https://www.ee.columbia.edu/%7Eajaimes/Pubs/ajaimes_iccv05.pdf

Multimodal human-computer interaction: A survey

Architecture for implementing a perceptual user interface. The architecture comprises alternative modalities for controlling computer application programs and manipulating on-screen objects through hand gestures or a combination of hand gestures and verbal commands. The perceptual user interface system includes a tracking component that detects object characteristics of at least one of a plurality of objects within a scene, and tracks the respective object. Detection of object characteristics is based at least in part upon image comparison of a plurality of images relative to a course mapping of the images. A seeding component iteratively seeds the tracking component with object hypotheses based upon the presence of the object characteristics and the image comparison. A filtering component selectively removes the tracked object from the object hypotheses and/or at least one object hypothesis from the set of object hypotheses based upon predetermined removal criteria.

Architecture for controlling a computer using hand gestures

Body posture and finger pointing are a natural modality for human-machine interaction, but first the system must know what it's seeing.

Vision-based hand-gesture applications

The intention of this paper is to provide an overview on the subject of Human-Computer Interaction. The overview includes the basic definitions and terminology, a survey of existing technologies and recent advances in the field, common architectures used in the design of HCI systems which includes unimodal and multimodal configurations, and finally the applications of HCI. This paper also offers a comprehensive number of references for each concept, method, and application in the HCI.

Human-Computer Interaction: Overview on State of the Art

In this paper we review the major approaches to multimodal human computer interaction from a computer vision perspective. In particular, we focus on body, gesture, gaze, and affective interaction (facial expression recognition, and emotion in audio). We discuss user and task modeling, and multimodal fusion, highlighting challenges, open issues, and emerging applications for Multimodal Human Computer Interaction (MMHCI) research.

Multimodal human computer interaction: a survey

Although speech and gesture recognition has been studied extensively, all the successful attempts of combining them in the unified framework were semantically motivated, e.g., keyword-gesture cooccurrence. Such formulations inherited the complexity of natural language processing. This paper presents a Bayesian formulation that uses a phenomenon of gesture and speech articulation for improving accuracy of automatic recognition of continuous coverbal gestures. The prosodic features from the speech signal were coanalyzed with the visual signal to learn the prior probability of co-occurrence of the prominent spoken segments with the particular kinematical phases of gestures. It was found that the above co-analysis helps in detecting and disambiguating visually small gestures, which subsequently improves the rate of continuous gesture recognition. The efficacy of the proposed approach was demonstrated on a large database collected from the weather channel broadcast. This formulation opens new avenues for bottom-up frameworks of multimodal integration.

Sanshzar Kettebekov

Papers

Prosody Based Co-analysis for Continuous Recognition of Coverbal Gestures