@article {317,
	title = {Efficient FFT-accelerated approach to invariant optical-LIDAR registration},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {46},
	year = {2008},
	pages = {3917 - 3925},
	abstract = {<p>This paper presents a fast Fourier transform (FFT)-accelerated approach designed to handle many of the difficulties associated with the registration of optical and light detection and ranging (LIDAR) images. The proposed algorithm utilizes an exhaustive region correspondence search technique to determine the correspondence between regions of interest from the optical image with the LIDAR image over all translations for various rotations. The computational cost associated with exhaustive search is greatly reduced by exploiting the FFT. The substantial differences in intensity mappings between optical and LIDAR images are addressed through local feature mapping transformation optimization. Geometric distortions in the underlying images are dealt with through a geometric transformation estimation process that handles various transformations such as translation, rotation, scaling, shear, and perspective transformations. To account for mismatches caused by factors such as severe contrast differences, the proposed algorithm attempts to prune such outliers using the random sample consensus technique to improve registration accuracy. The proposed algorithm has been tested using various optical and LIDAR images and evaluated based on its registration accuracy. The results indicate that the proposed algorithm is suitable for the multimodal invariant registration of optical and LIDAR images.</p>
},
	keywords = {Fast Fourier Transform, fast Fourier transforms, feature mapping transformation optimization, FFT-accelerated approach, geometric distortions, geometric transformation estimation process, geophysical techniques, image registration, LIDAR images, light detection and ranging image registration, multimodal invariant registration, optical image registration, optical radar},
	issn = {0196-2892},
	doi = {http://dx.doi.org/10.1109/TGRS.2008.2001685},
	author = {A Wong and J Orchard}
}
@inproceedings {648,
	title = {Phase-based methods for Fourier shape matching},
	booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {2004},
	address = {Montreal},
	abstract = {<p>Interest in the problem of shape matching has been recently reignited by the huge proliferation of images on the Internet, particularly huge computer databases containing thousands or millions of images. Although many typical image database algorithms rely on a variety of shading, texture, and colour attributes, there are significant opportunities for the use of shape as a discriminator, particularly for binary or high-contrast images. Fourier shape descriptors have been studied extensively for shape comparison, however the descriptor phases have been mostly neglected - either ignored entirely or treated simplistically. This paper formalizes the use of phase in shape matching, and derives shape discriminators, applicable to both simple and complex shapes.</p>
},
	keywords = {binary images, complex shapes, fast Fourier transforms, Fourier shape descriptors, Fourier shape matching, high-contrast images, huge computer databases, image database algorithms, image matching, image retrieval, Internet, phase-based methods, shape discriminators, simple shapes, very large databases, visual databases},
	doi = {http://dx.doi.org/10.1109/ICASSP.2004.1326572},
	author = {P Fieguth}
}
