Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7937
標題: 次空間投影技術於影像分割之應用
The Application of Subspace Projection Techniques to Image Segmentation
作者: 吳坤倫
Wu, Kun-Lun
關鍵字: feature extraction
特徵擷取
SSC algorithm
RBF Gaussian function
coordinate reconstruction
SSC演算法
RBF高斯核心
座標重建
出版社: 電機工程學系所
引用: [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。
摘要: 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。
In 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.
URI: http://hdl.handle.net/11455/7937
其他識別: U0005-1108200820084200
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1108200820084200
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