Three-dimensional shape recovery from focused image surface

Choi, Tae‐Sun; Asif, Muhammad; Yun, Joungil

doi:10.1109/icassp.1999.757539

Cited by 33 publications

(13 citation statements)

References 4 publications

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“…FIS can be improved by a piecewise curved surface rather than piecewise planar approximation. This was proposed by Choi and J. Yun [6]. They estimated the piecewise curved surface by interpolation using second order Lagrange polynomial.…”

Section: Approximation Methodsmentioning

confidence: 99%

Depth Estimation by Finding Best Focused Points Using Line Fitting

Malik

Choi

2008

Lecture Notes in Computer Science

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This paper presents a method for depth estimation using image focus based on the linear regression model. For each pixel, we select two datasets based on the maximum value which is calculated using Laplacian operator. Then linear regression model is used to find lines that approximate these datasets. The best fit lines are found using least squares method. After approximating the two lines, their intersection point is calculated and weights are assigned to calculate the new value for the depth map. The proposed method is compared with four depth estimation algorithms.

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Section: Approximation Methodsmentioning

confidence: 99%

Depth Estimation by Finding Best Focused Points Using Line Fitting

Malik

Choi

2008

Lecture Notes in Computer Science

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“…This estimate is then refined by searching for a planar surface that maximizes the Focus Measure computed over pixels on FIS. (Choi et al, 1999) proposed the approximation of FIS by a piecewise curved surface rather than through the use of a piecewise planar approximation, where the piecewise curved surface is estimated by interpolation using a second order Lagrange polynomial. (Asif & Choi, 2001) used Neural Networks on GLV result to learn the shape of FIS by optimizing the Focus Measure over small 3D windows, as due to their nonlinear characteristics, neural networks can be used to approximate any arbitrary function.…”

Section: Approximation Methodsmentioning

confidence: 99%

“…(Subbarao & Choi, 1995) used energy of Laplacian as the focus measure and used window of size 15x15 to compute the focus measure to implement the Focused Image Surface method. For initial estimate, (Choi et al, 1999) used Sum of Modified Laplacian as the focus measure for curved window technique and used window of size 15x15. (Asif & Choi, 2001) used Gray Level Variance (GLV) as the focus measure for their Neural Network based method with window of size 7x7.…”

Section: Illumination and Window Sizementioning

confidence: 99%

Consideration of Various Noise Types and Illumination Effects for 3D Shape Recovery

Malik¹,

Choi²

2008

Brain, Vision and AI

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“…Later, Choi and Yun (2000) proposed the approximation of FIS by a piecewise curved surface rather than piecewise planar approximation to improve the accuracy for the objects with complex geometry. The piecewise curved surface was estimated by interpolation using second order Lagrange polynomial.…”

Section: Shape Refinement Techniquesmentioning

confidence: 99%

Accurate shape from focus based on focus adjustment in optical microscopy

Shim

Malik

Choi

2008

Microscopy Res & Technique

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Optical microscopy allows a magnified view of the sample while decreasing the depth of focus. Although the acquired images from limited depth of field have both blurred and focused regions, they can provide depth information. The technique to estimate the depth and 3D shape of an object from the images of the same sample obtained at different focus settings is called shape from focus (SFF). In SFF, the measure of focus--sharpness--is the crucial part for final 3D shape estimation. The conventional methods compute sharpness by applying focus measure operator on each 2D image frame of the image sequence. However, such methods do not reflect the accurate focus levels in an image because the focus levels for curved objects require information from neighboring pixels in the adjacent frames too. To address this issue, we propose a new method based on focus adjustment which takes the values of the neighboring pixels from the adjacent image frames that have approximately the same initial depth as of the center pixel and then it re-adjusts the center value accordingly. Experiments were conducted on synthetic and microscopic objects, and the results show that the proposed technique generates better shape and takes less computation time in comparison with previous SFF methods based on focused image surface (FIS) and dynamic programming.

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Three-dimensional shape recovery from focused image surface

Cited by 33 publications

References 4 publications

Depth Estimation by Finding Best Focused Points Using Line Fitting

Depth Estimation by Finding Best Focused Points Using Line Fitting

Consideration of Various Noise Types and Illumination Effects for 3D Shape Recovery

Accurate shape from focus based on focus adjustment in optical microscopy

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