• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.571-574
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• 2005

In this paper, we propose an efficient image segmentation technique for large-sized monochrome images using a hybrid approach which combines threshold and region-based techniques. First, an image is partitioned into fixed-size blocks and for each block the representative intensity is determined by averaging pixel intensities within the block. Next, the neighborhood blocks that have similar characteristics with respect to a specific threshold are merged in order to form candidate regions. Finally, those candidate regions are refined to get final target object regions by merging regions considering the spatial locality and certain criteria. We have performed experiments on images selected from various domains and showed that our technique was able to extract target object regions appropriately from most images.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.6
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• pp.210-217
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• 2000

Noncontact measuring methodology of 3-dimensional profile using CCD camera are very attractive because of it's high measuring speed and it's high sensitivity. Especially when projecting a grid pattern over the object, the captured image have 3 dimensional information of the object. Projection moire extract 3-D information with another grid pattern in front of CCD camera. However phase measuring profilometry(PMP) obtain similar results without additional grid pattern. In this paper, the projection moire are compared with the PMP mathematically, and it is shown that PMP can generate moire image with simple mathematical computations. Experimental works are also carried out showing the same results. It is shown that using a single gird pattern, moire image can be obtained directly without any mathematical operation when some conditions are satisfied.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.510-516
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• 2015

This paper describes an approach to detect interesting objects using satellite images. This paper focuses on the interesting objects that have common special patterns but do not have identical shapes and sizes. The previous technologies are still insufficient for automatic finding of the interesting objects based on operation of special pattern analysis. In order to overcome the circumstances, this paper proposes a methodology to obtain the special patterns of interesting objects considering their common features and their related characteristics. This paper applies MSER(Maximally Stable Extremal Regions) for the region detection and corner detector in order to extract the features of the interesting object. This paper conducts a case study and obtains the experimental results of the case study, which is efficient in reducing processing time and efforts comparing to the previous manual searching.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.56-59
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• 1987

In this study the moving information extraction techniques of moving objects are processed digital imaqe data by sampling three frames in a fixed-bacqround two-dimensional line sequence image the brightness of interframe are compared to extract difference image and difference image are two level formed and neighber averged From neigbber averaged image the parameters for recoqnition of the object are the number of contorur pixels, the number of vertex points and the distance between the vertex points Agtercomparing the same object the moving distance obtained from the coordinate which is constructed by the bit processing of the digital data and the moving velocity is obtained from the moving distance and the time interval between the first andsecond sampled frames.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.15-25
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• 2014

In this paper, we propose a loitering detection using trajectory probability distribution and local direction descriptor for intelligent surveillance system. We use a background modeling method for detecting moving object and extract the motion features from each moving object for making feature vectors. After that, we detect the loitering behavior person using K-Nearest Neighbor classifier. We test the proposed method in real world environment and it can achieve real time and robust detection results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.175-178
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• 2010

Slow movement of snail can be a benefit since it means less speed of tracking is required to get accurate movement track, but in the other side it is difficult to extract the object because the snail is almost as static as the background. In this paper, we present a technique to track the snail by using one of its common characteristic, dark color of its shell. The technique needs to be robust to illumination change since the experiment is usually to observe the movement of snail both at bright and dim condition. Snail position coordinate in 3D space is calculated using orthogonal stereo vision which combines the information from two images taken from cameras at the top and in front of the aquarium. Experimental results show this technique does not need prior background image extraction and robust to gradual or sudden illumination change.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.5
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• pp.1109-1122
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• 1996

Generally, moving images contain the various components in motions, which reange from a static object and background to a fast moving object. To extract the accurate motion parameters, we must consider the various motions. That requires a wide search egion in motion estimation. The wide search, however, causes a high computational complexity. If we have a few knowledge about the motion direction and magnitude before motion estimation, we can determine the search location and search window size using the already-known information about the motion. In this paper, we present a local adaptive motion estimation approach that predicts a block motion based on spatio-temporal neighborhood blocks and adaptively defines the search location and search window size. This paper presents a technique for reducing computational complexity, while having high accuracy in motion estimation. The proposed algorithm is introduced the forward and backward projection techniques. The search windeo size for a block is adaptively determined by previous motion vectors and prediction errors. Simulations show significant improvements in the qualities of the motion compensated images and in the reduction of the computational complexity.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.465-467
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• 1992

This paper shows 3D vision system based on halftone image analysis. Any halftone image has its own surface vector normal to surface patch. To classily the given 3D images, all the patch on 3D object are transformed to black/white halftone. First we extract the general learning patterns which represents required slopes and their attributes. And next we propose 3D segmentation by searching intensity, slope and density. Artificial neural network is found to be very suitable in this approach, because it has powerful learning quality and noise tolerant. In this study, 3D shape reconstruct using pyramidian model. Our results are evaluated to enhance the quality.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.919-920
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• 2008

In this paper, we present early processing techniques for visual inspection of metallic parts. Since metallic surfaces give rise to specular reflections, it is difficult to extract object boundaries using elementary segmentation techniques such as edge detection or binary thresholding. In this paper, we present two techniques for finding object boundaries on micro bit images. First, we explain a technique for detecting blade boundaries using a directional correlation mask. Second, a line and angle extraction technique based on Harris corner detector and Hough transform is described. These techniques have been effective for detecting blade boundaries, and a number of experimental results are presented using real images.

• ETRI Journal
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• v.42 no.3
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• pp.420-432
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• 2020

The reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition and computer vision. Therefore, we implement an algorithm with the help of a 3D model to perform reconstruction from the real datasets using the slope features. This approach avoids the problem of gaps created through the loss of parts of the artifacts. Therefore, the aim of this study is to assemble the object without previous knowledge about the form of the original object. We utilize the edges of the fragments as an important feature in reconstructing the objects and apply multiple procedures to extract the 3D edge points. In order to assign the positions of the unknown parts that are supposed to match, the contour must be divided into four parts. Furthermore, to classify the fragments under reconstruction, we apply a backpropagation neural network. We test the algorithm on several models of ceramic fragments. It achieves highly accurate results in reconstructing the objects into their original forms, in spite of absent pieces.