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    題名: 階層式多物件影像分割應用於家用物品之研究
    其他題名: Research of hierarchical methods for multi-objects segmentation in home environment
    作者: 黎均毅;Li, Chun-I
    貢獻者: 淡江大學電機工程學系碩士班
    江正雄;Chiang, Jen-Shiun
    關鍵詞: 機器視覺;立體視覺;影像分割;物件辨識;Robotic vision;Stereo Vision;Image Segmentation;Object recognition
    日期: 2012
    上傳時間: 2012-06-21 06:48:48 (UTC+8)
    摘要: 近年來隨著科技與工業的發展,機器人的技術目前已越來越加成熟,並已成功應用在不同領域上。在居家服務與照料上,有許多項目漸漸可由機器人所取代,居家服務機器人的需求與日俱增。然而設計一位居家服務機器人,必須具有導航定位、與家人互動、與協助服務照料家人等等功能。而在解決上述問題之前,其最重要的輸入端為機器人之雙眼,機器視覺作為系統輸入端的影像分析判斷是相當重要的。在居家服務的環境中,居家物件的辨識為相當重要的一個環節。物件辨識的方法有許多種,如直方圖比對、樣版比對、特徵點提取、Adaboost、SVM、類神經網路學習演算法等等….儘管如此,現今大多的物件辨識方法都依賴已建立的資料庫或是仰賴大量樣本訓練學習才能有效進行物件辨識,一旦在居家環境中出現了系統內資料庫未建立的居家物品,就得靠off-line的手動建置模型,增加了居家辨識系統或是機器人在環境中 服務的不方便性。
    為解決以上問題,本論文提出了一個結合深度影像與Grab Cut的影像分割與建置模型的方法,本系統分割物件為階層式設計,在Coarse Layer中利用深度影像找出多個物件的大概位置與大小,經由後端Fine Layer使用Grab Cut精細切割出物件邊緣並建置模型。一開始透過雙眼視覺輸入經匹配產生視差圖,以視差圖為基礎,感知環境中的立體資訊,同時濾除過遠背景與透過視差圖的直方圖分割將物品個別分割,並透過Grab Cut對遮罩影像進行收斂分割出完整的物件邊緣,最後進行SIFT/SURF特徵點提取影像辨識更新資料庫。完成全自動化且多物件的分割建置模型。透過上述程序,不同於其他物件辨識,本研究能在非固定背景下針對靜態居家物品完成全自動建模,且物件資訊完整度能接近手動建置模型的完整度,並且在更新的資料庫下辨識能保持一定的辨識率。
    As the growth of technology and industry in the past few years, the robotic technique has been fully developed and applied in many different fields. In homecare and home service, many function can be replace by robots. To design a homecare robot must be proved with many functions, like navigation and positioning, interaction, serving for family members and so on. Before solving the following problems, robot vision is very important for system input to analysis images. There are many methods to recognize objects for robot vision, such as histogram compare, Adaboost, SVM, template matching, and etc. But most of them depend on database to classify the models in database and object recognition. If some objects which don’t exist in the database appear in images, it must construct models for database by manual. It is inconvenient to recognize objects and increase the cost of home service robots.
    This paper proposed a new method which combined depth image processing and Grab Cut to segment objects and saving models from image, it can solve above problems. We present a hierarchical scheme with coarse layer segmentation and fine layer segmentation for object segmentation. It can fine the coarse location and area of multi-objects by processing depth image in coarse layer. Then, it can well segment objects and find its’ contour by using Grab Cut in fine layer. Finally, the proposed method uses SIFT/SURF to extract feature points and recognize objects. The proposed method can automatically segment static objects in different home environment and correct data of objects can be close to segmentation by manual. The automatic constructed image database can maintain good recognition accuracy rate.
    顯示於類別:[電機工程學系暨研究所] 學位論文

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