13 April 2018 3D shape recovery from image focus using gray level co-occurrence matrix
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 1069628 (2018) https://doi.org/10.1117/12.2309446
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
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.
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Fahad Mahmood, Fahad Mahmood, Umair Munir, Umair Munir, Fahad Mehmood, Fahad Mehmood, Javaid Iqbal, Javaid Iqbal, } "3D shape recovery from image focus using gray level co-occurrence matrix", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 1069628 (13 April 2018); doi: 10.1117/12.2309446; https://doi.org/10.1117/12.2309446

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