A system for face model adaptation combining active tracking is presented. Input from an active camera is used for MPEG4 model based coding. First, the background is compensated considering a moving camera (tilt or pa...
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A system for face model adaptation combining active tracking is presented. Input from an active camera is used for MPEG4 model based coding. First, the background is compensated considering a moving camera (tilt or pan). Second, the talking face is segmented from the compensated background using fusion of frame differences. A morphological filter is then applied to make the system less sensitive to noise. Third, Hough transform and deformable template coupled with color information are exploited to detect the facial features, e.g., eyes, mouth. Fourth, a wireframe model is adapted to the extracted face by an extended dynamic mesh. The feasibility of the proposed system is demonstrated using several real active video sequences.
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 p...
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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.
A method is developed for the measurement of short-range visual motion in image sequences, making use of the motion of image features such as edges and points. Each feature generates a Gaussian activation profile in a...
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A method is developed for the measurement of short-range visual motion in image sequences, making use of the motion of image features such as edges and points. Each feature generates a Gaussian activation profile in a spatiotemporal neighborhood of specified scale around the feature itself; this profile is then convected with motion of the feature. The authors show that image velocity estimates can be obtained from such dynamic activation profiles using a modification of familiar gradient techniques. The resulting estimators can be formulated in terms of simple ratios of spatiotemporal filters (i.e. receptive fields) convolved with image feature maps. A family of activation profiles of varying scale must be utilized to cover a range of possible image velocities. They suggest a characteristic speed normalization of the estimate obtained from each filter in order to decide which estimate is to be accepted. They formulate the velocity estimators for dynamic edges in 1-D and 2-D image sequences, as well as that for dynamic feature points in 2-D image sequences.< >
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