請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/31949
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李瑞庭(Anthony J.T. Lee) | |
dc.contributor.author | Hsiang-Ting Lan | en |
dc.contributor.author | 籃湘婷 | zh_TW |
dc.date.accessioned | 2021-06-13T03:25:35Z | - |
dc.date.available | 2006-08-01 | |
dc.date.copyright | 2006-08-01 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-07-27 | |
dc.identifier.citation | [1] H. Alt, B. Behrends, and J. Blomer, Approximate matching of polygonal shapes, Annals of Mathematics and Artificial Intelligence, 1995, pp. 251-265.
[2] M.J. Aitkenhead and A.J.S. McDonald, A neural network face recognition system, Engineering Applications of Artificial Intelligence, Vol. 16, 2003, pp. 167-176. [3] H. Blum, A transformation for extracting new descriptors of shape, In Models for the Perception of Speech and Visual Forms, MIT Press, Cambridge, MA, 1967, pp. 362-380. [4] H. Blum and R. Nagel, Shape description using weighted symmetric axis features, Pattern Recognition, Vol. 10, 1978, pp. 167-180. [5] G. Chen, Q. Wei, D. Liu and G. Wets, Simple association rules (SAR) and the SAR-based rule discovery, Computers and Industrial Engineering, Vol. 43, 2002, pp.721-733. [6] T. D’Orazio, C. Guaragnella and M. Leo, A. Distante, A new algorithm for ball recognition using circle Hough transform and neural classifier, Pattern Recognition, Vol. 37, 2004, pp.393-408. [7] A. Haar, Zur Theorie der orthogonalen Funktionensysteme, Mathematische Annalen, Vol. 69, 1910, pp. 331-371. [8] J. Hartigan, Clustering Algorithms, John Wiley & Sons, Inc., New York, NY, 1975. [9] M. K. Hu, Visual pattern recognition by moment invariants, IRE Transactions in Information Theory, Vol. 8, 1962, pp. 179-187. [10] A. Jain and R. Dubes, Algorithms for Clustering Data, Prentice-Hall, Inc., Upper Saddle River, NJ, 1988. [11] H. Kauppinen, T. Seppanen, and M. Pietikainan, An experimental comparison of autoregressive and fourier-based descriptors in 2d shape classification, IEEE Transactions on Pattern Analysis and Machine Inteligence, Vol. 17, 1995, pp. 201-207. [12] S. Lambert, E. de Leau, and L. Vuurpijl, Using pen-based outlines for object-based annotation and image-based queries, Proceedings of the Third International Conference on Visual Information and Information Systems, Amsterdam, The Netherlands, LNCS 1614, Springer, June 1999, pp. 585-592. [13] W. J. Li and T. Lee, Object recognition and articulated object learning by accumulative Hopfield matching, Pattern Recognition, Vol. 35, 2002, pp. 1933-1948. [14] Y. L. Lee and R. H. Park, A surface-based approach to 3-D object recognition using a mean field annealing neural network, Pattern Recognition, Vol. 35, 2002, pp.299-316. [15] B. Leibe and B. Schiele, Analyzing appearance and contour based methods for object categorization, Proceeding of International Conference on Computer Vision and Pattern Recognition, 2003. [16] J. Li, H. Shen and R. Topor, Mining the optimal class association rule set, Knowledge-based Systems, Vol. 15, 2002, pp. 399-405. [17] F. Mokhtarian, S. Abbasi, and J. Kittler, Robust and efficient shape indexing through curbature scale space, Proceeding of International Conference on British Machine Vision, 1996. [18] T. Pavlidis, A review of algorithms for shape analysis, Computer Graphics Image Processing, Vol. 7, 1978, pp. 243-258. [19] T. Pavlidis, S. L. Horowitz, Segmentation of plane curves, IEEE Transactions on Computers, Vol. 2x 3, 1974, pp. 860-870. [20] E. G. M. Petrakis, A. Diplaros, and E. Millos, Matching and retrieval of distorted and occluded shapes using dynamic programming, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, 2002, pp. 1501-1516. [21] E. Person and K. Fu, Shape discrimination using fourier descriptors, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 4, 1974, pp. 371-378. [22] S. Peleg and A. Rosenfeld, A min-max medial axis transformation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 3, 1981, pp. 208-210. [23] R. J. Prokop and A. P. Reeves, A survey of moment-based techniques for unoccluded object representation and recognition, CVGIP: Graphical Models Image Processing, Vol. 54, 1992, pp. 438-460. [24] R. Rastogi and K. Shim, Mining optimized support rules for numeric attributes, Information Systems, Vol. 26, 2001, pp. 425-444. [25] J. A. Rushing, H. Ranganath, T. H. Hinke and S. J. Graves, Image segmentation using association rule features, IEEE Transactions on Image Processing, Vol. 11, No. 5, May 2002, pp. 558-567. [26] D. Sanchez, J. Chamorro-Martinez and