is then used to recognize facial expressions in two different ways. The first method uses the physics-based model directly, by recognizing expressions through comparison of estimated muscle activations. The second method uses the physics-based model to generate spatio-temporal motion-energy templates

A motion sequence may be represented as a single pattern in x-y-t space; a velocity of motion corresponds to a three-dimensional orientation in this space. Motion sinformation can be extracted by a system that responds to the oriented spatiotemporal energy. We discuss a class of models for human

In this paper we introduce a template-based method for recognizing human actions called Action MACH. Our approach is based on a Maximum Average Correlation Height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection

is based on two basic pieces of information, pixel brightness and motion. The motion information is gathered as the optical flow information. Such information islSOO used for definition of an energy function for imagela elgeS This energy function represents a Markov randomfiel (MRF) posterior distribution

We present a novel approach to detecting human silhouettes in monocular sequences that achieves very low rates of both false positives and negatives by combining shape and motion information. To this end, we use sequences of moving silhouettes built using motion capture data that we match against

Abstract. Our goal is automatic recognition of basic human actions, such as stand, sit and wave hands, to aid in natural communication between a human and a computer. Human actions are inferred from human body joint motions, but such data has high dimensionality and large spatial and temporal

Recent work in human motion perception has conceptualized motion detection in terms of filters selective for spatio-temporal (ST) energy. One class of such models are called "energy models. " They code motion energy, not velocity as such, but we describe a velocity coding model

. In this paper, we propose and evaluate a distributed community detection approach that allows individual users to detect their own communities based on local observations. Our proposed template-matching method derives dynamic spatial and temporal characteristics of social communities by exploiting human’s

accurately recovers 3D shape and 3D motion by optimizing the positions of the vertices of the animated mesh. This optimization is driven by an energy function which incor-porates multi-view and inter-frame photo-consistency, smoothness of the spatio-temporal surface and of the velocity field. Central to our

of the moving targets is completed via a combination of a temporal boosted classifier and spatiotemporal “motion energy ” analysis. We illustrate that a temporal boosted classifier can be designed that successfully recognizes five object categories: person(s), bicycle, motorcycle, vehicle, and person