出處： Signal Processing and Communications Applications, 2006 IEEE

作者： Asanterabi Malima, Erol Ozgur, and Miijdat Cetin

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指導教授：張財榮學生：陳建宏學號： M97G0209

OUTLINE

Introduction

Hand Gesture Recognition

Experimental Results

Conclusion

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Introduction

Vision-based automatic hand gesture recognition

Human computer interaction

Robot control

Sign language interpretation

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Introduction

Visual gesture recognition

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Introduction

Chinese Sign Language Recognition Based on Gray-Level Co-Occurrence Matrix and Other Multi-features Fusion

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Introduction

A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation

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Introduction

The general problem

complex static and dynamic hand gestures

complex background

occlusions

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Introduction

Our focus the recognition of a fixed set of manual commands by a robot

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Hand Gesture Recognition

Localizing Hand-like Regions by Skin Detection

Segmentation and False-Region Elimination

Finding the Centroid and Farthest Distance

Constructing a Circle

Extracting a 1D Signal and Classifilcation

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Localizing Hand-like Regions by Skin Detection

red/green (R/G) ratio is a discriminative characteristic feature for human skin color

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Localizing Hand-like Regions by Skin Detection

skin thresholds Resulting in a black and white image output

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Segmentation and False-Region Elimination

Our assumption the largest connected white region corresponds to the hand

Threshold ： 20% of total white parts

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70%

19%

11%

Finding the Centroid and Farthest Distance

Calculate center of gravity(COG)

Farthest Distance

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Constructing a Circle

Radius= 0.7 * Farthest Distance

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Extracting a 1D Signal and Classifilcation

Tracking the circle constructed

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Extracting a 1D Signal and Classifilcation

Counting the number of zero-to-onesubtracting one (for the wrist)

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Scale, Rotation, and Translation Invariance

Just counts the number of active fingers

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Experimental Results

Source ： 4 Mega-Pixel digital camera Recognition rate ： 96/105=0.9142

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Conclusion

We proposed a fast and simple algorithm for a hand gesture recognition problem.

The segmentation portion of our algorithm is too simple

Reliable performance of hand gesture recognition techniques are still mostly beyond the current state of the art.

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