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dc.contributor.authorWasin Thubsaengen_US
dc.contributor.authorAram Kawewongen_US
dc.contributor.authorKarn Patanukhomen_US
dc.description.abstractThis paper presents a new method for vehicle logo detection and recognition from images of front and back views of vehicle. The proposed method is a two-stage scheme which combines Convolutional Neural Network (CNN) and Pyramid of Histogram of Gradient (PHOG) features. CNN is applied as the first stage for candidate region detection and recognition of the vehicle logos. Then, PHOG with Support Vector Machine (SVM) classifier is employed in the second stage to verify the results from the first stage. Experiments are performed with dataset of vehicle images collected from internet. The results show that the proposed method can accurately locate and recognize the vehicle logos with higher robustness in comparison with the other conventional schemes. The proposed methods can provide up to 100% in recall, 96.96% in precision and 99.99% in recognition rate in dataset of 20 classes of the vehicle logo. © 2014 IEEE.en_US
dc.subjectComputer Scienceen_US
dc.titleVehicle logo detection using convolutional neural network and pyramid of histogram of oriented gradientsen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2014 11th Int. Joint Conf. on Computer Science and Software Engineering: "Human Factors in Computer Science and Software Engineering" - e-Science and High Performance Computing: eHPC, JCSSE 2014en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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