• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.77-77
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• 2000

The shape of small or curved object is usually reconstructed using a single camera by moving its lens position to find a sequence of the focused images. Most conventional methods have used a window with fixed shape to test the focus measure, which resulted in a deterioration of accuracy. To solve this problem, this paper proposes a new approach of using a shape adaptive window. It estimates the shape of the object at every step and applies the same shape of window to calculate the focus measure. Focus measure is based on the variance of the pixels inside the window. This paper includes the experimental results.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1007-1008
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• 2008

This paper introduces a new focus measure using Principal Component Analysis (PCA) for Shape from Focus (SFF). A neighborhood consisting of seven pixels is taken and the focus quality is computed over the whole sequence. The experimental results demonstrate effectiveness and robustness of the proposed method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.8-13
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• 2010

Shape form focus (SFF) is a technique that reconstructs 3D shape of an object using image focus. Although many SFF methods have been proposed, there are still notable inaccuracy effects due to noise and non-optimization of image characteristics. In this paper, we propose a noise filter technique for noise reduction and genetic algorithm (GA) for focus measure optimization. The proposed method is analyzed with a statistical criteria such as Root Mean Square Error (RMSE) and correlation.

• Current Optics and Photonics
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• v.8 no.2
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• pp.170-182
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• 2024

The dimensional accuracy and consistency of a dual oil circuit centrifugal fuel nozzle are important for fuel distribution and combustion efficiency in an engine combustion chamber. A point cloud measurement method was proposed to solve the geometric accuracy detection problem for the fuel nozzle. An improved variance focus measure operator was used to extract the depth point cloud. Compared with other traditional sharpness evaluation functions, the improved operator can generate the best evaluation curve, and has the least noise and the shortest calculation time. The experimental results of point cloud slicing measurement show that the best window size is 24 × 24 pixels. In the height measurement experiment of the standard sample block, the relative error is 2.32%, and in the fuel nozzle cone angle measurement experiment, the relative error is 2.46%, which can meet the high precision requirements of a dual oil circuit centrifugal fuel nozzle.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.627-630
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• 1999

For depth extraction from the focus and recovery the shape, determination of criterion function for focus measure and size of the criterion window are very important. However, Texture, illumination, and magnification have an effect on focus measure. For that reason, depth map has a partial high and low peak. In this paper, we propose a depth extraction method from focused images using the error interpolation. This method is modified the error depth into mean value between two normal depth in order to improve the depth map.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.2
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• pp.151-159
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• 2002

The method of accumulating a sequence of focused images is usually used for reconstruction of 3D object\\`s shape. To acquire a focused image, the conventional methods must calculate the focus measures of all pixels resulting in a long measurement time. This paper proposes a new method of reducing the computation time spent for deciding the focused pixels in the input image, which predicts the area in the image to calculate the focus measure based on a priori information on the object to be measured. The proposed algorithm estimates the area to consider in the next measurement based on the focused area in the present measurement. As the focus measure, Laplacian measure was used in this paper and the experiments have shown that the preposed algorithm may significantly reduce the calculation time. Although, as implied, this algorithm can be applied to only simple objects at this stage, advanced representation schemes will eliminate the restrictions on application domain.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.37-43
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• 2003

A faster autofocusing method is proposed. The searching speed of microscope camera is limited by the long focusing time due to too much sampled digital image data. The improvement of autofocusing speed based on the down sampling is discussed analytically and is proved by experiments. The anticipated aliasing is found negligible in shilling rate of focus measure.

• 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.