@conference {14896,
	title = {Finding structurally consistent motion correspondences},
	booktitle = {Pattern Recognition, 1994. Vol. 1 - Conference A: Computer Vision Image Processing., Proceedings of the 12th IAPR International Conference on},
	volume = {1},
	year = {1994},
	month = {1994/10//},
	pages = {650 -653 vol.1 - 650 -653 vol.1},
	abstract = {Much work on deriving scene structure and motion from features assumes as input a set of tracked image features that share a common 3D motion. Producing this input requires segmenting independent motions, and detecting image features that do not correspond to 3D features, originating instead, for example in occlusion boundaries or specularities. We derive a linear program that tells when a set of tracked points might have come from 3D points that share a single motion, assuming affine motion and bounded error. We can also use linear programming to place conservative bounds on the structure of the scene that corresponds to tracked points. We implement and test this algorithm on real images},
	keywords = {3D, boundaries;, common, consistent, correspondences;, estimation;, features;, image, independent, linear, MOTION, motion;, occlusion, programming;, scene, segmentation;, specularities;, structurally, structure;, tracked},
	doi = {10.1109/ICPR.1994.576388},
	author = {Jacobs, David W. and Chennubhotla,C.}
}
@conference {14881,
	title = {Segmenting independently moving, noisy points},
	booktitle = {Motion of Non-Rigid and Articulated Objects, 1994., Proceedings of the 1994 IEEE Workshop on},
	year = {1994},
	month = {1994/11//},
	pages = {96 - 103},
	abstract = {There has been much work on using point features tracked through a video sequence to determine structure and motion. In many situations, to use this work, we must first isolate subsets of points that share a common motion. This is hard because we must distinguish between independent motions and apparent deviations from a single motion due to noise. We propose several methods of searching for point-sets with consistent 3D motions. We analyze the potential sensitivity of each method for detecting independent motions, and experiment with each method on a real image sequence},
	keywords = {3D, common, consistent, estimation;, features;, image, independently, linear, MOTION, motion;, moving, noisy, point, points;, programming;, real, segmentation;, sequence;, sequences;, video},
	doi = {10.1109/MNRAO.1994.346249},
	author = {Jacobs, David W. and Chennubhotla,C.}
}
