臺灣博碩士論文加值系統

傳統的嘴唇偵測方法是由可分別嘴唇與非嘴唇的二元分類器，緊接著某些種搜尋的方法，搜尋的方法可能是依照某些人體解剖學的資訊或是經驗法則。這類的搜尋方法當它們在搜尋的時並不會學習任何結構的資訊。在此提出一個新的嘴唇偵測方法。這個方法依照臉上的結構資訊同時解決分類與搜尋。這個新提出的方法有三個主要的部份。首先，運用顏色與形狀的資訊來做人臉偵測並用局部自動相關函式(local autocorrelation function)以及全面最大差異﹙global maximum difference ﹚做為臉部特徵。第二，特徵然後藉由自組網路(self organizing map)來聚集。第三，藉由將嘴唇偵測以馬可夫決策過程(Markovian decision process)來模型，加強式學習(reinforcement learning)被用來找出最佳方針(optimal policy)，藉由實驗結果來說明這個方法的能力。

Traditional lips detection methods consist of a binary classifiers which can classify lips and nonlips followed by some search algorithms. The search algorithms may depend on some face anatomatical information or heuristics. They do not learn any structural inforamiton while searching. A new lips detection approach is proposed. It solves classificaition and search at the same time based on structural infomation in faces. There are three main parts in this proposed approach. First, face detection is based on color and shape information and autocorrelation functions and golbal maximum difference are used as features for face image. Second, features are then clustered by self organizing map to form states. Third, by modeling the lips detection as a Markovian decision process, reinforcment learning is applied to find a optimal policy. The capability of this approach is demostrated by experimental results.

第一章導論
第二章 人臉偵測與特徵選取
第一節 人臉偵測
第二節 特徵選取
第三章 自組網路
第四章 加強式學習
第一節 有限離散時間的馬可夫決策過程
第二節 使用Q-learning找出最佳策略
第五章 實驗結果
第六章 結論

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