• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.31-36
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• 2003

This paper proposes an acceleration method of PCA(Principal Component Analysis) based feature detection. The feature detection method makes decision whether the target feature is included in a given image, and if included, calculates the position and extent of the target feature. The position and scale of the target feature or face is not known previously, all the possible locations should be tested for various scales to detect the target. This is a search Problem in huge search space. This Paper proposes a fast face and feature detection method by reducing the search space using the multi-stage prediction map and contour Prediction map. A Proposed method compared to the existing whole search way, and it was able to reduce a computational complexity below 10％ by experiment.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.1
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• pp.8-17
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• 2009

This paper proposes robust ship wake search method in the target evasion environment. Moving surface ships generate a long trailing wake in the rear of a surface ship. Wake homing torpedo sensing this wake can detect the surface target and engage it automatically. In wake homing torpedo, wake search method is important element to maximize effectiveness of wake homing torpedo. This paper proposes one-side, two-side and centering mode according to passing wake boundary scenarios. Also, wake deflection angle is deduced by using the principle of deflection angle of acoustic torpedo. The representative experimental results using monte-carlo simulation demonstrate that the searching method using one-side mode is superior to two-side and centering mode in the target evasion environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.406-411
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• 2005

In this paper, we present the area-based Q-learning to search a target object using multiple robot. To search the target in Markovian space, the robots should recognize their surrounding at where they are located and generate some rules to act upon by themselves. Under area-based Q-learning, a robot, first of all, obtains 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to search for a target object while navigating in a unknown hallway where some obstacles were placed. In the end of this paper, we presents the results of three algorithms - a random search, area-based action making (ABAM), and hexagonal area-based Q-teaming.

• Journal of the Ergonomics Society of Korea
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• v.31 no.6
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• pp.705-713
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• 2012

Objective: The aim of this study is to predict human visual target search using ACT-R cognitive architecture in real scene images. Background: Human uses both the method of bottom-up and top-down process at the same time using characteristics of image itself and knowledge about images. Modeling of human visual search also needs to include both processes. Method: In this study, visual target object search performance in real scene images was analyzed comparing experimental data and result of ACT-R model. 10 students participated in this experiment and the model was simulated ten times. This experiment was conducted in two conditions, indoor images and outdoor images. The ACT-R model considering the first saccade region through calculating the saliency map and spatial layout was established. Proposed model in this study used the guide of visual search and adopted visual search strategies according to the guide. Results: In the analysis results, no significant difference on performance time between model prediction and empirical data was found. Conclusion: The proposed ACT-R model is able to predict the human visual search process in real scene images using salience map and spatial layout. Application: This study is useful in conducting model-based evaluation in visual search, particularly in real images. Also, this study is able to adopt in diverse image processing program such as helper of the visually impaired.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.11-21
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• 2015

In order to achieve success in ground operations, searching for moving targets is one of critical factors. Usually, the system of searching for adversary ground moving targets has complex properties which includes target's moving characteristics, camouflage level, terrain, weather, available search time window, distance between target and searcher, moving speed, target's tactics, etc. The purpose of this paper is to present a practical quantitative method for effectively searching for infiltrated moving targets considering aforementioned complex properties. Based upon search theories, this paper consists of two parts. One is infiltration route analysis, through terrain and mobility analysis. The other is building dynamic probability maps through Monte Carlo simulation to determine the prioritized searching area for moving targets. This study primarily considers ground moving targets' moving pattern. These move by foot and because terrain has a great effect on the target's movement, they generally travel along a constrained path. With the ideas based on the terrain's effect, this study deliberately performed terrain and mobility analysis and built a constrained path. In addition, dynamic probability maps taking terrain condition and a target's moving speed into consideration is proposed. This analysis is considerably distinct from other existing studies using supposed transition probability for searching moving targets. A case study is performed to validate the effectiveness and usefulness of our methodology. Also, this study suggests that the proposed approach can be used for searching for infiltrated ground moving target within critical time window. The proposed method could be used not only to assist a searcher's mission planning, but also to support the tactical commander's timely decision making ability and ensure the operations' success.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4929-4947
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• 2017

