There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactio ns with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.

A review of vision based hand gestures recognition

Recent methods and databases in vision-based hand gesture recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a precis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.

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Sign Language Recognition

Real-time gesture recognition is mandatory for gesture based human-robot natural interaction. Several glove-based approaches are introduced for this purpose, but they are un-natural and force user to wear some cumbersome glove. This paper presents a vision-based gesture recognition method for human-robot interface. Hand poses and face are detected using multiple feature-based template matching techniques from the three largest skin-like regions that are segmented using skin color information from YIQ color space. Gesture is recognized using rule-based approach whenever the three skin-like regions at a particular image frame match with the predefined gesture. Gesture command is sent to robot through a knowledge based software platform for human-robot interface. In this paper, a method has also been proposed to detect left hand and right hand relative to face position as well as, to detect and locate its position. The effectiveness of the proposed method has been demonstrated over the interaction with a pet robot named AIBO

Real-time Vision-based Gesture Recognition for Human Robot Interaction

Adaptive visual gesture recognition for human-robot interaction using a knowledge-based software platform

Face Detection and Facial Feature Localization for Human-machine Interface

On Tracking of Eye for Human-Robot Interface

This paper presents a novel person to camera distance measuring system based on eye-distance. The distance between centers of two eyes is used for measuring the person to camera distance. The variation in eye-distance (in pixels) with the changes in camera to person distance (in inches) is used to formulate the distance measuring system. The system starts with computing the distance between two eyes of a person and then person to camera distance is measured. The proposed distance measurement system is relatively simple and inexpensive to implement as it does not require any other external distance measuring tools. Experimental results show the effectiveness of the system with an average accuracy of 94.11%.

Haruki Ueno

Papers

Real-time Vision-based Gesture Recognition for Human Robot Interaction

Adaptive visual gesture recognition for human-robot interaction using a knowledge-based software platform

Face Detection and Facial Feature Localization for Human-machine Interface

On Tracking of Eye for Human-Robot Interface

Person to Camera Distance Measurement Based on Eye-Distance