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Image Mosaicing based on Neural Networks

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 1
Year of Publication: 2016
Authors:
Tamer A. A. Alzohairy, Emad El-Dein H. A. Masameer, Mahmoud S. Sayed
10.5120/ijca2016908338

Tamer A A Alzohairy, Emad El-Dein H A Masameer and Mahmoud S Sayed. Article: Image Mosaicing based on Neural Networks. International Journal of Computer Applications 136(1):25-31, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Tamer A. A. Alzohairy and Emad El-Dein H. A. Masameer and Mahmoud S. Sayed},
	title = {Article: Image Mosaicing based on Neural Networks},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {136},
	number = {1},
	pages = {25-31},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

The main concept behind image mosaic is image registration. In image mosaicing several overlapping images are assembled in order to constitute one panoramic image. In this paper a new feature-based approach will be presented for automated image to image registration and mosaicing. The proposed method is implemented on real complex images. The proposed method is based on five main steps. First, the Harris algorithm is used to extract the feature points in the reference and sensed images. Second, feature matching is established using the Euclidean distance of the signature vectors obtained using pulse coupled neural network (PCNN). Third, transformation parameters are obtained using the least-square rule based on general affine transformation. Fourth, the image resampling and transformation are performed using bilinear interpolation to get the registered image. Finally, the mosaicing image is obtained. Experimental results show that the proposed algorithm shows excellent results when applied and tested on real complex images.

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Keywords

Registration, Mosaicing, Reference image, Sensed image, Affine transformation, Pulse Coupled Neural Network (PCNN) and blending.