TY - JOUR
T1 - Image Transformations and Blurring
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
Y1 - 2009
A1 - Domke, Justin
A1 - Aloimonos, J.
KW - reconstruction
KW - restoration
KW - sharpening and deblurring
KW - smoothing.
AB - 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” and "real” 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,” we show how this can be done despite not knowing the blurring kernel.
VL - 31
SN - 0162-8828
CP - 5
M3 - http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.133
ER -
TY - JOUR
T1 - Structure From Planar Motion
JF - Image Processing, IEEE Transactions on
Y1 - 2006
A1 - Li,Jian
A1 - Chellapa, Rama
KW - algebra;road
KW - analysis;matrix
KW - camera;surveillance
KW - directional
KW - matrix;planar
KW - MOTION
KW - motion;stationary
KW - perspective
KW - processing;
KW - reconstruction
KW - signal
KW - system;image
KW - uncertainty;measurement
KW - vehicles;surveillance;video
KW - videos;vehicle
AB - 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
VL - 15
SN - 1057-7149
CP - 11
M3 - 10.1109/TIP.2006.881943
ER -
TY - CONF
T1 - 3D face reconstruction from video using a generic model
T2 - Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on
Y1 - 2002
A1 - Chowdhury, A.R.
A1 - Chellapa, Rama
A1 - Krishnamurthy, S.
A1 - Vo, T.
KW - 3D
KW - algorithm;
KW - algorithms;
KW - analysis;
KW - Carlo
KW - chain
KW - Computer
KW - Face
KW - from
KW - function;
KW - generic
KW - human
KW - image
KW - Markov
KW - MCMC
KW - methods;
KW - model;
KW - Monte
KW - MOTION
KW - optimisation;
KW - OPTIMIZATION
KW - processes;
KW - processing;
KW - recognition;
KW - reconstruction
KW - reconstruction;
KW - sampling;
KW - sequence;
KW - sequences;
KW - SfM
KW - signal
KW - structure
KW - surveillance;
KW - video
KW - vision;
AB - 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.
JA - Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference on
VL - 1
M3 - 10.1109/ICME.2002.1035815
ER -