Byungin Choi

Realistic infrared sequence generation by physics-based infrared target modeling for infrared search and track

Infrared search and track pursues the detection of sea-skimming infrared targets incoming from long distance. This paper presents a realistic synthetic target simulator for the development of infrared target detection and tracking algorithms. The proposed simulator consists of a 2-D background modeling part and a 3-D infrared target modeling part. Real infrared background images are used for the realistic modeling of background. Synthetic infrared target images are obtained by the consecutive processing of 3-D geometric modeling and radiometric modeling of targets according to target types, target distances, and atmospheric transmissivity. The experimental results validate the realistic modeling of the proposed method by comparing real observation sequence data.

Three plot correlation-based small infrared target detection in densesun-glint environment for infrared search and track

This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust detection performance of the proposed method via real infrared test sequences including synthetic targets.

Improved small target detection for IR point target

In the target detection algorithm based on the bivariate cubic facet fitting (BCFF), the target position can be selected so that the position has the maximum value or energy value of the filtered result. However, in the cluttered environment, it may generate a large number of clutters. In this paper, we propose the target detection algorithm which applies the maximum local contrast as the target selection method. Our proposed algorithm can considerably improve the detection rate more than the method using the maximum energy value.

Interval Type-2 Fuzzy Membership Function Generation Methods for Representing Sample Data

Type-2 fuzzy sets (T2 FSs) have been shown to manage uncertainty more effectively than type-1 fuzzy sets (T1 FSs) in several areas of engineering. However, computing with T2 FSs can require an undesirably large amount of computations since it involves numerous embedded T2 FSs. To reduce the complexity, interval type-2 fuzzy sets (IT2 FSs) can be used, since the secondary memberships are all equal to one. In this chapter, three novel interval type-2 fuzzy membership function (IT2 FMF) generation methods are proposed. The methods are based on heuristics, histograms, and interval type-2 fuzzy C-means (IT2 FCM). For each method, the footprint of uncertainty (FOU) is only required to be obtained, since the FOU can completely describe an IT2 FMF. The performance of the methods is evaluated by applying them to back-propagation neural networks (BPNNs). Experimental results for several data sets are given to show the effectiveness of the proposed membership assignments.

Adaptive contrast enhancement based on temperature and histogram for an infrared image

In general, an infrared camera which is 1-dimensional scanning type has the fixed pattern noises which are shown in the form of horizontal line. The noises probably caused by non-uniformity of the detectors, optical characteristics or noises from inside heat source. Those noises are shown very strongly resulted by the contrast enhancement when the camera views a uniform background such as a clear and cold sky. In this paper, we propose adaptive contrast enhancement method based on the temperature and histogram range of scenes to reduce the fixed pattern noise. In our proposed method, the weight value is extracted from the temperature and histogram range of the input image. The strength of the contrast is then regulated by the weight value. Therefore, our proposed method can effectively reduce the fixed pattern noise. To show the validity of our proposed method, we present experimental results for several infrared images.

Image and sensor los-based automatic horizontal line detection and tracking for infrared search and track

It is important to stably extract horizon lines for sea-based infrared search and track. Most of the existing solutions for the problems only use single image to detect horizon line. Although this results in good accuracy for some images, it often fails to detect horizons in foggy, occluded environments. In this paper, we propose a novel horizon detection and tracking method that is robust to sensor vibrations and occlusions using image and sensor-based initial horizon detection and robust statistics. Local horizon optimization and tracking produce stable horizons in occluded environments. The experimental results validate the robustness of the proposed method in real infrared images.

Search and tracking system architecture using 1-D scanning sensors

In the maritime environment, It is necessary for ships self protection to search ad track approaching targets. We developed high performance search and tracking system with Infrared sensors. Our system can obtain high performance with several FPGAs and COTS processing boards. Dual band IR sensor (MWIR and LWIR) also gives two types of target detection and tracing abilities. Our system designed to automatically detect and track both air and surface targets such as sea skimming missiles, small ships, and aircrafts at a long range. In this paper, we describe technologies in our search and tracking system architecture. We describe software architecture for signal processing and target detection and tracking algorithms as well.

A novel template update method for IR seeker

In general, the centroid and block matching tracking algorithms have been used for target tracking systems such as IR seekers. However, the former may induce large tracking error in complex background due to low contrast between targets and background. Moreover, the latter can be suffers from variation of target size when the observer approach to a target. In this paper, we propose a simple and effective template update method for the block matching algorithm. Our proposed method performs the block matching algorithm using the original target template and the zooming target template. If the correlation obtained by the zoomed target template is larger than that of the original template, we update the original template. Therefore, our proposed algorithm can robustly track targets although target size varies.

Automatic tracking system with target classification

In this paper, we propose an overall target tracking scheme performing image stabilization, detection, tracking, and classification in the IR sensored image. Firstly, in the image stabilization stage, a captured image is stabilized from visible frame-to-frame jitters caused by camera shaking. After that, the background of the image is modeled as Gaussian. Based on the results of the background modeling, the difference image between a Gaussian background model and a current image is obtained, and regions with large differences are considered as targets. The block matching method is adopted as a tracker, which uses the image captured from the detected region as a template. During the tracking process, positions of the target are compensated by the Kalman filter. If the block matching tracker fails to track targets as they hide themselves behind obstacles, a coast tracking method is employed as a replacement. In the classification stage, key points are detected from the tracked image by using the scale-invariant feature transform (SIFT) and key descriptors are matched to those of pre-registered template images.

A novel clustering method using weighted sub-sampling for an infrared search and track system

In an infrared search and tracking (IRST) system, the clustering procedure which merges target pixels into one cluster requires larger computational load according to increasing clutters. In this paper, we propose a novel clustering method based on weighted sub-sampling to reduce clustering time and obtain suitable cluster in cluttered environment. A conventional sub-sampling method can reasonably reduce clustering time but cause large error, when obtaining cluster center. However, our proposed clustering method perform sub-sampling and assign specific weights which is the number of target pixels in sampling region to sub-sampled pixels to obtain suitable cluster center. After performing clustering procedure, the cluster center position is properly obtained using sampled pixels and their weights in the cluster. Therefore, our proposed method can not only reduce clustering time using a sub-sampling method, but also obtain proper cluster center using our proposed weights. To validate our proposed method, experimental results for several infrared and noise images are presented.