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研究生:曾昱翔
研究生(外文):Yu-Hsiang Tseng
論文名稱:針對機器人足球賽之新式顏色辨識與模糊為基底的色彩補正方法
論文名稱(外文):A Novel Color Detection Method and Fuzzy-based Color Correction Method for Robot Soccer Competition
指導教授:蔡舜宏蔡舜宏引用關係
口試委員:陳大道鄭志強蕭俊祥
口試日期:2012-07-20
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
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:2012
畢業學年度:100
語文別:中文
論文頁數:68
中文關鍵詞:機器人足球顏色辨識模糊控制器視覺系統
外文關鍵詞:Robot SoccerColor DetectionFuzzy ControllerVision System
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本論文針對機器人足球賽,建立一資料庫並提出一新式顏色辨識演算法以降低程式執行時間並提高識別對象的辨識成功率。且透過實驗結果驗證其辨識成功率被提升,且其執行時間能有效降低。
由於比賽用球為光滑材質的橘色球,光滑材質在高亮度下,顏色集群將產生非線性變化,並會造成色彩失真。因此,本論文提出一個基於模糊理論的色彩補正方法去修補色彩失真的區塊,使其失真部分能被正確辨識。
此外,本論文提出一種適用於機器人足球賽的色彩分割方法,此方法能夠使得主體目標顏色更容易設定及擷取。最後透過實驗證實,所提出方法能提升機器人足球賽視覺系統之效能。


In this thesis, a database is constructed and a novel color detection algorithm is proposed to reduce the running time and increase the success rate for recognizing the object. Through the experiment result, one can observe that the proposed method indeed can increase the success rate and reduce the running time.
Since the material of orange game ball is glossy, the color clusters of glossy materials under high brightness will vary with a non-linear trend and may cause color distortion. Therefore, in this thesis, a fuzzy-based color correction method is propounded to correct the distortion for obtaining the exact color detection.
Furthermore, this thesis proposes a color segmentation method which is especially suitable for the robot soccer competition. It provides a simpler method for user to capture the colors of the main object. Finally, the experiment results demonstrate that the proposed method can improve the efficiency in the robot soccer competition vision system


中文摘要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 論文架構 4
第二章 機器人足球系統與色彩空間簡介 5
2.1 人形機器人足球系統簡介 5
2.2 人形機器人足球系統之硬體設備 7
2.3 色彩空間簡介 10
第三章 以模糊為基底的色彩補正方法 17
3.1 橘球顏色定義 17
3.2 方法架構 20
3.2.1 過亮橘色定義 21
3.2.2 元件連通法 22
3.2.3 模糊理論 24
3.2.4 過亮橘球色彩補正模糊系統設計 27
3.3 實驗結果 31
第四章 新式顏色辨識方法 34
4.1 前言 34
4.2 顏色偵測 35
4.2.1 傳統顏色偵測方法 35
4.2.2 資料庫的建立 36
4.3 新型影像處理演算流程 38
4.3.1 形態學(Morphology) 39
4.3.2 邊緣偵測 41
4.3.3 RCD圓形偵測 42
4.4 球偵測實驗測試 43
4.5 應用新式顏色辨識方法之偵測結果分析比較 44
第五章 機器人影像辨識系統 50
5.1 彩色影像分割 50
5.2 機器人目標資訊 53
5.3 場地校正 57
5.3.1 場地旋轉校正 57
5.3.2 影像高度校正 62
5.4 實際測試結果 63
第六章 結論 65
參考文獻 66


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