• Journal of the Korea Society of Computer and Information
• /
• v.22 no.7
• /
• pp.47-54
• /
• 2017

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.

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

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.9
• /
• pp.349-358
• /
• 2021

Recently, a method of finding the attention area or localization area for an object of an image using CAM (Class Activation Map)[1] has been variously carried out as a study of WSOL (Weakly Supervised Object Localization). The attention area extraction from the object heat map using CAM has a disadvantage in that it cannot find the entire area of the object by focusing mainly on the part where the features are most concentrated in the object. To improve this, using CAM and Selective Search[6] together, we first expand the attention area in the heat map, and a Gaussian smoothing is applied to the extended area to generate retraining data. Finally we train the data to expand the attention area of the objects. The proposed method requires retraining only once, and the search time to find an localization area is greatly reduced since the selective search is not needed in this stage. Through the experiment, the attention area was expanded from the existing CAM heat maps, and in the calculation of IOU (Intersection of Union) with the ground truth for the bounding box of the expanded attention area, about 58% was improved compared to the existing CAM.

• Journal of the military operations research society of Korea
• /
• v.35 no.1
• /
• pp.21-32
• /
• 2009

In this study, the author uses a MATLAB simulation to develop and test a generalization of the traditional Random Search model which allows both the searcher and target to move and to be in different, but overlapping, areas. Also the best evasion speed for a randomly moving target against a Systematic Search is studied.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.5
• /
• pp.293-302
• /
• 2019

In mountain accident events, it is important for the search team commander to determine the search area in order to secure the Golden Time. Within this period, assistance and treatment to the concerned individual will most likely prevent further injuries and harm. This paper proposes a method to determine the search priority area based on missing persons behavior and missing persons incidents statistics. GIS (Geographic Information System) and MCDM (Multi Criteria Decision Making) are integrated by applying WLC (Weighted Linear Combination) techniques. Missing persons were classified into five types, and their behavioral characteristics were analyzed to extract seven geographic analysis factors. Next, index values were set up for each missing person and element according to the behavioral characteristics, and the raster data generated by multiplying the weight of each element are superimposed to define models to select search priority areas, where each weight is calculated from the AHP (Analytical Hierarchy Process) through a pairwise comparison method obtained from search operation experts. Finally, the model generated in this study was applied to a missing person case through a virtual missing scenario, the priority area was selected, and the behavioral characteristics and topographical characteristics of the missing persons were compared with the selected area. The resulting analysis results were verified by mountain rescue experts as 'appropriate' in terms of the behavior analysis, analysis factor extraction, experimental process, and results for the missing persons.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.7
• /
• pp.1255-1262
• /
• 2009

The present study explained the phenomenon that low frequency oscillation is synchronized with discrete data obtained from a wide area system, and a sync search method. When a disturbance occurs in an power system, various controllers operate in order to maintain synchronization. If the system's damping is poor, low frequency oscillations continue for a long time and the oscillations are synchronized with one another at specific frequency. The present study estimated dominant modes, magnitude and phase of signals by applying parameter estimation methods to discrete signals obtained from an power system, and performed sync search among wide area signals by comparing the estimated data. Sync search was performed by selecting those with the same frequency and damping constant from dominant oscillation modes included in a large number of signals, and comparing their magnitude and phase. In addition, we defined sync indexes in order to indicate the degree of sync between areas in a wide area system. Furthermore, we proposed a wide area sync search method by normalizing mode magnitude in discrete data obtained from critical generator of the wide area. By applying the sync search method and sync indexes proposed in this study to two area systems, we demonstrated that sync scanning can be performed for discrete signals obtained from power systems.

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

• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10b
• /
• pp.389-391
• /
• 1999

영상 압축 기법에는 여러 가지 알고리즘을 적용되고 있다. 이런 알고리즘들에는 주파수 영역 중복을 제거하기 위한 DCT, 시간 중복성 제거를 위한 움직임 측정, 압축기법에 의해서 만들어진 정보를 부호화하는 VLC들이 있다. 이런 부호화 알고리즘들은 부호화기를 구현하는데 많은 시간을 요구하며 특히 움직임 추정은 부호화기의 절반에 가까운 시간을 소비한다. 움직임 측정 기술의 복잡도는 search algorithm, cost function, search range parameter의 요인으로 나타낼 수 있다. 본 논문에서는 기존의 Full Search 알고리즘 대신에 three-step 알고리즘을 사용하여 움직임 측정 시간을 줄였다. Full Search 알고리즘은 search area에서 모든 지역에 대해 cost function을 사용하여 이전 블록과 얼마나 유사한지를 조사한다. 따라서 이전 블록과 가장 유사한 부분을 찾는 좋은 방법이지만 그만큼 시간이 많이 사용한다. Three-step 알고리즘은 search area의 일정 지역에 대해 cost function를 사용하여 이전 블록과의 유사성을 찾는 fast 알고리즘이다. Three-step 알고리즘을 사용한 경우 기존의 full search 알고리즘을 사용할 때 보다 60% 정도의 시간이 단축되었다. 그리고 생성되는 압축 데이터의 크기는 full search 알고리즘을 사용할 때 보다 많이 차지한다. 생성되는 H.263파일의 화질에서는 Three-step 알고리즘을 사용한 경우일지라도 full search 알고리즘을 사용한 경우와 거의 비슷한 화질을 보여준다.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.859-863
• /
• 1993

There are two methods to get 3-dimensional information by matching image pair feature-based matching and area-based matching. One of the problems in the area-based matching is how the optimal search region which gives accurate correlation between given point and its neighbors can be selected. In this paper, we proposed a new area-based matching algorithm which uses edge-features used in the conventional feature-based matching. It first selects matching candidates by feature-based and matches image pair with area-based method by taking these candidates as guidance to decision of search area. The results show that running time is reduced by optimizing search area(considering edge points and continuity of disparity), keeping on the precision as the conventional area-based matching method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.3
• /
• pp.341-346
• /
• 2012

In rehabilitation where ultrasonographic diagnosis is not popular, it could be subjective by medical expert's experience. Thus, it is necessary to develop an objective automative procedure in ultrasonic image analysis. A disadvantage of existing automative analytic procedure in musculoskeletal system is to designate an incorrect muscle area when the figure of fascia is vague. In this study, we propose a new procedure to extract more accurate muscle area in abdomen ultrasonic image for that purpose. After removing unnecessary noise from input image, we apply End-in Search algorithm to enhance the contrast between fascia and muscle area. Then after extracting initial muscle area by Up-Down search, we trace the fascia area with a mask based on morphological and directional information. By this tracing of mask movements, we can emphasize the fascia area to extract more accurate muscle area in result. This new procedure is proven to be more effective than existing methods in experiment using convex ultrasound images that are used in real world rehabilitation diagnosis.