Optimal Sampling for Shape from Focus by Using Gaussian Process Regression

Jang, Hoon-Seok; Yun, Guhnoo; Mahmood, Muhammad Tariq; Kang, Min-Koo

doi:10.1109/icce46568.2020.9043150

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Therefore, the curve corresponding to the focus estimator in consecutive images should have the character of a normal distribution. The other authors use this relationship to estimate depth by using Gaussian function interpolation [29]. The impact of interference in real systems makes it necessary to use more robust methods.…”

Section: Methods Of Comparing the Focus Of A Scene Point Between Imagesmentioning

confidence: 99%

Focus Estimation Methods for Use in Industrial SFF Imaging Systems

Sioma,

Lenty

2024

Applied Sciences

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A Shape-From-Focus (SFF) is a three-dimensional imaging method based on focus information. It is not yet widely used for in-line industrial inspection or measurement tasks. The main reasons are the time it takes to capture a 3D image of the inspected product and the presence of interference affecting image quality. This paper compares operators for estimating focus in source images in the scope of their use in constructing an industrial 3D scanner. Interferences were introduced by using additional illuminators and changing the acquisition parameters. The use of industrial-grade cameras, industrial-grade illuminators, and electrically controlled optics are discussed. A novel approach in the research is using an electrically tunable lens to move the position of the image plane during 3D image acquisition. The research was conducted on various surfaces found on typical industrial products. The research showed which focus estimation operators can be applied to SFF imaging within the range of interference considered. It was also confirmed that using the centre of gravity method for scene reconstruction allows for an increase in resolution compared to the maximum method.

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Section: Methods Of Comparing the Focus Of A Scene Point Between Imagesmentioning

confidence: 99%

Focus Estimation Methods for Use in Industrial SFF Imaging Systems

Sioma,

Lenty

2024

Applied Sciences

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“…A popular technique in this category involves using a Gaussian distribution to find the peak in the image stack that represents the highly focused image [3]. Other methods include Gaussian process regression for focus curve fitting [29], weighted least squares regression for focus curve fitting [30], using the gradient of the focus measure curve with the adaptive derivative step to find the best-focused position [31], the phase correlation method that applies a discrete Fourier transform on the focus volume for peak detection [32], and optimizing the focus volume through energy minimization by exploiting the structural similarity between the image sequence and the initially obtained focus volume [33]. Postprocessing techniques refine the depth map obtained from the initial focus volume.…”

Section: Introductionmentioning

confidence: 99%

Directional Ring Difference Filter for Robust Shape-from-Focus

Ashfaq

Mahmood

2023

Mathematics

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In the shape-from-focus (SFF) method, the quality of the 3D shape generated relies heavily on the focus measure operator (FM) used. Unfortunately, most FMs are sensitive to noise and provide inaccurate depth maps. Among recent FMs, the ring difference filter (RDF) has demonstrated excellent robustness against noise and reasonable performance in computing accurate depth maps. However, it also suffers from the response cancellation problem (RCP) encountered in multidimensional kernel-based FMs. To address this issue, we propose an effective and robust FM called the directional ring difference filter (DRDF). In DRDF, the focus quality is computed by aggregating responses of RDF from multiple kernels in different directions. We conducted experiments using synthetic and real image datasets and found that the proposed DRDF method outperforms traditional FMs in terms of noise handling and producing a higher quality 3D shape estimate of the object.

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A simplified approach using deep neural network for fast and accurate shape from focus

Mutahira

Muhammad

et al. 2020

Microscopy Res & Technique

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Three-dimensional shape recovery is an important issue in the field of computer vision. Shape from Focus (SFF) is one of the passive techniques that uses focus information to estimate the three-dimensional shape of an object in the scene. Images are taken at multiple positions along the optical axis of the imaging device and are stored in a stack. In order to reconstruct the three dimensional shape of the object, the best-focused positions are acquired by maximizing the focus curves obtained via application of a focus measure operator. In this article, Deep Neural Network (DNN) is employed to extract the more accurate depth of each object point in the image stack. The size of each image in the stack is first reduced and then provided to the proposed DNN network to aggregate the shape. The initial shape is refined by applying a median filter, and later the reconstructed shape is sized back to original by utilizing bi-linear interpolation. The results are compared with commonly used focus measure operators by employing root mean squared error (RMSE), correlation, and image quality index (Q). Compared to other methods, the proposed SFF method using DNN shows higher precision and low computational time consumption.

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Optimal Sampling for Shape from Focus by Using Gaussian Process Regression

Cited by 6 publications

References 14 publications

Focus Estimation Methods for Use in Industrial SFF Imaging Systems

Focus Estimation Methods for Use in Industrial SFF Imaging Systems

Directional Ring Difference Filter for Robust Shape-from-Focus

A simplified approach using deep neural network for fast and accurate shape from focus

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