• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.878-887
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• 2005

The passive focusing method finds the in-focus position by analyzing images captured by a camera. In this paper, we propose an efficient passive auto-focusing algorithm for video measuring systems. The sum of modified Laplacian of Gaussian is used to calculate focus values from images and Gaussian curve fitting is applied to estimate the optimal in-focus position. The Proposed method is tested for various objects and illuminations. The test result is compared with other methods to verify accuracy and efficiency of the proposed algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.781-784
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• 2005

홍채 인식은 동공과 흰자위 사이에 존재하는 도넛 모양의 홍채 패턴(Iris pattern)을 이용하여 자신인지 타인인지 판별하는 매우 신뢰도가 높은 생체인식기술 가운데 하나이다. 홍채 인식은 홍채 영상의 홍채 패턴으로부터 홍채 코드(Iris code)를 추출하여 인식하기 때문에 좋은 질의 홍채영상을 취득하는 것은 정확한 홍채 인식을 위해서 매우 중요하다. 이러한 홍채 영상의 질을 결정하는 중요한 요소 가운데 하나가 초점(focus)이다. 초점이 맞지 않아 흐려진(blurring) 영상은 홍채 인식에서 자신임에도 불구하고 타인으로 인식하는 FRR(false reject error)를 증가시킨다. 홍채 인식 시스템의 카메라는 고정 초점 방식과 가변 초점 방식이 있다. 고정 초점 방식은 초점렌즈가 고정되어 있어서 초점이 맞지 않는 영상을 취득할 경우 사용자에게 다시 요구하여 입력받도록 한다. 이는 사용자에게 불편을 초래한다. 가변 초점 방식은 사용자와의 거리를 측정하여 초점렌즈를 움직여서 초점이 잘 맞은 선명한 영상을 얻는다. 하지만, 초점렌즈를 움직이기 위해서 사용자와의 거리를 측정하는 센서와 초점렌즈를 움직이는 모터등과 같은 부가 장비가 필요하다. 따라서 카메라의 부피가 커지고, 가격이 상승하게 되는 문제점이 있다. 그리므로 본 논문은 고정 초점 카메라를 사용하여 부가 장비 없이 홍채 영상 복원 알고리즘을 사용하여 소프트웨어적으로 초점이 맞지 않아 흐려진 영상을 처리하는 방법을 제안한다. 본 논문은 초점값을 이용하여 열화(degradation)의 정도를 판단하였으며, 초점값(focus value)에 따라 점확산함수(point spread function)를 설계하여 홍채영상을 복원하였다.

• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.585-594
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• 2006

Due to the size limit, the voice coil motor (VCM) is adopted in most of the mobile phone camera to control auto-focus instead of step motor. The optical system using the VCM has the property that the focus values are varying even though the same current is induced. It means that an error of the lens position was taken placed due to the characteristics of the VCM. In this paper, a algorithm was proposed to compensate the lens position error using the step size and the search count of each stage. In the proposed algorithm -7 step middle searching stage is inserted the conventional searching algorithm for the fast auto-focus searching and the final searing step size was set to +1 for the precise focus control, respectively. In the experimental results, the focus values was found more fast in the proposed algorithm than the conventional. And more the image quality by the proposed algorithm was superior to that of the conventional.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.80-87
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• 2011

In this paper we present subject region-based auto-focusing algorithm using noise robust focus measure. The proposed algorithm automatically estimates the main subject using entropy and solves the traditional problems with a subject position or high frequency component of background image. We also propose a new focus measure by analyzing the discrete cosine transform coefficients. Experimental results show that the proposed method is more robust to Gaussian and impulse noises than the traditional methods. The proposed algorithm can be applied to Pan-tilt-zoom (PTZ) cameras in the intelligent video surveillance system.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.78-84
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• 2015

Depth estimation is often required for robot vision, 3D modeling, and motion control. Previous method is based on the focus measures which are calculated for a series of image by a single camera at different distance between and object. This method, however, has disadvantage of taking a long time for calculating the focus measure since the mask operation is performed for every pixel in the image. In this paper, we estimates the depth by using the focus measure of the boundary pixels located between the objects in order to minimize the depth estimate time. To detect the boundary of an object consisting of a straight line and a circle, we use the Hough transform and estimate the depth by using the focus measure. We performed various experiments for PCB images and obtained more effective depth estimation results than previous ones.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.777-780
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• 1998

