Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7937
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dc.contributor廖和恩zh_TW
dc.contributor廖俊睿zh_TW
dc.contributor.advisor陶金旭zh_TW
dc.contributor.author吳坤倫zh_TW
dc.contributor.authorWu, Kun-Lunen_US
dc.contributor.other中興大學zh_TW
dc.date2009zh_TW
dc.date.accessioned2014-06-06T06:40:45Z-
dc.date.available2014-06-06T06:40:45Z-
dc.identifierU0005-1108200820084200zh_TW
dc.identifier.citation[1] J.C. Harsanyi and C.I. Chang, “Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection,” IEEE Trans. Geosci. Remote Sensing, vol.32, no.4, pp.779-784, Jul. 1994. [2] T.M.Tu, C.H.Chen, and C.I.Chang, “A posteriori least squares orthogonal subspace projection approach to desired signature extraction and detection,” IEEE Trans. Geosci. Remote Sensing, vol.35, no.1, pp.127-139, Jan. 1997. [3] C.I.Chang, X.L.Zhao, Mark L.G.Althouse, andJ.J.Pan, “Least squares subspace projection to mixed pixel classification for hyperspectral images,” IEEE Trans. Geosci. Remote Sensing, vol.36, no.3,pp.898-912, May 1998. [4] B.Schölkopf and A.Smola. “Learning with kernels,” MIT Press,Cambridge, Ma, 2002. [5] J.S. Taur, G.H Lee, C.W Tao, C.C. Chen, C.W. Yang, “Segmentation of psoriasis vulgaris images using multiresolution-based orthogonal subspace techniques,” IEEE Trans. On system, Man and Cybern. Part-B, vol.36, no.2, pp.390-402, Apr. 2006. [6] H. Kwon and N. M. Nasrabadi, “Kernel orthogonal subspace projection for hyperspectral signal classification,” IEEE Trans. Geosci. Remote Sensing, vol. 43, no. 12, pp. 2952-2962, Dec. 2005. [7] H. Kwon and N. M. Nasrabadi, “Kernel matched subspace detectors for hyperspectral target detection,” IEEE Trans. Pattern Anal. Machine Intell., vol. 28, no. 2, pp. 178-194, Feb. 2006. [8] B.Scholkopf, A.J.Smola, and K.-R.Muller, “Kernel Principal Component Analysis,” Neural Computation, no.10, pp.1299-1319, 1999. [9] Homas B.Criss, Marilyn M.South(1998), “Multiple Image Coordinate Extraction (MICE) Technique for Rapid Targeting of Precision Guided Munitions, ” Jons Hopkins Apl Technical Degest, vol.19, no.4 [10] Rafael C.Gonzalez, Richard E.Woods, Steven L.Eddins著;繆紹綱譯, “數位影像處理-運用Matlab,” 台灣東華書局股份有限公司出版,民國94年9月初版。zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/7937-
dc.description.abstract本論文主要著墨於兩個部份。第一個部分屬於影像分割的部份,包括了特徵擷取的技術、修正型SSC(signature subspace classifier)演算法、以及用RBF高斯核心核化SSC演算法等。而SSC演算法是將一觀測像素分解至信號次空間與雜訊空間,藉此可大幅降低雜訊的影響。而核化SSC的技術部份,它可探測非線性訊號的相關性,以增進分割的效能。第二部分在描述目標物三維座標重建的技術,空拍影像可以得到拍攝時飛機、攝影機相關的地理位置資訊,由飛機與目標物座標的相對關係,運用攝影圖學法之成像技術,進而獲得目標物的座標資訊,達到影像定位技術的目的。zh_TW
dc.description.abstractIn this thesis, we focus on two research topics. The first one is image segmentation. The techniques include feature extraction, Modified SSC (signature subspace classifier) algorithm, and kernelized SSC techniques with the RBF Gaussian function. The SSC algorithm decomposes the feature vector for a region around an observed pixel into a signature space and a noise space so that noise effects can be greatly reduced. The kernelized SSC can exploit the nonlinear correlations of signature and improve the efficiency of segmentation. The second topic is three dimensional coordinate reconstruction. We apply photogrammetry techniques and use the information obtained when taking pictures to reconstruct three dimensional coordinate of the target.en_US
dc.description.tableofcontents第一章 前言..........................................................................................1 第二章 特徵擷取.................................................................................2 2.1 前言..................................................................................2 2.2 模糊紋理頻譜..................................................................2 2.3 二維模糊色彩長條圖......................................................5 2.4 偵測同質區域..................................................................7 2.4.1同質區域的分類......................................................9 2.4.2訓練區域................................................................10 第三章 影像分割演算法...................................................................12 3.1 前言................................................................................12 3.2 SSC演算法....................................................................12 3.3 核化SSC技術(KSSC)..................................................14 第四章 影像三維座標重建...............................................................20 4.1 前言................................................................................20 4.2 座標幾何模型................................................................20 4.3 三維座標重建................................................................22 4.4單張影像座標重建之運用.............................................24 第五章 實驗結果與討論...................................................................26 5.1 影像分割結果................................................................26 5.2 影像三維座標重建模擬結果........................................39 第六章 結論與未來展望...................................................................48 參考文獻..................................................................................................49zh_TW
dc.language.isoen_USzh_TW
dc.publisher電機工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1108200820084200en_US
dc.subjectfeature extractionen_US
dc.subject特徵擷取zh_TW
dc.subjectSSC algorithmen_US
dc.subjectRBF Gaussian functionen_US
dc.subjectcoordinate reconstructionen_US
dc.subjectSSC演算法zh_TW
dc.subjectRBF高斯核心zh_TW
dc.subject座標重建zh_TW
dc.title次空間投影技術於影像分割之應用zh_TW
dc.titleThe Application of Subspace Projection Techniques to Image Segmentationen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.grantfulltextnone-
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