@conference {13124,
	title = {Human detection using iterative feature selection and logistic principal component analysis},
	booktitle = {Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/05//},
	pages = {1691 - 1697},
	abstract = {We present a fast feature selection algorithm suitable for object detection applications where the image being tested must be scanned repeatedly to detected the object of interest at different locations and scales. The algorithm iteratively estimates the belongness probability of image pixels to foreground of the image. To prove the validity of the algorithm, we apply it to a human detection problem. The edge map is filtered using a feature selection algorithm. The filtered edge map is then projected onto an eigen space of human shapes to determine if the image contains a human. Since the edge maps are binary in nature, Logistic Principal Component Analysis is used to obtain the eigen human shape space. Experimental results illustrate the accuracy of the human detector.},
	keywords = {algorithm;logistic, analysis;edge, analysis;probability;, applications;principal, belongness, component, DETECTION, detection;feature, detection;iterative, detection;principal, extraction;filtering, feature, filtering;human, MAP, methods;object, PCA;object, probability;edge, selection, theory;iterative},
	doi = {10.1109/ROBOT.2008.4543444},
	author = {Abd-Almageed, Wael and Davis, Larry S.}
}
@article {12586,
	title = {Super-Resolution of Face Images Using Kernel PCA-Based Prior},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {888 - 892},
	abstract = {We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements},
	keywords = {analysis;learning-based, analysis;probability;, component, Face, image, method;prior, model;face, principal, probability, recognition;image, reconstruction;image, reconstruction;kernel, resolution;principal, super-resolution;high-resolution},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893346},
	author = {Chakrabarti,Ayan and Rajagopalan, AN and Chellapa, Rama}
}
