Abstract— The research aims to develop the Gun’s Type and Models Classification (GTMC) system using image recognition theory. It is expected that this study can serve as a guide for law enforcement agencies or at least serve as the catalyst for a similar type of research. Master image storage and image recognition are the two main processes. The procedures involved original images, scaling, gray scale, canny edge detector, SUSAN corner detector, block matching template, and finally gun type’s recognition. Of the 505 images, 80 were control or master images, and 425 were experimental images of the eight gun types. The finding from the experiment indicated that the GTMC was able to classify the images of the semi-automatic gun with the highest accuracy of 99.06 percent, and the average accurate gun image classification was 81.25 percent respectively.
Index Terms— Canny, gun type classification, image recognition theory, SUSAN.
M. L. Kulthon Kasemsan is with the Faculty of Information Technology, Rangsit University, Pathumtani, Thailand (e-mail: kkasemsan@rsu.ac.th).
[PDF]
Cite: M. L. Kulthon Kasemsan, " The Classification of Gun’s Type Using Image Recognition Theory," International Journal of Information and Electronics Engineering vol. 4, no. 1, pp. 54-58, 2014.