M. A. Vila, Modelling subjectivity in visual perception of orientation for image retrieval, Information Processing and Management, Vol. 39, 2003, pp. 251-266. [27] Di Sciascio, E., Donini, F.M., Mongiello, M., Structured knowledge representation for image retrieval, Journal of Artificial Intelligence Research, Vol. 16, 2002, pp. 209-257. [28] Kang B. Sun and Boaz J. Super, Classification of contour shapes using class segment sets, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. [29] M. Turk, A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 71-86. [30] T. Wallace and P. Wintz, An efficient three-dimensional aircraft recognition algorithm using normalized fourier descriptors, Computer Graphics Image Processing, Vol. 13, 1980, pp. 99-126. [31] C. Zahn and R. Roskies, Fourier descriptor for plane closed curves, Computer Graphics Image Processing, Vol. 21, 1972, pp. 269-281. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/31949 | - |
dc.description.abstract | 物件辨識的目的是識別圖片中的物件並且將物件歸類到具有相同特性的類別中。在本篇論文中,我們利用資料探勘方法找出特徵樣式以便辨識物件,我們提出的方法主要包括三個步驟。在預先處理階段,先正規化圖片以處理仿射轉換(affine transformation)後所帶來的影響。針對取出的輪廓點座標做小波轉換(wavelet transform)取得32個係數來當作整體特徵,接著將每一個輪廓點當作一個視窗的中心,計算角度直方圖(angle histogram)以取得一些向量當作局部特徵。在訓練階段,我們利用這些整體和局部特徵找出各種類別物件的特徵樣式(feature pattern)。在測試階段,利用一些圖片來檢驗各類別特徵樣式的有效性。針對每一張測試圖片,計算符合每一類整體和局部特徵樣式的比例,再利用權重將這兩個比例整合。最後,這張圖片會被歸類到擁有最高比例的類別。實驗結果顯示出,我們的方法在樹葉資料庫可達到98.15%的分類準確率,在ETH資料庫可達到97.62%的分類準確率,均勝過由Sun和Super所提出的方法。 | zh_TW |
dc.description.abstract | The goal of object recognition is to identify the object in an image. In this thesis, we proposed a data mining approach to realize object recognition. Our proposed method consists of three phases. In the preprocessing phase, we normalize the image to make our method invariant to translation, scale and rotation. We do Haar discrete wavelet transform on the coordinates of the extracted contour and get 32 coefficients to be the global feature. Then we use each contour point to be the centroid of a window, and calculate the angle histogram to get some vectors as local features. In the training phase, we use these global and local features to find the patterns of each class. In the testing phase, some images are used to test the effectiveness of the representative patterns. For each test image, we calculate the ratios of the global and local patterns of the test image conformed to each class, and use a weight to combine both ratios. Finally, the image is classified into the class with the highest ratio. The experimental results show that the classification accuracy rate of our method achieves 98.15% in the leaves database and 97.62% in the ETH object database and outperforms the method proposed by Sun and Super. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T03:25:35Z (GMT). No. of bitstreams: 1 ntu-95-R93725015-1.pdf: 646858 bytes, checksum: bd5490b85fc8ae921221b57b62a0edad (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | Table of Contents......................................i
List of Figures.......................................ii List of Tables.......................................iii Chapter 1 Introduction.................................1 Chapter 2 Our Proposed Approach........................5 2.1 Preprocessing phase..............................5 2.2 Training phase..................................11 2.2.1 Getting global patterns.....................11 2.2.2 Getting local patterns......................12 2.3 Testing phase...................................14 2.3.1 Testing on global patterns..................14 2.3.2 Testing on local patterns...................14 2.3.3 Testing on global and local patterns........15 Chapter 3 Performance Analysis........................16 3.1 Leaves database.................................16 3.2 ETH object database.............................18 3.3 Discussions.....................................21 Chapter 4 Concluding Remarks and Future Work..........24 References............................................26 | |
dc.language.iso | en | |
dc.title | 利用整體和局部特徵辨識物件 | zh_TW |
dc.title | Object Recognition Using Global and Local Features | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 沈錳坤(Man-Kwan Shan),陳良華(Liang-Hua Chen) | |
dc.subject.keyword | 物件辨識,特徵樣式,小波轉換,角度直方圖, | zh_TW |
dc.subject.keyword | object recognition,feature pattern,wavelet transform,angle histogram, | en |
dc.relation.page | 29 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-07-29 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-95-1.pdf 目前未授權公開取用 | 631.7 kB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。