跳到主要內容

臺灣博碩士論文加值系統

(34.204.169.230) 您好!臺灣時間:2024/02/21 23:29
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:邱駿展
研究生(外文):Chun-Chan Chiu
論文名稱:三維物件之辨識與姿態估測
論文名稱(外文):3D Object Recognition and Pose Estimation
指導教授:陳金聖陳金聖引用關係
口試委員:顏炳郎李恆寬
口試日期:2009-01-22
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:87
中文關鍵詞:物體識別視覺追蹤特徵比對
外文關鍵詞:object recognitionvisual trackingfeature matching
相關次數:
  • 被引用被引用:6
  • 點閱點閱:582
  • 評分評分:
  • 下載下載:31
  • 收藏至我的研究室書目清單書目收藏:0
物體識別(Object recognition)與姿態估測在目前影像伺服與機器視覺等相關應用中仍是一個重要且基礎的問題。本篇論文主要在發展一個三維物體識別與姿態估測之視覺系統,對特定的目標物進行識別且鎖定,並利用立體視覺量測的方式獲得該物體的三維座標與姿態。
本論文之視覺系統之架構可分為離線訓練階段與線上操作階段,離線訓練階段進行目標物的特徵提取(Feature detector)與特徵描述(Feature descriptor)計算、建立粗與細之搜尋結構和相機校正,線上操作階段進行輸入影像的特徵提取與特徵描述計算、目標物識別和估測目標物位置與姿態。因本視覺系統同時考量穩定性與即時性,所以本論文提出改良的直覺式角點外觀偵測方式,以達到快速特徵點提取的效果,特徵描述部份使用SIFT的描述方式,並透過PCA的方式來降低其描述空間的維度,不同的是,離線訓練階段使用多解析度的特徵描述範圍,但線上操作階段只使用單一解析度的特徵描述範圍,在目標物識別時本論文提出階層式的比對方式,此方式利用縮減搜尋空間來加快比對速度。由實驗結果顯示,使用本論文所提出之特徵點能夠迅速識別出單一目標物,而在一般雜亂之環境下仍然有足夠的能力識別出目標物,並能進一步求得其三維空間上的座標位置,了解該目標物的在空間上的姿態。
3D object recognition and pose estimation is a fundamental technique for many applications of machine vision, including target tracking, visual servo, robot vision, just to name a few. This thesis proposes a high speed and robust vision system for 3D objects recognition and poses estimation based on stereo projective.
The algorithm architecture of this paper consists of two phases: (1) the off-line training phase, and (2) the on-line operating phase. In the training phase, the data set corresponding to the targets are collected, then the feature points are detected and the feature descriptions are represents. With this information, the algorithm creates the hierarchical structures of feature descriptions to improve the speed of patch matching. In the other hand, the camera calibration is also finished in this phase. For the operating phase, the target images are input and the same processes for feature detection and representation are done. Finally, the recognition algorithm based on the hierarchical structures improves the performance of object detection and the 3D pose is further estimated. This thesis proposes the modified intuitive corner detection to quickly extract the features, and the feature descriptions based on SIFT and PCA are applied. In patch matching process, the multi-resolution patch in training phase and single resolution patch in operating phase are loaded respectively and the two stages matching, coarse and fine matching based on hierarchical structures of feature descriptions to reduce the range of candidates, are proposed to reduce the matching computation time. Experimental results show that the proposed algorithms can rapidly detect the target even in the complex environment. Furthermore, the pose of 3D object can be easily estimated using the transformation formula from 2D to 3D.
