El işareti tanıma sistemi.

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Türkiye

Tezin Onay Tarihi: 2010

Tezin Dili: İngilizce

Öğrenci: Emrah Gingir

Eş Danışman: GÖZDE AKAR, MEHMET METE BULUT

Özet:

This thesis study presents a hand gesture recognition system, which replaces input devices like keyboard and mouse with static and dynamic hand gestures, for interactive computer applications. Despite the increase in the attention of such systems there are still certain limitations in literature. Most applications require different constraints like having distinct lightning conditions, usage of a specific camera, making the user wear a multi-colored glove or need lots of training data. The system mentioned in this study disables all these restrictions and provides an adaptive, effort free environment to the user. Study starts with an analysis of the different color space performances over skin color extraction. This analysis is independent of the working system and just performed to attain valuable information about the color spaces. Working system is based on two steps, namely hand detection and hand gesture recognition. In the hand detection process, normalized RGB color space skin locus is used to threshold the coarse skin pixels in the image. Then an adaptive skin locus, whose varying boundaries are estimated from coarse skin region pixels, segments the distinct skin color in the image for the current conditions. Since face has a distinct shape, face is detected among the connected group of skin pixels by using the shape analysis. Non-face connected group of skin pixels are determined as hands. Gesture of the hand is recognized by improved centroidal profile method, which is applied around the detected hand. A 3D flight war game, a boxing game and a media player, which are controlled remotely by just using static and dynamic hand gestures, were developed as human machine interface applications by using the theoretical background of this study. In the experiments, recorded videos were used to measure the performance of the system and a correct recognition rate of ~90% was acquired with nearly real time computation.