13 April 2018 3D shape recovery from image focus using Gabor features
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961F (2018) https://doi.org/10.1117/12.2309440
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
Recovering an accurate and precise depth map from a set of acquired 2-D image dataset of the target object each having different focus information is an ultimate goal of 3-D shape recovery. Focus measure algorithm plays an important role in this architecture as it converts the corresponding color value information into focus information which will be then utilized for recovering depth map. This article introduces Gabor features as focus measure approach for recovering depth map from a set of 2-D images. Frequency and orientation representation of Gabor filter features is similar to human visual system and normally applied for texture representation. 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, Jawad Mahmood, Jawad Mahmood, Ayesha Zeb, Ayesha Zeb, Javaid Iqbal, Javaid Iqbal, } "3D shape recovery from image focus using Gabor features", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961F (13 April 2018); doi: 10.1117/12.2309440; https://doi.org/10.1117/12.2309440
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