@article {14230,
	title = {Motion segmentation using occlusions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/06//},
	pages = {988 - 992},
	abstract = {We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo).},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, Computer vision, Filling, hidden feature removal, Image Enhancement, Image Interpretation, Computer-Assisted, image motion, Image motion analysis, Image segmentation, Layout, MOTION, Motion detection, Motion estimation, motion segmentation, Movement, Object detection, occlusion, occlusions, optical flow, ordinal depth, Pattern Recognition, Automated, Photography, Reproducibility of results, segmentation, Semiconductor device modeling, Sensitivity and Specificity, video analysis., Video Recording},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2005.123},
	author = {Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {18323,
	title = {Tracking rigid motion using a compact-structure constraint},
	booktitle = {Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {198 -205 vol.1 - 198 -205 vol.1},
	abstract = {An approach for tracking the motion of a rigid object using parameterized flow models and a compact-structure constraint is proposed. While polynomial parameterized flow models have been shown to be effective in tracking the rigid motion of planar objects, these models are inappropriate for tracking moving objects that change appearance revealing their 3D structure. We extend these models by adding a structure-compactness constraint that accounts for image motion that deviates from a planar structure. The constraint is based on the assumption that object structure variations are limited with respect to planar object projection onto the image plane and therefore can be expressed as a direct constraint on the image motion. The performance of the algorithm is demonstrated on several long image sequences of rigidly moving objects},
	keywords = {compact-structure constraint, image motion, Image sequences, Motion estimation, parameterized flow models, polynomial parameterized flow models, Polynomials, rigid motion tracking, rigid object, rigidly moving objects, structure-compactness constraint, tracking},
	doi = {10.1109/ICCV.1999.791218},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {18324,
	title = {A framework for modeling appearance change in image sequences},
	booktitle = {Computer Vision, 1998. Sixth International Conference on},
	year = {1998},
	month = {1998/01//},
	pages = {660 - 667},
	abstract = {Image ldquo;appearance rdquo; may change over time due to a variety of causes such as: 1) object or camera motion; 2) generic photometric events including variations in illumination (e.g. shadows) and specular reflections; and 3) ldquo;iconic changes rdquo; which are specific to the objects being viewed and include complex occlusion events and changes in the material properties of the objects. We propose a general framework for representing and recovering these ldquo;appearance changes rdquo; in an image sequence as a ldquo;mixture rdquo; of different causes. The approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion},
	keywords = {appearance change, camera motion, complex occlusion events, generic photometric events, iconic changes, illumination, image motion, Image sequences, Motion estimation, optical flow},
	doi = {10.1109/ICCV.1998.710788},
	author = {Black,M. J and Fleet,D. J and Yacoob,Yaser}
}
@conference {18321,
	title = {Learning parameterized models of image motion},
	booktitle = {Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on},
	year = {1997},
	month = {1997/06//},
	pages = {561 - 567},
	abstract = {A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion},
	keywords = {image motion, Image sequences, learning, learning (artificial intelligence), model-based recognition, Motion estimation, multi-resolution scheme, non-rigid motion, optical flow, optical flow estimation, parameterized models, Principal component analysis, training set},
	doi = {10.1109/CVPR.1997.609381},
	author = {Black,M. J and Yacoob,Yaser and Jepson,A. D and Fleet,D. J}
}
