@conference {12706,
	title = {3D model refinement using surface-parallax},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 285-8 vol.3 - iii - 285-8 vol.3},
	abstract = {We present an approach to update and refine coarse 3D models of urban environments from a sequence of intensity images using surface parallax. This generalizes the plane-parallax recovery methods to surface-parallax using arbitrary surfaces. A coarse and potentially incomplete depth map of the scene obtained from a digital elevation map (DEM) is used as a reference surface which is refined and updated using this approach. The reference depth map is used to estimate the camera motion and the motion of the 3D points on the reference surface is compensated. The resulting parallax, which is an epipolar field, is estimated using an adaptive windowing technique and used to obtain the refined depth map.},
	keywords = {3D, adaptive, arbitrary, camera, coarse, compensation;, Computer, DEM;, depth, digital, ELEVATION, environments;, epipolar, estimation;, field;, image, incomplete, INTENSITY, map;, model, MOTION, parallax;, plane-parallax, reconstruction;, recovery;, refinement;, sequence;, sequences;, surface, surfaces;, urban, vision;, windowing;},
	doi = {10.1109/ICASSP.2004.1326537},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {12687,
	title = {Robust ego-motion estimation and 3D model refinement using depth based parallax model},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {4},
	year = {2004},
	month = {2004/10//},
	pages = {2483 - 2486 Vol. 4 - 2483 - 2486 Vol. 4},
	abstract = {We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse, noisy and partial depth map using a depth based parallax model and brightness derivatives extracted from an image pair. Given a coarse, noisy and partial depth map acquired by a range-finder or obtained from a Digital Elevation Map (DFM), we first estimate the ego-motion by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth map estimate, motion of the 3D points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. Instead of assuming a smooth parallax field or locally smooth depth models, we locally model the parallax magnitude using the depth map, formulate the problem as a generalized eigen-value analysis and obtain better results. In addition, confidence measures for depth estimates are provided which can be used to remove regions with potentially incorrect (and outliers in) depth estimates for robustly estimating ego-motion in the next iteration. Results on both synthetic and real examples are presented.},
	keywords = {3D, algorithm;, analysis;, and, based, camera;, coarse, compensation;, DEM;, depth, digital, ego-motion, eigen-value, eigenfunctions;, eigenvalues, ELEVATION, epipolar, estimation;, extraction;, feature, field;, iteration, iterative, map;, method;, methods;, model, model;, MOTION, parallax, partial, range-finding;, refinement;, refining;, surface},
	doi = {10.1109/ICIP.2004.1421606},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {13210,
	title = {Uncalibrated stereo rectification for automatic 3D surveillance},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1357 - 1360 Vol.2 - 1357 - 1360 Vol.2},
	abstract = {We describe a stereo rectification method suitable for automatic 3D surveillance. We take advantage of the fact that in a typical urban scene, there is ordinarily a small number of dominant planes. Given two views of the scene, we align a dominant plane in one view with the other. Conjugate epipolar lines between the reference view and plane-aligned image become geometrically identical and can be added to the rectified image pair line by line. Selecting conjugate epipolar lines to cover the whole image is simplified since they are geometrically identical. In addition, the polarities of conjugate epipolar lines are automatically preserved by plane alignment, which simplifies stereo matching.},
	keywords = {3D, AUTOMATIC, conjugate, epipolar, image, lines;, matching;, method;, processing;, rectification, scene;, stereo, surveillance;, uncalibrated, urban},
	doi = {10.1109/ICIP.2004.1419753},
	author = {Lim,S.-N. and Mittal,A. and Davis, Larry S. and Paragios,N.}
}
@conference {12697,
	title = {View independent human body pose estimation from a single perspective image},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {2},
	year = {2004},
	month = {2004/07/02/june},
	pages = {II-16 - II-22 Vol.2 - II-16 - II-22 Vol.2},
	abstract = {Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images.},
	keywords = {3D, analysis;, biomechanics;, body, body-centric, camera;, capture, coordinate, coordinates;, detection;, epipolar, equation, estimation;, geometry;, human, image, image;, images;, model-based, models;, MOTION, object, optical, perspective, physiological, polynomial, polynomials;, pose, real, single, synthetic, system;, systems;, torso, tracking;, twist;, uncalibrated},
	doi = {10.1109/CVPR.2004.1315139},
	author = {Parameswaran, V. and Chellapa, Rama}
}
