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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/35896

    Title: 以邊緣偵測為基礎之多重影像物件切割與追蹤
    Other Titles: Image segmentation and tracking on multiple objects by edge detection method
    Authors: 莊欽龍;Chuang, Cheng-long
    Contributors: 淡江大學電機工程學系碩士班
    蕭瑛東;Hsiao, Ying-tung;簡丞志;Chien, Cheng-chih
    Date: 2005
    Issue Date: 2010-01-11 07:17:39 (UTC+8)
    Abstract:   現今許多新式之影像壓縮技術如MPEG-4與MPEG-7,皆支援以物件為基礎(object-based)之多媒體編碼方式,使得視訊影像中的物件可支援互動、搜尋、以及交換等功能。在支援這些功能的同時,針對視訊影像中所有物件的切割與追蹤則為非常重要的關鍵技術。本文將提出一套具系統性之演算法,分別結合了邊緣偵測(edge detection)、影像切割(image segmentation)、以及軌跡預測(trajectory estimation)之技術,來完成影像物件追蹤之功能。

      本文所提出之切割影像方法是以物件輪廓為基礎。為了切割出更精確的影像物件,一般舊有的影像輪廓偵測技術並不敷需求;例如Sobel演算法可以有效地偵測明顯物件之輪廓,但許多細緻之邊緣與背景邊緣則無法順利萃取;或如DoE(difference of exponential)演算法,雖然可以偵測出更強烈的邊緣,但仍舊無法偵測細緻的邊緣,然而,細緻邊緣在影像中時常扮演著極重要角色,卻也因為細緻之故,以致於普遍被忽略或是無法處理。本文採用型態影像學(mathematical morphology)之理論,發展出具有加強描繪細微特徵之輪廓偵測演算法。利用邊緣存在之背景之平均灰階值以及標準差,我們可以判斷該邊緣是否應該被強化,以便在對整個影像使用全域閥值時,得以成功的萃取出該細緻邊緣。

      影像物件切割的部份,過去有許多影像切割演算法被提出,例如蛇模型(snake energy model)以及分水嶺演算法(watershed algorithm)等。蛇模型初始時需要一個初始輪廓,並且利用其內部能量(internal energy)與外部能量(external energy)來引導蛇模型之輪廓去逼近需要切割物體之輪廓。而其中內部能量乃為蛇模型之輪廓曲張之程度,而外部能量則為輪廓所經過位置之影像梯度,當兩個能量達到平衡收斂時,蛇模型之輪廓即達到理想狀態。分水嶺演算法則是模擬淹水的原理,在影像低點開始注入水,當兩個注水來源相遇時,該分界線則為分水嶺。而然,分水嶺演算法確有著過度分割的問題存在。本文所提出之影像切割演算法,可適用於全域影像分割,或使用於特定影像物件分割。利用在影像中植入區域生長點(growing seed),使所有點蔓延(region growing)整張圖或特定影像物件後,進行區域結合(region merging),即可將影像或物件萃取出來。而區域生長點之生長規則,則是由一個類似蛇模型之能量公式控制,使得生長過程中得以萃取出具有意義之物件邊緣。


    Many video compression standards, such as MPEG-4 and MPEG-7, have supported object-based multimedia coding that allows user to interact, search and exchange the objects in the images or video sequences. For supporting these features, the object segmentation and tracking in the video sequences play an essential and important role. This thesis proposes a solution algorithm to track one or multiple moving objects in frames of a video sequence, including edge detection algorithm, image segmentation algorithm and trajectory estimation functions.

    The object segmentation algorithm proposed in this thesis is based on exploring contour of the image. Therefore, to extract the desired objects with more precisely, an effective method for extracting the contour of the image is needed. The conventional edge detection algorithm is no longer satisfy there requirements. For example, Sobel’s edge detector can successfully sketch out the apparently contour of the objects. However, most of the thin edges in the image normally be eliminated by Sobel’s edge detector. The DoE (difference of exponential) method is able to track the stronger edges of the image, but the performance of extracting thin edges is not acceptable. However, in many cases, thin edges represent important features in the image, and should not be eliminated or discarded. This thesis presents a novel mathematical morphology based edge detector to enhance the performance of extracting thin edges in a still image. According to the mean value and standard derivation of the pixel in the image, the proposed method can enhance thin edges in the image for extracting by a global threshold value.

    After the edge detection process, this thesis proposes for applying a novel object segmentation algorithm to the image for extracting objects of the image. There are several popular algorithm had been developed for image segmentation, such as snake energy model and watershed algorithm. For snake energy model, it requires a manually-drawn initial snake and adjusts weighting parameters in the snake model. The snake is controlled by two energies, which are internal energy and external energy. The snake iterations are converged when these two energies reach to a balanced state. As to the watershed algorithm, it has the drawback of over-segmentation problem. This thesis presents a novel edge-based image segmentation algorithm that is capable of performing global image segmentation or segmenting desired objects in an image. The proposed algorithm provides more effective segmentation result than other methods by region growing method. A snake-energy-like cost function is developed to control the growing process for the algorithm to produce better segmentation results. While the growing phase is completed, the algorithm combines homogeneous regions together to extract more meaningful image objects.

    The segmentation algorithm proposed in this thesis is initialized by planting growing seeds into the image. Therefore, to extent our algorithms to video object tracking, this study proposes a scheme based on the previously segmentation result for automatically planting growing seeds into following video frames to extract the same objects on the following frames by the proposed scheme.

    Experimental results show that the proposed algorithms produce good performance on object segmentation and tracking.
    Appears in Collections:[電機工程學系暨研究所] 學位論文

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