- 學位論文
基於隱藏式馬可夫模型與深度資訊之手語辨識 系統
Sign Language Recognition based on HMMs and Depth Information
摘要
一個完整的手語辨識系統會受到系統參數及特徵參數影響其辨識率，而其中，後者對於辨識率的影響程度遠大於前者。因此，在手語辨識的架構中，一個好的特徵參數將可有效的提升整體之辨識率。本論文中，我們在手語辨識系統中加入深度資訊可以更有效地定位雙手的位置。然而這樣的資訊會因為手語者不同，導致無法辨識的情形發生，所以我們利用三維座標在單位時間上的變化量作為辨識特徵，藉以解決上述問題。首先我們紀錄三維座標在時間上的變化，接著利用隱藏式馬可夫模型能學習時間軸資訊的特性，以辨識在時間上動作的變化所組成之各種手語。由於加入深度資訊，可以辨識更多樣化的手語，以及解決手語者不同的問題，同時也可獲得更高的辨識率。
並列摘要
The recognition rate of the complete sign language recognition system will be influenced by the system parameters and feature parameters. And the latter is more important. Therefore, in the architecture of sign language recognition, a good feature parameter can effectively promote the recognition rate of the system. In this paper, we add the depth information to effectively locate the position of the hands in the sign language recognition system. However, the information will be changed by the different testers. Also, it will happen that we can’t do the recognition. So, we use the incremental changes of the three-dimensional coordinates on a unit time as the feature parameter to fix the above problem. First, we record the changes of the three-dimensional coordinates on time, then using the hidden Markov models with the characteristic of learning the time-axis information to recognize the variety of sign language movement changing on the time. Since we have added the depth information, we can recognize more variety of sign language and solve the problem of different signers. Moreover, we can get the higher rate of recognition.