臺灣博碩士論文加值系統

物件抽取技術(video object extraction)之龐大計算量是MPEG-4物件導向(Object Based) 功能尚未被工業界廣泛應用的主要原因之一。本論文提出一套多重解析度之物件抽取演算法來降低計算量，並維持穩定的物件抽取品質。一般而言，在適當的低解析度上，物件輪廓仍十分鮮明，我們的想法是在適當解析度上作物件抽取，以達到降低計算量的目的。然而，視訊次取樣 (Subsampling) 到適當之低解析度, 再投影回原解析度之過程中會衍生許多問題，例如: 次取樣層次越多，物件邊緣資訊也會流失越多，所以在得到低解析度的過程中，如何盡量減少重要邊緣資訊的流失是一個挑戰；再者，由低解析度投射回原解析度會衍生鋸齒方塊效應的問題，我們也提出解決的辦法。
之後我們又提出一套動態背景之物件抽取演算法，用應用於戶外監視系統之物件抽取。我們使用動作向量(Motion-vector)作為判斷物件之依據，再利用平均(mean) 觀念與中值濾波(median filter)將背景動作向量壓抑，如此物件動作向量就可被凸顯出來，之後再利用簡單的判斷式將物件抽取出來，如此便可解決之背景改變之問題。

The complex computation and quality of video object extraction is one of the main reasons that the object-oriented function of MPEG-4 could not be widely applied.In this paper, we propose a multi-resolution video object extraction to decrease the computation complexity and maintain the stable quality of video extraction. In general, the object contour is still clear in the adequate low resolution, thus we will extract the object in the proper resolution to decrease the computation complexity. However, there exists some problems when video subsamples to the adequate goal resolution and then projects to original resolution. By the more and more subsample layer, the object edge data lose more, so how to decrease the lost of significant edge data in the low resolution will be challengeable. Moreover, we also propose a method to solve the problem of block effect when projecting from low resolution to original resolution.
we propose a dynamic-background object extraction algorithm on object extraction of outdoor surveillance system. First, we adopt a method by applying the Motion-vector as the foundation of judging object and then adopt the conception of mean and median filter to suppress the motion vector of background. As a result, the motion vector of object can be presented obviously. Secondly, we use simple formulation to extract the object. Thus, we can solve the problem of the change of background.

第一章：緒論，簡單介紹背景及方法。
第二章：多重解析度之背景介紹，canny-edge detection以及型態濾波的介紹及物件粹取演算法之評估準則。
第三章：針對視訊分割演算法過於繁複的問題，提出多重解析度之概念來降低演算法之複雜度，並針對多重解析度帶來之問題(ex:鋸齒效應，目標解析度)，提出解決以及建議，並利用動量保持法，建立靜態背景資訊。
第四章：針對動態背景，提出一套針對動態背景之物件粹取演算法。
第五章：結論與未來方向。

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