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研究生:蘇信宏
研究生(外文):Xin-Hong Su
論文名稱:數位學習情意偵測專心程度之影像處理
論文名稱(外文):The Image Processing of E-Learning Affective Detection on the Degree of Concentration
指導教授:蘇祖澤蘇祖澤引用關係
指導教授(外文):Tsu-Tse Su
學位類別:碩士
校院名稱:北台灣科學技術學院
系所名稱:機電整合研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:60
中文關鍵詞:人臉偵測影像處理
外文關鍵詞:Face DetectionImage Processing
相關次數:
  • 被引用被引用:7
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  • 下載下載:119
  • 收藏至我的研究室書目清單書目收藏:2
數位學習已推展多年,但仍被視為輔助教學,其主因為鬆散的自我學習或自主學習,其成效不易彰顯。有鑑於此,本研究提出一影像處理與專心程度分析模式,利用網路攝影機偵測與紀錄學習過程中,臉部與眼睛的影像變化,再進行專心程度分析。藉由此分析結果,可提供後續學習環境、教學方法與教學進度等數位學習情境之調整,以提升自我學習之成效。
本文所提出的專心程度分析模式,是以目前普遍的網路攝影機攝取學習者的平面影像,取像時間為隨機,每次取連續的五張影像,再整合自動白平衡、直方圖均衡、膚色辨識、形態學斷開與閉合與元件標記等各項影像處理技術,找出影像中人臉位置,接著尋找人臉範圍中的雙眼,最後再由雙眼影像分析開閉狀況,藉此辨別學習者的專心程度等級。
本文所提出之影像處理與專心程度分析模式,具以下優點:(1)可自動化掌握學習者情意-專心程度;(2)以一般市售低成本之網路攝影機與個人電腦即可進行數位學習,有助於數位學習的推展與普及;(3)利用電腦有系統的偵測與紀錄多個學習者的學習狀態,遠優於傳統教學單一教師的片段記憶。經實驗證明,相關演算法確實可行,且數位學習時均能快速反應於學習螢幕,不需太多的等待,故本研究可充分推廣於線上數位學習。
E-Learning has promoted many years, but is still regarded as the assistance teaching, the dominate factor was the loosely-coupled self-study or independent study was not easily showcased the result. Therefore, this research proposes an image processing and the absorbed degree analysis mode, using the webcam to detect and record the change of face images and eye images captured during a course study, then carries on the concentration degree analysis. Affiliation from this analysis result, May provide the following learning environment, the teaching method and the teaching progress and so on contextual adaptation of E-Learning, to promote result of the self-study.
In this article, it proposes the concentration degree analysis mode, captures the learner image by the universal webcam, takes likes the time for to be stochastic, takes continuous five images each time, performs the integration of auto-white-balance, histogram equalizer, color recognizing, dilation and erosion of morphology, the part marking and so on image processing technology, locates the person face position in the image, and seeks eyes in the person face scope, finally analysis opens the eye or shut the eye condition by the binocular image, by use of this distinguishes the learner concentration degree.
This article proposes image processing and the concentration degree analysis pattern, has following merits: (1) may automatically capture the learner affection-concentration degree; (2) may easily start E-Learning by the general market low cost webcam and the personal computer that is beneficial to E-Learning promoting with the popularization; (3) may systematically use the computer to detect and record many learners’ study conditions, far surpasses the traditional teaching sole teacher's fragment memory. Proved after the experiment, the corresponding algorithms are truly feasible, also when E-Learning can rapidly response from the monitor, not have too much waiting, therefore this research may fully promote to on-line E-Learning.
摘 要 i
ABSTRACT iii
致謝 v
目錄 vi
圖目錄 x
表目錄 xii
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 論文架構 2
1.4 文獻探討 4
第二章 彩色影像擷取 7
2.1 前言 7
2.2 系統環境 7
2.3 偵測流程 8
2.4 影像擷取 9
2.5 結論 11
第三章 影像前級處理 13
3.1 前言 13
3.2 直方圖等化 13
3.3 自動白平衡 15
3.4 色彩正規化 17
3.5 結論 18
第四章 膚色偵測 19
4.1 前言 19
4.2 膚色辨識模型 19
4.3 背景藍色去除 21
4.4 結論 23
第五章 區塊通連群聚 24
5.1 前言 24
5.2 二值化 24
5.3 雜訊去除 25
5.4 區塊通連群聚 26
5.5 結論 29
第六章 人臉偵測與辨識 30
6.1 前言 30
6.2 人臉偵測 30
6.3 人臉辨識 31
6.4 結論 34
第七章 人眼偵測與辨識 35
7.1 前言 35
7.2 人眼偵測 35
7.3 人眼辨識 37
7.4 人眼張閉判斷 44
7.5 結論 47
第八章 實驗成果與結論 48
8.1 前言 48
8.2 專心程度決策準則 48
8.3 實驗成果 49
8.4 結論 57
8.5 未來展望 57
參考文獻 59
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