• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2299-2311
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• 2013

As portable multimedia devices become more popular and smaller, the use of portable projectors is also rapidly increasing. However, when portable projectors are used in mobile environments in which a dedicated planar screen is not available, the problem of geometric distortion of the projected image often arises. In this paper, we present a geometric image compensation method for portable projectors to compensate for geometric distortions of images projected on various types of planar or nonplanar projection surfaces. The proposed method is based on extraction of the two-dimensional (2D) geometric information of a projection area, setting of the compensation area, and prewarping using 2D homography. The experimental results show that the proposed method allows effective compensation for waved and arbitrarily shaped projection areas, as well as tilted and bent surfaces that are often found in the mobile environment. Furthermore, the proposed method is more computationally efficient than conventional image compensation methods that use 3D geometric information.

• Fashion & Textile Research Journal
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• v.15 no.4
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• pp.504-513
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• 2013

This study evaluates the difference of visual effect according to pant style and geometric pattern. The researcher made 28 stimuli-combination of four pant Stiles (classic, baggy, skinny, and bell-bottom) and seven geometric pattern (large vertical stripe, small vertical stripe, large horizontal stripe, small horizontal stripe, large check, small check, and hound's tooth check). The test involved 96 female college students. The stimuli were made with the i-Designer computer program. The panels tested the computer screen images of all manikins wearing pants. A 7-point scale was used to evaluate each image. For the data analysis, ANOVA and Duncan-test were applied along with an SPSS program. The results of this study are as follows. Three factors (lower-body compensation, abdomen highlight, and length compensation) influenced the visual effect pant styles and geometric patterns. The skinny style and large vertical stripe evaluated positively in elongated height and leg length and a slimmer overall body. It was shown that the vertical stripe pattern was evaluated as more positive than the horizontal stripe pattern in the visual effect; particularly, the results showed distinct aspects in the classic pants style. The mutual influence of the visual effect (according to pants style and geometric pattern) were indicated as two factors of lower-body compensation and length compensation. A more positive visual effects resulted in a higher mutual influence on pant style and geometric pattern.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.16-22
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• 2012

In this paper, we present spiral pattern which suits for optical simulator to calibrate fisheye lens and compensate geometric distortion. Using spiral pattern, we present calibration without mathematical modeling in advance. Proposed spiral pattern used to input image of optical simulator. Using fisheye lens image, we calibrate a fisheye lens by matching geometrically moved dots to corresponding original dots which leads not to need mathematical modeling. Proposed algorithm calibrates using dot matching which matches spiral pattern image dot to distorted image dot. And this algorithm does not need modeling in advance so it is effective. Proposed algorithm is enabled at processing of pattern recognition which has to get the exact information using fisheye lens for digital zooming. And this makes possible at compensation of geometric distortion and calibration of fisheye lens image applying in various image processing.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.27-35
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• 2010

In this paper, we propose a method that can compensate the geometric distortions of image caused from an arbitrary nonflat display surface(or wall) under the environment of portable overhead projector without a flat screen. In the proposed method, we first project a grid pattern to an arbitrary nonflat display surface and then derive an equation of straight line that represents the geometry relationship between the distorted grid pattern and the original grid pattern. Next, after determining the proper size of the original grid pattern according to the form of the display surface, we generate a compensation pattern from the derived equation of straight line, which can symmetrically compensate for the distorted image. Finally, we compensate for the geometric distortions of the projected image by segmenting the real image to be projected from portable overhead projector and prewarping it according to the compensation pattern. To evaluate the proposed method, we performed experiments of image compensation on inclined surface, bent surface and curved surface that are frequently occurred in the environment of portable overhead projector without a flat screen. From the experimental results, we found that the proposed method could be very effective in compensating for the general types of the geometric distortions of the projected images.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.202-207
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• 2002

