Please use this identifier to cite or link to this item:
DC FieldValueLanguage
dc.contributor.authorHuang, Lu-Weien_US
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.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.subjectStereo Visionen_US
dc.subjectVideo Sequenceen_US
dc.subjectHuman Detectionen_US
dc.titleHuman detection in video sequences using stereo visionen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
Appears in Collections:電機工程學系所
Show simple item record
TAIR Related Article

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.