@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 {12480,
	title = {Tracking via object reflectance using a hyperspectral video camera},
	booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {44 - 51},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Recent advances in electronics and sensor design have enabled the development of a hyperspectral video camera that can capture hyperspectral datacubes at near video rates. The sensor offers the potential for novel and robust methods for surveillance by combining methods from computer vision and hyperspectral image analysis. Here, we focus on the problem of tracking objects through challenging conditions, such as rapid illumination and pose changes, occlusions, and in the presence of confusers. A new framework that incorporates radiative transfer theory to estimate object reflectance and the mean shift algorithm to simultaneously track the object based on its reflectance spectra is proposed. The combination of spectral detection and motion prediction enables the tracker to be robust against abrupt motions, and facilitate fast convergence of the mean shift tracker. In addition, the system achieves good computational efficiency by using random projection to reduce spectral dimension. The tracker has been evaluated on real hyperspectral video data.},
	keywords = {Computer vision, electronic design, hyperspectral datacubes, hyperspectral image analysis, Hyperspectral imaging, Hyperspectral sensors, hyperspectral video camera, Image motion analysis, Image sensors, lighting, Motion estimation, motion prediction, Object detection, object reflectance tracking, random projection, Reflectivity, robust methods, Robustness, sensor design, spectral detection, Surveillance, tracking, Video surveillance},
	isbn = {978-1-4244-7029-7},
	doi = {10.1109/CVPRW.2010.5543780},
	author = {Nguyen,Hien Van and Banerjee, A. and Chellapa, Rama}
}
@conference {14209,
	title = {Compound eye sensor for 3D ego motion estimation},
	booktitle = {2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings},
	volume = {4},
	year = {2004},
	month = {2004/10/28/Sept.},
	pages = {3712- 3717 vol.4 - 3712- 3717 vol.4},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We describe a compound eye vision sensor for 3D ego motion computation. Inspired by eyes of insects, we show that the compound eye sampling geometry is optimal for 3D camera motion estimation. This optimality allows us to estimate the 3D camera motion in a scene-independent and robust manner by utilizing linear equations. The mathematical model of the new sensor can be implemented in analog networks resulting in a compact computational sensor for instantaneous 3D ego motion measurements in full six degrees of freedom.},
	keywords = {3D camera motion estimation, CAMERAS, compound eye vision sensor, Computer vision, Equations, Eyes, Geometry, Image sensors, Insects, linear equations, Motion estimation, robot vision, Robustness, sampling geometry, Sampling methods, Sensor phenomena and characterization},
	isbn = {0-7803-8463-6},
	doi = {10.1109/IROS.2004.1389992},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Brajovic,V.}
}
@conference {12055,
	title = {Polydioptric camera design and 3D motion estimation},
	booktitle = {2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings},
	volume = {2},
	year = {2003},
	month = {2003/06/18/20},
	pages = {II- 294-301 vol.2 - II- 294-301 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Most cameras used in computer vision applications are still based on the pinhole principle inspired by our own eyes. It has been found though that this is not necessarily the optimal image formation principle for processing visual information using a machine. We describe how to find the optimal camera for 3D motion estimation by analyzing the structure of the space formed by the light rays passing through a volume of space. Every camera corresponds to a sampling pattern in light ray space, thus the question of camera design can be rephrased as finding the optimal sampling pattern with regard to a given task. This framework suggests that large field-of-view multi-perspective (polydioptric) cameras are the optimal image sensors for 3D motion estimation. We conclude by proposing design principles for polydioptric cameras and describe an algorithm for such a camera that estimates its 3D motion in a scene independent and robust manner.},
	keywords = {3D motion estimation, Algorithm design and analysis, Application software, CAMERAS, Computer vision, Eyes, field-of-view camera, Image motion analysis, image sampling, image sensor, Image sensors, Layout, light ray, Motion estimation, multiperspective camera, optimal camera, optimal image formation, optimal sampling pattern, pinhole principle, polydioptric camera design, ray space, scene independent estimation, space structure analysis, stereo image processing, visual information processing},
	isbn = {0-7695-1900-8},
	doi = {10.1109/CVPR.2003.1211483},
	author = {Neumann, J. and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
