Web of Science: Proceedings Paper

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Keywords:

Image Recognition  SURF  Make and Model Recognition of Cars  Landmark Detection  Bag of Features  Scalable Vocabulary Tree  Pyramid Match 


Abstract:

The choice of local image features is crucial for many computer vision applications. Scale Invariant Feature Transform (SIFT) features [1] and their upgraded version – Speeded-Up Robust Features (SURF) [2], are the most successful and popular ones for different object and scene recognition tasks, in terms of non-real and real time requirements, respectively. However, local features are not the only means building up the potential for fast and user-friendly solutions. Methods applied to process extracted features and their descriptors at the next steps are also critical. Three selected approaches of these type, based on to Bag of Features [3], Scalable Vocabulary Tree [4] and Pyramid Match [5] methods, respectively, are examined in the paper. Their effectiveness with regard to real-time make and model recognition of cars as well as visual building and places identification is reported and discussed as a final result of performed examination.


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