• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.852-859
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• 2009

Normally, to extract the defect in TFT-LCD inspection system, the image is obtained by using line scan camera or area scan camera which is achieved by CCD or CMOS sensor. Because of the limited dynamic range of CCD or CMOS sensor as well as the effect of the illumination, these images are frequently degraded and the important features are hard to decern by a human viewer. In order to overcome this problem, the feature vectors in the image are obtained by using the average intensity difference between defect and background based on the weber's law and the standard deviation of the background region. The defect detection method uses non-linear SVM (Supports Vector Machine) method using the extracted feature vectors. The experiment results show that the proposed method yields better performance of defect classification methods over conveniently method.

• Journal of Digital Contents Society
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• v.14 no.2
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• pp.275-282
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• 2013

In medical image processing field, object recognition is usually carried out by computerized processing of various input information such as brightness, shape, and pattern. If the information mentioned does not make sense, however, many limitations could occur with object recognition during computer processing. Therefore, this paper suggests effective object recognition method based on the magnetic resonance (MR) theory to resolve the basic limitations in computer processing. We propose the efficient method of robust gray-white matter segmentation by texture analysis through the Susceptibility Weighted Imaging (SWI) for contrast enhancement. As a result, an average area difference of 5.2%, which was higher than the accuracy of conventional region segmentation algorithm, was obtained.

• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.953-960
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• 2007

To correctly detect muras, visual defects in TFT-LCD panels, image distortion occurring on the profess of capturing panels should be corrected. In general vision systems, there are several known methods to restore the observed image. However, the vignetting effect particularly shown only in panel images cannot be easily restored through traditional methods because it is combined with background non-uniformity due to the unique characteristic of panel. To increase the reliability of image restoration, the vignetting effect should be properly corrected after being separated from image background. Therefore, in this paper we present a new method to analyze and correct the vignetting effect of panel images using principal component analysis. Experimental results for a total of 175 test images showed that the average contrast error of the muras in the distorted images was reduced from 37% to 11% and the mura misidentification rate was decreased from 14.8% to 2.2% by image restoration.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.12-21
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• 2006

In this paper, we propose a technique for extracting attentive objects in images using feature maps, regardless of the complexity of images and the position of objects. The proposed method uses feature maps with edge and color information in order to extract attentive objects. We also propose a reference map which is created by integrating feature maps. In order to create a reference map, feature maps which represent visually attentive regions in images are constructed. Three feature maps including edge map, CbCr map and H map are utilized. These maps contain the information about boundary regions by the difference of intensity or colors. Then the combination map which represents the meaningful boundary is created by integrating the reference map and feature maps. Since the combination map simply represents the boundary of objects we extract the candidate object regions including meaningful boundaries from the combination map. In order to extract candidate object regions, we use the convex hull algorithm. By applying a segmentation algorithm to the area of candidate regions to separate object regions and background regions, real object regions are extracted from the candidate object regions. Experiment results show that the proposed method extracts the attentive regions and attentive objects efficiently, with 84.3% Precision rate and 81.3% recall rate.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.17-26
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• 2006

The error diffusion method is one of the digital halftoning processes that transforms the continuous-tone image to the binary image and the method has the excellent reproduction ability. However the error diffusion method using the permanent threshold has difficulty in proper binarization, so the method has the periodic pattern and is unpleasant to the eye. In this paper, to reduce defects and to binarize properly, we propose the error diffusion method using the adaptive threshold. Depending on the intensity distribution of the input gray scale image, we decided on the adaptive threshold with the average of the intensities. The error diffusion method with the adaptive threshold has the better performance than the existing method and is evaluated with experiments and comparisons.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.165-173
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• 2004

Since the conventional image segmentation methods using mathematical morphology tend to yield over-segmented results, they normally need postprocess which merges small regions to obtain larger ones. To solve this over-segmentation problem without postprocess had to increase size of structuring element used marker extraction. As size of structuring element is very large, edge of region segments incorrectly. Therefore, this paper selectively applies structuring element of mathematical morphology to improve performance of image segmentation and classifies input image into texture region, edge region and simple region using averaged local variance and image gradient. Proposed image segmentation method removes the cause for over-segmentation of image as selectively applies size of structuring element to each region. Simulation results show that proposed method correctly segment for pixel region of similar luminance value and more correctly search texture region and edge region than conventional methods.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.139-146
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• 2011

The color correction is the important process of influencing on the picture quality of the 3D stereoscopic images. Existing colorcorrecting methods handle the processing intensifying a correspondence among a left and right image using a histogram based on any one side. In case of color correction based on a histogram, it is difficult to correct tone of image, because the color temperature is not converted enough. And in this paper, the color temperature correction and color consistency correction is proposed without using histogram. The proposed color correction method by color temperature gives 3 in CIE-${\Delta}E$ for each pixel on the images captured with same illuminants and the conventional gives similar results. For color consistency, the proposed gives 9 in CIE-${\Delta}E$ on the images captured with different illuminants while the conventional gives 18. The proposed method shows better results than the conventional in color consistency processing.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.106-111
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• 2009

Real-time video stabilization technology is used in correction for the camera vibrations of the hand-held camera by hand or fixed camera by external condition. This paper is about the counterplan to cope with the vibration of the movie generated by the large external cause relatively. we use the movie stabilization parameters with the phase correlation method based the DFT to get the displacements of the current frame to the reference frame. we use the kalman filter for the efficient and stable searching works on the phase correlation map and present the proper conditions for the real-time processing through the experiments. We propose the measure to evaluate the capability of the video stabilizer which is the standard deviation of the brightness of the center block. and compare the capability for the video sequences randomly shifted and the jittered video sequences obtained from camera.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.3 s.309
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• pp.67-77
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• 2006

This thesis proposes an effi챠ent method for extracting a prostate volume from 3D ultrasound image by using wavelet transform and SVM classification. In the proposed method, a modulus image for each 2D slice is generated by averaging detail images of horizontal and vertical orientations at several scales, which has the sharpest local maxima and the lowest noise power compared to those of all single scales. Prostate contour vertices are determined accurately using a SVM classifier, where feature vectors are composed of intensity and texture moments investigated along radial lines. Experimental results show that the proposed method yields absolute mean distance of on average 1.89 pixels when the contours obtained manually by an expert are used as reference data.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.100-104
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• 2013

In this paper, we present an optimal threshold setting method for flame detection of infrared thermal image. Conventional infrared flame detection methods used fixed intensity threshold to segment candidate flame regions and further processing is performed to decide correct flame detection. So flame region segmentation step using the threshold is important processing for fire detection algorithm. The threshold should be change in input image depends on camera types and operation conditions. We have analyzed the conventional thresholds composed of fixed-intensity, average, standard deviation, maximum value. Finally, we extracted that the optimal threshold value is more than summation of average and standard deviation, and less than maximum value. it will be enhance flame detection rate than conventional fixed-threshold method.