3D Shape Recovery by Aggregating 3D Wavelet Transform-Based Image Focus Volumes Through 3D Weighted Least Squares

Ali, Usman; Mahmood, Muhammad Tariq

doi:10.1007/s10851-019-00918-8

Cited by 17 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the weighted L1 (Kim et al, 2017) scheme, the same standard deviation volume u p was incorporated as the guidance volume. We also compared the proposed method with two related approaches (Ali et al, 2019; Ali & Mahmood, 2020) that have been proposed recently in SFF. These approaches minimize weighted L2-based energy function, and so does the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…This technique employs the image sequence as the guidance volume while doing regularization. In Ali & Mahmood (2020) (WL2-FVA), 3D discrete wavelet transform is applied on image volume and a number of focus volumes are obtained at different scales. Then, the weighted least squares scheme is applied on them for cross-scale aggregation.…”

Section: Resultsmentioning

confidence: 99%

“…Jeon et al (2019) proposed an efficient FM operator that has a ring-and-disk-like structure and, thus, has properties of local and non-local FMs. Recently, Ali et al (2019) and Ali & Mahmood (2020) have proposed to improve the focus volumes through regularization. Mutahira et al (2020) employed a simple deep neural network (DNN) to aggregate 3D shape.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus

Ali

Mahmood

2021

Microsc Microanal

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus

Ali

Mahmood

2021

Microsc Microanal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Gradient-based operators take the first derivative of an image to compute the focus value, [13]- [16]. Laplacian-based operators measure the focus by calculating the second derivative of an image, [15]- [19]. Wavelet-based operators consider the wavelet transform to describe an image's spatial and frequency contents, [15], [20], [21], whereas, statisticalbased operators take into account different statistics of an image to measure the focus, [22], [23], [19], [16].…”

Section: Related Workmentioning

confidence: 99%

Focus Measurement in Color Space for Shape From Focus Systems

et al. 2021

View full text Add to dashboard Cite

Shape from Focus (SFF) has been studied extensively in computer vision for 3D shape and depth recovery. The first stage in SFF methods is to compute the focus value of every pixel by converting the colored images into gray scale and then apply the focus measure operator. Converting colored values in the images into gray scale values may lead to imprecise mapping of pixels with different colored values onto the same gray scale value, this affects the overall accuracy of the system. In a colored image, the focused pixels maintain a considerable color difference from their neighboring pixels as compared to the defocused ones, which are blended into their neighborhood. This article presents an alternative method to measure the degree of focus by directly processing colored images. The color differences of the neighbor pixels with respect to the central pixel are obtained and summed together, this is followed by calculating their spread. The sum and the spread are combined to measure the degree of focus of the pixel in consideration. The proposed focus measure is then used for shape recovery of various simulated and real objects and is compared with previous techniques. The comparison results show the proposed method has the highest correlation and smallest RMSE values confirming the effectiveness of using color images for shape recovery.

show abstract

“…Discrete Cosine Transform ( DCT )‐based FMs estimate the focus level by using DCT coefficients (Shen and Chen (2006); Lee, Yoo, Kumar, and Kim (2009)). Three dimensional modification focus measurements have also been proposed by (Yan, Hu, Qian, Qiao, and Zhang (2020a); Ali and Mahmood (2020)). Other operators that cannot be related to any category listed above are grouped into a miscellaneous FMs category (Sun, Duthaler, and Nelson (2004); Santos et al (1997)).…”

Section: Introductionmentioning

confidence: 99%

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

Mutahira

Muhammad

et al. 2020

Microscopy Res & Technique

View full text Add to dashboard Cite

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.

show abstract

3D Shape Recovery by Aggregating 3D Wavelet Transform-Based Image Focus Volumes Through 3D Weighted Least Squares

Cited by 17 publications

References 30 publications

Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus

Depth Hypotheses Fusion through 3D Weighted Least Squares in Shape from Focus

Focus Measurement in Color Space for Shape From Focus Systems

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

Contact Info

Product

Resources

About