Yayın:
Impact of Artificial Dataset Enlargement on Performance of Deformable Part Models

Basit öğe sayfası
dc.contributor.authorYilmaz, Bedir
dc.contributor.authorAmasyali, Mehmet Fatih
dc.contributor.authorBalcilar, Muhammet
dc.contributor.authorUslu, Erkan
dc.contributor.authorYavuz, Sirma
dc.date.accessioned2026-06-27T13:58:42Z
dc.date.issued2016
dc.description.abstractThere is a remarkable body of work for increasing the performance of Deformable Part Models (DPM), which is one of the most popular algorithms that are being used for object detection from digital images. The contribution that has been made to the object detection performance of the DPM algorithm via usage of larger datasets that has been created via production of artificial images from original images has been examined in this study. Various artificial dataset enlargement techniques that require no additional effort of labeling or data gathering have been compared on INRIA dataset and positive impact of artificial dataset enlargement has been observed. An increase on object detection performance has been noted both on the original INRIA dataset and its subsets.en
dc.identifier.endpage196
dc.identifier.isbn978-1-5090-1679-2
dc.identifier.startpage193
dc.identifier.urihttps://hdl.handle.net/20.500.14981/56165
dc.identifier.wos000391250900026
dc.language.isotur
dc.publisherIEEE
dc.relation.conference24th Signal Processing and Communication Application Conference (SIU)
dc.relation.ispartof2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU)
dc.subjectDeformable Part Models
dc.subjectArtificial Dataset Enlargement
dc.subjectPattern Recognition
dc.subjectHuman Victim Detection
dc.subjectPedestrian Detection
dc.subjectEngineering
dc.titleImpact of Artificial Dataset Enlargement on Performance of Deformable Part Models
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

Dosyalar

Koleksiyonlar

Yayınlar