@article {19299,
	title = {Utilizing Hierarchical Multiprocessing for Medical Image Registration},
	journal = {IEEE Signal Processing Magazine},
	volume = {27},
	year = {2010},
	month = {2010},
	pages = {61 - 68},
	abstract = {This work discusses an approach to utilize hierarchical multiprocessing in the context of medical image registration. By first organizing application parallelism into a domain-specific taxonomy, an algorithm is structured to target a set of multicore platforms.The approach on a cluster of graphics processing units (GPUs) requiring the use of two parallel programming environments to achieve fast execution times is demonstrated.There is negligible loss in accuracy for rigid registration when employing GPU acceleration, but it does adversely effect our nonrigid registration implementation due to our usage of a gradient descent approach.},
	keywords = {Acceleration, application parallelism, Biomedical imaging, domain-specific taxonomy, GPU acceleration, gradient descent approach, Graphics processing unit, hierarchical multiprocessing, image registration, Magnetic resonance imaging, Medical diagnostic imaging, medical image processing, medical image registration, multicore platform set, Multicore processing, PARALLEL PROCESSING, parallel programming, Robustness, Signal processing algorithms, Ultrasonic imaging},
	isbn = {1053-5888},
	author = {Plishker,W. and Dandekar,O. and Bhattacharyya, Shuvra S. and Shekhar,R.}
}
@conference {18470,
	title = {Virtual audio system customization using visual matching of ear parameters},
	booktitle = {16th International Conference on Pattern Recognition, 2002. Proceedings},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {1003- 1006 vol.3 - 1003- 1006 vol.3},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Applications in the creation of virtual auditory spaces (VAS) and sonification require individualized head related transfer functions (HRTFs) for perceptual fidelity. HRTFs exhibit significant variation from person to person due to differences between their pinnae, and their body sizes. We propose and preliminarily implement a simple HRTF customization based on the use of a published database of HRTFs (Algazi et al., 2001) that also contains geometrical measurements of subject pinnae. We measure some of these features via simple image processing, and select the HRTF that has features most closely corresponding to the individual{\textquoteright}s features. This selection procedure is implemented along with the virtual auditory system described in (Zotkin et al., 2002), and listener tests conducted comparing the customized HRTF and a fixed HRTF. Despite the simplicity of the method, tests reveal average improvement in localization accuracy of about 25 percent, though performance improvement varies with source location and individuals.},
	keywords = {acoustic signal processing, Audio systems, Auditory system, Computer vision, database, Ear, ear parameter matching, geometrical measurements, Head, head related transfer functions, HRTF customization, Image databases, IMAGE PROCESSING, medical image processing, performance improvement, Position measurement, sonification, Spatial databases, System testing, Transfer functions, virtual audio system customization, virtual auditory spaces, virtual auditory system, visual matching},
	isbn = {0-7695-1695-X},
	doi = {10.1109/ICPR.2002.1048207},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S. and Mohan,A. and Raykar,V.}
}
