腦部損傷及其相關疾病在台灣甚或全世界都是主要的健康問題。生物學家為了瞭解神經血管網絡在損傷後的病生理特徵與修復機制，藉由分析腦損傷前後之斑馬魚的行為，進而了解神經血管網絡再造與癒合所對應的生物行為模式。因此在本論文中，我們提出了一個追蹤多隻斑馬魚的軟體工具，個別追蹤斑馬魚並且記錄其軌跡，再加以量化腦損傷與修復模式其相對的行為，以利於生物學家的研究與觀察。
而在影像處理之領域中，多物件追蹤問題已被探討多年。在多物件影像追蹤技術裡，物件與物件之間的遮蔽現象(Occlusion)為首要面對的問題之一，Occlusion現象的發生會造成資訊被遮蔽，導致多物件追蹤容易發生錯誤。所以在本論文中，針對多隻斑馬魚之Occlusion現象，我們提出了兩種分離技術(1)外插法(Extrapolation method)與(2)骨骼分析法(Skeleton analysis method)分離發生遮蔽的物件。這兩種方法都是基於分析由影像形態學(Morphological Image Processing)所萃取的魚隻骨骼。藉由比對骨架所提供的資訊，找出影片中物件前後之關聯性，以達到個別追蹤多隻斑馬魚的目的。由論文的最後模擬結果來看，我們所提出的追蹤方法提供有效率的追蹤以及可接受的追蹤準確率。

Brain injury and its related diseases is the major health problem in Taiwan as well as around the world. For biological purpose, biologists may want to understand the corresponding behavior change of a fish between brain lesion and reconstruction. So in this thesis we present a computerized video-tracking tool, by using the image processing techniques to tracking multiple zebrafish, recording their swimming path and quantify their behavior.
In the viewpoint of video processing, multiple object tracking have been studied for a long time and handling the occlusion also has been one of the major problems. Once occlusion occurs, objects are occluded by others, it may cause tracking errors. So separating occlusion objects is an important task for multiple object tracking. In this thesis, in order to solve occlusion problems, we propose two structural separating methods (i) extrapolation method and (ii) skeleton analysis method. Both methods are based on analyzing the fish skeleton which is obtained by morphological image processing. And then by matching the associations which are extracted from skeleton between consecutive frames, we can track fish individually. At the final part of this thesis, the experiment results show our separating methods are efficient and having acceptable accuracy.

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