@conference {12444,
	title = {Face tracking in low resolution videos under illumination variations},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {781 - 784},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In practical face tracking applications, the face region is often small and affected by illumination variations. We address this problem by using a new feature, namely the Gradient-Logarithmic Field (GLF) feature, in the particle filter framework. The GLF feature is robust under illumination variations and the GLF-based tracker does not assume any model for the face being tracked and is effective in low-resolution video. Experimental results show that the proposed GFL-based tracker works well under significant illumination changes and outperforms some of the state-of-the-art algorithms.},
	keywords = {Adaptation models, Computational modeling, Face, face recognition, face tracking, GLF-based tracker, gradient methods, gradient-logarithmic field feature, illumination variations, lighting, low resolution videos, low-resolution, particle filter, particle filter framework, particle filtering (numerical methods), Robustness, tracking, video signal processing, Videos, Visual face tracking},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6116672},
	author = {Zou, W.W.W. and Chellapa, Rama and Yuen, P.C.}
}
@conference {19261,
	title = {Modeling and optimization of dynamic signal processing in resource-aware sensor networks},
	booktitle = {2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)},
	year = {2011},
	month = {2011},
	pages = {449 - 454},
	abstract = {Sensor node processing in resource-aware sensor networks is often critically dependent on dynamic signal processing functionality - i.e., signal processing functionality in which computational structure must be dynamically assessed and adapted based on time-varying environmental conditions, operating constraints or application requirements. In dynamic signal processing systems, it is important to provide flexibility for run-time adaptation of application behavior and execution characteristics, but in the domain of resource-aware sensor networks, such flexibility cannot come with significant costs in terms of power consumption overhead or reduced predictability. In this paper, we review a variety of complementary models of computation that are being developed as part of the dataflow interchange format (DIF) project to facilitate efficient and reliable implementation of dynamic signal processing systems. We demonstrate these methods in the context of resource-aware sensor networks.},
	keywords = {Adaptation models, Aerodynamics, Computational modeling, data flow graphs, dataflow interchange format, Dynamic scheduling, dynamic signal processing, power consumption overhead, Program processors, resource-aware sensor networks, run-time adaptation, Schedules, sensor node processing, Signal processing, Wireless sensor networks},
	author = {Bhattacharyya, Shuvra S. and Plishker,W. and Sane, N. and Chung-Ching Shen and Wu, Hsiang-Huang}
}