In visual object tracking, Correlation Filter-based Tracking (CFT) systems have arouse recently to be the most accurate and efficient methods. The CFT's circularly shifts the larger search window to find most likely position of the target. The need of larger search window to cover both background and object make an algorithm sensitive to the background and the target occlusions. Further, the use of fixed-sized windows for training makes them incapable to handle scale variations during tracking. To address these problems, we propose two layer target representation in which both global and local appearances of the target is considered. Multiple local patches in the local layer provide robustness to the background changes and the target occlusion. The target representation is enhanced by employing additional reversed RGB channels to prevent the loss of black objects in background during tracking. The final target position is obtained by the adaptive weighted average of confidence maps from global and local layers. Furthermore, the target scale variation in tracking is handled by the statistical model, which is governed by adaptive constraints to ensure reliability and accuracy in scale estimation. The proposed structural enhancement is tested on VTBv1.0 benchmark for its accuracy and robustness.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.2
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• pp.89-114
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• 2010

This study empirically investigated the effects of the target costing system as well as information technology(IT) infrastructure on the knowledge management processes(i.e., socialization, externalization, combination and internalization) and the performance of a firm. This study also examined an impact of the social capital(i.e., inter-departmental communication, trust, cooperation and integration) on the adoption and development of the target costing and the IT infrastructure. The results of this study showed that inter-departmental communication, trust and integration have a significant positive impact on the adoption of the target costing. It was also found that the effects of inter-departmental communication and integration on the development of storage and transfer infrastructure are significant and positive. However, in the adoption of search infrastructure, only the impact of inter-departmental integration was significant. The results of regression analyses presented that the target costing has significant influence on the four processes of knowledge management. It was also observed that the effects of storage and transfer infrastructure on combination are significant and positive. In search infrastructure, the impact on combination and internalization was significant. According to the results of this study, it was found that when the adoption level of the target costing is high, search infrastructure mainly affects the three processes(i.e., socialization, combination and internalization). However, under a low adoption level of the target costing, the impact of storage and transfer infrastructure on the whole processes was significant and positive. Thus, it is assumed that storage and transfer infrastructure complements a low level of the target costing adoption through the active transfer and sharing of explicit and tacit knowledge.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.225-234
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• 2018

In this paper, we consider a problem of partitioning a search area into smaller rectangular regions, so that multiple platforms can conduct search operations independently without requiring unnecessary coordination among themselves. The search area consists of cells where each cell has some prior information regarding the probability of target existence. The detection probability in particular cell is evaluated by multiplying the observation probability of the platform and the target existence probability in that cell. The total detection probability within the search area is defined as the cumulative detection probability for each cell. However, since this search area partitioning problem is NP-Hard, we decompose the problem into three sequential phases to solve this computationally intractable problem. Additionally, we discuss a special case of this problem, which can provide an optimal analytic solution. We also examine the performance of the proposed approach by comparing our results with the optimal analytic solution.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.43-52
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• 2008

We study the problem of search in which a server contains various multimedia contents and a user wishes to retrieve some multimedia items containing a specific without revealing to the server which items they are. Recently, Ogata and Kurosawa introduced a search scheme by using the notion of oblivious transfer. In their scheme, a user must inefficiently search and compare all the data stored in the seuer for each search query. In this paper, we propose an efficient oblivious search by using the oblivious transfer, in which a user needs not to search and compare all the data. We formally prove that the proposed scheme is secure under the hardness of RSA known target inversion problem.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.1
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• pp.25-33
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• 1998

This paepr presents an algorithm for searching an object in a fast way which contains a continuous moving object in multi-dimensional spatical databases. This algorithm improves the search method of R-tree for the case that a target object is continuously moving in a spatial database. It starts the searching from the current node instead of the root of R-tree. Thus, the algorithm will find the target object from the entries of current node or sibling nodes in the most cases. The performance analysis shows that it is more efficient than the existing algorithm for R-tree when search windows or target objects are continuously moving.