Dr. Sara Saravi Profile

Dr. Sara Saravi

Lecturer at Loughborough Univ

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Author

Publications (5)

Proceedings Article | 13 May 2015 Paper

The application of machine learning in multi sensor data fusion for activity recognition in mobile device space

Asmaa Marhoubi, Sara Saravi, Eran Edirisinghe

Proceedings Volume 9481, 94810G (2015) https://doi.org/10.1117/12.2177115

KEYWORDS: Sensors, Gyroscopes, Mobile devices, Machine learning, Data fusion, Cell phones, Denoising, Optical sensors, Environmental sensing, Analytical research

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Proceedings Article | 13 May 2015 Paper

Illumination modelling of a mobile device environment for effective use in driving mobile apps

Asmaa Marhoubi, Sara Saravi, Eran Edirisinghe, Helmut Bez

Proceedings Volume 9481, 94810R (2015) https://doi.org/10.1117/12.2177087

KEYWORDS: Light sources, Mathematical modeling, Mobile devices, Modeling, Sensors, Performance modeling, Optical sensors, Environmental sensing, 3D modeling, Machine learning

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SPIE Journal Paper | 20 September 2013

Multiexposure and multifocus image fusion with multidimensional camera shake compensation

Alexis Lluis Gomez, Sara Saravi, Eran Edirisinghe

OE, Vol. 52, Issue 10, 102007, (September 2013) https://doi.org/10.1117/12.10.1117/1.OE.52.10.102007

KEYWORDS: Image fusion, Cameras, Image registration, Transform theory, Image enhancement, High dynamic range imaging, Wavelets, Image processing, Optical engineering, Image quality

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Multiexposure image fusion algorithms are used for enhancing the perceptual quality of an image captured by sensors of limited dynamic range. This is achieved by rendering a single scene based on multiple images captured at different exposure times. Similarly, multifocus image fusion is used when the limited depth of focus on a selected focus setting of a camera results in parts of an image being out of focus. The solution adopted is to fuse together a number of multifocus images to create an image that is focused throughout. A single algorithm that can perform both multifocus and multiexposure image fusion is proposed. This algorithm is a new approach in which a set of unregistered multiexposure/focus images is first registered before being fused to compensate for the possible presence of camera shake. The registration of images is done via identifying matching key-points in constituent images using scale invariant feature transforms. The random sample consensus algorithm is used to identify inliers of SIFT key-points removing outliers that can cause errors in the registration process. Finally, the coherent point drift algorithm is used to register the images, preparing them to be fused in the subsequent fusion stage. For the fusion of images, a new approach based on an improved version of a wavelet-based contourlet transform is used. The experimental results and the detailed analysis presented prove that the proposed algorithm is capable of producing high-dynamic range (HDR) or multifocus images by registering and fusing a set of multiexposure or multifocus images taken in the presence of camera shake. Further, comparison of the performance of the proposed algorithm with a number of state-of-the art algorithms and commercial software packages is provided. In particular, our literature review has revealed that this is one of the first attempts where the compensation of camera shake, a very likely practical problem that can result in HDR image capture using handheld devices, has been addressed as a part of a multifocus and multiexposure image enhancement system.

Proceedings Article | 1 May 2012 Paper

Subjectively optimised multi-exposure and multi-focus image fusion with compensation for camera shake

A. Lluis-Gomez, S. Saravi, E. Edirisinghe

Proceedings Volume 8436, 84360Q (2012) https://doi.org/10.1117/12.922712

KEYWORDS: Image fusion, Image registration, Cameras, Wavelets, Transform theory, Image enhancement, Image quality, High dynamic range imaging, Image processing, Fusion energy

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Proceedings Article | 5 May 2010 Paper

Real-time speaker identification for video conferencing

S. Saravi, I. Zafar, E. Edirisinghe, R. Kalawsky

Proceedings Volume 7724, 77240D (2010) https://doi.org/10.1117/12.854846

KEYWORDS: Laser induced plasma spectroscopy, Video, Facial recognition systems, Detection and tracking algorithms, Edge detection, Sensors, Image processing algorithms and systems, Image registration, Information visualization, Mouth

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