@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}
}
@inbook {12640,
	title = {Computer Vision, Statistics in},
	booktitle = {Encyclopedia of Statistical SciencesEncyclopedia of Statistical Sciences},
	year = {2006},
	month = {2006///},
	publisher = {John Wiley \& Sons, Inc.},
	organization = {John Wiley \& Sons, Inc.},
	abstract = {Computer Vision (CV) broadly refers to the discipline where extraction of useful 2D and/or 3D information from one or more images is of interest. Useful information could consist of features such as edges, lines, curves and textures, or information about depth, motion, object descriptions, etc. CV problems are usually ill-posed inverse problems. Since the image data is usually obtained from sensors such as video cameras, infrared, radar, etc., the information extraction processes often have to deal with data that is corrupted by noise from the sensors and the environment. Statistics can help in obtaining robust and accurate solutions to these inverse problems by modeling the noise processes. We present here a broad overview of the applications of statistics to different computer vision problems and explain in detail two particular applications, tracking and motion analysis, where statistical approaches have been used very successfully.},
	keywords = {Computer vision, sampling, Statistics, structure from motion, tracking},
	isbn = {9780471667193},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/0471667196.ess3129/abstract},
	author = {Chellapa, Rama and Chowdhury,Amit K. Roy}
}
@article {12689,
	title = {Bayesian self-calibration of a moving camera},
	journal = {Computer Vision and Image Understanding},
	volume = {95},
	year = {2004},
	month = {2004/09//},
	pages = {287 - 316},
	abstract = {In this paper, a Bayesian self-calibration approach using sequential importance sampling (SIS) is proposed. Given a set of feature correspondences tracked through an image sequence, the joint posterior distributions of both camera extrinsic and intrinsic parameters as well as the scene structure are approximated by a set of samples and their corresponding weights. The critical motion sequences are explicitly considered in the design of the algorithm. The probability of the existence of the critical motion sequence is inferred from the sample and weight set obtained from the SIS procedure. No initial guess for the calibration parameters is required. The proposed approach has been extensively tested on both synthetic and real image sequences and satisfactory performance has been observed.},
	keywords = {self-calibration, Sequential Monte Carlo methods, structure from motion, video analysis},
	isbn = {1077-3142},
	doi = {10.1016/j.cviu.2004.03.009},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314204000396},
	author = {Qian,Gang and Chellapa, Rama}
}
@article {14184,
	title = {A hierarchy of cameras for 3D photography},
	journal = {Computer Vision and Image Understanding},
	volume = {96},
	year = {2004},
	month = {2004/12//},
	pages = {274 - 293},
	abstract = {The view-independent visualization of 3D scenes is most often based on rendering accurate 3D models or utilizes image-based rendering techniques. To compute the 3D structure of a scene from a moving vision sensor or to use image-based rendering approaches, we need to be able to estimate the motion of the sensor from the recorded image information with high accuracy, a problem that has been well-studied. In this work, we investigate the relationship between camera design and our ability to perform accurate 3D photography, by examining the influence of camera design on the estimation of the motion and structure of a scene from video data. By relating the differential structure of the time varying plenoptic function to different known and new camera designs, we can establish a hierarchy of cameras based upon the stability and complexity of the computations necessary to estimate structure and motion. At the low end of this hierarchy is the standard planar pinhole camera for which the structure from motion problem is non-linear and ill-posed. At the high end is a camera, which we call the full field of view polydioptric camera, for which the motion estimation problem can be solved independently of the depth of the scene which leads to fast and robust algorithms for 3D Photography. In between are multiple view cameras with a large field of view which we have built, as well as omni-directional sensors.},
	keywords = {Camera design, Multi-view geometry, Polydioptric cameras, Spatio-temporal image analysis, structure from motion},
	isbn = {1077-3142},
	doi = {10.1016/j.cviu.2004.03.013},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314204000505},
	author = {Neumann, Jan and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@article {12726,
	title = {Face reconstruction from monocular video using uncertainty analysis and a generic model},
	journal = {Computer Vision and Image Understanding},
	volume = {91},
	year = {2003},
	month = {2003/07//},
	pages = {188 - 213},
	abstract = {Reconstructing a 3D model of a human face from a monocular video sequence is an important problem in computer vision, with applications to recognition, surveillance, multimedia, etc. However, the quality of 3D reconstructions using structure from motion (SfM) algorithms is often not satisfactory. One of the reasons is the poor quality of the input video data. Hence, it is important that 3D face reconstruction algorithms take into account the statistics representing the quality of the video. Also, because of the structural similarities of most faces, it is natural that the performance of these algorithms can be improved by using a generic model of a face. Most of the existing work using this approach initializes the reconstruction algorithm with this generic model. The problem with this approach is that the algorithm can converge to a solution very close to this initial value, resulting in a reconstruction which resembles the generic model rather than the particular