Paper Info
Title
Automated detection in complex objects using a tracking algorithm in multiple X-ray views
Abstract
We propose a new methodology to detect parts of interest inside of complex objects using multiple X-ray views. Our method consists of two steps: `structure estimation', to obtain a geometric model of the multiple views from the object itself, and `parts detection', to detect the object parts of interest. The geometric model is estimated by a bundle adjustment algorithm on stable SIFT keypoints across multiple views that are not necessary sorted. The detection of the object parts of interest is performed by an ad-hoc segmentation algorithm (application dependent) followed by a tracking algorithm based on geometric and appearance constraints. It is not required that the object parts have to be segmented in all views. Additionally, it is allowed to obtain false detections in this step. The tracking is used to eliminate the false detections without discriminating the object parts of interest. In order to illustrate the effectiveness of the proposed method, several applications - like detection of pen tips, razor blades and pins in pencil cases and detection of flaws in aluminum die castings used in the au-tomative industry - are shown yielding a true positive rate of 94.3% and a false positive rate of 5.6% in 18 sequences from 4 to 8 views.
Year
DOI
Venue
2011
10.1109/CVPRW.2011.5981715
CVPR 2011 WORKSHOPS
Keywords
Field
DocType
complex object automated detection,tracking algorithm,multiple X-ray views,structure estimation',geometric model,SIFT keypoints,object part detection,ad-hoc segmentation algorithm,pen tips detection,razor blades detection,pins detection,flaw detection,aluminum die castings,automative industry,nondestructive testing
False positive rate,Scale-invariant feature transform,Computer science,Image segmentation,Artificial intelligence,Computer vision,Object detection,Pattern recognition,Bundle adjustment,Segmentation,Geometric modeling,Algorithm,Feature extraction
Conference
Volume
Issue
ISSN
2011
1
2160-7508
ISBN
Citations 
PageRank 
978-1-4577-0529-8
3
0.48
References 
Authors
14
1
Name
Order
Citations
PageRank
Domingo Mery146642.09