• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.7-14
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.41-47
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• Journal of radiological science and technology
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• v.39 no.4
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• pp.631-636
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• 2016

Hitherto, spatial resolution has commonly been evaluated by test patterns or phantoms built on some specific distances (from close to far) between two objects (or double targets). This evaluation method's shortcoming is that resolution is restricted to target distances of phantoms made for test. Therefore, in order to solve the problem, this study proposes and verifies a new method to efficiently test spatial resolution with a single target. For the research I used PSF and JND to propose an idea to measure spatial resolution. After that, I made experiments by commonly used phantoms to verify my new evaluation hypothesis inferred from the above method. To analyse the hypothesis, I used LabVIEW program and got a line pixel from digital image. The result was identical to my spatial-resolution hypothesis inferred from a single target. The findings of the experiment proves only a single target can be enough to relatively evaluate spatial resolution on a digital image. In other words, the limit of the traditional spatial-resolution evaluation method, based on double targets, can be overcome by my new evaluation one using a single target.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.23-29
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• 2014

The emergence of high resolution satellite images and the evolution of spatial resolution facilitate various studies using high resolution satellite images. Above all, target detection algorithms are effective for monitoring of traffic flow and military surveillance and reconnaissance because vehicles, airplanes, and ships on broad area could be detected easily using high resolution satellite images. Recently, many satellites are launched from global countries and the diversity of satellite images are also increased. On the contrary, studies on comparison about the spatial resolution or target detection, especially, are insufficient in domestic and foreign countries. Therefore, in this study, effects of spatial resolution on target detection are analyzed using the PSO target detection algorithm. The resampling techniques such as nearest neighbor, bilinear, and cubic convolution are adopted to resize the original image into 0.5m, 1m, 2m, 4m spatial resolutions. Then, accuracy of target detection is assessed according to not only spatial resolution but also resampling method. As a result of the study, the resolution of 0.5m and nearest neighbor among the resampling methods have the best accuracy. Additionally, it is necessary to satisfy the criteria of 2m and 4m resolution for the detection of airplane and ship, respectively. The detection of airplane need more high spatial resolution than ship because of their complexity of shape. This research suggests the appropriate spatial resolution for the plane and ship target detection and contributes to the criteria of satellite sensor design.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.65-77
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• 2014

To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1973-1974
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• 2006

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local maxima of a similarity measure between the color histograms of the target and candidate image. However, the mean shift tracking algorithm using only color histograms has a serious defect. It doesn't use the spatial information of the target. Thus, it is difficult to model the target more exactly. And it is likely to lose the target during the occlusions of other objects which have similar color distributions. To deal with these difficulties we use both color information and spatial information of the target. Our proposed algorithm is robust to occlusions and scale changes in front of dynamic, unstructured background. In addition, our proposed method is computationally efficient. Therefore, it can be executed in real-time.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.676-683
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• 2011

In this paper, we propose a robust method which can detect and track a high-speed small approaching target in a cluttered environment for Korean Active Protection System. The proposed method uses a temporal and spatial filter, tracking filter to detect and track a single target in consecutive order. And it is comprised of a candidate target detection step, a prior target selection step and a target tracking. Field tests on real infrared image sequences show that the proposed method could stably track a high speed and small target in complex background and target occlusion.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1418-1425
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• 2014

In ultrasonic medical imaging, spatial compounding of images is a technique where ultrasonic beam is steered to examine patient tissues in multiple angles. In the conventional ultrasonic diagnostic imaging, the steering of the ultrasonic beam is achieved electronically using the phased array transducer elements. In this paper, a spatial compounding approach is presented where the ultrasonic probe element is rotated mechanically and the beam steering is achieved mechanically. In the spatial compounding, target position is computed using the value of the rotation axis and the transducer array angular position. However, in the process of the rotation mechanism construction and the control system there arises the inevitable uncertainties in these values. These geometric parameter errors result in the target position error, and the consequence is a blurry compounded image. In order to reduce these target position errors, we present a spatial compounding scheme where error correcting transformation matrices are computed and applied to the raw images before spatial compounding to reduce the blurriness in the compounded image. The proposed scheme is illustrated using phantom and live scan images of human knee, and it is shown that the blurriness is effectively reduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5459-5473
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• 2017

Recently, a tracking algorithm called the spatial-temporal context model has been proposed to locate a target by using the contextual information around the target. This model has achieved excellent results when the target undergoes slight occlusion and appearance changes. However, the target location in the current frame is based on the location in the previous frame, which will lead to failure in the presence of fast motion because of the lack of a prediction mechanism. In addition, the spatial context model is updated frame by frame, which will undoubtedly result in drift once the target is occluded continuously. This paper proposes two improvements to solve the above two problems: First, four possible positions of the target in the current frame are predicted based on the displacement between the previous two frames, and then, we calculate four confidence maps at these four positions; the target position is located at the position that corresponds to the maximum value. Second, we propose a target reliability criterion and design an adaptive threshold to regulate the updating speed of the model. Specifically, we stop updating the model when the reliability is lower than the threshold. Experimental results show that the proposed algorithm achieves better tracking results than traditional STC and other algorithms.

• Journal of the Korea Society of Computer and Information
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• v.21 no.8
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• pp.21-28
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• 2016

In this paper, we propose a fast small target detection method using multiple filters, and describe system implementation using IPP libraries. To detect small targets in Infra-Red images, it is mandatory that you should apply a filter to eliminate a background and identify the target information. Moreover, by using a suitable algorithm for the environments and characteristics of the target, the filter must remove the background information while maintaining the target information as possible. For this reason, in the proposed method we have detected small targets by applying multi area(spatial) filters in a low luminous environment. In order to apply the multi spatial filters, the computation time can be increased exponentially in case of the sequential operation. To build this algorithm in real-time systems, we have applied IPP library to secure a software optimization and reduce the computation time. As a result of applying real environments, we have confirmed a detection rate more than 90%, also the computation time of the proposed algorithm have been improved about 90% than a typical sequential computation time.