Determining Noise Performance of Co-Occurrence GMuLBP on Object Detection Task

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridALPASLAN, Nuh/0000-0002-6828-755X
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidALPASLAN, Nuh/B-2199-2018
dc.contributor.authorAlpaslan, Nuh
dc.contributor.authorTurhan, Mehmet Murat
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:39:42Z
dc.date.available2024-08-04T20:39:42Z
dc.date.issued2013
dc.departmentİnönü Üniversitesien_US
dc.description6th International Conference on Machine Vision (ICMV) -- NOV 16-17, 2013 -- London, ENGLANDen_US
dc.description.abstractObject detection is currently one of the most actively researched areas of computer vision, image processing and analysis. Image co-occurrence has shown significant performance on object detection task because it considers the characteristic of objects and spatial relationship between them simultaneously. CoHOG has achieved great success on different object detection tasks, especially human detection. Whereas, CoHOG is sensitive to noise and it does not consider gradient magnitude which significantly effects the object detection accuracy. To overcome these disadvantages the CoGMuLBP was proposed. CoGMuLBP uses a new statistical orientation assignment method based on uniform LBP instead of using the common gradient orientation In this study, detection accuracies of CoGMuLBP and CoHOG are calculated on three different datasets with NN classifier. In addition, to evaluate the noise performance of the methods, gaussian noises were added to test images and performances were recalculated. Numerical experiments performed on three different datasets show that 1) CoGMuLBP has higher detection accuracy than CoHOG; 2) using uniform LBP based gradient orientation improves detection accuracy; and 3) CoGMuLBP is more robust to gaussian noise and illumination changes. These results provide the effectiveness of CoGMuLBP for object detection.en_US
dc.description.sponsorshipSichuan Univ,Huazhong Normal Univ,Aim Shams Univ,Singapore Inst Elect,Univ Portsmouth,China Commun,Sci & Engn Insten_US
dc.identifier.doi10.1117/12.2053138
dc.identifier.isbn978-0-8194-9996-7
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.scopus2-s2.0-84901305021en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1117/12.2053138
dc.identifier.urihttps://hdl.handle.net/11616/96458
dc.identifier.volume9067en_US
dc.identifier.wosWOS:000339220200069en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpie-Int Soc Optical Engineeringen_US
dc.relation.ispartofSixth International Conference on Machine Vision (Icmv 2013)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectObject Detectionen_US
dc.subjectCoHOGen_US
dc.subjectCoGMuLBPen_US
dc.titleDetermining Noise Performance of Co-Occurrence GMuLBP on Object Detection Tasken_US
dc.typeConference Objecten_US

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