中文摘要 i
英文摘要 ii
誌 謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與目的 1
1.2 文獻回顧 2
1.3 系統規劃與研究概述 3
1.4 論文架構 7
第二章 特徵提取與描述方式 8
2.1 特徵點偵測 8
2.1.1 Harris特徵點偵測演算法 8
2.2 特徵描述 18
2.2.1 方向矯正原理 18
2.2.2 SIFT的描述方式 21
2.2.3 PCA降維方式 23
第三章 特徵對應與識別方式 26
3.1 相關背景 26
3.2 目標物體之資料庫建立 27
3.3 特徵資料搜尋與對應 30
3.3.1 資料對應方式 30
3.3.2 資料搜尋方式 31
3.3.3 幾何限制估測 35
3.3.4 整體識別流程 36
第四章 位置與姿態的估測 38
4.1相機投影幾何 38
4.1.1 相機投影模型 38
4.1.2 相機參數 39
4.1.3 平面投影轉換 42
4.1.4 徑向透鏡扭曲 44
4.2 雙眼立體視覺原理 46
4.3 同軸幾何與基礎矩陣 48
4.3.1 同軸限制 48
4.3.2 基礎矩陣 49
4.4 目標物體之三維空間資訊估算 52
第五章 實驗測試與評估 54
5.1 實驗平台架構 54
5.2 實驗測試評估與討論 56
5.2.1 特徵點重現性實驗 56
5.2.2 特徵點重現性實驗之討論 65
5.2.3 物體識別性能測試實驗之設置 65
5.2.4 物體識別性能測試實驗(測試環境1) 69
5.2.5 物體識別性能測試實驗(測試環境1)之討論 72
5.2.6 物體識別性能測試實驗(測試環境2) 73
5.2.7 物體識別性能測試實驗(測試環境2)之討論 76
5.2.8 物體之三維位置估測實驗 77
5.2.9 物體之三維位置估測實驗之討論 83
第六章 結論與未來方向 84
參考文獻 85
[1] S. Benhimane and E. Malis, "Homography-based 2D visiual servoing," Proc. IEEE International Conference on Robotics and Automation, pp.2397-2402, May 2006.
[2] E. Marchand and F. Chaumette, "Feature tracking for visual servoing purposes," Robotics and Autonomous Systems, Vol. 52, No. 1, pp.53-70, June 2005.
[3] M. Pressigout and E. Marchand, "Real-time hybrid tracking using edge and texture information, " International Journal of Robotics Research, Vol. 26, No. 7, pp.689-713, July 2007.
[4] T. Drummond and R. Cipolla, "Real-Time visual tracking of complex structures," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp.932-946, July 2002.
[5] D. Kragic, M. Bjorkman, H.I. Christensen, J. Eklundh, "Vision for robotic object manipulation in domestic settings," Autonomous Systems 52, pp.85-100, June 2005.
[6] T. Olsson, High-Speed vision and force feedback for motion-controlled industrial manipulators, Lund University, Lund, May 2007.
[7] J. Wang, H. Zha and R. Cipolla, "Coarse-to-fine vision-based localization by indexing scale-invariant features," IEEE Transactions on Systems, Part B: Cybernetics, Vol. 36, No. 2, April 2006.
[8] S. Ahn, M. Choi, J. Choi and W.K. Chung, "Data association using visual object recognition for EKF-SLAM in home environment," Proc. IEEE/RSJ International Conference on Intelligent Robots and Syatems, pp.2588-2594, October 2006.
[9] N. Zhang, M. Li and B. Hong, "Active mobile robot simultaneous localization and mapping," Proc. IEEE International Conference on Robotics and Biomimetrics, pp.1676-1681, December 2006.
[10] S. Frintrop, P. Jensfelt and H.I. Christensen, "Attentional landmark selction for visual SLAM," Proc. IEEE/RSJ International Conference on Intelligent Robots and Syatems, pp.2582-2587, October 2006.
[11] M. Pollefeys, Visual 3d modeling from images, Tutorial Notes, USA.
[12] L. Van Gool, M. Vergauwen, F. Verbiesst, K. Cornelis and J. Tops, "Visual modeling with a hand-held camera," International Journal of Computer Vision, Vol. 59, No. 3, pp.207-232, 2004.
[13] I. Gordon and D.G Lowe, "Scene modeling, recognition and tracking with invariant image features," International Symposium on Mixed and Augmented Reality, pp.110-119, 2004.
[14] G. Klein, Visual tracking for augmented reality, Ph.D. Thesis, University of Cambridge, January 2006.
