We introduce a discriminative hidden-state approach for the recognition of human gestures.
Gesture sequences often have a complex underlying structure, and models that can …

We describe algorithms for recognizing human motion in monocular video sequences,
based on discriminative conditional random fields (CRFs) and maximum entropy Markov …

Sign language is the most natural and expressive way for the hearing impaired. Because of
this, automatic sign language recognition has long attracted vision researchers. It offers …

We consider the problem of computing the likelihood of a gesture from regular, unaided
video sequences, without relying on perfect segmentation of the scene. Instead of requiring …

Recognition of complex activities from surveillance video requires detection and temporal
ordering of its constituent “atomic” events. It also requires the capacity to robustly track …

This paper proposes the control of smart home environments such as lights and curtains
using body gestures. We use a forward spotting scheme that executes gesture segmentation …

In the following work we present a new approach to recognition of hand gesture based on
pseudo three-dimensional hidden Markov model (P3DHMM), a technique which can …

The present thesis is concerned with the development and evaluation (in terms of accuracy
and utility) of systems using hand postures and hand gestures for enhanced Human …

Human has always been a part of the computational loop. So, what do we mean by human-
centered computing (HCC)? aren't humans always the focus of computations some how …

Spotting patterns of interest in an input signal is a very useful task in many different fields
including medicine, bioinformatics, economics, speech recognition and computer vision …