Bu kaydın yasal hükümlere uygun olmadığını düşünüyorsanız lütfen sayfa sonundaki Hata Bildir bağlantısını takip ederek bildirimde bulununuz. Kayıtlar ilgili üniversite yöneticileri tarafından eklenmektedir. Nadiren de olsa kayıtlarla ilgili hatalar oluşabilmektedir. MİTOS internet üzerindeki herhangi bir ödev sitesi değildir!

Vehicle Logo Recognition Using Image Processing Methods

BROWSE_DETAIL_CREATION_DATE: 20-02-2017

BROWSE_DETAIL_IDENTIFIER_SECTION

BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Albera, Sumia A. A (Author),

BROWSE_DETAIL_CONTRIBUTERS: Şengül, Gökhan (Advisor),

BROWSE_DETAIL_TAB_KEYWORDS

Vehicle logo recognition, Vehicle logo classification, SURF algorithm, Local Binary recognition, Gray Level Co-occurrence Matrix


BROWSE_DETAIL_TAB_ABSTRACT

Vehicle logo recognition is the ability to recognize and classify the vehicle logos in different conditions with high accuracy. This system plays significant role in monitoring systems, security and surveillance systems, such as the control system in government buildings and military camps. Vehicle logo recognition starts with reading the logo as an image, goes on analyzing and classifying of the logo. The goal of this study is to compare the performance of three methods used for vehicle logo recognition and determine the accuracy of each method in noisy environments and from images captured from different directions. The main methods used for vehicle logo recognition in this thesis are: SURF algorithm, LBP and GLCM. In addition, KNN is used as a classifier with LBP and GLCM features. These methods are tested on the data sets collected in two ways: gathering logo images from the website of the manufacturers and capturing logo images by a standard camera. Best result in this thesis for vehicle logo recognition was achieved by the SURF algorithm


BROWSE_DETAIL_TAB_TOC



BROWSE_DETAIL_TAB_DESCRIPTION



BROWSE_DETAIL_TAB_RIGHTS



BROWSE_DETAIL_TAB_NOTES



BROWSE_DETAIL_TAB_REFERENCES


BROWSE_DETAIL_TAB_REFERENCED_BYS

BROWSE_DETAIL_GOTO_LIST

 

TEXT_STATS

  • TEXT_RECORD_STATS
    • TEXT_STATS_THIS_MONTH: 0
    • TEXT_STATS_TOTAL: 2414
  • TEXT_ONLINE_STATS
    • TEXT_ONLINE_STATS_TOTALONLINEVISITOR: 20
    • TEXT_ONLINE_STATS_TOTALONLINEUSER: 0
    • TEXT_STATS_TOTAL: 20

LINK_STATS