EOC (Electro-Optical Camera) of KOMPSAT-1 (Korea Multi-Purpose SATellite) has been producing land imageries of the world since January 2000. After image data are acquired by EOC, they are transmitted from satellite to ground via X-band RF signal. Then, EOC image data are generated and pass through radiometric and geometric corrections to generate standard products of EOC images. After radiometric correction on EOC image data, Modulation Transfer Function (MTF) compensation is applicable on EOC images with user's request for better image quality. MTF compensation is concerned with filtering EOC images to minimize the effect of degradations. For Image Receiving and Processing System (IRPE) at KOMPSAT Ground Station (KGS), Wiener filter is used in MTF compensation for EOC images. If the Pointing Spread Function (PSF) of EOC system is known, signal-to-noise power spectra ratio is the only factor in the determination of Wiener filter. In this paper, MTF compensation in IRPE at KGS is introduced and MTF compensated EOC 1R images are generated using Wiener filters with various signal-to-noise power spectra ratios. MTF compensated EOC 1R images are correlated with EOC 1R images for observing linearities between them. As a result, the effect of signal-to-noise power spectra ratio is shown on MTF compensated EOC 1R images.

• Korean Journal of Remote Sensing
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• v.19 no.1
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• pp.43-52
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• 2003

EOC (Electro-Optical Camera) of KOMPSAT-1 (Korea Multi-Purpose SATellite) has been producing land imageries of the world since January 2000. After image data are acquired by EOC, they are transmitted from satellite to ground via X-band RF signal. Then, EOC image data are retrieved and pass through radiometric and geometric corrections to generate standard products of EOC images. After radiometric correction on EOC image data, Modulation Transfer Function (MTF) compensation is applicable on EOC images with user's request for better image quality. MTF compensation is concerned with filtering EOC images to minimize the effect of degradations. For Image Receiving and Processing System (IRPE) at KOMPSAT Ground Station (KGS), Wiener filter is used for MTF compensation of EOC images. If the Pointing Spread Function (PSF) of EOC system is known, signal-to-noise (SNR) power spectra ratio is the only variable which determines the shape of Wiener filter In this paper, MTF compensation in IRPE at KGS is briefly addressed, and MTF compensated EOC images are generated using Wiener filters with various SNR power spectra ratios. MTF compensated EOC images are compared with original EOC 1R images to observe correlations between them. As a result, the effect of SNR power spectra ratio on MTF compensated EOC images is shown.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1418-1425
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• 2014

In ultrasonic medical imaging, spatial compounding of images is a technique where ultrasonic beam is steered to examine patient tissues in multiple angles. In the conventional ultrasonic diagnostic imaging, the steering of the ultrasonic beam is achieved electronically using the phased array transducer elements. In this paper, a spatial compounding approach is presented where the ultrasonic probe element is rotated mechanically and the beam steering is achieved mechanically. In the spatial compounding, target position is computed using the value of the rotation axis and the transducer array angular position. However, in the process of the rotation mechanism construction and the control system there arises the inevitable uncertainties in these values. These geometric parameter errors result in the target position error, and the consequence is a blurry compounded image. In order to reduce these target position errors, we present a spatial compounding scheme where error correcting transformation matrices are computed and applied to the raw images before spatial compounding to reduce the blurriness in the compounded image. The proposed scheme is illustrated using phantom and live scan images of human knee, and it is shown that the blurriness is effectively reduced.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.67-75
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• 2011

This paper presents a scale and rotation-invariant gait recognition method using R-transform, which is computed by projecting squared coefficients of Radon transform. Since R-transform is invariant to translation, rotation, and scaling, it particularly suitable for extracting object poses without camera calibration. Coefficients of R-transform are used to compute correlation, and the maximum correlation value determines the similarity between two gait images. The proposed method requires neither camera calibration nor geometric compensation, and as a result, it makes robust gait recognition possible without additional compensation for translation, rotation, and scaling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.213-224
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• 2010

In this paper, we propose an algorithm both geometric correction using a grid point image and radiometric adaptive projection that dependent upon the luminance of the input image and that of the background. This method projects and captures the grid point image then calculates the geometrically corrected position by difference between the two images. Next, to compensate color, a corrected image is calculated by the ratio divided luminance of an input image by luminance of arbitrary surface. In addition, we found the scaling factor which controls the contrast to avoid clipping error. At this time, the scaling factor is dependent on mean image lightness when background is determined. Experimental results show that the proposed method achieves good performance and is able to reduce the perceived color clipping and artifacts, better approximating the projection on a white screen.