ARG-based segmentation of overlapping objects in multi-energy x-ray image of passenger accompanied baggage

Abstract

This paper addresses part of the problem dealing with the automatic detection of hazardous articles in accompanied baggage based on multi-energy X-ray imagery for Station Security. In this detection problem, segmentation is the first significant stage to extract interested objects in the images for detailed analysis and recognition at following stages. Due to complexity of articles in passenger baggage, the X-ray images generally contain regions in which different objects are overlapping. In order to obtain the integrated objects for subsequent analysis and recognition, these regions should be multi-segmented and allocated to different objects simultaneously. In this paper, we propose an ARG-based segmentation method using fuzzy attributed relational subgraph (ARSG) matching based on neighborhood structure ARSG model base (MB). The proposed segmentation strategy consists of two phases: pre-segmentation and post-segmentation. In the pre-segmentation phase, an X-ray image is segmented into non-overlapping segments using multi-threshold and statistical techniques according to color and texture features and represented by an attributed relational graph (ARG). Subsequently, in the post-segmentation phase, we propose a graph-matching algorithm using fuzzy similarity distance (FSD) that represents the similarity of the attributed relation between the vertex neighborhood and a certain model. Finally, the Number of Layer value of the vertices, which describe the number of objects overlapping in correspond region, are all obtained, and the ARG of image is completed and the integrated segments of objects in image can be extracted using relational attribute and space information. The results show a good average integrity of objects segmented from experiment images.