• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.25 no.3
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• pp.253-259
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• 2023

Precise evaluation of the feeders, fistulous points, and draining veins plays a key role for successful embolization of intracranial dural arteriovenous fistulas (DAVF). Digital subtraction angiography (DSA) is a gold standard diagnostic tool to assess the exact angioarchitecture of DAVFs. With the advent of new image postprocessing techniques, we lately have been able to apply image fusion techniques with two different image sets obtained with flat panel detector rotational angiography. This new technique can provide additional and better pretherapeutic information of DAVFs over the conventional 2D and 3D angiographies. In addition, it can be used during the endovascular treatment to help the accurate and precise navigation of the microcatheter and microguidwire inside the vessels and identify the proper location of microcatheter in the targeted shunting pouch. In this study, we briefly review the process of an image fusion technique and introduce our clinical application for treating DAVFs, especially focused on the transvenous embolization.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.66-77
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• 2002

This paper deals with an implementation of the navigation parameter extraction technique using the TMS320C80 multimedia video processor (MVP). Especially, this Paper focuses on the relative position estimation algorithm which plays an important role in real-time operation of the overall system. Based on the relative position estimation algorithm using the images obtained at two locations, we develop a fast algorithm that can reduce large amount of computation time and fit into fixed-point processors. Then, the algorithm is reconfigured for parallel processing using the 4 parallel processors in the MVP. As a result, we shall demonstrate that the navigation parameter extraction system employing the MVP can operate at full-frame rate, satisfying real-time requirement of the overall system.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.651-662
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• 2010

This paper describes a method to identify objects for autonomous navigation of an outdoor mobile robot. To identify objects, the robot recognizes the object from an image taken by moving robot on outdoor environment. As a beginning, this paper presents the candidates for a segment of region to building of artificial object, sky and trees of natural objects. Then we define their characteristics individually. In the process, we segment the regions of the objects included by preprocessing using multiple features. Multiple features are HSI, line segments, context information, hue co-occurrence matrix, principal components and vanishing point. An analysis of building identifies the geometrical properties of building facet such as wall region, windows and entrance. The building as intersection in vertical and horizontal line segment of vanishing point extracts the mesh. The wall region of building detect by merging the mesh of the neighbor parallelograms that have similar colors. The property estimates the number of story and rooms in the same floors by merging skewed parallelograms of the same color. We accomplish the result of image segmentation using multiple features and the geometrical properties analysis of object through experiments.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.509-514
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• 2003

The virtual bronchoscopy was implemented using chest CT images to visualize inside of tracheo-bronchial wall. The optical endoscopy procedures are invasive, uncomfortable for patients and sedation or anesthesia may be required. Also, they have serious side effects such as perforation, infection and hemorrhage. In order to determine the navigation path, we segmented the tracheo-bronchial wall from the chest CT image. We used the coordinates as a navigation path for virtual camera that were calculated from medial axis transformation. We used the perspective projection and marching cube algorithm to render the surface from volumetric CT image data. The tracheobronchial disease was classified into tracheobronchial stenosis causing from inflammation or lung cancer, bronchiectasis and bronchial cancer. The virtual bronchoscopy is highly recommended as a diagnosis tool with which the specific place of tracheobronchial disease can be identified and the degree of tracheobronchial disease can be measured qualitatively, Also, the virtual bronchoscopy can be used as an education and training tool for endoscopist and radiologist.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.561-566
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• 2003

This paper proposes a new algorithm that generates smooth and realistic transition views from one viewpoint to another view point on the panorama based navigation system. The proposed algorithm is composed with two steps. One is prewarping that aligns the viewing direction in two panorama image, the other is bidirectional disparity morphing (BDM) that generates the intermediate scene from the aligned panorama images. For the prewarping, first of all, we compute the phase correlation between two images in order to get the information such as the displacement, rotation, and scale. Then we align the original images using these information. As soon as finishing the prewarping, we compute the block based disparity vectors (DVs) and smooth them using the two occluding patterns. As we apply these DVs to the BDM, we can get the elaborate intermediate scenes. We make an experiment on the proposed algorithm with real panoramic images and we can get the satisfactory results.

