@conference {12471,
	title = {Automatic target recognition based on simultaneous sparse representation},
	booktitle = {Image Processing (ICIP), 2010 17th IEEE International Conference on},
	year = {2010},
	month = {2010/09//},
	pages = {1377 - 1380},
	abstract = {In this paper, an automatic target recognition algorithm is presented based on a framework for learning dictionaries for simultaneous sparse signal representation and feature extraction. The dictionary learning algorithm is based on class supervised simultaneous orthogonal matching pursuit while a matching pursuit-based similarity measure is used for classification. We show how the proposed framework can be helpful for efficient utilization of data, with the possibility of developing real-time, robust target classification. We verify the efficacy of the proposed algorithm using confusion matrices on the well known Comanche forward-looking infrared data set consisting of ten different military targets at different orientations.},
	keywords = {(artificial, algorithm;feature, based, classification;iterative, classification;learning, Comanche, data, dictionary;matching, extraction;image, forward-looking, infrared, intelligence);military, learning, MATCHING, matrix;dictionary, measure;military, methods;learning, orthogonal, pursuit, pursuit;confusion, recognition;class, recognition;target, representation;feature, representation;sparse, set;automatic, signal, similarity, simultaneous, sparse, supervised, systems;object, target, target;simultaneous, tracking;},
	doi = {10.1109/ICIP.2010.5652306},
	author = {Patel, Vishal M. and Nasrabadi,N.M. and Chellapa, Rama}
}
@conference {12482,
	title = {Robust RVM regression using sparse outlier model},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {1887 - 1894},
	abstract = {Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision problems such as age, head pose, 3D human pose and lighting estimation. However, the presence of outliers in the training dataset makes the estimates from these regression techniques unreliable. In this paper, we propose robust versions of the RVM regression that can handle outliers in the training dataset. We decompose the noise term in the RVM formulation into a (sparse) outlier noise term and a Gaussian noise term. We then estimate the outlier noise along with the model parameters. We present two approaches for solving this estimation problem: (1) a Bayesian approach, which essentially follows the RVM framework and (2) an optimization approach based on Basis Pursuit Denoising. In the Bayesian approach, the robust RVM problem essentially becomes a bigger RVM problem with the advantage that it can be solved efficiently by a fast algorithm. Empirical evaluations, and real experiments on image de-noising and age estimation demonstrate the better performance of the robust RVM algorithms over that of the RVM reg ression.},
	keywords = {3D, analysis;, approach;Gaussian, denoising;computer, denoising;lighting, denoising;regression, estimation;relevance, human, machine;robust, model;Gaussian, noise;basis, noise;computer, outlier, pose;Bayesian, pursuit, regression;sparse, RVM, vector, vision;image},
	doi = {10.1109/CVPR.2010.5539861},
	author = {Mitra, K. and Veeraraghavan,A. and Chellapa, Rama}
}
