@article {12029,
title = {Image Transformations and Blurring},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {31},
year = {2009},
month = {2009///},
pages = {811 - 823},
abstract = {Since cameras blur the incoming light during measurement, different images of the same surface do not contain the same information about that surface. Thus, in general, corresponding points in multiple views of a scene have different image intensities. While multiple-view geometry constrains the locations of corresponding points, it does not give relationships between the signals at corresponding locations. This paper offers an elementary treatment of these relationships. We first develop the notion of "ideal{\textquotedblright} and "real{\textquotedblright} images, corresponding to, respectively, the raw incoming light and the measured signal. This framework separates the filtering and geometric aspects of imaging. We then consider how to synthesize one view of a surface from another; if the transformation between the two views is affine, it emerges that this is possible if and only if the singular values of the affine matrix are positive. Next, we consider how to combine the information in several views of a surface into a single output image. By developing a new tool called "frequency segmentation,{\textquotedblright} we show how this can be done despite not knowing the blurring kernel.},
keywords = {reconstruction, restoration, sharpening and deblurring, smoothing.},
isbn = {0162-8828},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.133},
author = {Domke, Justin and Aloimonos, J.}
}
@article {12604,
title = {Structure From Planar Motion},
journal = {Image Processing, IEEE Transactions on},
volume = {15},
year = {2006},
month = {2006/11//},
pages = {3466 - 3477},
abstract = {Planar motion is arguably the most dominant type of motion in surveillance videos. The constraints on motion lead to a simplified factorization method for structure from planar motion when using a stationary perspective camera. Compared with methods for general motion , our approach has two major advantages: a measurement matrix that fully exploits the motion constraints is formed such that the new measurement matrix has a rank of at most 3, instead of 4; the measurement matrix needs similar scalings, but the estimation of fundamental matrices or epipoles is not needed. Experimental results show that the algorithm is accurate and fairly robust to noise and inaccurate calibration. As the new measurement matrix is a nonlinear function of the observed variables, a different method is introduced to deal with the directional uncertainty in the observed variables. Differences and the dual relationship between planar motion and planar object are also clarified. Based on our method, a fully automated vehicle reconstruction system has been designed},
keywords = {algebra;road, analysis;matrix, camera;surveillance, directional, matrix;planar, MOTION, motion;stationary, perspective, processing;, reconstruction, signal, system;image, uncertainty;measurement, vehicles;surveillance;video, videos;vehicle},
isbn = {1057-7149},
doi = {10.1109/TIP.2006.881943},
author = {Li,Jian and Chellapa, Rama}
}
@conference {12765,
title = {3D face reconstruction from video using a generic model},
booktitle = {Multimedia and Expo, 2002. ICME {\textquoteright}02. Proceedings. 2002 IEEE International Conference on},
volume = {1},
year = {2002},
month = {2002///},
pages = {449 - 452 vol.1 - 449 - 452 vol.1},
abstract = {Reconstructing a 3D model of a human face from a 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 common method of overcoming this problem is to use a generic model of a face. 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. 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. A 3D estimate is obtained purely from the video sequence using SfM algorithms without use of the generic model. 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 local regions in the two models. The optimization is done using a Markov chain Monte Carlo (MCMC) sampling strategy. 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. The evolution of the 3D model through the various stages of the algorithm is presented.},
keywords = {3D, algorithm;, algorithms;, analysis;, Carlo, chain, Computer, Face, from, function;, generic, human, image, Markov, MCMC, methods;, model;, Monte, MOTION, optimisation;, OPTIMIZATION, processes;, processing;, recognition;, reconstruction, reconstruction;, sampling;, sequence;, sequences;, SfM, signal, structure, surveillance;, video, vision;},
doi = {10.1109/ICME.2002.1035815},
author = {Chowdhury, A.R. and Chellapa, Rama and Krishnamurthy, S. and Vo, T.}
}