• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.712-714
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• 2000

In the field of computer graphics, it approached the investigation of outstanding performance and high speed. Although most introduced Anti-Aliasing method were to meet these, it was not to improve speed. Because Anti-Aliasing method was focus on only qualify. Anti-Aliasing Effect is compensated from movement of object on the screen. Speed is important in the REAL-TIME application program like as 3D games. Cause Anti-Aliasing which needs great amount of time is not used in general. This Paper suggest the Efficient Anti-Aliasing method which apply Two-Point Anti-Aliasing Method that informs brightness data of the screen and use adjacent brightness data for real-time rendering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.69-72
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• 2011

In this paper, we propose a method to track the movement of camera from the video sequences. This method is useful for video analysis and can be applied as pre-processing step in some application such as video stabilizer and marker-less augmented reality. First, we extract the features in each frame using corner point detection. The features in current frame are then compared with the features in the adjacent frames to calculate the optical flow which represents the relative movement of the camera. The optical flow is then analyzed to obtain camera movement parameter. The final step is camera movement estimation and correction to increase the accuracy. The method performance is verified by generating a 3D map of camera movement and embedding 3D object to the video. The demonstrated examples in this paper show that this method has a high accuracy and rarely produce any jitter.

• Journal of Industrial Technology
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• v.16
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• pp.247-256
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• 1996

The multilevel grid file(MLGF) is a dynamic multidimensional file organization supporting multi-attribute accesses efficiently. The paper proposes new method for batch-constructing MLGFs. Our method consists of two phases. The first phase begins by relocating all the objects in order that logically adjacent objects in multidimensional domain space are clustered in one dimensional physical space. For this, our method employs the Z-ordering scheme, which effectively maps multidimensional space into one dimensional space preserving proximity. The second phase paginates the relocated objects and creates leaf level directory entries, each of which corresponds to a object page. Simultaneously, it performs same actions on the directory entries recursively in a bottom-up fashion until the root directory fits in a page. For performance evaluation, we analyze our method in terms of the number of page accesses. The result shows the optimality of our method.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.639-642
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• 1999

There exist geometrically invariant relations in single-view images under a specific geometrical structure. This invariance may be utilized for 3D object recognition. Two types of invariants are compared in terms of the robustness to the variation of the feature points. Deviation of the invariant relations are measured by adding random noise to the feature point location. Zhu’s invariant requires six points on adjacent planes having two sets of four coplanar points, whereas the Kaist method requires four coplanar points and two non-coplanar points. Experimental results show that the latter method has the advantage in choosing feature points while suffering from weak robustness to the noise.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1014-1023
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• 2017

In this paper, we propose a method for estimating the human height by using downward depth images. We detect a point with the lowest depth value in an object as top of the head and estimate the height by calculating the depth difference with the floor. Since the depth of the floor varies depending on the angle of the camera, the correction formula is applied. In addition, the binarization threshold is variably applied so that height can be estimated even when several people are adjacent. Simulation results show that the proposed method has better performance than the conventional methods. The proposed method is expected to be widely used in body measurement, intelligent surveillance, and marketing.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.268-273
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• 2000

A signaling system for a single-track railroad has been specified in CafeOBJ. In this paper, we describe the specification of arbitrary two adjacent stations connected by a single line that is called a two-station system. The system consists of two stations, a railroad line (between the stations) that is also divided into some contiguous sections, signals and trains. Each object has been specified in terms of their behavior, and by composing the specifications with projection operators the whole specification has been described. A safety property that more than one train never enters a same section simultaneously has also been verified with CafeOBJ.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.60-67
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• 1996

In this paper, we propose a new temporal interpolation method based on spatial filtering. Compared with the conventional method, the proposed one may use a few adjacent frames and apply temporal lowpass filtering. To develop this method, we follow the basic approach of sampling rate conversion. Additionally, we use some assumption of video sequence : moving object has constant velocity rigid translational motion. From them, spatial filtering for temporal sampling rate conversion is described. This method has a lot of noise immunity on a motion vector and doesn't make a great difference from the original frame. The interpolated frame shows moderate change even there is a great time difference. This method has exactly same description of motion adaptive spatial filter which has an efficient temporal band-limiting characteristics. It imposes the possibility to make video sequence with good pictural quality.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.656-659
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• 2004

최근 많은 연구에서, 동일한 영상그룹들로부터 추출된 저수준의 특징들을 이용해서 고수준의 정보를 분석한 뒤, 이를 이용해서 영상을 분류하는 방법들을 소개하고 있다. 이러한 연구는 CBIR의 인덱싱에서 저수준의 특징만을 사용할 때 발생하는 의미적인 차이(semantic gap)문제를 해결하여, 검색의 효율을 높일 수 있게 한다. 하지만 이들 연구는 대부분 전경(scenery)영상만을 대상으로 하고 있다. 한편 영상을 객체 단위로 다루는 것은 CBIR의 성능을 크게 향상 시킬 수 있는 요인이 된다. 왜냐하면 대부분의 사용자는 관심있는 객체가 포함된 영상을 검색하기 원하기 때문이다. 본 논문에서는 영상의 객체를 인공객체와 자연객체로 분류하는 방법을 제안한다. 인공객체의 경우 자연객체에 비해 상대적으로 직선형태의 에지가 많이 발견되며 객체를 구성하는 패턴이 규칙적이고 방향성을 가진다. 또한 인공객체는 자연객체에 비해 객체영역의 경계가 직선에 의한 단순한 형태로 나타난다. 이러한 특징들을 EDH(edge Direction Histogram)의 에너지, EDAS(Energy Difference of Adjacent Sector)와 가버 필터를 통해 추출하여 분류에 이용한다. 실험을 통하여 각 특징들을 개별적으로 사용해서 76%에서 84% 사이의 분류 정확성을 얻었으며, 제안한 머징 방법을 이용하여 최종적으로 약 90%의 정확성으로 분류하였다.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1297-1309
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• 2016

In this paper, an efficient connected component labeling (CCL) method was proposed. The proposed method is based on GPU parallelism. The CCL is very important in various applications where images are analysed. However, the label of each pixel is dependent on the connectivity of adjacent pixels so that it is not very easy to be parallelized. In this paper, a GPU-based parallel CCL techniques were proposed and applied to the analysis of radar signal. Since the radar signals contains complex and large data, the efficiency of the algorithm is crucial when realtime analysis is required. The experimental results show the proposed method is efficient enough to be successfully applied to this application.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.