• Journal of Korea Society of Industrial Information Systems
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• v.17 no.4
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• pp.61-70
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• 2012

We propose a method that finds candidate targets based on morphology and detects a small target from them using modified gaussian distance function. The existing small target detection methods use predictive filters or morphology. The methods using predictive filters take long to approach least errors. The methods using morphology are weak at clutters and need to consider size of a small target when selecting size of structure elements. We propose a robust method for small target detection to complete the existing methods. First, the proposed method deletes clutters using a median filter. Next, it does closing and opening operation using various size of structure elements, and figures target candidate pixels with subtraction operation between the results of closing and opening operation. It detects an exact small target using a gaussian distance function from the candidates target areas. The proposed method is less sensitive to clutters, and shows a detection rate of 98%.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.53-60
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• 2013

We propose a method for improving detection speed of small target detection system using SAD algorithm. First, the proposed method deletes clutters using a median filter. Next, it does closing and opening operation using various size of structure elements, and extracts candidate pixels for a target with subtraction operation between the results of closing and opening operation. It finally detects a small target using a gaussian distance function from the candidate pixels. To improve detection speed, it detects a target performing SAD algorithm only for the predicted target areas for next every 7 frames. The proposed method not only enables a real time process because it considers only predicted area but also shows detecting rate of 97%.

• Journal of Korea Multimedia Society
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• v.10 no.4
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• pp.432-440
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• 2007

In this paper, we propose a small target detection algorithm for FLIR image with complex background. First, we compute the motion information of target from the difference between the current frame and the created background image. However, the slow speed of target cause that it has the very low gray level value in the difference image. To improve the gray level value, we perform the local gamma correction for difference image. So, the detection index is computed by using statistical characteristics in the improved image and then we chose the lowest detection index a true target. Experimental results show that the proposed method has significantly the good detection performance.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.293-299
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• 2017

Small unmanned platforms maneuvering underwater are the key naval future forces, utilized as the asymmetric power in war. As a method of detecting and identifying such platforms, we introduce a property estimation technique based on an iterative numerical analysis. The property estimation technique can estimate not only the position of a target but also its physical properties. Moreover, it will have a potential in detecting and classifying still target or multiple targets. In this study, we have conducted the property estimation of an small underwater target using the data acquired from the lake experiment. As a result, it shows that the properties of a small platform may be roughly estimated from the in site data even using one channel.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.130-135
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• 2012

In this paper, we propose a technique to detect the size and location of the small target in images by using Gaussian kernel repeatedly. In order to detect the size and location of the small target, we find the local maximum value by applying the facet model and then use the $3{\times}3$ Gaussian kernel repeatedly. we determine the size of small target by comparing the local maximum value $D_2$ according to the number of iteration. To reduce the computational complexity, we use the Gaussian pyramid when using the kernel repeatedly. Through the experiment, we verified that the size and location of the small target is detected by the number of iterations and results show improvements from conventional methods.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.1-13
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• 2017

In This Paper, we find the Target Candidate Region and the Location of the Candidate Region by Performing the Morphological Difference Calculation and Pixel Labeling for Robust Small Target Detection in Infrared Image with low SNR. Conventional Target Detection Methods based on Morphology Algorithms are low in Detection Accuracy due to their Vulnerability to Clutter in Infrared Images. To Address the Problem, Target Signal Enhancement and Background Clutter Suppression are Achieved Simultaneously by Combining Moravec Algorithm and LCM (Local Contrast Measure) Algorithm to Classify the Target and Noise in the Candidate Region. In Addition, the Proposed Algorithm can Efficiently Detect Multiple Targets by Solving the Problem of Limited Detection of a Single Target in the Target Detection method using the Morphology Operation and the Gaussian Distance Function Which were Developed for Real time Target Detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1C
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• pp.8-16
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• 2010

This paper presents a new small target detection method using the cross product of the temporal pixels based on the temporal profile in infrared (IR) image sequences. The temporal characteristics of small targets and the various backgrounds are different. A new algorithm classifies target pixels and the background pixels through the hypothesis testing using the cross product of pixels on the temporal profile and predicts the temporal backgrounds based on the results. The small targets are detected by subtracting the predicted temporal background profile from the original temporal profile. For the performance comparison between the proposed algorithm and the conventional algorithms, the receiver operating characteristics (ROC) curves is computed in experiment. Experimental results show that the proposed algorithm has better discrimination and a lower false alarm rate than the conventional methods.

• ICROS
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• v.20 no.4
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• pp.37-43
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• 2014

소형 무인항공기가 발달함에 따라 영상센서의 활용이 증가하였다. 특히 발전된 컴퓨팅 파워와 소형화된 영상센서의 발달로 무겁고 값비싼 레이더 장비의 기술을 영상센서로 구현하는 연구가 활발히 진행 중이다. 영상 이동표적표시(Moving Target Indication MTI) 기법은 이런 시대의 흐름에 따라 발달된 기술이며 현재 미군의 소형무인항공기 RQ-11 Raven에 상용화 되었다. 본 논문에서는 소형 무인항공기용 영상 MTI 기법의 원리를 설명하고 영상 MTI에서 주로 사용하는 카메라 운동 보 정 기법인 매개변수/비매개변수 방식의 영상정합의 접근법을 위주로 설명하였다. 또한 소형 무인항공기용 영상감시시스템 구현에서 가장 큰 문제점인 시차/항공기 진동 보정에 대한 진행 중인 연구논문도 소개하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.33-38
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• 2013

In this paper, we propose an algorithm to track multi-small targets in infrared image sequences in case of dissipation or creation of targets by using the background estimation filter, Kahnan filter and mean shift algorithm. We detect target candidates in a still image by subtracting an original image from an background estimation image, and we track multi-targets by using Kahnan filter and target selection. At last, we adjust specific position of targets by using mean shift algorithm In the experiments, we compare the performance of each background estimation filters, and verified that proposed algorithm exhibits better performance compared to classic methods.