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研究生:鄭芝怡
研究生(外文):Chih-I, Cheng
論文名稱:利用電腦視覺在室內走廊環境作障礙物偵測與距離估測
論文名稱(外文):Obstacle Detection and Distance Estimation by Computer Vision for Indoor Corridor Environment
指導教授:李祖添李祖添引用關係
指導教授(外文):Tsu-Tian Lee
學位類別:碩士
校院名稱:國立交通大學
系所名稱:電機與控制工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:40
中文關鍵詞:估測對應切割影像
外文關鍵詞:stereo visionmatchcolor-segmentation
相關次數:
  • 被引用被引用:11
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為了能使自動導航車在室內環境中,有效地閃避障礙物與估測出物體的距離,所以在本篇論文中,我們將建立一套雙眼立體視覺系統,針對對應問題( stereo matching ),利用立體視覺系統中,左右影像之間的特性,找出實際環境中障礙物成像至影像平面的投影點關係,以得到障礙物的實際深度,也就是得到相機本身與障礙物之間的距離關係,作為自動導航車的行進方向距離依據。找出對應點的關係是本論文的重點之一,另外為了減少尋找對應點的處理時間,需要先分辨出障礙物與環境,所以我們先針對走廊環境影像做分析,依據其影像特質,建立一個環境顏色database,再利用環境與障礙物的差異將障礙物切割出來,之後只要針對被切割後得到的障礙物影像找尋對應點。一旦這些點決定後,得到對應點的的位移差,則相機與障礙物間的距離便可得知。實驗結果充分顯示所提出的方法可行性及實用性。
In this thesis, we will develop a binocular stereo vision system to be used as an automatic navigational system. The main purpose of the vision system is obstacle detection and distance estimation for the collision avoidance of the automatic navigational system. A binocular stereo vision system includes a pair of left and right cameras. To find the correspondence points of stereo images (stereo matching) is a main problem of the stereo vision system. The pixels in the left and right images have similar structure. We can use these characteristics to find the conjugate pair corresponding to projection points of the scene points in the left and right images and then calculate the depth of scene points by perspective projection and triangular geometry. Determining the corresponding points is the main purpose in this thesis. And in order to reduce the time for determining corresponding points of stereo images, we need to identify obstacles in the environment first. Thus we analyze color space of corridor images. Based on the characteristic feature of images, we set up a database of environment color distribution. The color-segmentation method is employed to separate the obstacles from environment. Then the corresponding points of obstacles images are determined and the disparity of corresponding points are measured. As a result, the distance between cameras and obstacle is determined. Experimental results are provided to illustrate validity and applicability of the developed algorithm.
Contents
致 謝 I
Abstract in Chinese II
Abstract in English III
List of Figure IV
List of Table VI
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Survey on related work 1
1.2.1 Survey of obstacle detection 2
1.2.2 Survey of stereo matching 4
1.2.3 Survey of rectification 5
1.3 Thesis of organization 5
Chapter 2 Image Processing 7
2.1 Depth of image 7
2.1.1 Image geometry 7
2.1.2 Stereo imaging 8
2.2 Image rectification 10
2.2.1 Introduction 10
2.2.2 The method of the rectification 12
2.2.3Experimental results 14
Chapter 3 Obstacle Detection 16
3.1 Problem formulation 16
3.2 Color-segmented method 16
3.2.1 Initial environments estimated 16
3.2.2 Color segmentation 18
3.3 Image filtering 19
3.4 Component labeling 21
Chapter 4 Stereo matching 23
4.1 Area-based and feature-based techniques 23
4.1.1 Area-based technique 23
4.1.2 Feature-based technique 23
4.2 Constraints of matching 24
4.3 Block matching 26
Chapter 5 Experiment results and discussion 27
5.1 Result of our method 27
5.2 Discussion 34
Chapter 6 Conclusions 37
References
References
[1] Olivier Faugeras, “ Three-Dimensional Computer Vision”, the MIT Press, 1993.
[2] Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, “ Machine Vision”, the MIT Press and McGraw-Hill, Inc. 1995.
