Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/46003
標題: An adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits
作者: Jiang, J.A.
楊曼妙
Chang, H.Y.
Wu, K.H.
Ouyang, C.S.
Yang, M.M.
Yang, E.C.
Chen, T.W.
Lin, T.T.
關鍵字: X-ray
insect pest inspection
quarantine
image processing
adaptive
thresholding
deboned poultry
classification
期刊/報告no:: Computers and Electronics in Agriculture, Volume 60, Issue 2, Page(s) 190-200.
摘要: Although X-ray scanners are commonly used in airports or customs for security inspection, practical application of X-ray imaging in quarantine inspection to prevent propagation of alien insect pests in imported fruits is still unavailable. The first step to identify insect infestation in fruit by X-ray imaging technique is image acquisition. This is followed by the image segmentation procedure, which can locate sites of infestation. Since the grey level of X-ray images depends on the density and thickness of the test samples, the relative contrast of infestation site to the intact region inside a typical fruit varies with its position. To accurately determine whether a fruit has signs of insect infestation, we have developed an adaptive image segmentation algorithm based on the local pixels intensities and unsupervised thresholding algorithm. This paper presents the detailed image processing procedure including the grid formation, local thresholding, threshold value interpolation, background removal, and morphological filtering for the determination of infestation sites of a fruit in X-ray image. The real-time image processing procedure was tested with X-ray images of several types of fruit such as citrus, peach, guava, etc. Additional tests and analyses were also performed using the developed algorithm on the X-ray images obtained with different image acquisition parameters. (C) 2007 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/11455/46003
ISSN: 0168-1699
文章連結: http://dx.doi.org/10.1016/j.compag.2007.08.006
Appears in Collections:昆蟲學系

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