• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.384-390
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• 2012

In this paper, we analyzed the navigation error of TERCOM (TErrain COntour Matching), which is TRN (Terrain Referenced Navigation) batch processing, caused by scale factor error of radar altimeter and proved the possibility of false position fix when we use the TERCOM's feature matching algorithm. Based on these, we proposed the new TRN batch processing algorithm using the slope measurements of terrain. The proposed technique measures on periodic changes in the slope of the terrain elevation profile, and these measurements are used in the feature matching algorithm. By using the slope of terrain data, the impact of scale factor errors can be compensated. By simulation, we verified improved outcome using this approach compared to the result using the conventional method.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.8
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• pp.407-414
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• 2004

In this paper, a new gray scale expression method that divides the scan lines into multiple blocks is suggested. The proposed method can drive 16 sub-fields per 1 TV field in the panel with XGA ($1366{\times}768$) resolution. The on and off states of even subfields depend on the condition of odd subfields. The write address mode is used in the odd subfields, while the erase address mode is used in the even subfields. Because the ramp reset pulse is applied every 2 sub-fields, both the contrast ratio and the dynamic voltage margin are sufficiently obtained in comparison with previous AWD (Address While Display) methods. In realizing 16 subfields, shortening the scan time in the erase address period was important. The X bias voltage in the erase address period affected the minimum address voltage but did not the delay time of the address discharge. The delay time of the address discharge was affected by the address voltage and the time interval between the last sustain discharge and the scanning time. We also evaluated the dynamic false contour. New method shows an improved image quality in horizontal moving, but discontinuous lines were observed at the boundaries of each block in vertical moving

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.21-29
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• 2007

Anisotropic diffusion is one of the widely used techniques in the field of image segmentation. In the conventional anisotropic diffusion [1]-[6], usually 4-neighborhood directions are used: north, south, west and east, except the image diagonal directions, which results in the loss of image details and causes false contours. Existing methods for image segmentation using conventional anisotroplc diffusion can't preserve contour information, or noises with high gradients become more salient as the umber of times of the diffusion increases, resulting in over-segmentation when applied to watershed. In this paper, to overcome the shortcoming of the conventional anisotropic diffusion method, a new arusotropic diffusion method based on diagonal edges is proposed. And a method of watershed segmentation is applied to the proposed method. Experimental results show that the process time of the proposed method including diagonal edges over conventional methods can be up to 2 times faster and the Circle image quality improvement can be better up to $0.45{\sim}2.33(dB)$. Experiments also show that images are segmented very effectively without over segmentation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.93-101
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• 2004

A face boundary can be approximated by an ellipse with five-dimensional parameters. This property allows an ellipse detection algorithm to be adapted to detecting faces. However, the construction of a huge five-dimensional parameter space for a Hough transform is quite unpractical. Accordingly, we Propose a selectively attentional Hough transform method for detecting faces from a symmetric contour in an image. The idea is based on the use of a constant aspect ratio for a face, gradient information, and scan-line-based orientation decomposition, thereby allowing a 5-dimensional problem to be decomposed into a two-dimensional one to compute a center with a specific orientation and an one-dimensional one to estimate a short axis. In addition, a two-point selection constraint using geometric and gradient information is also employed to increase the speed and cope with a cluttered background. After detecting candidate face regions using the proposed Hough transform, a multi-layer perceptron verifier is adopted to reject false positives. The proposed method was found to be relatively fast and promising.