• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.43-49
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• 2018

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.115-118
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• 2003

In this paper we implemented a field-sequential stereoscopic endoscope system that can generate stereoscopic images with different perspective depth using LCD stutter. Re stereoscopic image is generated from stereoscopic adapter that has LCD shutter. We have compared the stereoscopic depth of a field-sequential stereoscopic endoscope system with that of the conventional endoscope system. And the implemented system is verified by evaluation the field-sequential stereoscopic image on a Monitor. This system will be use to medical instruments in time.

• Spatial Information Research
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• v.18 no.3
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• pp.73-83
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• 2010

This study presents a method to segment an image, employing the statistics observed at each pixel location across sequential frame images. In the acquisition and analysis of spatial information, utilization of digital image processing technique has very important implications. Various image segmentation techniques have been presented to distinguish the area of digital images. In this study, based on the analysis of the spectroscopic characteristics of sequential frame images that had been previously researched, an image segmentation method was proposed by using the randomness occurring among a sequence of frame images for a same scene. First of all, we computed the mean and standard deviation values at each pixel and found reliable pixels to determine seed points using their standard deviation value. For segmenting an image into individual regions, we conducted region growing based on a T-test between reference and candidate sample sets. A comparative analysis was conducted to assure the performance of the proposed method with reference to a previous method. From a set of experimental results, it is confirmed that the proposed method using a sequence of frame images segments a scene better than a method using a single frame image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.6
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• pp.669-678
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• 2009

Real-time image georeferencing technologies are required to generate spatial information promptly from the image sequences acquired by a multi-sensor system. We thus derive a sequential adjustment algorithm based on the combined adjustment model. By adopting the sequential adjustment model, we develop a sequential AT(Aerial Triangulation) algorithm to georeference image sequences in real-time. The proposed algorithm enables to perform AT rapidly with the minimum computation at the current stage by using the results computed at the previous stage whenever a new image is added. Experiments with simulated data were conducted to verify the effectiveness of the proposed algorithm. The results of the experiments show that the georeferencing of each image took very short time and its accuracy was determined within ${\pm}4cm$ on the ground control points comparing to the results of the existing simultaneous AT.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.906-915
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• 2005

In this paper we propose content based image retrieval method using sequential clustering and projection information. Proposed method uses the mean of color in clustered color regions by sequential clustering and the projection information in each clustered color regions, which combines spatial information with color information in images efficiently. The experimental results showed that the proposed method retrieval efficiency improved 11.6 percent over conventional methods. In addition, the proposed method robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.224-229
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• 2011

Cellular Automata is a discrete dynamical system that can be completely described in terms of local relation. For any given image, the system can save its features as well as increase or decrease the brightness of it locally through consideration of optimized transition in succession. These transitions in succession satisfy the function "Lyapunov" and have sequential movements. This study suggests the way of noise reduction for each image with the use of the Sequential Cellular Automata system. The mentioned transition in succession gives stable results with high-convergence performance to random noises and PSNR (Peak Signal-to-Noise Ratio) using histograms and MSE (Mean Square Error) for verification of effectiveness.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.3
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• pp.176-184
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• 1998

We present a sequential factorization method using singular value decomposition (SVD) for recovering both the three-dimensional shape of an object and the motion of camera from a sequence of images. We employ paraperpective projection [6] for camera model to handle significant translational motion toward the camera or across the image. The proposed mthod not only quickly gives robust and accurate results, but also provides results at each frame becauseit is a sequential method. These properties make our method practically applicable to real time applications. Considerable research has been devoted to the problem of recovering motion and shape of object from image [2] [3] [4] [5] [6] [7] [8] [9]. Among many different approaches, we adopt a factorization method using SVD because of its robustness and computational efficiency. The factorization method based on batch-type computation, originally proposed by Tomasi and Kanade [1] proposed the feature trajectory information using singular value decomposition (SVD). Morita and Kanade [10] have extenened [1] to asequential type solution. However, Both methods used an orthographic projection and they cannot be applied to image sequences containing significant translational motion toward the camera or across the image. Poleman and Kanade [11] have developed a batch-type factorization method using paraperspective camera model is a sueful technique, the method cannot be employed for real-time applications because it is based on batch-type computation. This work presents a sequential factorization methodusing SVD for paraperspective projection. Initial experimental results show that the performance of our method is almost equivalent to that of [11] although it is sequential.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2064-2068
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• 2010

In this paper, we introduced a sequential intelligent image analysis system(SIIAS). This system is implemented using PTZ camera with intelligent analysis algorithm and TI's Davinci DM6446. Enter, abandon, removal and cross functions are included in our system. These functions can be used individually or in combination for object monitoring and tracking. Sequential intelligent function processing is more efficient than the previous one by virtue of accurate observation, wide area monitoring and low cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1341-1347
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• 2007

The displacement field (Optical flow) has been calculated by bottom-up approaches based on local processing. In contrast with them, in this paper, a top-down approach based on expanding in turn from the lowest order mode the whole motion in an image pair of sequential images is proposed. The intensity of medical images usually represents a quantity which is conserved during the motion. Hence sequential images are ideally related by a coordinate transformation. The displacement field can be determined from the generalized moments of the two images. The equations which transform arbitrary generalized moments from a source image to a target image are expressed as a function of the displacement field. The appareent displacement field is then computed iteratively by a projection method which utilizes the functional derivatives of the linearized moment equations. This method is demonstrated using a pair of sequential heart images. For comparative evaluation, we applied Horn and Schunck's method, a standard multigrid method, and our proposed algorithm to sequential image.

• KIEAE Journal
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• v.9 no.2
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• pp.81-90
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• 2009

Relating to grasping sequential landscape characteristics in consideration of the behavioral characteristics of the subject experiencing visual perception, this study was made on the subject of main walking line section for visitors of three treasures of Buddhist temples. Especially, as a method of obtaining data for grasping sequential visual perception landscape, the researcher employed [momentum sequential viewpoint setup] according to [the interval of pointers arbitrarily] and fisheye-lens-camera photography using the obtained omni-directional angle visual perception information. As a result, in terms of viewpoint selection, factors like approach road form, change in circulation axis, change in the ground surface level, appearance of objects, etc. were verified to make effect, and among these, approach road form and circulation axis change turned out to be the greatest influences. In addition, as a result of reviewing the effectiveness via the subjects, for the sake of qualitative evaluation of landscape components using the VR picture image obtained in the process of acquiring omni-directional angle visual perception information, a positive result over certain values was earned in terms of panoramic vision, scene reproduction, three-dimensional perspective, etc. This convinces us of the possibility to activate the qualitative evaluation of omni-directional angle picture information and the study of landscape through it henceforth.