Fast model-based feature matching technique applied to airport lighting

Abstract

Irwin, G. (2008Abstract: The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting.The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade -Lucus -Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

Multiple Luminaire Identification in Airborne Images of Airport's Approach Lighting Using Mathematical Morphology With Variable Length Structuring Element

“…It has already been reported in [44] that the existing KLT and SIFT techniques give and success rates in tracking luminaires, whereas model based matching techniques produce a 76% success rate. An experiment to compare the proposed pattern based identification technique with the model based matching technique was performed using the real image data.…”

“…Using this technique they achieved a success rate which is higher than that of the KLT and SIFT technique. This model based technique [44] is a good example of bottom-up approach. All the luminaires need to be mapped individually with the corresponding entity in the model or template to define the final pattern of the ALS.…”

“…In their study, Niblock et al concluded that both of these techniques show very poor success rate. The KLT performed at accuracy whereas SIFT has shown only success rate [44]. To overcome the poor accuracy of tracking the luminaires, Niblock et al proposed a fast model based feature matching technique [44].…”

“…The KLT performed at accuracy whereas SIFT has shown only success rate [44]. To overcome the poor accuracy of tracking the luminaires, Niblock et al proposed a fast model based feature matching technique [44]. Initially they constructed a template based on the ALS luminaire data.…”

“…Niblock et al [44] studied the application of two existing tracking techniques applied to airport lighting image data. These being the Kanade-Lucus-Tomasi (KLT) [45], [46] and scale invariant feature transform (SIFT) [47], [48].…”