• Title/Summary/Keyword: 표적지 크기

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Small Target Detection using Morphology and Gaussian Distance Function in Infrared Images (적외선 영상에서 모폴로지와 가우시안 거리함수를 이용한 소형표적 검출)

  • Park, Jun-Jae;Ahn, Sang-Ho;Kim, Jong-Ho;Kim, Sang-Kyoon
    • 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%.

세기조절방사선치료 조사면의 최소 조각 크기에 대한 치료중 표적 움직임의 효과

  • 서예린;이병용;안승도;이상욱;김종훈;신성수;신승애;최은경
    • Proceedings of the Korean Society of Medical Physics Conference
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    • 2003.09a
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    • pp.37-37
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    • 2003
  • 목적 : 일반적으로 세기조절방사선치료 조사면의 작은 조각 크기에 대해, 이상적인 플루언스 지도 혹은 치료계획장치로부터의 최적화된 결과에 가까운 선량분포에서 더 좋은 leaf sequence를 얻을 수 있다. 한편, 치료중 장기의 움직임이 가장 작은 조각 크기의 선택을 방해하는 문제는 항상 존재한다. 게다가, 전통적인 정지 조사면과 달리 표적이 움직이는 동안 조사면 자체도 움직이므로 움직이는 표적에 대한 세기조절방사선치료의 경우에서 적절한 표적 마진에 관한 질문이 제기되어왔다. 따라서, 이 연구에서는 조각 크기에 대한 치료중 표적 움직임의 효과를 연구하였다. 대상 및 방법 : 세기조절방사선치료 플루언스 지도에 대해, 다양한 크기 - 0.5$\times$0.5, 1.0$\times$1.0, $1.5\times$1.5, 2.0$\times$2.0, 3.0$\times$3.0, 4.0$\times$4.0, 5.0$\times$5.0 $ extrm{cm}^2$ - 의 정사각형 패턴들을 설계하였고, Leaf sequence 는 step-and-shoot 빔 전달 방법을 이용하였다. 인접 조각들 사이의 세기 비율은 0.2, 0.4, 0.6, 0.8, 1.0로 하였고, 표적 움직임은 범위가 0.5-2.0 cm인 사인곡선 형태로 가정하였다. 움직임 묘사를 위해 동적 leaf 의 움직임이 표적의 움직임 을 반영하도록 계산되었고 움직임의 효과를 분석하기 위해 필름선량측정을 수행하였다. 결과 : 인접 조각의 세기 비율은 모든 경우에서 저하되었고, 호흡 진폭의 반보다 작은 조각 크기에 대한 선량분포는 임상적으로 유의할만큼 저하된 세기 지도를 보였다. 조각에 대해 방사선 조사시간의 두 호흡주기이상에 대해서는, 표적 마진 주위의 선량분포가 통상적인 정지 조사면에서와 같았다. 결론 : 플루언스 지도에서 세기조절방사선치료 조각의 최소 크기는 치료중 장기 움직임을 고려한 후 선택되어야 한다. 조각에 대한 방사선 조사시간의 두 호흡주기이상에 대해서는, 표적 마진을 기존의 정지 조사면과 같게 정의할 수 있었다.

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Object Tracking Based on Centroids Shifting with Scale Adaptation (중심 이동 기반의 스케일 적응적 물체 추적 알고리즘)

  • Lee, Suk-Ho;Choi, Eun-Cheol;Kang, Moon-Gi
    • Journal of Korea Multimedia Society
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    • v.14 no.4
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    • pp.529-537
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    • 2011
  • In this paper, we propose a stable scale adaptive tracking method that uses centroids of the target colors. Most scale adaptive tracking methods have utilized histograms to determine target window sizes. However, in certain cases, histograms fail to provide good estimates of target sizes, for example, in the case of occlusion or the appearance of colors in the background that are similar to the target colors. This is due to the fact that histograms are related to the numbers of pixels that correspond to the target colors. Therefore, we propose the use of centroids that correspond to the target colors in the scale adaptation algorithm, since centroids are less sensitive to changes in the number of pixels that correspond to the target colors. Due to the spatial information inherent in centroids, a direct relationship can be established between centroids and the scale of target regions. Generally, after the zooming factors that correspond to all the target colors are calculated, the unreliable zooming factors are filtered out to produce a reliable zooming factor that determines the new scale of the target. Combined with the centroid based tracking algorithm, the proposed scale adaptation method results in a stable scale adaptive tracking algorithm. It tracks objects in a stable way, even when the background colors are similar to the colors of the object.

