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We extend the work of Black and Yacoob on the tracking and recognition of human facial expressions using parameterized models of optical flow to deal with the articulated motion of human limbs. We define a " cardboard person model " in which a person's limbs are represented by a set of connected planar patches. The parameterized image motion of these(More)
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid(More)
A f r a m e w o r k f o r m o d e l i n g a n d recognition o f t e m-poral activities is proposed. T h e m o d e l i n g o f s e t s of e x e m p l a r activities is achieved by p a r a m e t e r i z-i n g t h e i r r e p r e s e n t a t i o n in t h e f o r m o f p r i n c i p a l c o m p o n e n t s. R e c o g n i t i o n o f spatio-temporal vari-a n t s(More)
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid(More)
This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states(More)
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(More)
We propose a generalized model of image \appear-ance change" in which brightness variation over time is represented as a probabilistic mixture of diierent causes. We deene four generative models of appearance change due to: 1) object or camera motion; 2) illumination phenomena; 3) specular reeections; and 4) \iconic changes" which are speciic to the objects(More)
Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex(More)