[15] V. Ferrari, T. Tuytelaars and L. Van Gool, "Integrating multiple model views for object recognition," Proc. IEEE Computer Society Conference on Computer Vision and Pattren Recognition, Vol. 2, pp.105-112, 2004.
[16] K. Mikolajczyk, C. Schmid and A. Zisserman, "Human detection based on a probabilistic assembly of robust part detectors," Eighth European Conference on Computer Vision, pp.68-82, May 2004.
[17] F.F. Li, R. Fergus and P. Perona, "One-Shot learning of object categories," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp.594-611, April 2006.
[18] H.P. Moravec, "Toward automatic visual obstacle avoidance," Proc. Fifth of International Joint Conference on Artificial Intelligence, Vol. 14, pp.584, August 1977.
[19] C. Harris and M. Stephens, "A combined corner and edge detector," Alvey Vision Conference, pp.147-151, 1988.
[20] S.M. Smith and M. Stephens, "SUSAN: A new approach to low level image processing," International Journal of Computer Vision, pp.45-78, 1997.
[21] Z. Zhang, R. Deriche, O. Faugeras and Q.T. Luong, "A robust technique for matching two unicalibrated images through the recovery of unknown epipolar geometry," Artificial Intelligence, Vol. 78, pp.87-119, 1995.
[22] T. Lindeberg, "Feature detection with automatic scale selection," International Journal of Computer Vision, Vol. 30, No. 2, pp.79-116, 1998.
[23] K. Mikolajczyk and C. Schmid, "Indexing based on scale invariant interest points," Proc. Eighth International Conference on Computer Vision, pp.525-531, 2001.
[24] K. Mikolajczyk and C. Schmid, "An affine invariant interest point detector," Proc. Seventh European Conference on Computer Vision, pp.128-142, 2002.
[25] T. Tuytelaars and L. Van Gool, "Matching widely separated views based on affine invariant regions," International Journal of Computer Vision, Vol. 1, No. 59, pp.61-58, 2004.
[26] D. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol. 60, No. 2, pp.91-110, 2004.
[27] D. Lowe, "Local feature view clustering for 3d object recognition," IEEE Conference on Computer Vision and Pattern Recognition, pp.682-688, 2001.
[28] Y. Ke and R. Sukthankar, "PCA-SIFT: A more distinctive representation for local image descriptors," IEEE Conference on Computer Vision and Pattern Recognition, Vol. 2, pp.506-513. 2004.
[29] C. Schmid , R. Mohr and C. Bauckhage, "Evaluation of interest point detectors," International Journal of Computer Vision, Vol. 37, No. 2, pp.151-172, 2000.
[30] K. Mikolajczyk, T. Tuytelaars, C.Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool, "A Comparison of affine region detectors," International Journal of Computer Vision, 2006.
[31] K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," Proc. Conference on Computer Vision and Patten recognition, pp.257-264, 2003.
[32] K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Transactions on Patten analysis and Machine Intelligence, Vol. 27, No. 10, pp.1615-1630, October 2005.
[33] E. Rosten, High performance rigid body tracking, Ph.D. Thesis, University of Cambridge, February 2006.
[34] V. Lepetit and P. Fua, "Towards recognizing feature points using classification trees," Technical Report IC, EPFL, 2004.
[35] V. Lepetit and P. Fua, "Keypoint recognition using randomized trees," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 9, pp.1465-1479, Septemper 2006.
[36] T.T.H. Tran and E. Marchand, "Real-time keypoints matching: application to visual servoing," IEEE Conference on Robotics and Automation, 2007.
[37] I.T. Joliffe, "Principal component analysis,"Springer-Verlag, 1986.
[38] J. Liu and R. Hubbold, "Automatic camera calibration and scene reconstruction with scale-invariant features," ISCV, Springer-Verlag, pp.558-568, 2006.
[39] N. Fischler and R.C. Bolles, "Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography," CACM, Vol. 24, No. 6, pp.381-395, June 1981.
[40] R.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, pp.323-344, August 1987.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top