Infrared (IR) imagery is frequently used in security/surveillance and military image processing applications. In this article we will consider the problem of outlining military naval vessels in such images. Obtaining these outlines is important for a number of applications, for instance in vessel classification.
Detecting this outline is basically a very complex image segmentation task. We will use a special neural network for this purpose. Neural networks have recently shown great promise in a wide range of image processing applications, image segmentation is no exception in this regard. The main drawback when using neural networks for this purpose is the need for substantial amounts of data in order to train the networks. This problem is of particular concern for our application due to the difficulty in obtaining IR images of military vessels.
In order to alleviate this problem we have experimented with using alternatives to true IR images for the training of the neural networks. Although such data in no way can capture the exact nature of real IR images, they do capture the nature of IR images to a degree where they contribute substantially to the training and final performance of the neural network.
A test system with four cameras in the infrared and visual spectra is under development at FFI (The Norwegian Defence Research Establishment). The system can be mounted on a high speed jet aircraft, but may also be used in a land-based version. It can be used for image acquisition as well as for development and test of automatic target recognition (ATR) algorithms. The sensors on board generate large amounts of data, and the scene may be rather cluttered or include anomalies (e.g. sun glare). This means we need image processing and pattern recognition algorithms which are robust, fast (real-time), and able to handle complex scenes. Algorithms based on order statistics are known to be robust and reliable. However, they are in general computationally heavy, and thus often unsuitable for real time applications. But approximations to order statistics do exist. Median of medians is one example. This is a technique where an approximation of the median of a sequence is found by first dividing the sequence in subsequences, and then calculating median (of medians) recursively. The algorithm is very efficient, the processing time is of order O(n). By utilizing such techniques for estimating image statistics, the computational challenge can be overcome. In this paper we present strategies for how approximations to order statistics can be applied for developing robust and fast algorithms for image processing, especially visualization and segmentation.
A research platform with four cameras in the infrared and visible spectral domains is under development at the Norwegian Defence Research Establishment (FFI). The platform will be mounted on a high-speed jet aircraft and will primarily be used for image acquisition and for development and test of automatic target recognition (ATR) algorithms. The sensors on board produce large amounts of data, the algorithms can be computationally intensive and the data processing is complex. This puts great demands on the system architecture; it has to run in real-time and at the same time be suitable for algorithm development. In this paper we present an architecture for ATR systems that is designed to be exible, generic and efficient.
The architecture is module based so that certain parts, e.g. specific ATR algorithms, can be exchanged without affecting the rest of the system. The modules are generic and can be used in various ATR system configurations. A software framework in C++ that handles large data ows in non-linear pipelines is used for implementation. The framework exploits several levels of parallelism and lets the hardware processing capacity be fully utilised. The ATR system is under development and has reached a first level that can be used for segmentation algorithm development and testing. The implemented system consists of several modules, and although their content is still limited, the segmentation module includes two different segmentation algorithms that can be easily exchanged. We demonstrate the system by applying the two segmentation algorithms to infrared images from sea trial recordings.
The work presented in this paper is based on a dataset recorded with an airborne sensor. It comprises targets like M-60,
M-47, M-113, bridge layers, tank retrievers, and trucks in various types of scenes.
The background-object segmentation consists of first estimating the ground level everywhere in the scene, and then for
each sample simply subtracting the measured height and ground level height. No assumptions concerning flat terrain etc.
Samples with height above ground level higher than a certain threshold are clustered by utilizing a straightforward
agglomerative clustering algorithm. Around each cluster the bounding box with minimum volume is determined. Based
on these bounding boxes, too small as well as too large clusters can easily be removed.
However, vehicle-sized clutter will not be removed. Clutter detection is based on estimating the normal vector for a
plane approximation around each sample. This approach is based on the fact that the surface normals of a vehicle is more
“modulo 90°” distributed than clutter.
The aim of the classification has been to classify main battle tanks (MBTs) Two types of algorithms have been studied,
one based on Dempster Shafer fusion theory, and one model based.
Our dataset comprises clusters of 269 vehicles (among them 131 MBTs), and 253 clutter objects (i.e. in practice vehiclesized
bushes). The experiments we have carried out show that the segmentation extracts all vehicles, the clutter detection
removes 90% of the clutter, and the classification finds more than 95% of the MBTs as well as removes half of the
Architecture and technology of imaging intrusion detection sensor OPAK are presented. Applications and desired features for IDS and UGS are compared. Started developments of OPAK towards an unattended ground sensor system are described.