Pattern recognition of Vehicle Logo using Tchebichef and Legendre moment

In this paper, we propose a Vehicle Logo Recognition (VLR) approach which uses moment invariant for feature extraction. Moment invariants and Minimum-Mean Distance (MMD) classifier are adopted to recognize six different types of vehicle logos from a public dataset. Vehicle logos obtained from coarse...

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Bibliographic Details
Published in2015 IEEE Student Conference on Research and Development (SCOReD) pp. 82 - 86
Main Authors Foo Chong Soon, Hui Ying Khaw, Joon Huang Chuah
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Conferences
Feature extraction
Image recognition
Image segmentation
Legendre
Minimum-Mean Distance (MMD)
Pattern recognition
Research and development
Tchebichef
Vehicle-Logo Recognition (VLR)
Vehicles
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Summary:In this paper, we propose a Vehicle Logo Recognition (VLR) approach which uses moment invariant for feature extraction. Moment invariants and Minimum-Mean Distance (MMD) classifier are adopted to recognize six different types of vehicle logos from a public dataset. Vehicle logos obtained from coarse and fine segmentation, are recognized using Tchebichef and Legendre moment invariants. In either coarse or fine segmented vehicle logo images, Tchebichef moment invariants perform better than the Legendre's. With the experimental accuracy results of 88.3% on the 240 dataset images of six different types of vehicle logos, it has demonstrated the effectiveness of the proposed method in recognizing the fine segmented vehicle logo, which supports the use of the system for real application.
DOI:10.1109/SCORED.2015.7449438