Proceedings ArticleDOI
3D shape recovery from image focus using gray level co-occurrence matrix
Fahad Mahmood,Umair Munir,Fahad Mehmood,Javaid Iqbal +3 more
- Vol. 10696, pp 1069628
TLDR
Gray Level Co-occurrence Matrix is proposed along with its statistical features for computing the focus information of the image dataset and is considered as superior alternative to most of recently proposed 3-D shape recovery approaches.Abstract:
Recovering a precise and accurate 3-D shape of the target object utilizing robust 3-D shape recovery algorithm is an ultimate objective of computer vision community. Focus measure algorithm plays an important role in this architecture which convert the color values of each pixel of the acquired 2-D image dataset into corresponding focus values. After convolving the focus measure filter with the input 2-D image dataset, a 3-D shape recovery approach is applied which will recover the depth map. In this document, we are concerned with proposing Gray Level Co-occurrence Matrix along with its statistical features for computing the focus information of the image dataset. The Gray Level Co-occurrence Matrix quantifies the texture present in the image using statistical features and then applies joint probability distributive function of the gray level pairs of the input image. Finally, we quantify the focus value of the input image using Gaussian Mixture Model. Due to its little computational complexity, sharp focus measure curve, robust to random noise sources and accuracy, it is considered as superior alternative to most of recently proposed 3-D shape recovery approaches. This algorithm is deeply investigated on real image sequences and synthetic image dataset. The efficiency of the proposed scheme is also compared with the state of art 3-D shape recovery approaches. Finally, by means of two global statistical measures, root mean square error and correlation, we claim that this approach –in spite of simplicity generates accurate results.read more
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References
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Journal ArticleDOI
Shape from focus
Shree K. Nayar,Y. Nakagawa +1 more
TL;DR: The shape from focus method presented here uses different focus levels to obtain a sequence of object images and suggests shape fromfocus to be an effective approach for a variety of challenging visual inspection tasks.
Journal ArticleDOI
Analysis of focus measure operators for shape-from-focus
TL;DR: A methodology to compare the performance of different focus measure operators for shape-from-focus is presented and applied and the selected operators have been chosen from an extensive review of the state-of-the-art.
Proceedings ArticleDOI
Depth from focus with your mobile phone
TL;DR: This work introduces the first depth from focus (DfF) method capable of handling images from mobile phones and other hand-held cameras, solving a novel uncalibrated DfF problem and aligning the frames to account for scene parallax.
Journal ArticleDOI
A heuristic approach for finding best focused shape
Muhammad Ahmad,Tae-Sun Choi +1 more
TL;DR: A fast heuristic model based on dynamic programming is proposed for the search of FIS shape, which searches the optimal focus measure in the whole image volume, instead of the small volume as adopted in previous methods.
Journal ArticleDOI
Shape from focus using multilayer feedforward neural networks
Muhammad Asif,Tae-Sun Choi +1 more
TL;DR: In this article, a shape-from-focus (SFF) method is proposed based on representation of 3D FIS in terms of neural network weights, where the neural networks are trained to learn the shape of the FIS that maximizes the focus measure.
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