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Author: 楊依哲
Yi-che Yang
Thesis Title: 自動模糊影像的偵測與分類系統
An Automatic System for Blurred Image Detection and Classification
Advisor: 吳怡樂
Yi-Leh Wu
Committee: 陳建中
Jiann-Jone Chen
唐政元
Cheng-Yuan Tang
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2012
Graduation Academic Year: 100
Language: 英文
Pages: 37
Keywords (in Chinese): 模糊影像奇異值分解特徵值梯度Hough Transform直線偵測
Keywords (in other languages): Blurred regions detection and classification, Singular value features, Gradient, Hough Transform, Dilation algorithm, Erosion algorithm
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  • 由於數位相機與數位相片普及化,使得接踵而來的數位資料劇增。使用者可以輕易透過數位設備取得數位影像資料,但是礙於設備的優劣或使用者本身的習慣,往往因為數位影像取得的不當而造成影像的模糊。模糊影像的處理一直是多媒體分析上的問題,我們提出一個自動模糊影像的偵測與分類系統,可以為使用者從大量的數位影像中偵測模糊影像,並將其分類。

    在偵測方法上,我們分析模糊影像的奇異值分解結果,同時擷取重要的特徵值,將兩者作為模糊影像偵測的標準。在模糊影像分類的部分,我們計算模糊影像的梯度,並利用奇異值分解與梯度之間的關係,透過Hough Transform的直線偵測方法達到模糊影像分類的目的。由實驗結果顯示我們的方法確實有助於改善模糊影像的處理,同時為多媒體分析與電腦視覺的應用提供了良好的準備工作。


    This work proposes an effective and simple automatic image blurred region detection and classification method. Many digital photos contain defocus blurred or motion blurred regions. To detect the image blurred regions, we examine the singular value feature for each image pixel by pixel. And for the image blurred classification, we compute the gradient with the Hough Transform for each image pixels, and use the dilation / erosion algorithms to increase accuracy. Experiment results show that the proposed method can detect image blurred regions effectively, and classify defocus blur regions and motion blur regions accurately.

    論文摘要 Abstract Contents List of Figures List of Tables Chapter 1. Introduction Chapter 2. Design Overview Chapter 3. Proposed Methods 3.1 Blurred Image Detection 3.1.1 Singular Value Decomposition (SVD) 3.1.2 SVD in Proposed Technique 3.2 Blurred Image Classification 3.2.1 Estimated Gradient 3.2.1.1 Cross Type 3.2.1.2 Adjacent Type 3.2.2 The Hough Transform 3.2.3 Dilation and Erosion Operators Chapter 4. Experiment 4.1 Experiment Data 4.2 Experiment Result Chapter 5. Conclusion and Future Work Reference

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