[3] R. C. Gonzalez, R. E. Woods, “ Digital Image Processing”, Addison-Wesley
Publishing Company, 1992.
[4] U. R. Dhond, J. K. Aggarwal, “ Structure from Stereo-A Review”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 6, pp. 1489-1510, 1989.
[5] C. H. Ku, W. H. Tsai, “ Obstacle Avoidance for Autonomous Lane Vehicle Navigation in Indoor Environments by Quadratic Classifier”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 29, No. 3, pp. 416-426, June 1999
[6] M. Bertozzi, A. Broggi, A. Fascioli, “ Vision-Based Intelligent Vehicles: State of the Art and Perspectives”, Robotics and Autonomous Systems, Vol. 32, pp. 1-16, 2000.
[7] R. Bishop, “A survey of intelligent vehicle applications worldwide”, Intelligent Vehicles Symposium Proceedings of the IEEE, pp. 25-30, 2000.
[8] A. Broggi, M. Bertozzi, A. Fascioli, C. Guarino, L. Bianco, A. Piazzi, “ Visual Perception of Obstacles and Vehicles for Platooning”, IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 3, pp. 164-176, 2000.
[9] M. Bertozzi, A. Broggi, G. Conte, A. Fascioli, “ Obstacle and Lane Detection on ARGO”, Intelligent Transportation System, pp. 1010-1015, 1997.
[10] E. Haritaoglu, L. Davis, “ Multiple Vehicle Detection and Tracking in Hard Real-time”, Proceedings IEEE Intelligent Vehicles Symposium, pp. 351-356, 1996.
[11] M. Tabb, N. Abuja, “Multiscale Image Segmentation by Integrated Edge and Region Detection”, IEEE Trans. on Image Processing, Vol. 6, No. 5, pp. 642-655, May 1997.
[12] M. Cheriet , J.N. Said , C.Y. Suen, “A Recursive Thresholding Technique for Image Segmentation”, Image Processing IEEE, Vol. 7, No. 6, pp. 918-921, 1998.
[13] X. Yang, L. Jia, C.Y. Yu, Z.C. Yan, “The Adaptive Threshold for Image Segmentation in Shape Metering of Hot-rolling Strip Steel”, 38th Annual Conference Proceedings of SICE Annual, pp. 1095-1098, 1999.
[14] T. I. Hentea, “Algorithm for Automatic Threshold Determination for Image Segmentation”, Electrical and Computer Engineering, Vol. 1, pp.535-538, 1993.
[15] J.P. Fan, D.K.Y. Yau, A.K. Elmagarmid, W.G. Aref, “Automatic Image Segmentation by Integrating Color-edge Extraction and Seeded Region Growing”, Image Processing IEEE, Vol. 10, No. 10, pp. 1454-1466, 2001.
[16] M. R. R., P. M. J. V. Zwet, B. P. F. Lelieveldt, R. J. Greest, H. C. Hohan, “A Multiresplution Image Segmentation Technique Based on Pyramid Segmentation and Fuzzy Clustering”, Image processing IEEE, Vol. 9, No. 7, pp. 1238-1248, 2000.
[17] M.P. Tremblay, A. Zaccarin, “Transmission of the Color Information Using Quad-trees and Segmentation-based Approaches for the Compression of Color Images with Limited Palette”, Image processing IEEE, Vol. 3, pp. 967-971, 1994.
[18] Q. Ji, R. M. Haralick, “ Quantitative Evaluation of Edge Detectors Using the Minimum Kernel Variance Criterion”, IEEE International Conf. On Image Processing, Vol. 2, pp. 705-709, 1999.
[19] M. Heath, S. Sarker, T. Sanocki, K. Bowyer, “Comparison of Edge Detectors: A Methodology and Initial Study”, Computer Vision and Pattern Recognition Proc. IEEE, pp. 143-148, 1996.
[20] P.Garcia, F. Pla, I. Garcia, “Detecting Edges in Color Images Using Dichromatic Differences”, Seventh International IEEE Conf. On Image Processing and ITS Applications, Vol. 1, pp. 363-367, 1999.
