A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery

We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to...

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Published inPattern recognition Vol. 46; no. 9; pp. 2420 - 2436
Main Authors Flitton, Greg, Breckon, Toby P., Megherbi, Najla
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.09.2013
Elsevier
Subjects
3D feature descriptors
3D SIFT
Airports
Applied sciences
Baggage
CT baggage scan
CT object recognition
Density
Detection, estimation, filtering, equalization, prediction
Detectors
Exact sciences and technology
Image processing
Imagery
Information, signal and communications theory
Object recognition
Pattern recognition
RIFT (reactor in flight test)
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Threat detection
Three dimensional
CT object recognition
3D SIFT
CT baggage scan
Threat detection
Object recognition
3D feature descriptors
Performance evaluation
Histogram
False positive
Measurement sensor
Artefact
Pattern recognition
Computerized tomography
Three dimensional model
Volume
Imaging
Airport
Object detection
Target detection
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Abstract We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex CT imagery domain containing a high degree of noise and imaging artefacts, a specific instance object recognition system using simpler descriptors appears to outperform a more complex RIFT/SIFT solution. Recognition rates in excess of 95% are demonstrated with minimal false-positive rates for a set of exemplar 3D objects. ► Rigid item threat detection in 3D CT baggage imagery. ► CT imagery contains high degree of artefacts which hinder descriptor performance. ► Performance using a variety of local descriptors is compared. ► 3D SIFT orientation invariance methodology weak in this imagery. ► 95% detection rate is achieved using simple local descriptors.
AbstractList We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex CT imagery domain containing a high degree of noise and imaging artefacts, a specific instance object recognition system using simpler descriptors appears to outperform a more complex RIFT/SIFT solution. Recognition rates in excess of 95% are demonstrated with minimal false-positive rates for a set of exemplar 3D objects.
We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex CT imagery domain containing a high degree of noise and imaging artefacts, a specific instance object recognition system using simpler descriptors appears to outperform a more complex RIFT/SIFT solution. Recognition rates in excess of 95% are demonstrated with minimal false-positive rates for a set of exemplar 3D objects. ► Rigid item threat detection in 3D CT baggage imagery. ► CT imagery contains high degree of artefacts which hinder descriptor performance. ► Performance using a variety of local descriptors is compared. ► 3D SIFT orientation invariance methodology weak in this imagery. ► 95% detection rate is achieved using simple local descriptors.
Author Flitton, Greg
Megherbi, Najla
Breckon, Toby P.
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Issue 9
Keywords CT object recognition
3D SIFT
CT baggage scan
Threat detection
Object recognition
3D feature descriptors
Performance evaluation
Histogram
False positive
Measurement sensor
Artefact
Pattern recognition
Computerized tomography
Three dimensional model
Volume
Imaging
Airport
Object detection
Target detection
Language English
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Snippet We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The...
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SubjectTerms 3D feature descriptors
3D SIFT
Airports
Applied sciences
Baggage
CT baggage scan
CT object recognition
Density
Detection, estimation, filtering, equalization, prediction
Detectors
Exact sciences and technology
Image processing
Imagery
Information, signal and communications theory
Object recognition
Pattern recognition
RIFT (reactor in flight test)
Signal and communications theory
Signal processing
Signal, noise
Telecommunications and information theory
Threat detection
Three dimensional
Title A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery
URI https://dx.doi.org/10.1016/j.patcog.2013.02.008
https://search.proquest.com/docview/1365153502
https://search.proquest.com/docview/1513462637
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