@conference {14054,
	title = {A boundary element method for electromagnetic scattering by multiple cylinders},
	booktitle = {Antennas and Propagation Society International Symposium, 2003. IEEE},
	volume = {3},
	year = {2003},
	month = {2003/06//},
	pages = {516 - 519 vol.3 - 516 - 519 vol.3},
	abstract = {An integral equation approach is derived for an electromagnetic scattering from M multiple cylinders. The problem is two-dimensional and the integral equation is solved using the Nystrom method. We give numerical examples that illustrate the algorithm.},
	keywords = {algorithm;, boundary, boundary-elements, cylinders;, electromagnetic, element, equations;, Fredholm, integral, method;, methods;, multiple, Nystrom, scattering;, two-dimensional, type, wave},
	doi = {10.1109/APS.2003.1219899},
	author = {Seydou,F. and Duraiswami, Ramani and Seppanen,T.}
}
@conference {12718,
	title = {Rank constrained recognition under unknown illuminations},
	booktitle = {Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on},
	year = {2003},
	month = {2003/10//},
	pages = {11 - 18},
	abstract = {Recognition under illumination variations is a challenging problem. The key is to successfully separate the illumination source from the observed appearance. Once separated, what remains is invariant to illuminant and appropriate for recognition. Most current efforts employ a Lambertian reflectance model with varying albedo field ignoring both attached and cast shadows, but restrict themselves by using object-specific samples, which undesirably deprives them of recognizing new objects not in the training samples. Using rank constraints on the albedo and the surface normal, we accomplish illumination separation in a more general setting, e.g., with class-specific samples via a factorization approach. In addition, we handle shadows (both attached and cast ones) by treating them as missing values, and resolve the ambiguities in the factorization method by enforcing integrability. As far as recognition is concerned, a bootstrap set which is just a collection of two-dimensional image observations can be utilized to avoid the explicit requirement that three-dimensional information be available. Our approaches produce good recognition results as shown in our experiments using the PIE database.},
	keywords = {albedo, approach;, constrained, database;, databases;, decomposition;, factorization, field;, illumination, image;, information;, Lambertian, lighting;, model;, object, object-specific, PIE, rank, recognition;, reflectance, samples;, singular, three-dimensional, two-dimensional, value, variations;, visual},
	doi = {10.1109/AMFG.2003.1240818},
	author = {Zhou, S. and Chellapa, Rama}
}
@article {12748,
	title = {Optimal edge-based shape detection},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/11//},
	pages = {1209 - 1227},
	abstract = {We propose an approach to accurately detecting two-dimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-dimensional (1-D) optimal step edge operator, which minimizes both the noise power and the mean squared error between the input and the filter output. This operator is found to be the derivative of the double exponential (DODE) function, originally derived by Ben-Arie and Rao (1994). We define an operator for shape detection by extending the DODE filter along the shape{\textquoteright}s boundary contour. The responses are accumulated at the centroid of the operator to estimate the likelihood of the presence of the given shape. This method of detecting a shape is in fact a natural extension of the task of edge detection at the pixel level to the problem of global contour detection. This simple filtering scheme also provides a tool for a systematic analysis of edge-based shape detection. We investigate how the error is propagated by the shape geometry. We have found that, under general assumptions, the operator is locally linear at the peak of the response. We compute the expected shape of the response and derive some of its statistical properties. This enables us to predict both its localization and detection performance and adjust its parameters according to imaging conditions and given performance specifications. Applications to the problem of vehicle detection in aerial images, human facial feature detection, and contour tracking in video are presented.},
	keywords = {1D, 2D, aerial, analysis;, boundary, conditions;, contour, cross, detection;, DODE, double, edge, edge-based, error, error;, exponential, extraction;, facial, feature, filter, filter;, Filtering, function;, geometry;, global, human, images;, imaging, localization, mean, methods;, NOISE, operator;, optimal, optimisation;, output;, performance;, pixel;, power;, propagation;, properties;, section;, SHAPE, square, squared, statistical, step, theory;, tracking;, two-dimensional, vehicle, video;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.800896},
	author = {Moon, H. and Chellapa, Rama and Rosenfeld, A.}
}
@article {15596,
	title = {Approximating large convolutions in digital images},
	journal = {Image Processing, IEEE Transactions on},
	volume = {10},
	year = {2001},
	month = {2001/12//},
	pages = {1826 - 1835},
	abstract = {Computing discrete two-dimensional (2-D) convolutions is an important problem in image processing. In mathematical morphology, an important variant is that of computing binary convolutions, where the kernel of the convolution is a 0-1 valued function. This operation can be quite costly, especially when large kernels are involved. We present an algorithm for computing convolutions of this form, where the kernel of the binary convolution is derived from a convex polygon. Because the kernel is a geometric object, we allow the algorithm some flexibility in how it elects to digitize the convex kernel at each placement, as long as the digitization satisfies certain reasonable requirements. We say that such a convolution is valid. Given this flexibility we show that it is possible to compute binary convolutions more efficiently than would normally be possible for large kernels. Our main result is an algorithm which, given an m times;n image and a k-sided convex polygonal kernel K, computes a valid convolution in O(kmn) time. Unlike standard algorithms for computing correlations and convolutions, the running time is independent of the area or perimeter of K, and our techniques do not rely on computing fast Fourier transforms. Our algorithm is based on a novel use of Bresenham{\textquoteright}s (1965) line-drawing algorithm and prefix-sums to update the convolution incrementally as the kernel is moved from one position to another across the image},
	keywords = {2D, algorithm;binary, approximation;mathematical, convolution, convolution;binary, convolutions;Bresenham{\textquoteright}s, convolutions;convex, convolutions;geometric, images;discrete, kernel;convex, kernel;digital, line-drawing, morphology;, morphology;approximation, object;image, polygonal, processing;large, processing;mathematical, theory;convolution;image, two-dimensional},
	isbn = {1057-7149},
	doi = {10.1109/83.974567},
	author = {Mount, Dave and Kanungo,T. and Netanyahu,N. S and Piatko,C. and Silverman,R. and Wu,A. Y}
}
