• Journal of the Korean Society for Precision Engineering
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• v.32 no.9
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• pp.807-813
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• 2015

In the present study, an interferometer system, which integrates the laser sensitivity control technique based on the theory of electronic speckle pattern interferometry, one of non-contact non-destructive analysis methods, was developed. This interferometry system receives an image from CCD cameras for each reference and object, and compares the photosensitivity of the object and reference images from imagification. For the purpose of this study, the photosensitivity of object and reference light is measured with power meters, and the amount of light was controlled with an ND filter with a reference light port matching photosensitivity. Using the plate specimen as the object, 0.6, 0.9, 1.2, and $1.5{\mu}m$ of out-plane deformation was made, and images were compared according to the difference in photosensitivity. After analysis, larger object deformations showed larger numbers of stripe patterns. Images became clearer and data error was reduced when the photosensitivity of object and reference light matched.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.586-591
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• 2009

In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.10
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• pp.1219-1227
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• 2013

A new type of wearable robot is developed for a disabled person with disabled limbs, that is, a person who cannot intentionally move his/her legs and arms. This robot can enable the disabled person to grip an object using eye movements. A gaze tracking algorithm is employed to detect pupil movements by which the person observes the object to be gripped. By using this gaze tracking 2D information, the object is identified and the distance to the object is measured using a Kinect device installed on the robot shoulder. By using several coordinate transformations and a matching scheme, the final 3D information about the object from the base frame can be clearly identified, and the final position data is transmitted to the DSP-controlled robot controller, which enables the target object to be gripped successfully.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.55-63
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• 2008

This paper presents a panorama background generation and object tracking technique using a Pan-Tilt-Zoom camera. The proposed method estimates local motion vectors rapidly using phase correlation matching at the prespecified multiple local regions, and it makes minimized estimation error by vector quantization. We obtain the required image patches, by estimating the overlapped region using local motion vectors, we can then project the images to cylinder and realign the images to make the panoramic image. The object tracking is performed by extracting object's motion and by separating foreground from input image using background subtraction. The proposed PTZ-based object tracking method can efficiently generated a stable panorama background, which covers up to 360 degree FOV The proposed algorithm is designed for real-time implementation and it can be applied to many commercial applications such as object shape detection and face recognition in various surveillance video systems.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.227-228
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• 2013

본 논문에서는 연속적으로 입력되는 스테레오 입체 영상으로부터 2차원과 3차원의 특징을 결합하여 군집화함으로써 대상 물체를 보다 강건하게 분할하는 기법을 제안한다. 제안된 방법에서는 촬영된 장면의 좌우 영상으로부터 스테레오 정합 알고리즘을 이용해 영상의 각 화소별로 카메라와 물체 사이의 거리를 나타내는 깊이 특징을 추출한다. 그런 다음, 깊이와 색상 특징을 효과적으로 군집화하여 배경에 해당하는 영역을 제외하고, 전경에 해당하는 대상 물체를 감지한다. 실험에서는 제안된 방법을 여러가지 영상에 적용하여 테스트를 해 보았으며, 제안된 방법이 기존의 2차원 기반의 물체 분리 방법에 비해 보다 강건하게 대상물체를 분할함을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.767-771
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• 2003

In this study, stereo vision system is applied to a mobile manipulator for effective tasks. The robot can recognize a target and compute the position of the target using a stereo vision system. While a monocular vision system needs properties such as geometric shape of a target, a stereo vision system enables the robot to find the position of a target without additional information. Many algorithms have been studied and developed for an object recognition. However, most of these approaches have a disadvantage of the complexity of computations and they are inadequate for real-time visual servoing. However, color information is useful for simple recognition in real-time visual servoing. In this paper, we refer to about object recognition using colors, stereo matching method, recovery of 3D space and the visual servoing.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.219-222
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• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• Asia pacific journal of information systems
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• v.4 no.2
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• pp.3-34
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• 1994

In this paper we describe a new data modeling language we call $O^{2}LDM$. $O^{2}LDM$ incorporates features from object-oriented and logic approaches. In $O^{2}LDM$ there is a rich collection of objects organized in a type hierarchy. It is possible to compose queries that involve field selection, function application and other constructs which transcend the usual, strictly syntactic, matching of PROLOG. We give the features of $O^{2}LDM$ and motivate its utility for conceptual modeling. We have a prototype implementation for the language, which we have written in ML. In this paper we describe an executable semantics of the deductive process used in the language. We work some examples to illustrate the expressive power of the language, and compare $O^{2}LDM$ to PROLOG.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.83-87
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• 2006

Similarity search in moving object trajectories is an active area of research. In this paper, we introduce a new concept of measure that computes spatial distance (similarity) between two trajectories of moving objects on road networks. In addition, we propose an algorithm that generates a sequence of matching edge pairs for two trajectories that ate to be compared and computes spatial distance between them which is non Euclidian in nature. With an example, we explain how our algorithm works to show spatial similarity between trajectories of moving objects in spatial network.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.3
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• pp.193-198
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• 2015

A large number of vision applications rely on matching keypoints across images. Pedestrian detection is under constant pressure to increase both its quality and speed. Such progress allows for new application. A higher speed enables its inclusion into large systems with extensive subsequent processing, and its deployment in computationally constrained scenarios. In this paper, we focus on improving the speed of pedestrian detection using HOG(histogram of oriented gradient) and multi frame operation which is robust to illumination changes in cluttering images. The result of our simulation indicates that the detection rate and speed of the proposed method is much faster than that of conventional HOG and differential images.