Research on Artillery Target Size Determination Method Considering Ballistic and Terrain Characteristics (탄도 및 지형 특성을 고려한 포병 표적지 크기 결정 방안 연구)

  • Juhee Kim;Kieun Sung
    • Journal of the Korea Institute of Military Science and Technology
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    • v.27 no.3
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    • pp.355-363
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    • 2024
  • This study proposes a method for determining the optimal target size for an artillery range considering ballistics and environmental conditions. To this end, the size of the probable error of each type of ammunition and charge determined during shooting were considered, and the effect of the firing position and target terrain characteristics on the target size was analyzed. In conclusion, the size of the target increased as the range increased, and a larger target size was required for the DPICM than for the general high explosive. Accordingly, the optimal target size must be determined by considering various factors such as topographical characteristics, shooting position location, and shooting range safety standards.

Reducing Computational Complexity for Local Maxima Detection Using Facet Model (페이싯 모델을 이용한 국부 극대점 검출의 처리 속도 개선)

  • Lee, Gyoon-Jung;Park, Ji-Hwan;Joo, Jae-Heum;Nam, Ki-Gon
    • 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.

Small Target Detection in Multi-Resolution Image Using Facet Model (다중 해상도 영상에서 페이싯 모델을 이용한 초소형 표적 검출)

  • Park, Ji-Hwan;Lee, Min-Woo;Lee, Chul-Hun;Joo, Jae-Heum;Nam, Ki-Gon
    • Journal of the Institute of Convergence Signal Processing
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    • v.12 no.2
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    • pp.76-82
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    • 2011
  • In this paper, we propose the technique to detect the location and size of the small target in multi-resolution image using cubic facet model. The input image is reduced by the multi-resolution and we obtain the multi-resolution images. We apply the facet model and the local maxima conditions to the multi-resolution images of each level. And then, we detect the location of the small target. We estimate that the location at the maximum of the $D_2$ which means the local maxima value of the facet model in the multi-resolution images is the location of the small target. We can detect the small target of the various size about the multi-resolution images of each level. In this paper, we experimented in the various infrared images with the small target. The method using the typical facet model applies a mask. However, the proposed method applies a mask in the multi-resolution images. We verified to vary the mask size and differ the size of the small target. The proposed algorithm can detect the location and size of the small target.

Target Detection Technique in a DBS(Doppler Beam Sharpening) Image (DBS(Doppler Beam Sharpening) 영상에서 표적 탐지 방안)

  • Kong, Young-Joo;Kwon, Jun-Beom;Kim, Hong-Rak;Woo, Seon-Keol
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.28 no.5
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    • pp.373-381
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    • 2017
  • DBS(Doppler Beam Sharpening) algorithm is a way to improve azimuth resolution performance in radar. Since DBS image includes the is information about the search area of radar, various clutter components exist besides the target to be detected. To detect and track the desired target in a DBS image, it must be able to identify a target and the clutter components. In this paper, we describe how to use image size and terrain information(DTED) to identify the target in a DBS image. By using morphological filter and chain code, it acquires image size and excludes the clutter components. By matching with DTED, we determine target.

Design of a SIFT based Target Classification Algorithm robust to Geometric Transformation of Target (표적의 기하학적 변환에 강인한 SIFT 기반의 표적 분류 알고리즘 설계)

  • Lee, Hee-Yul;Kim, Jong-Hwan;Kim, Se-Yun;Choi, Byung-Jae;Moon, Sang-Ho;Park, Kil-Houm
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.1
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    • pp.116-122
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    • 2010
  • This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

Improvement of detecting speed of small target using SAD algorithm (SAD 알고리즘을 이용한 소형표적 검출속도 개선)

  • Son, Jung-Min;Ahn, Sang-Ho;Kim, Jong-Ho;Kim, Sang-Kyoon
    • 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%.

Adaptive Target Detection Algorithm Using Gray Difference, Similarity and Adjacency (밝기 차, 유사성, 근접성을 이용한 적응적 표적 검출 알고리즘)

  • Lee, Eun-Young;Gu, Eun-Hye;Yoo, Hyun-Jung;Park, Kil-Houm
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38B no.9
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    • pp.736-743
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    • 2013
  • In IRST(infrared search and track) system, the small target detection is very difficult because the IR(infrared) image have various clutter and sensor noise. The noise and clutter similar to the target intensity value produce many false alarms. In this paper. We propose the adaptive detection method which obtains optimal target detection using the image intensity information and the prior information of target. In order to enhance the target, we apply the human visual system. we determine the adaptive threshold value using image intensity and distance measure in target enhancement image. The experimental results indicate that the proposed method can efficiently extract target region in various IR images.