臺灣博碩士論文加值系統

近年來，由於MPEG-4快速的發展，以物件為基礎的視訊編碼及應用更是引起廣泛的研究興趣。從影像中擷取出來物件可以應用在基於物件之虛擬視訊會議環境和監視系統…等等。而從影像中將物件切割出來的技術最主要有兩個困難點，第一是物件本身跟和低階的特徵沒有相對應的關係，第二是如何有效地從影像中將物件切割出來。
在以往相關的研究中，大部分的人都是使用靜止的攝影機並且是在一個固定不動的背景情況下運作，如此便限制了使用者的活動區域。在本論文中，我們提出一個穩健且快速的從影像中切割出物體的方法和一個以主動式攝影機穩定的追蹤物體的方法。這個方法完全不需要事先知道物體的形狀。在整個實驗系統中，我們可以將物體和背景分離，並使用主動式攝影機加以追蹤，如此可將切割出來的物體鎖定在影像的中心區域。除此之外，整個系統可以在沒有特殊限制的環境中使用，且不需要其他特別的硬體設備。
所提出的物件切割方法主要是利用背景相減法、型態運算子、區域成長法、適應性機制、樣版比對和一些自行定義的運算。而整個系統可以每秒處理15張 176 x 144 (QCIF) 的影像。

Growing interest arises in segmentation for object-based video clips since the development of MPEG-4 standard. The moving object extraction can also be applied to the object-based videoconference, surveillance, and so on. The difficulties of moving object segmentation are that physical objects are normally not homogeneous with respect to low-level features and it’s usually tough to segment them efficiently.
The previous related researches are only operated with a static camera and in a stationary background. In this thesis, we propose a robust and fast segmentation algorithm and a reliable tracking strategy without knowing the shape of the object in advance. The system can segment the foreground from the background and track the moving object with an active (pan-tilt zoom) camera such that the moving object always stays around the center of images. Especially, the system can work in an unrestricted environment without the need for special purpose hardware.
The proposed segmentation algorithm is based on the background subtraction, morphological operations, region growing, adaptive mechanism, template matching, and some innovative operations. The system can segment a moving object at 15 frames per second over a 176 x 144 pixel image.

Abstracti
Table of Contentsii
Chapter 1: Introduction1
1.1Moving Object Extraction1
1.2Motivation2
1.3Thesis Organization2
Chapter 2: Construction of Mosaic Images3
2.1 Image Alignment4
2.2 Image Integration7
2.3 Mosaic Construction and Utilization in our System8
Chapter 3: Moving Object Extraction and Tracking13
3.1 Overview of the Proposed System14
3.2 Object Segmentation Based on Background Subtraction Method15
3.3 Tracking of Moving Object and Dynamic Segmentation23
3.3.1 Template Matching25
3.3.2 Detecting the Color of Skin27
3.3.3 Detection and Tracking of the Moving Object27
Chapter 4: Experimental Results29
4.1 Updating Background Model29
4.2 Object Extraction31
4.3 Detecting the Skin Color32
4.4 Tracking the Feature33
4.5 Template Matching34
Chapter 5: Summary and Future Work35
Reference37

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