@article {218,
	title = {An adaptive Monte Carlo approach to phase-based multimodal image registration},
	journal = {IEEE Transactions on Information Technology in Biomedicine},
	volume = {14},
	year = {2010},
	pages = {173 - 179},
	abstract = {<p>In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.</p>
},
	keywords = {adaptive Monte Carlo scheme, adaptive sampling approach, Algorithms, Computer-Assisted, Diagnostic Imaging, geometric transformation, Humans, image interpretation, image phase moments, image processing, image registration, iterative methods, Least-Squares Analysis, medical image processing, Monte Carlo Method, Monte Carlo methods, multidimensional sampling distribution, multidimensional solution space, multiresolution algorithm, optimal image alignment, Pearson type VII error, phase based multimodal image registration, random solution candidate generation, sampling methods},
	issn = {1089-7771},
	doi = {http://dx.doi.org/10.1109/TITB.2009.2035693},
	author = {A Wong}
}
@inproceedings {368,
	title = {Robust invariant descriptor for symbol-based image recognition and retrieval},
	booktitle = {1st IEEE International Conference on Semantic Computing},
	year = {2007},
	abstract = {<p>This paper presents a robust invariant descriptor for symbol-based image recognition and retrieval. A modified Hough-based Transform is used to extract parameter space information (i.e., position data and angular data) from a symbol image to derive an invariant descriptor. The proposed descriptor provides a compact representation of a symbol image that can be evaluated efficiently. The extracted descriptor is highly robust against geometric transformations such as translation, rotation, reflection, and scaling, and image degradation. A series of experiments were conducted using a set of architectural and engineering symbols subjected to geometric transformations and image degradation. The experimental results clearly show that the proposed descriptor can be used effectively for symbol recognition and retrieval.</p>
},
	keywords = {content-based retrieval, Feature Extraction, geometric transformation, Hough transforms, Hough-based transform, image degradation, image recognition, image representation, image retrieval, robust invariant descriptor, symbol-based image recognition},
	doi = {http://dx.doi.org/10.1109/ICSC.2007.24},
	author = {A Wong and W Bishop}
}