[21] A. Broggi, “A Massively Parallel Approach to Real-Time Vision-Based Road Markings Detection”, Intelligent Vehicles ''95 Symposium., Proceeding, pp. 84-89. 1995.
[22] A. Broggi, “ An Image Reorganization Procedure for Automotive Road Following Systems”, Image Processing on Proceeding, Vol. 3, pp. 532-535, 1995.
[23] M. Bertozzi, A. Broggi, “GOLD: a Parallel Real-time Stereo Vision System for Generic Obstacle and Lane Detection”, IEEE Transactions Image Processing, Vol.7, pp. 62-81, 1998.
[24] W. Kruger, W. Enkelmann, S. Rossle, “Real-time Estimation and Tracking of Optical Flow Vectors for Obstacle Detection”, in Proc. IEEE Intelligent Vehicles Symp., Detroit, MI, Sept. , pp. 304—309, 1995.
[25] T. Suzuki, T. Kanade, “Measurement of Vehicle Motion and Orientation Using Optical Flow”, in Proc. IEEE Int. Conf. Intelligent Transportation Systems, Tokyo, Japan, pp. 25—30, 1999.
[26] H. Maeda, S. Okada, T. Irie, “A New Method for Stereo Matching Problem in Computer Vision Using Synergetics”, Systems, Man, and Cybernetics IEEE, Vol. 3, pp. 503-208, 1999.
[27] A. Fusiello, V. Roberto, E. Trucco, “Efficient Stereo with Multiple Windowing”, Computer Vision and Pattern Recognition, pp.858-863, 1997.
[28] S. B. Marapane, M. M. Trivedi, “Multi-primitive Hierarchical (MPH) Stereo Analysis”, Pattern Analysis and Machine Intelligence, Vol. 16, No. 3, pp. 227-240, 1994.
[29] C. H. Huang, J. H. Wang, ” Stereo Correspondence Using Hopfield Network with Multiple Constraints”, IEEE International Conference on Systems, Man, and Cybernetics, Vol. 2, pp. 1518-1523, 2000.
[30] F. Dornaika, R. Chung, “ Cooperative Stereo-Motion: Matching and Reconstruction”, Computer Vision and Image Understanding on IDEAL, Vol. 79, No. 3, pp. 408-427, September 2000.
[31] S. B. Kang, R. Szeliski, H. Y. Shum, “A Parallel Feature Tracker for Extended Image Sequences”, Computer Vision and Image Understanding, Vol. 67, No. 3, pp. 296-310, 1997.
[32] G. A. Jones, “Constraint, Optimization, and Hierarchy: Reviewing Stereoscopic Correspondence of Complex Features”, Computer Vision and Image Understanding, Vol. 65, No. 1, pp. 57-78, 1997.
[33] F. Candocia, M. Adjouadi, “A similarity measure for stereo feature matching”, Image Processing, Vol. 10, No. 10, pp. 1460-1464, 1997.
[34] J. H. Wang, C. P. Hsiao, “Stereo Matching by Neural Network That Uses Sobel Feature Data”, Neural Networks, Vol. 3, pp. 1801 —1806, 1996.
[35] S. Tamas, C. Marton, “Texture Classification and Segmentation by Cellular Neural Networks Using Genetic Learning”, Computer Vision and Image Understanding, Vol. 71, No. 3, pp. 255-270, 1998.
[36] D. V. Papadimitriou, T. J. Dennis, “ Epipolar Line Estimation and Rectification for Stereo Image Pairs”, IEEE Transaction on Image Processing, Vol. 5, No. 4, pp. 672-676, April 1996.
[37] N. Ayache, C. Hansen, “Rectification of Images for Binocular and Trinoclur Stereovision”, Pattern Recognition, Vol. 1, pp. 11-16, 1988.
[38] H. H. Wu, T. H. Chen, “Optimal Rectification of Binocular Stereo Image Pairs”, Computer Vision, Graphics and Image Processing, pp.797-803, 1999.
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