3-D Shape Recovery from Image Focus Using Gray Level Co-Occurrence Matrix

F. Mahmood, U. Munir, Fahad Mehmood and J. Iqbal
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F. Mahmood: GCUF - Government College University of Faisalabad
Fahad Mehmood: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

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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. \textcopyright 2018 Copyright SPIE.

Keywords: 3-D shape recovery; depth map; focus measure.; Gaussian mixture model; gray level co-occurrence matrix; Shape from focus (search for similar items in EconPapers)
Date: 2017-11-13
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Published in 2017-11-15, Nov 2017, Vienna, Austria. ⟨10.1117/12.2309446⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04317817

DOI: 10.1117/12.2309446

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