@article {12654,
	title = {Statistical bias in 3-D reconstruction from a monocular video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {14},
	year = {2005},
	month = {2005/08//},
	pages = {1057 - 1062},
	abstract = {The present state-of-the-art in computing the error statistics in three-dimensional (3-D) reconstruction from video concentrates on estimating the error covariance. A different source of error which has not received much attention is the fact that the reconstruction estimates are often significantly statistically biased. In this paper, we derive a precise expression for the bias in the depth estimate, based on the continuous (differentiable) version of structure from motion (SfM). Many SfM algorithms, or certain portions of them, can be posed in a linear least-squares (LS) framework Ax=b. Examples include initialization procedures for bundle adjustment or algorithms that alternately estimate depth and camera motion. It is a well-known fact that the LS estimate is biased if the system matrix A is noisy. In SfM, the matrix A contains point correspondences, which are always difficult to obtain precisely; thus, it is expected that the structure and motion estimates in such a formulation of the problem would be biased. Existing results on the minimum achievable variance of the SfM estimator are extended by deriving a generalized Cramer-Rao lower bound. A detailed analysis of the effect of various camera motion parameters on the bias is presented. We conclude by presenting the effect of bias compensation on reconstructing 3-D face models from rendered images.},
	keywords = {3D face models, 3D video reconstruction, algorithms, artifacts, Artificial intelligence, Automated;Signal Processing, bias compensation, bundle adjustment, camera motion estimation, Computer simulation, Computer-Assisted;Imaging, Computer-Assisted;Subtraction Technique;Video Recording;, depth estimate, error covariance estimation, error statistics, generalized Cramer-Rao lower bound, Image Enhancement, Image Interpretation, Image reconstruction, initialization procedures, least squares approximations, linear least-squares framework, monocular video, motion compensation, Motion estimation, statistical bias, Statistical;Pattern Recognition, structure from motion algorithms, Three-Dimensional;Information Storage and Retrieval;Models, video signal processing},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.849775},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama}
}
@conference {17386,
	title = {Supporting creativity with powerful composition tools for artifacts and performances},
	booktitle = {Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000},
	year = {2000},
	month = {2000/01/04/7},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Modern software such as word processors, slide preparation/presentation tools, or music composition packages are designed to produce artifacts or performances. Now, some designers are expanding their goals to include creativity support into their software. This essay builds on the genex framework for creativity, which has four phases and eight activities. It focuses on the composition activity by considering tools to support initiation, revision, and evaluation. Some existing tools provide partial support that suggests ways in which to develop more ambitious low, middle, and high level tools. The goal is to enable more people to be more creative more of the time.},
	keywords = {artifacts, Artificial intelligence, composition tools, Computer science, Context modeling, creativity, creativity support, Educational institutions, Information retrieval, Laboratories, Manufacturing, music composition, performances, Software design, software libraries, software tools, US Department of Transportation},
	isbn = {0-7695-0493-0},
	doi = {10.1109/HICSS.2000.926896},
	author = {Shneiderman, Ben}
}
