博碩士論文 955201019 詳細資訊




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姓名 林仕庭(Shih-ting Lin)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 使用分級時序記憶實作視角無關手勢辨識問題
(View-Independent Hand Gesture Recognition using Hierarchical Temporal Memory)
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摘要(中) 肢體語言辨識(Gesture Recognition)為人機互動(Human Computer Interaction)運用中一項重要的技術,其中視角無視辨識(View-Independent Recognition)為機器視覺辨識的難題。為了賦予機器學習模型(Machine Learning model)辨識事物於不同視角的能力,時間資訊的運用是一道線索。然而多數的機器學習模型本質為辨識模型(discriminative model),運算複雜度的問題使其困難於運用時間資訊,並被認為欠缺對輸入訓練資料的歸納性(generalization)與藉由過去經驗幫助新事物學習,增進學習(incremental learning)的能力。
分級時序記憶(Hierarchical Temporal Memory)為近年新發展的機器學習模型。根據人類大腦皮質的運算假說:記憶預測架構,建構非辨識模型(non-discriminative model)。分級時序記憶利用時間資訊行使非監督式學習,使機器學習模型具備歸納訓練資料與增進學習的能力,同時達到可信賴的辨識結果。本論文使用電腦視覺演算法與分級時序記憶實作兩個手勢辨識問題,於視角變動的連續影像的單張辨識中(snap shot)分別得到辨識正確率91%與84%的辨識結果。
摘要(英) Gesture Recognition is importance in designing efficient Human Computer Interaction (HCI) applications and View-Independent Recognition is one of a difficult computer vision gesture recognition problem. Temporal information is a clue to provide the ability to recognize object in variant phase for Machine Learning model. However, most of the Machine Learning Model is discriminative model. It has computational complexity problem for using temporal information and proves inadequate at the ability of training data generalization and incremental learning essentially.
Hierarchical Temporal Memory is a novel Machine Learning model studying in recent years. According to the memory prediction framework hypothesis of brain new cortex, Hierarchical Temporal Memory builds a non-discriminative model using temporal information to do unsupervised learning. Try to achieve training data generalization and incremental learning ability without losing recognition reliability. Combining computer vision image process algorithm and Hierarchical Temporal Memory Machine Learning model, a hand gesture recognition system was built in this paper. Two continuous view-point change recognition problems was tested, the continuous image sequence snap shot recognition accuracy results were 91% and 84% respectively.
關鍵字(中) ★ 分級時序記憶
★ 手勢辨識
★ 視角無關辨識
★ 機器學習
關鍵字(英) ★ Hierarchical Temporal Memory
★ Hand Gesture Recognition
★ View-Independent Recognition
★ Machine Learning
論文目次 第一章 緒論 .......................................................................................................................................... 1
1.1 研究背景與目的 .......................................................................................................................... 1
1.2 過去手勢辨識研究回顧 ............................................................................................................... 1
1.3 論文組織 ..................................................................................................................................... 4
第二章 辨識問題與分級時序記憶 ....................................................................................................... 5
第三章 分級時序記憶演算介紹 ......................................................................................................... 10
3.1 分級時序記憶網路架構 ............................................................................................................. 10
3.2 節點運算 .................................................................................................................................... 11
3.2.1 學習階段運算 ...................................................................................................................... 12
3.2.1.1 樣式記憶 ......................................................................................................... 12
3.2.1.2 轉換機率學習 ................................................................................................. 13
3.2.1.3 時序分群 ......................................................................................................... 15
3.2.2 推論階段運算 ...................................................................................................................... 16
3.3 階層運算 ................................................................................................................................... 17
3.3.1 階層運算流程 ..................................................................................................................... 17
3.3.2 貝氏訊息傳播 ..................................................................................................................... 19
3.4 分級時序記憶歸納性與增進學習 ............................................................................................ 21
3.5 非辨識模型 ............................................................................................................................... 21
第四章 影像處理演算法 ..................................................................................................................... 23
4.1 使用統計模型的前景擷取演算法 ............................................................................................ 23
4.2 物件連通演算與雜訊去除 ........................................................................................................ 25
4.3 膚色偵測 ................................................................................................................................... 26
4.4 邊緣偵測 ................................................................................................................................... 27
4.5 手掌區域偵測 ............................................................................................................................ 28
4.6 影像維度正規化 ........................................................................................................................ 30
4.7 賈伯濾波器 ............................................................................................................................... 31
4.8 分級時序記憶機器學習模型 .................................................................................................... 32
4.9 支持向量機分類器 .................................................................................................................... 32
第五章 手勢辨識 ................................................................................................................................ 34
5.1 辨識問題敘述 ............................................................................................................................ 34
5.2 辨識環境 ................................................................................................................................... 35
5.3 分級時序記憶訓練流程 ............................................................................................................ 35
5.4 辨識效能驗證 ............................................................................................................................ 38
5.4.1 辨識錯誤分析 ..................................................................................................................... 39
5.4.2 猜拳手勢辨識 ..................................................................................................................... 43
5.4.2.1 剪刀手勢 ......................................................................................................... 43
5.4.2.2 石頭手勢 ......................................................................................................... 44
5.4.2.3 布手勢 ............................................................................................................. 45
5.4.3 手指數手勢辨識 ................................................................................................................. 47
5.4.3.1 手指數一 ......................................................................................................... 47
5.4.3.2 手指數二 .......................................................................................................... 48
5.4.3.3 手指數三 ......................................................................................................... 49
5.4.3.4 手指數四 ......................................................................................................... 50
5.4.3.5 手指數五 ......................................................................................................... 51
5.5 分級時序記憶生成模型特性 .................................................................................................... 53
5.6 時序推論輸出統計辨識 ............................................................................................................ 55
第六章 結論與未來展望 ..................................................................................................................... 60
6.1 結論 ........................................................................................................................................... 60
6.2 未來展望 ................................................................................................................................... 61
參考文獻 .............................................................................................................................................. 62
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指導教授 陳竹一、魏慶隆
(Jwu E Chen、Chin-Long Wey)
審核日期 2009-8-16
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