face in the video which needs to be modeled. In this paper, we propose a method of 3D reconstruction of a human face from video in which the 3D reconstruction algorithm and the generic model are handled separately. We show that it is possible to obtain a reasonably good 3D SfM estimate purely from the video sequence, provided the quality of the input video is statistically assessed and incorporated into the algorithm. The final 3D model is obtained after combining the SfM estimate and the generic model using an energy function that corrects for the errors in the estimate by comparing the local regions in the two models. The main advantage of our algorithm over others is that it is able to retain the specific features of the face in the video sequence even when these features are different from those of the generic model and it does so even as the quality of the input video varies. The evolution of the 3D model through the various stages of the algorithm and an analysis of its accuracy are presented.},
	keywords = {Energy function minimization, Face modeling, Generic model, stochastic approximation, structure from motion, uncertainty analysis},
	isbn = {1077-3142},
	doi = {10.1016/S1077-3142(03)00079-1},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314203000791},
	author = {Roy Chowdhury,Amit K. and Chellapa, Rama}
}
@article {12060,
	title = {Visual space-time geometry - A tool for perception and the imagination},
	journal = {Proceedings of the IEEE},
	volume = {90},
	year = {2002},
	month = {2002/07//},
	pages = {1113 - 1135},
	abstract = {Although the fundamental ideas underlying research efforts in the field of computer vision have not radically changed in the past two decades, there has been a transformation in the way work in this field is conducted. This is primarily due to the emergence of a number of tools, of both a practical and a theoretical nature. One such tool, celebrated throughout the nineties, is the geometry of visual space-time. It is known under a variety of headings, such as multiple view geometry, structure from motion, and model building. It is a mathematical theory relating multiple views (images) of a scene taken at different viewpoints to three-dimensional models of the (possibly dynamic) scene. This mathematical theory gave rise to algorithms that take as input images (or video) and provide as output a model of the scene. Such algorithms are one of the biggest successes of the field and they have many applications in other disciplines, such as graphics (image-based rendering, motion capture) and robotics (navigation). One of the difficulties, however is that the current tools cannot yet be fully automated, and they do not provide very accurate results. More research is required for automation and high precision. During the past few years we have investigated a number of basic questions underlying the structure from motion problem. Our investigations resulted in a small number of principles that characterize the problem. These principles, which give rise to automatic procedures and point to new avenues for studying the next level of the structure from motion problem, are the subject of this paper.},
	keywords = {3-D motion estimation, Buildings, Computer vision, Geometry, Graphics, Image sequences, Layout, Mathematical model, mathematical theory, model building, Motion estimation, multiple view geometry, multiple views, Navigation, optical flow, optical illusions, patch correspondence, Rendering (computer graphics), Robotics and automation, Solid modeling, structure from motion, three-dimensional models, visual space-time},
	isbn = {0018-9219},
	doi = {10.1109/JPROC.2002.801440},
	author = {Ferm{\"u}ller, Cornelia and Baker, P. and Aloimonos, J.}
}
@article {14234,
	title = {New eyes for building models from video},
	journal = {Computational Geometry},
	volume = {15},
	year = {2000},
	month = {2000/02//},
	pages = {3 - 23},
	abstract = {Models of real-world objects and actions for use in graphics, virtual and augmented reality and related fields can only be obtained through the use of visual data and particularly video. This paper examines the question of recovering shape models from video information. Given video of an object or a scene captured by a moving camera, a prerequisite for model building is to recover the three-dimensional (3D) motion of the camera which consists of a rotation and a translation at each instant. It is shown here that a spherical eye (an eye or system of eyes providing panoramic vision) is superior to a camera-type eye (an eye with restricted field of view such as a common video camera) as regards the competence of 3D motion estimation. This result is derived from a geometric/statistical analysis of all the possible computational models that can be used for estimating 3D motion from an image sequence. Regardless of the estimation procedure for a camera-type eye, the parameters of the 3D rigid motion (translation and rotation) contain errors satisfying specific geometric constraints. Thus, translation is always confused with rotation, resulting in inaccurate results. This confusion does not happen for the case of panoramic vision. Insights obtained from this study point to new ways of constructing powerful imaging devices that suit particular tasks in visualization and virtual reality better than conventional cameras, thus leading to a new camera technology. Such new eyes are constructed by putting together multiple existing video cameras in specific ways, thus obtaining eyes from eyes. For a new eye of this kind we describe an implementation for deriving models of scenes from video data, while avoiding the correspondence problem in the video sequence.},
	keywords = {model building, shape reconstruction, structure from motion, video analysis},
	isbn = {0925-7721},
	doi = {10.1016/S0925-7721(99)00044-9},
	url = {http://www.sciencedirect.com/science/article/pii/S0925772199000449},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J. and Brodsk{\'y},Tom{\'a}{\v s}}
}
