Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7933
DC FieldValueLanguage
dc.contributor林正堅zh_TW
dc.contributor歐陽彥杰zh_TW
dc.contributor.advisor陶金旭zh_TW
dc.contributor.author黃祿倭zh_TW
dc.contributor.authorHuang, Lu-Weien_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.identifierU0005-1108200819564900zh_TW
dc.identifier.citation[1] D. Schulz, W. Burgard, D. Fox, and A. Cremes, “Tracking multiple moving objects with a mobile robot,” Proc. IEEE CVPR, vol.1, pp.371-377, 2001. [2] D. A. Forsyth, and J. Ponce, Computer vision: a modern approach, Prentice Hall, 2002. [3] R. Collins, C. J. Taylor, and J. Graham, “Special issue on video surveillance and monitoring,” IEEE Trans. Pattern Anal. and Machine Intell., vol. 22, pp. 745-746, 2000. [4] D. M. Gvarila, “The visual analysis of human movement: a survey,” Computer Vision and Image Understanding, vol. 73, pp. 82-98, Jan. 1999. [5] V. Pavlovic, R. Sharma, and T. S. Huang, “Visual interpretation of hand gestures for human computer interaction: a review,” IEEE Trans. Pattern Anal. and Machine Intell., vol. 19, no. 7, pp. 677-695, Jul. 1997. [6] Y. Wu, and T. Yu, “A filed model for human detection and tracking,” IEEE Trans. Pattern Anal. and Machine Intell., vol. 28, no. 5, pp. 753-765, May 2006. [7] S. Ioffe, and D. A. Forsyth, “Probabilistic methods for finding people,” IJCV, 43(1), pp. 45-68, 2001. [8] A. Mohan, C. Papageorgiou and T. Poggio, “Example-based object detection in Images by components,” IEEE Trans. PAMI, 23(4), pp. 349-361, 2001. [9] Christophe Simon, Frederique Bicking and Thierry Simon, ”Estimation of depth on thick edges from sharp and blurred images,” IEEE Instrumentation and Measurement Technology Conference, vol. 1, pp. 323-328. [10] Cassandra Swain, Alan Peters and Kazuhiko Kawamura, ”Depth estimation from image defocus using fuzzy logic,” Proceedings of the 3rd IEEE International Conference on Fuzzy System, vol. 1, P9, 94-99, 1994. [11] F. Deschen, D. Ziou, P. Fuchs, ”Homotopy-based estimation of depth cues in spatial domain,” IEEE International Conference on Pattern Recognition, vol. 3, pp. 627-630, 2002. [12] F. Deschen, D. Ziou, ”Homotopy-based computation of defocus blur and affine transform,” in Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'03), vol. 1, pp. I-398-404, 2003. [13] L. Li, Y. T. Koh, S. S. Ge, and W. Huang, “Stereo-based human detection for mobile service robots,” Proc. IEEE Conferecne on Control, Automation, Robotics, and Computer Vision, pp. 74-79, Dec. 2004. [14] L. Li, S. S. Ge, T. Sim, Y. T. Koh, and X. Hunag, “Object-oriented scale-adaptive filtering for human detection from stereo images,” Proc. IEEE Conference on Cybernetics and Intelligent Systems, pp. 135-140, Dec. 2004. [15] D.C. Brown, “Decentering Distortion of Lenses,” Photogrammetric Eng., vol. 32, No. 3, pp. 444-462, May 1966. [16] Roger Y. Tsai, “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and Lenses,” IEEE Journal of Robotics and Automation, vol. ra-3, No.4, Aug. 1987. [17] Z. Zhang, “Flexible camera calibration by viewing a plane from unknown