• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.6
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• pp.26-35
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• 2009

In this paper, we present morphology-based step region extraction and regularized iterative point-spread-function (PSF) estimation methods. The proposed PSF estimation method uses canny edge detector to extract the edge of the input image. We extract feasible vertical and horizontal edges using morphology analysis, such as the hit-or-miss transform. Given extracted edges we estimate the optimal step-response using flattening and normalization processes. The PSF is finally characterized by solving the equation which relates the optimal step response and the 2D isotropic PSF. We shows the restored image by the estimated PSF. The proposed algorithm can be applied a fully digital auto-focusing system without using mechanical focusing parts.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.81-85
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• 1998

점확산 함수(point spread function; PSF)의 정확한 추정은 복원결과가 원 영상에 얼마나 근접할 수 있는가를 결정한다는 점에서 영상처리의 중요한 연구 주제중의 하나가 된다. 본 논문에서는 PSF를 추정하기 위한 알고리즘을 제안하고, 이를 영상복원에 적용한 후 이를 기반으로 디지털 자동초점시스템을 제안한다. 초점불안전 열화시스템을 구현하기 위한 과정은 두 단계로 구성되어 있는데, 즉 입력 영상에서 에지분류를 통한 PSF 추정과, 이를 이용한 영상복원이다. 보다 구체적으로, 입력 영상에 특정 에지가 있는 임의의 크기의 블록을 선정해부면, 그 블록으로부터 자동으로 에지방향이 기저영상을 이용해서 구해지며 확산원의 크기를 추정하여 1차원 단위 계단응답과 영역을 구하여 평균한 후, 2차원 등방성 PSF를 추정한다. 마지막으로 추정된 PSF를 사용하여 복원을 수행함으로써 초점이 맞는 영상을 구한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.7-13
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• 2009

This paper presents a circularly symmetric point spread function (PSF) estimation technique for a fully digital auto-focusing system. The proposed algorithm provides realistic, unsupervised PSF estimation by establishing the relationship between one-dimensional ideal step response and two-dimensional circularly symmetric PSF.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.159-167
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• 2012

Eye tracking technology tracks human eyes movements to understand user's intention. This technology has been improving slowly and should be used for a variety of occasions now. For example, it enables persons with disabilities to operate a computer with their eyes. This article will show a typical implementation of an eye tracking system for persons with disabilities, after introducing the design principles and specific implementation details of an eye tracking system. The article discussed the realization of self-adapting regulation algorithm in detail. The self-adapting algorithm is based on feedback signal controlling the lens movements to realize automatic focus, and to get a clear eyes image. This CCD camera automatic focusing method has self-adapting capacity for changes of light intensity on the external environment. It also avoids the trouble of manual adjustment and improves the accuracy of the adjustment.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.57-62
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• 1999

Estimation of the point spread function (PSF) is one of the main research topic of image processing, because it determines the performance of the auto-focusing system. In this paper, a new algorithm for PSF estimation is proposed, and its application to image restoration is also presented. The procedure for complete realization of the auto-focusing system consists of two steps: PSF estimation based on edge classification, and image restoration using the estimated PSF. More specifically, we divide imput image into multiple small image or block, estimate unit step response and average them on the blocks which contain edge, and estimate 2-dimensional isotropic PSF from the 1 dimensional step response. Finally we obtain in-focused image by using image restoration based on the estimated PSF.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1727-1730
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• 2003

In this paper we propose a sub-pixel point spread function (PSF) estimation method for a fully digital auto-focusing system. We assume that the amount of out-of-focus is the same along the concentric circle. In order to estimate the accurate PSF, sub-pixel information is considered in the proposed PSF estimation procedure. The feasibility of the proposed algorithm is experimentally demonstrated.

• Journal of radiological science and technology
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• v.36 no.2
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• pp.111-122
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• 2013

This study was carried out to reduce patient dose through focus-detector distance, kilovoltage, and a combination of copper filters. In the C, L-spine lateral, Skull AP views were obtained by making changes of 60-100 kV in tube voltage and of 100-200 cm in focus-detector distance and by adding a copper filter when using an auto exposure control device in the digital radiography equipment. The incident dose showed 90 kV, 0.3 mmCu in C-spine lateral with 0.06 mGy under the condition of 200 cm; 100 kV, 0.3 mmCu with 0.40 mGy under the condition of 200 cm and 90 kV 0.3 mmCu in Skull AP with the lowest value of 0.24 mGy under the condition of 140 cm. It was observed that entrance surface dose decreased the most when was increased by 150 cm, 70 kV (C-spine lateral), 81 kV (L-spine lateral). It was also found out that as the between the focus-detector increased in the expansion of the video decreased but the difference was not significant when the distance was 180 cm or more. Skull AP showed the most reduction in the entrance surface dose when the tube voltage was changed by 80 kV, 0.1 mmCu, and 120 cm. Therefore, when using the automatic exposure control device, it is recommended to use the highest tube voltage if possible and to increase focus-detector distance at least by 150~200 cm in wall and 120~140 cm in table in consideration of the radiotechnologist's physical conditions, and to combine 0.1~0.3 mmCu and higher filters. It is thus expected to reduce patient dose by avoiding distortion of images and reducing the entrance surface dose.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.325-332
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• 2014

It is generally accepted that classification accuracy is affected by the number of learning documents, but there are few studies that show how this influences automatic text classification. This study is focused on evaluating the deviation-based classification model which is developed recently for genre-based classification and comparing it to other classification algorithms with the changing number of training documents. Experiment results show that the deviation-based classification model performs with a superior accuracy of 0.8 from categorizing 7 genres with only 21 training documents. This exceeds the accuracy of Bayesian and SVM. The Deviation-based classification model obtains strong feature selection capability even with small number of training documents because it learns subject information within genre while other methods use different learning process.

• Journal of Digital Contents Society
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• v.11 no.1
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• pp.39-45
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• 2010

In this paper we propose a new fast peak searching method using the gradient and present simulation results. The proposed method is a solution to the problem that finds the peak(maximum) of the unimodal function on a finite interval with minimum searching steps. Its main application is the auto-focus in the mobile phone. We propose the three steps to find the peak; periodic search, gradient-based search and detail search. In simulation we generated the Gaussian functions with white noise and have the result of about 8 searching steps and 1.04 errors on average.

• Journal of IKEEE
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• v.14 no.3
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• pp.190-198
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• 2010

This paper describes a auto focus piezo actuator driver IC for portable digital camera. The 80[V] DC voltage is generated by a DC-DC converter and supplied to power of piezo moving control circuit. The voltage of piezo actuator needs range -20[V] to 80[V] proportional to 1[Vp-p] input control voltages. The dimensions and number of external parts are minimized in order to get a smaller hardware size. IIC(Inter-IC) interface logic is designed for data interface and it makes debugging easy, test for mass productions. The power consumption is around 40[mW] with supply voltage of 3.6[V]. This device has been fabricated in a 0.6[um] double poly, triple metal 100[V] BCD MOS process and whole chip size is 1600*1500 [$um^2$].