二維形狀特徵在圖像分類的評估｜國立清華大學博碩士論文庫

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研究生：	簡嘉漢 Chia-Han Chien
論文名稱：	二維形狀特徵在圖像分類的評估 Performance Evaluation of 2D Shape Features for Pattern Classification
指導教授：	陳朝欽 Chaur-Chin Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2005
畢業學年度：	93
語文別：	英文
論文頁數：	1冊
中文關鍵詞：	二維形狀、分類、輪廓、區域、動量
外文關鍵詞：	2D Shape, classification, contour, region, moment
相關次數：	點閱：2 下載：0
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近年來以內容為基礎的影像擷取系統已成為很熱門的研究領域，而影像特徵值的擷取和圖形分類在此系統架構佔極重要的部份。在影像裡形狀是人類視覺分別物體的重要特徵。本論文即針對二維形狀特徵在圖形分類上作評估，主要以區域(Region-Based)和輪廓(Contour-Based)兩個觀點，分別用Moment Invariants, Zernike Moments, Pseudo Zernkie Moments, Angular Radial Transform, Fourier Descriptor, Improved Moment Invariants等方法擷取特徵值。再把已擷取的特徵值分別用不同分類器，包括支撐向量機器來分類。我們使用了三個資料庫共252張影像來作測試，由實驗結果可知，以區域形狀的方法會有較佳的正確率。

Object shape features are powerful when used in similarity search and retrieval.
In a recognition system, feature extraction and classifiers play an important role. We just focus on both feature extraction and classifiers. In this thesis, we discuss two major ways to extract shape features: region-based shape, contour-based shape. The methods of extracting shape features include moment invariants, Zernike moments, Pseudo Zernike moments, Angular Radial Transform (ART), improved moments, and Fourier descriptors. Then we introduce a classifier SVM. A comparison with different feature extraction for different classifiers will be tested on 3 databases composed of 252 128x128 images, such as DB1, DB2, DB3 from PRIP Lab at NTHU. Finally, we show the simulation results and make a conclusion.

Chapter 1 Introduction ................................................................................................. 1
Chapter 2 Methods of Feature Extraction..................................................................... 3
    2.1 Moment Invariants.......................................................................................... 3
    2.2 Zernike Moments………................................................................................ 5
    2.3 Pseudo Zernike Moments............................................................................... 6
    2.4 Angular Radial Transform.............................................................................. 8
    2.5 Fourier Descriptors......................................................................................... 9
    2.6 Improved Moment Invariants.........................................................................11
Chapter 3 Support Vector Machine..............................................................................13
    3.1 The Maximal Margin Classifier.....................................................................13
    3.2 Soft Margin Optimization..............................................................................16
    3.3 Feature Transform and Kernel SVM.............................................................17
    3.4 Multi-calss classification…………...............................................................19
Chapter 4 Experimental Results .................................................................................20
    4.1 Data Description……… ...............................................................................20
    4.2 Experimental Results…….. ..........................................................................21
Chapter 5 Conclusion …………………......................................................................26
References ...................................................................................................................27

                                

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