本論文建構一個人類搭檔系統(HPS)，此系統包含聽覺、說話、手語與視覺功能。聽覺系統利用本論文提出之聲音偵測與分割方法萃取出聲音中有意義之成份。利用說話系統，HPS可以讀網頁、email、並與人對話。在視覺感測系統中，除了基本的人臉偵測、人臉辨識功能，本論文另引入變異係數(CV)去評估每個臉部特徵之重要程度，並運用於部份人臉偵測。並且，為讓HPS能在場景中搜尋目標物並辨識出此物品，本論文提出雙峰式―CAMSHIFT 演算法與擬人式學習法。此外，為服務聽障人士，本論文亦設計並實作出台灣手語辨識系統。此辨識系統採用隱藏式馬可夫模型(HMM)辨識手語之時序資料。然而，HMM有二個待解決之問題：一為狀態數之決定，二為結構之決定。為解決HMM之狀態數問題，本論文提出基於熵之K-means演算法，此方法提供一個視覺化的方式來判斷資料集的群數。最後，本論文利用蜂群(ABC)演算法來決定HMM的結構。

This dissertation constructs the HPS and introduces four sub-systems, the hearing system, speaking system, sign language recognition system and vision system. The hearing system utilizes the proposed voice detection and segmentation method to extract the meaning part of speech. The speaking system allows the HPS to read the webpage, read the new email and talk to the person. In the visual sensing system, besides the fundamental functions, face detection and face recognition, the partial face detection method also has been proposed and the CV value is introduced to appraise the degree of importance of the features. Besides, to allow the HPS to search for a target object and identify what this object is, the DPCAMSHIFT algorithm and the anthropomorphic learning process have been proposed to achieve these goals. For the hearing-impaired people, the sign language recognition system for home-service-related TSL words is also implemented in this dissertation so that HPS can assist them in their daily lives. The sign language recognition system utilizes HMM to classify the sequential data. However, there are two issues to be solved in HMM. One is the number of states, and the other is the determination of structure of HMM. To determine the number of states in HMM, this dissertation proposes the Entropy-Based K-means algorithm to plot the entropy diagram, which provides a visualization method to judge the number of clusters for real datasets. Finally, this dissertation utilizes the ABC algorithm to establish the structure of HMM.

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