A camera or display usually has a smaller dynamic range than the human eye. For this reason, objects that
can be detected by the naked eye may not be visible in recorded images. Lighting is here an important
factor; improper local lighting impairs visibility of details or even entire objects. When a human is observing
a scene with different kinds of lighting, such as shadows, he will need to see details in both the dark and
light parts of the scene. For grey value images such as IR imagery, algorithms have been developed in
which the local contrast of the image is enhanced using local adaptive techniques. In this paper, we present
how such algorithms can be adapted so that details in color images are enhanced while color information is
retained. We propose to apply the contrast enhancement on color images by applying a grey value contrast
enhancement algorithm to the luminance channel of the color signal. The color coordinates of the signal will
remain the same. Care is taken that the saturation change is not too high. Gamut mapping is performed
so that the output can be displayed on a monitor. The proposed technique can for instance be used by
operators monitoring movements of people in order to detect suspicious behavior. To do this effectively,
specific individuals should both be easy to recognize and track. This requires optimal local contrast, and is
sometimes much helped by color when tracking a person with colored clothes. In such applications, enhanced
local contrast in color images leads to more effective monitoring.
This paper describes the development and implementation of a low cost
camera system that uses polarisation features of visible light for
faster area reduction. The camera system will be mounted on a
mechanical minefield area reduction asset, namely an AT mine roller of
The HALO Trust. The automatic detection system will give an audible
alarm in order to stop the AT mine roller before the rollers detonate a mine.
This paper gives a comparison of two vehicle-mounted infrared
systems for landmine detection. The first system is a down-ward looking standard infrared camera using processing methods developed within the EU project LOTUS. The second system is using a forward-looking polarimetric infrared camera. Feature-based classification is used for this system. With these systems data have been acquired simultaneously of different test lanes from a moving platform. The performance of each system is evaluated using a leave-one-out method. On the training set the polarimetric infrared system performs better especially for low false alarm rates. On the independent evaluation set the differences are much smaller. On the ferruginous soil test lane the down-ward looking system performs better at certain points whereas on the grass test lane the forward-looking system performs better at certain points.
In this submission, we report on the successful field demonstration of the LOTUS landmine detection system that took place in August 2002 near the village of Vidovice, in the Northeast of Bosnia and Herzegovina.
High detection performance is required for an operational system for the detection of landmines. Humanitarian de-mining scenarios, combined with inherent difficulties of detecting landmines on an operational (vibration, motion, atmosphere) as well as a scenario level (clutter, soil type, terrain), result in high levels of false alarms for most sensors. To distinguish a landmine from background clutter one or more discriminating object features have to be found. The research described here focuses on finding and evaluating one or more features to distinguish disk-shaped landmines from background clutter in infrared images. These images were taken under controlled conditions, with homogenous soil types. Two methods are considered to acquire shape-based features in the infrared imagery. The first method uses a variation of the Hough transformation to find circular shaped objects. The second method uses the tophat filter with a disk-shaped structuring element. Furthermore, Mahalanobis and Fisher based classifiers are used to combine these features.
In this paper we introduce the concept of depth fusion for anti-personnel landmine detection. Depth fusion is an extension of common sensor-fusion techniques for landmine detection. The difference lies within the fact that fusion of sensor data is performed in different physical depth layers. In order to do so, it requires a sensor that provides depth information for object detections. Our ground-penetrating radar fulfills this requirement. Depth fusion is then taken as the combination of the output of sensor fusion of all layers. The underlying idea is that sensor fusion for the surface layer has a different weighing of the sensors when compared with the sensor fusion in the deep layers because of apparent sensor characteristics. For example, a thermal IR sensor hardly adds information to the sensor fusion in the deep layers. Furthermore, GPR has difficulties suppressing clutter in the surface layer. As such, the surface fusion should emphasize on the TIR sensor, whereas sensor fusion in the deep layers should have a higher weighing of the GPR. This a priori information can be made explicit by choosing for a depth-fusion approach. Experimental results form measurements at the TNO-FEL test facility are presented that validate our depth-fusion concepts.
To acquire detection performance required for an operational system for the detection of anti-personnel landmines, it is necessary to use multiple sensor and sensor-fusion techniques. This paper describes five decision-level sensor- fusion techniques and their common optimization method. The performance of the sensor-fusion techniques is evaluated by means of Receiver Operator Characteristics curves. These techniques are tested on an outdoor test facility. Three of four test lanes of this facility are used as training set and the fourth is used as evaluation set. The detection performance of naive Bayes, Dempster-Shafer, voting and linear discriminant are very similar on both the training and the evaluation set. This is probably caused by the flexibility of the sensor-fusion techniques resulting into similar optimal solutions independent of the fusion technique.