물체의 3차원적인 정보를 복원하는 일은 그 정보의 일련된 이용에 있어서 중요한 문제이다. 이를 위해 여러가지 방법들이 연구되고 있으며, 그 중 shape from focus(SFF) 방법은 영상의 초점이 맞는 렌즈의 위치를 찾아내어 렌즈 공식에 의해 초점이 맞는 부분의 거리 정보를 구할 수 있다. 기존의 이 방법은 초점이 맞았는지의 정도를 계산하기 위한 focus measure 값들을 카메라의 광학축에 수직인 단순한 평면으로 가정하여 그 합이 최대가 되는 위치를 찾아내었다. 이를 개선하기 위해서 focused image surface(FIS) 개념이 연구되었고 그로 인해 더욱 나아진 결과를 얻었다. 물체의 FIS는 카메라 렌즈에 의해 초점이 맞게된 물체의 점들의 집합으로 이루어진 공간상의 면이다. 기하광학에 의해 물체의 모양과 FIS 상이에는 일대일 대응 관계가 있고 FIS의 형태를 구하는것이 결국은 물체의 모양을 복원하는것이다. FIS 개념을 처음 적용할 때는 물체의 모양이 부분적으로 영상 탐지기(image detector)와 같은 평면으로 가정하여 3차원 공간상에서 가능한 모든 방향의 평면에 대한 focus measure를 구하여 그 값이 최대가 되는 렌즈의 위치를 구하였다. 그러나 이러한 방법은 focus measure의 합이 정사각형의 윈도우에서 계산되기 때문에 곡면으로 이루어진 실제 물체에서는 오차르 ㄹ가지게 된다. 본 논문에서는 이와는 달이 평면이 아닌 곡면에 대한 focus measure의 합이 최대가 되는 렌즈의 위치를 구하여 이전의 방법들 보다 정확한 복원이 가능함을 보인다.

• Korean Journal of Optics and Photonics
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• v.22 no.5
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• pp.230-238
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• 2011

There are many types of interchangeable zoom lens in the digital single lens reflex camera and the compact digital still camera system in order to meet various specifications such as the field angle. Thus special cases for which the focus adjustment using only an auto-focus group is not available in the focal point correction (that is, the focus adjustment) of both wide and tele-zoom positions are sometimes generated. In order to make each BFL(back focal length, BFL) coincide at wide and tele-zoom positions with each designed BFL, focus adjustment processes must be performed at least in these two points within the zoom lens system. In this paper, we propose a method of focus adjustment by using the concept of focus sensitivity, and we calculate a limit on focus adjustment distance by means of statistical analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.88-94
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• 2008

A DCT-based energy-ratio measure for autofocus in digital camera is proposed in this paper. This measure, namely AC2DC1 and AC5DC1, determines the sharpness of an image using a ratio between AC and DC energy in the DCT domain. This method is derived from energy analysis of DCT coefficients. Autofocus score calculation method is used to assess the performance of the proposed measure and to compare it with other measures. Experimental results under various conditions verify the robustness of the proposed focus measure for the Gaussian as well as impulsive noises.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.227-236
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• 2018

The focus control mechanism of the multi-purpose camera can be required for the better quality image acquisition. A good image acquisition through the hardware system including the optics and image sensor, has to be processed before the post correction for improvement of image quality. In the case of the high-resolution satellite camera, the focus control is not a necessity, unlike a normal camera due to a fixed optical system, but may be required due to various reasons. Although there is a basic focus control method using a motor for satellite electronic optical camera, a focus control method using thermal control can be a good alternative because of its various advantages in design, installation, operation, contamination, high reliability and etc. In this paper, we describe the design method and implementation results for the focus control mechanism using the temperature sensor and heater installed in the telescope structure. In the proposed focus control method, the measured temperature information is converted into temperature data by the Kalman filter and the converted temperature data are used in PI controller for the thermal focus control.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.3 s.357
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• pp.12-19
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• 2007

This paper proposes the enhanced auto-focus algorithm detecting several objects and selecting the target object. Proposed algorithm first detects some objects distributed in the image using focus measure operator and multi-window and then selects the target object through fuzzy reasoning with three fuzzy membership functions. Implementation can be simple because it only needs image sensor instead of infrared or ultrasonic equipment. Experimental result shows that the proposed algorithm can improve the quality of image by focusing to the target object.