• Journal of Advanced Navigation Technology
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• v.22 no.4
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• pp.295-301
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• 2018

Recently, GPS(Global Positioning System) array antenna technology is generally used and widely adopted as a national infrastructure structure and aero-vehicles for protection the GPS signal reception. Until now, the 2-dimensional planar array is universally used for its applications in the array antenna signal processing, however relatively higher altitude air vehicles such as UAV experiences additional null zones induced by low altitude GPS interferences which is located in a symmetry zone of antenna horizontal plane and this could make the receiving antenna pattern coverage reduction. In this paper, we improved 20% of the beam pattern receiving performance and 13 dB correlation value improvement by eliminating the interference mirror images.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.379-390
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• 2019

In this paper, we propose a novel deep neural network model for Vision-and-Language Navigation (VLN) named LVLN (Landmark-based VLN). In addition to both visual features extracted from input images and linguistic features extracted from the natural language instructions, this model makes use of information about places and landmark objects detected from images. The model also applies a context-based attention mechanism in order to associate each entity mentioned in the instruction, the corresponding region of interest (ROI) in the image, and the corresponding place and landmark object detected from the image with each other. Moreover, in order to improve the success rate of arriving the target goal, the model adopts a progress monitor module for checking substantial approach to the target goal. Conducting experiments with the Matterport3D simulator and the Room-to-Room (R2R) benchmark dataset, we demonstrate high performance of the proposed model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.936-942
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• 2019

Autonomous driving of drone indoor must move along a narrow path and overcome other factors such as lighting, topographic characteristics, obstacles. In addition, it is difficult to operate the drone in the hallway because of insufficient texture and the lack of its diversity comparing with the complicated environment. In this paper, we study an autonomous drone navigation using Convolution Neural Network(CNN) in indoor environment. The proposed method receives an image from the front camera of the drone and then steers the drone by predicting the next path based on the image. As a result of a total of 38 autonomous drone navigation tests, it was confirmed that a drone was successfully navigating in the indoor environment by the proposed method without hitting the walls or doors in the hallway.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1143-1148
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• 2006

사용자 친화형 유저 인터페이스 구현을 위해 인간의 손 형상을 실시간으로 인식하는 연구의 중요성이 부각되고 있다. 그러나 인간의 손은 자유도가 크기 때문에 손 형상을 정확히 인식하기란 매우 어렵고 또한 피부색과 유사한 색을 가지는 복잡한 배경에서는 더욱 곤란하다. 본 논문에서는 별도의 센서를 부착하지 않고 카메라를 사용하여 피부색 정보에 의한 손 형상을 분할한 후 손가락 끝 점을 찾는다. 찾은 손가락 끝점을 이용하여 방향을 탐지하는 알고리즘에 대해 기술한다. 이 방법은 템플리트 매칭을 이용하여 손가락 끝 점을 탐색한 후 찾은 손 가락 끝 점과 손목의 중심을 이용하여 전, 후, 좌, 우 방향을 탐지한다. 제안하는 방법을 이용하여 3D가상현실 공간에서의 Navigation에 응용하였으며, 실험결과 전진, 후진 및 좌측, 우측의 방향전환도 매우 좋은 결과를 보였다. 또한 본 논문에서 제안하는 방법은 마우스, 키보드, 조이스틱 등의 조작 없이 전, 후, 좌, 우 방향전환을 사용자가 직관적으로 지시함으로써 보다 자연스러운 인간과 컴퓨터의 상호작용을 제공할 수 있을 것이다.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.588-591
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• 2004

In this paper, we propose a ground plane detection algorithm, using a new image processing method (IPD). To extract the ground plane from the color image acquired by monocular camera, we use a new identical pixel detection method (IPD) and an edge detection method. This IPD method decides whether the pixel is identical with the ground plane pixel or not. The IPD method needs the reference area and its performance depends on the reference area size. So we propose the reference area auto-expanding algorithm in accordance with situation. And we evaluated the proposed algorithm by the experiments in the various environments. From the experiments results, we know that the proposed algorithm is efficient in the real indoor environment.