@article {15917,
	title = {Gradient-based Image Recovery Methods from Incomplete Fourier Measurements},
	journal = {IEEE Transactions on Image Processing},
	volume = {PP},
	year = {2012},
	month = {2012///},
	pages = {1 - 1},
	abstract = {A major problem in imaging applications such as Magnetic Resonance Imaging (MRI) and Synthetic Aperture Radar (SAR) is the task of trying to reconstruct an image with the smallest possible set of Fourier samples, every single one of which has a potential time and/or power cost. The theory of Compressive Sensing (CS) points to ways of exploiting inherent sparsity in such images in order to achieve accurate recovery using sub- Nyquist sampling schemes. Traditional CS approaches to this problem consist of solving total-variation minimization programs with Fourier measurement constraints or other variations thereof. This paper takes a different approach: Since the horizontal and vertical differences of a medical image are each more sparse or compressible than the corresponding total-variational image, CS methods will be more successful in recovering these differences individually. We develop an algorithm called GradientRec that uses a CS algorithm to recover the horizontal and vertical gradients and then estimates the original image from these gradients. We present two methods of solving the latter inverse problem: one based on least squares optimization and the other based on a generalized Poisson solver. After a thorough derivation of our complete algorithm, we present the results of various experiments that compare the effectiveness of the proposed method against other leading methods.},
	keywords = {Compressed sensing, Fourier transforms, Image coding, Image edge detection, Image reconstruction, L1{\textendash}minimization, minimization, Noise measurement, OPTIMIZATION, Poisson solver, Sparse recovery, Total variation, TV},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2011.2159803},
	author = {Patel, Vishal M. and Maleh,R. and Gilbert,A. C and Chellapa, Rama}
}
@article {12454,
	title = {A Fast Bilinear Structure from Motion Algorithm Using a Video Sequence and Inertial Sensors},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {33},
	year = {2011},
	month = {2011/01//},
	pages = {186 - 193},
	abstract = {In this paper, we study the benefits of the availability of a specific form of additional information-the vertical direction (gravity) and the height of the camera, both of which can be conveniently measured using inertial sensors and a monocular video sequence for 3D urban modeling. We show that in the presence of this information, the SfM equations can be rewritten in a bilinear form. This allows us to derive a fast, robust, and scalable SfM algorithm for large scale applications. The SfM algorithm developed in this paper is experimentally demonstrated to have favorable properties compared to the sparse bundle adjustment algorithm. We provide experimental evidence indicating that the proposed algorithm converges in many cases to solutions with lower error than state-of-art implementations of bundle adjustment. We also demonstrate that for the case of large reconstruction problems, the proposed algorithm takes lesser time to reach its solution compared to bundle adjustment. We also present SfM results using our algorithm on the Google StreetView research data set.},
	keywords = {3D urban modeling, algorithms, Artificial intelligence, CAMERAS, computer vision., Convergence, fast bilinear structure, Google StreetView research data set, Image Interpretation, Computer-Assisted, Image reconstruction, Image sensors, Image sequences, Imaging, Three-Dimensional, inertial sensors, Information Storage and Retrieval, Linear systems, minimization, MOTION, motion algorithm, Motion estimation, multiple view geometry, Pattern Recognition, Automated, Sensors, SfM equations, sparse bundle adjustment algorithm, structure from motion, Three dimensional displays, vertical direction, Video Recording, video sequence, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2010.163},
	author = {Ramachandran, M. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {12555,
	title = {Compressive wireless arrays for bearing estimation},
	booktitle = {Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on},
	year = {2008},
	month = {2008/04/31/4},
	pages = {2497 - 2500},
	abstract = {Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the sum of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, low dimensional random projections of the microphone signals can be used to determine multiple source bearings by solving an <i>l</i> <sub>1</sub>-norm minimization problem. Field data results are shown where only 10 bits of information is passed from each microphone to estimate multiple target bearings.},
	keywords = {acoustic, algorithms;sparse, arrays;acoustic, arrays;hypothesis, bandwidth;compressive, bearing, Estimation, estimation;signal, estimation;sparse, matrices;vectors;wireless, minimization, networks;, problem;communication, problem;microphone, problems;joint, PROCESSING, processing;array, processing;direction-of-arrival, processing;l<sub>1</sub>-norm, sensor, signal, signals;parameter, Testing, vector;wireless, wireless},
	doi = {10.1109/ICASSP.2008.4518155},
	author = {Cevher, V. and Gurbuz, A.C. and McClellan, J.H. and Chellapa, Rama}
}
@article {15720,
	title = {BFGS with Update Skipping and Varying Memory},
	journal = {SIAM Journal on Optimization},
	volume = {8},
	year = {1998},
	month = {1998///},
	pages = {1060 - 1083},
	abstract = {We give conditions under which limited-memory quasi-Newton methods with exact line searches will terminate in n steps when minimizing n-dimensional quadratic functions. We show that although all Broyden family methods terminate in n steps in their full-memory versions, only BFGS does so with limited-memory. Additionally, we show that full-memory Broyden family methods with exact line searches terminate in at most n + p steps when p matrix updates are skipped. We introduce new limited-memory BFGS variants and test them on nonquadratic minimization problems.},
	keywords = {bfgs, Broyden family, limited-memory, minimization, quasi-Newton, update skipping},
	doi = {10.1137/S1052623496306450},
	url = {http://link.aip.org/link/?SJE/8/1060/1},
	author = {Kolda,Tamara G. and O{\textquoteright}Leary, Dianne P. and Nazareth,Larry}
}
