DocumentCode :
254617
Title :
Joint Shape and Texture Based X-Ray Cargo Image Classification
Author :
Jian Zhang ; Li Zhang ; Ziran Zhao ; Yaohong Liu ; Jianping Gu ; Qiang Li ; Duokun Zhang
Author_Institution :
Nuctech, Beijing, China
fYear :
2014
fDate :
23-28 June 2014
Firstpage :
266
Lastpage :
273
Abstract :
Security & Inspection X-Ray Systems is widely used by custom to accomplish some security missions by inspecting import-export cargo. Due to the specificity of cargo X-Ray image, such as overlap, viewpoint dependence, and variants of cargo categories, it couldn´t be understood easily like natural ones by human. Even for experienced screeners, it´s very difficult to judge cargo category and contraband. In this paper, cargo X-Ray image is described by joint shape and texture feature, which could reflect both cargo stacking mode and interior details. Classification performance is compared with the benchmark method by top hit 1, 3, 5 ratio, and it´s demonstrated that good performance is achieved here. In addition, we also discuss X-Ray image property and explore some reasons why cargo classification under X-Ray is very difficult.
Keywords :
X-ray imaging; freight handling; image classification; image texture; inspection; shape recognition; tariffs; X-ray cargo image classification; X-ray image property; cargo interior details; cargo stacking mode; image joint shape; image texture feature; inspection; Feature extraction; Image edge detection; Joints; Shape; Stacking; Visualization; X-ray imaging; cargo X-Ray image classification; edge based BOW; joint shape and texture feature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
Type :
conf
DOI :
10.1109/CVPRW.2014.48
Filename :
6909993
Link To Document :
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