@inproceedings {287,
	title = {IceSynth: An image synthesis system for sea-ice segmentation evaluation},
	booktitle = {6th Canadian Conference on Computer and Robot Vision},
	year = {2009},
	month = {05/2009},
	address = {Kelowna, British Columbia, Canada},
	abstract = {<p>An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.</p>
},
	keywords = {automatic sea-ice monitoring, ice classification, ice types, IceSynth, image classification, image segmentation, image synthesis system, image texture, oceanographic techniques, polar regions, probability, probability distribution estimation, pseudo-ground truth data, remote sensing by radar, SAR imagery, sea ice, sea-ice segmentation evaluation, sea-ice structures, sea-ice textures, stochastic processes, stochastic sampling, synthetic aperture radar},
	doi = {http://dx.doi.org/10.1109/CRV.2009.27},
	author = {A Wong and W Zhang and D A. Clausi}
}
@inproceedings {300,
	title = {Stochastic channel prioritization for spectrum sensing in cooperative cognitive radio},
	booktitle = {Consumer Communications and Networking Conference},
	year = {2009},
	abstract = {<p>In this paper, a novel cooperative stochastic channel prioritization algorithm is presented for the purpose of improving spectrum sensing efficiency in cooperative cognitive radio systems. The proposed algorithm achieves the goal by prioritizing the channels for fine sensing based on both local statistics obtained by the cognitive radio as well as long-term spatiotemporal statistics obtained from other cognitive radios. Channel priority is determined in a stochastic manner by performing statistical fusion on the local statistics and statistics from neighboring cognitive radios to obtain a biasing density from which stochastic sampling can be used to identify the likelihood of channel availability. Therefore, the individual cognitive radios collaborate to improve the likelihood of each cognitive radio in obtaining available channels. Simulation results show that the proposed cooperative stochastic channel prioritization algorithm can be used to reduce both sensing overhead and percentage of missed opportunities when implemented in a complimentary manner with existing cooperative cognitive radio systems.</p>
},
	keywords = {cognitive radio, cooperative cognitive radio, long-term spatiotemporal statistic, spectrum sensing efficiency, statistical analysis, statistical fusion, stochastic channel prioritization algorithm, stochastic processes, stochastic sampling},
	url = {http://dx.doi.org/10.1109/CCNC.2009.4784893},
	author = {X Wang and A Wong and P-H Ho}
}
