• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.62-68
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• 2010

This study is to identify target locations with low false alarms on thermal infrared images obtained from natural environment. The proposed method is different from the previous researches because it uses morphology filters for Gabor response images instead of an intensity image in initial detection stage. This method does not need precise extracting a target silhouette to distinguish true targets or clutters. It comprises three distinct stages. First, morphological operations and adaptive thresholding are applied to the summation image of four Gabor responses of an input image to find out salient regions. The locations of extracted regions can be classified into targets or clutters. Second, local texture features are computed from salient regions of an input image. Finally, the local texture features are compared with the training data to distinguish between true targets and clutters. The multi-layer perceptron having three layers is used as a classifier. The performance of the proposed method is proved by using natural infrared images. Therefore it can be applied to real automatic target detection systems.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.756-763
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• 2013

Bilateral filter (BF), functioning by two Gaussian filters, domain and range filter is a nonlinear filter for sharpness enhancement and noise removal. In infrared (IR) small target detection field, the BF is designed by background predictor for predicting background not including small target. For this, the standard deviations of the two Gaussian filters need to be changed adaptively in background and target region of an infrared image. In this paper, the proposed bilateral filter make the standard deviations changed adaptively, using variance feature of mean values of surrounding block neighboring local filter window. And, in case the variance of mean values for surrounding blocks is low for any processed pixel, the pixel is classified to flat background and target region for enhancing background prediction. On the other hand, any pixel with high variance for surrounding blocks is classified to edge region. Small target can be detected by subtracting predicted background from original image. In experimental results, we confirmed that the proposed bilateral filter has superior target detection rate, compared with existing methods.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.57-67
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• 2015

In this paper, we proposed the geolocation method using target detection information in infrared images. Our method was applied to geolocation system of hostile targets in ground-to-ground field. The major distortion that has bad effect of geolocation was composed of optic, topography, GPS(Global Positioning System) and IMU(Inertial Measurement Unit) of reconnaissance unit. We proposed enhanced geolocation method to cope with optic and topography distortion using polynomial fitting and slant-range calculation model to overcome earth curvature problem, and the result showed that the performance of our method was good for system requirements.

• Journal of KIISE
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• v.44 no.4
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• pp.411-416
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• 2017

The spectral radiance received by an infrared (IR) sensor is mainly influenced by the surface temperature of the target itself. Therefore, the precise temperature prediction is important for generating an IR target image. In this paper, we implement the combined three-dimensional surface temperature prediction module against target attitudes, environments and properties of a material for generating a realistic IR signal. In order to verify the calculated surface temperature, we are using the well-known IR signature analysis software, OKTAL-SE and compare the result with that. In addition, IR signal modeling is performed using the result of the surface temperature through coupling with OKTAL-SE.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.31-38
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• 2016

In this paper, we propose multi-scale infrared target detection algorithm with varied filter size using integral image. Filter based target detection is widely used for small target detection, but it doesn't suit for large target detection depending on the filter size. When there are multi-scale targets on the sky background, detection filter with small filter size can not detect the whole shape of the large targe. In contrast, detection filter with large filter size doesn't suit for small target detection, but also it requires a large amount of processing time. The proposed algorithm integrates the filtering results of varied filter size for the detection of small and large targets. The proposed algorithm has good performance for both small and large target detection. Furthermore, the proposed algorithm requires a less processing time, since it use the integral image to make the mean images with different filter sizes for subtraction between the original image and the respective mean image. In addition, we propose the implementation of real-time embedded system using FPGA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1593-1600
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• 1999

This paper describes the acquisition modeling of an airborne target for target tracking simulation of infrared homing missiles. The modeling, of which key technologies are the sub-modeling for target infrared signature, atmospheric transmission, and receiver characteristics, shows the acquisition process of an airborne target under various tracking conditions determined by line-of-sight, distance, and atmospheric conditions. We confirm the validity of the modeling by applying it to simulations concerned with target tracking. The modeling gives a guideline to determine an optimum detector and a defection band for effective discrimination of the target among false targets.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1719-1723
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• 2005

An automatic target sensing and triggering system for small fire arms is proposed. The system consists of an optical collector, an infrared ray sensor responsive to human body temperature, an electric actuator and a trigger mechanism. TRIZ methodologies are used to develop solutions to several contradictory problems. Experimental results on the system performance is compared with predictions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.546-554
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• 2011

Near targets in the infrared (IR) images have the steady feature for inner region and the transient feature for the boundary region. Based on these features, this paper proposes a new method to extract the fine target shape of near targets in the IR images. First, we detect the boundary region of the candidate targets using the local variance weighted information entropy (WIE) of the original images. And then, a coarse target region can be estimated based on the labeling of the boundary region. For the coarse target region, we use the opening filter with an adaptive structure element to extract the fine target shape. The decision of the adaptive structure element size is optimized for the width information of target boundary by calculating the average WIE in the enlarged windows. The experimental results show that a proposed method has better extraction performance than the previous threshold algorithms.

• ETRI Journal
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• v.35 no.1
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• pp.80-88
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• 2013

A passive infrared or pyroelectric infrared (PIR) sensor is mainly used to sense the existence of moving objects in an indoor environment. However, in an outdoor environment, there are often outbreaks of false alarms from environmental changes and other sources. Therefore, it is difficult to provide reliable detection outdoors. In this paper, two algorithms are proposed to reduce false alarms and provide trustworthy quality to surveillance systems. We gather PIR signals outdoors, analyze the collected data, and extract the target features defined as window energy and alarm duration. Using these features, we model target and false alarms, from which we propose two target decision algorithms: window energy detection and alarm duration detection. Simulation results using real PIR signals show the performance of the proposed algorithms.