• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.201-207
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• 2015

The smartphone with camera can easily generate an image instead of a scanner. However the document image through a smartphone can have distortions related rotation or perspective. In this paper, we proposed a method to generate the document image in that distortions are reduced from the captured document image through a smartphone. For this, the original document image through a smartphone is preprocessed using the MSER-b technique to reduce the light effect. Then, the text area contour is extracted using the characteristics of the document image. Lastly, rotation or perspective distortions are reduced using the extracted text area contour. For experiments, the proposed method is compared two other products. Through experiments, we show that the distortions within the captured document image through smartphone can be effectively reduced.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.342-347
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• 2018

We proposed a fast detection of copy-move image forgery using four step search algorithm in the spatial domain. In the four-step search algorithm, the search area is 21 (-10 ~ +10), and the number of pixels to be scanned is 33. Our algorithm reduced computational complexity more than conventional copy move image forgery methods. The proposed method reduced 92.34 % of computational complexity compare to exhaustive search algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1244-1248
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• 2005

In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.91-95
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• 2011

In this paper, we present an improved multi-scale gradient algorithm. The proposed algorithm works the effectively handling of both step and blurred edges. In the proposed algorithm, the image sharpening operator is sharpening the edges and contours of the objects. This operation gives an opportunity to get noise reduced image and step edged image. After that, multi-scale gradient operator works on noise reduced image in order to get a gradient image. The gradient image is segmented by watershed transform. The approach of region merging is used after watershed transform. The region merging is carried out according to the region area and region homogeneity. The region number of the proposed algorithm is 36% shorter than that of the existing algorithm because the proposed algorithm produces a few irrelevant regions. Moreover, the computational time of the proposed algorithm is relatively fast in comparison with the existing one.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.5
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• pp.327-334
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• 1991

This paper proposes an efficient method of implementing the generalized Hough transform (GHT), which has been hindered by an excessive computing load and a large memory requirement. The conventional algorithm requires a parameter space of 4 dimensions in detection a rotated, scaled, and translated object in an input image. Prior to the application of GHT to the input image, the proposed method determines the angle of rotation and the scaling factor of the test image using the proportion of the edge components between the reference image and test image. With the rotation angle and the scaling factor already determined, the parameter spaceis to be reduced to a simple array of 2 dimensions by applying the unit GHT only one time. The experiments with the image of airplanes reveal that both of the computing time and the requires memory size are reduced by 95 percent, without any degradatationof accuracy, compared with the conventional GHT algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.658-663
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• 1999

2D sliced CT images hardly express the human disease in a space. This space expression can be reconstructed into 3D image by piling up the CT sliced image in succession. In medical image, in order to get the reconstructed 3D images, expensive system or much calculation time is needed. But by changing the method of reconstruction procedure and limit the range, the reconstruction time could be reduced. In this study, to reduce the processing time and memory, we suggested a method of interpolation and ray casting processing at the same time in a limited range. Such a limited range processing have advantages that we could reduce the unnecessary interpolation and ray casting. Through a experiment, it is founded that the reconstruction time and the memory was much reduced.

• Journal of Sensor Science and Technology
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• v.27 no.6
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• pp.362-367
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• 2018

A complementary metal oxide semiconductor (CMOS) binary image sensor is proposed for low-power and low-noise operation. The proposed binary image sensor has the advantages of reduced power consumption and fixed pattern noise (FPN). A gate/body-tied (GBT) p-channel metal-oxide-semiconductor field-effect transistor (PMOSFET)-type photodetector is used as the proposed CMOS binary image sensor. The GBT PMOSFET-type photodetector has a floating gate that amplifies the photocurrent generated by incident light. Therefore, the sensitivity of the GBT PMOSFET-type photodetector is higher than that of other photodetectors. The proposed CMOS binary image sensor consists of a pixel array with $394(H){\times}250(V)$ pixels, scanners, bias circuits, and column parallel readout circuits for binary image processing. The proposed CMOS binary image sensor was analyzed by simulation. Using the dynamic comparator, a power consumption reduction of approximately 99.7% was achieved, and this performance was verified by the simulation by comparing the results with those of a two-stage comparator. Also, it was confirmed using simulation that the FPN of the proposed CMOS binary image sensor was successfully reduced by use of the double sampling process.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.725-728
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• 1988

Image restoration technique using dual sensor is presented in this paper. Digital Radiography image (1024xlO24) is obtained by conventional resolution sensor. We also obtain local DR image data by high resolution sensor. Two dimensional maximum entropy power spectrum estimation (2-D ME PSE) is applied to low resolution image and high resolution image for the purpose of the power spectrum estimation of each image. A class of linear algebraic restoration filter, parametric projection filter (PPF), is derived from the power spectrums of each image. It is shown that the noise energy may be considerably reduced through the PPF.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.291-295
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• 2008

In this paper, we propose an input image reduction method to solve the problems of Radon transform which is a line structure analysis tool to correct a rotated object through a vision system. First we extract an object image removed background from the input image. Then we also select a reduced object image as a final input mage of Radon transform from the object image by considering slope. Finally we extract a rotated angle by using Radon transform with the final input image and correct the rotated object with the angle. In experimental results, we could improve the process time of about 64%, reduce the memory space of about 18% and make progress the line detection rate of about 18%.

• Journal of Biosystems Engineering
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• v.25 no.5
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• pp.409-414
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• 2000

A machine vision system was built to obtain single stationary image from an egg. This system includes a CCD camera, an image processing board and a lighting system. A computer program was written to acquire, enhance and get histogram from an image. To minimize the evaluation time, the artificial neural network with the histogram of the image was used for eggshell evaluation. Various artificial neural networks with different parameters were trained and tested. The best network(64-50-1 and 128-10-1) showed an accuracy of 87.5% in evaluating eggshell. The comparison test for the elapsed processing time per an egg spent by this method(image processing and artificial neural network) and by the processing time per an egg spent by this method(image processing and artificial neural network) and by the previous method(image processing only) revealed that it was reduced to about a half(5.5s from 10.6s) in case of cracked eggs and was reduced to about one-fifth(5.5s from 21.1s) in case of normal eggs. This indicates that a fast eggshell evaluation system can be developed by using machine vision and artificial neural network.