@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 {14267,
	title = {The statistics of optical flow: implications for the process of correspondence in vision},
	booktitle = {15th International Conference on Pattern Recognition, 2000. Proceedings},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {119-126 vol.1 - 119-126 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper studies the three major categories of flow estimation methods: gradient-based, energy-based, and correlation methods; it analyzes different ways of compounding 1D motion estimates (image gradients, spatio-temporal frequency triplets, local correlation estimates) into 2D velocity estimates, including linear and nonlinear methods. Correcting for the bias would require knowledge of the noise parameters. In many situations, however, these are difficult to estimate accurately, as they change with the dynamic imagery in unpredictable and complex ways. Thus, the bias really is a problem inherent to optical flow estimation. We argue that the bias is also integral to the human visual system. It is the cause of the illusory perception of motion in the Ouchi pattern and also explains various psychophysical studies of the perception of moving plaids. Finally, the implication of the analysis is that flow or correspondence can be estimated very accurately only when feedback is utilized},
	keywords = {Bias, Computer vision, correlation, correlation methods, energy-based method, flow estimation, Frequency estimation, gradient method, gradient methods, Image analysis, Image motion analysis, Image sequences, least squares, least squares approximations, Motion estimation, Nonlinear optics, Optical feedback, optical flow, Optical harmonic generation, Optical noise, Statistics, Visual perception},
	isbn = {0-7695-0750-6},
	doi = {10.1109/ICPR.2000.905288},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