orientations,” Proceedings of the IEEE International Conference on Computer Vision, vol. 1, pp. 666-673, 1999. [18] Q.T. Luong and O.D. Faugeras, “The fundamental matrix: theory, algorithms, and stability analysis,” route des Lucioles, B.P. 93, 2004. [19] K. Konolige, “Small vision system: hardware and implementation,” Proc. Int'l Symp. Robotics Research, pp. 111-116, Aug. 1997. [20] Deoffrey Egnal and Richard P. Wildes, “Detecting binocular half-occlusion: empirical comparisons of five approaches,” IEEE transactions on pattern analysis and machine intelligence, vol. 24, No. 8, 2008. [21] T. Lindegerg, “Scale-space theory: a basic tool for analyzing structures at different scales,” Jour. of App. Stat., 21(2), pp. 225-270, 1994. [22] S. Smith, and J. Brady, “SUSAN-a new approach to low level image processing,” IJCV, 23(1), pp. 45-78, 1997.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/7933-
dc.description.abstract在本論文中,我們大致可以將這個研究分成兩個部份,首先是取得立體像差影像,其中包括了雙攝影機的校正,再來則是如何準確且有效率的完成人體偵測。在人體偵測的部份,我們使用X-D平面的二維模糊歸屬直方圖來分析影像中可能存在人體的部份,並利用大小適應性濾波器來針對平均人體大小的區域做加強,更有利於直方圖的分析。經過分析的結果,我們可以發現影像中人類物件存在的部份確實有人型的分割區域產生,而我們也在一系列的序列影像中將人類物件存在的地方做一圈選的動作。zh_TW
dc.description.abstractIn this thesis, we focus on the detection of human bodies using two cameras. We can roughly divide this research into two steps. The first one is to obtain the stereo disparity images, which includes the calibration of cameras as preprocessing. The second step is to detect the human regions accurately and efficiently. We extract the human regions in the images by using the fuzzy 2D histogram on the X-D plane. Then we used a scale-adaptive filter to enhance the area with an average scale for human bodies. According to the experiment results, our method can produce a segmentation region of the human shape which contains a human object in the image. And we had tracked the movements of human objects in the video sequences.en_US
dc.description.tableofcontents第一章 緒論 1 1.1 前言 1 1.2 研究動機 1 1.3 論文簡介 2 1.4 相關文獻與研究方法 3 第二章 攝影機校正 4 2.1 前言 4 2.2 攝影機成像座標系統 4 2.3 攝影機參數與攝影機校正 5 2.3.1 透視攝影機與攝影機參數 6 2.3.2 攝影機內部參數 7 2.3.3 攝影機外部參數 8 2.4 攝影機參數求取 9 2.4.1 校正模型與其影像之平面投影 9 2.4.2 內部參數條件限制式 10 2.4.3 求解攝影機參數 11 2.4.4 內部參數求解 12 2.4.5 外部參數求解 13 第三章 立體視覺 14 3.1 前言 14 3.2 極線幾何 15 3.3 立體像差 17 3.4 SRI Small Vision System 19 3.5 雙眼視界 21 3.5.1 像差搜尋範圍 21 3.5.2 雙眼視界的調整 21 3.5.3 互動視窗大小 22 3.6 Filtering 23 3.6.1 Confidence Filter 23 3.6.2 Uniqueness Filter 24 3.6.3 Speckle Filter 25 第四章 研究方法 26 4.1 前言 26 4.2 X-D二維像差直方圖 27 4.3 大小適應性濾波 30 4.4 分水嶺處理 32 4.5 人類物件分割 34 第五章 實驗結果 36 5.1 前言 36 5.2 兩人並排行走 36 5.3 一對一行走 40 5.4 三人於不同距離 46 第六章 結論與未來展望 51 參考文獻 52zh_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=U0005-1108200819564900en_US
dc.subjectStereo Visionen_US
dc.subject立體視覺zh_TW
dc.subjectCalibrationen_US
dc.subjectVideo Sequenceen_US
dc.subjectHuman Detectionen_US
dc.subject校正zh_TW
dc.subject視訊影像zh_TW
dc.subject人體偵測zh_TW
dc.title立體視覺於視訊人體偵測之應用zh_TW
dc.titleHuman detection in video sequences using stereo visionen_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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