Scene Reconstruction, Pose Estimation and Tracking doc

Scene Reconstruction, Pose Estimation and Tracking Scene Reconstruction, Pose Estimation and Tracking Edited by Rustam Stolkin I-TECH Education and Publishing IV Published by the I-Tech Education and Publishing, Vienna, Austria Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the Advanced Robotic Systems International, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2007 I-Tech Education and Publishing www.ars-journal.com Additional copies can be obtained from: publication@ars-journal.com First published June 2007 Printed in Croatia A catalogue record for this book is available from the Austrian Library. Scene Reconstruction, Pose Estimation and Tracking, Edited by Rustam Stolkin p. cm. ISBN 978-3-902613-06-6 1. Vision Systems. 2. Scene Reconstruction. 3. Pose Estimation. 4. Tracking. V Preface This volume, in the ITECH Vision Systems series of books, reports recent advances in the use of pattern recognition techniques for computer and robot vision. The sciences of pattern recognition and computational vision have been inextricably intertwined since their early days, some four decades ago with the emergence of fast digital computing. All computer vi- sion techniques could be regarded as a form of pattern recognition, in the broadest sense of the term. Conversely, if one looks through the contents of a typical international pattern rec- ognition conference proceedings, it appears that the large majority (perhaps 70-80%) of all pattern recognition papers are concerned with the analysis of images. In particular, these sciences overlap in areas of low-level vision such as segmentation, edge detection and other kinds of feature extraction and region identification, which are the focus of this book. Those who were research students in the 1980s may recall struggling to find enough exam- ple images in digital form with which to work. In contrast, since the 1990s there has been an explosive increase in the capture, storage and transmission of digital images. This growth is continuing apace, with the proliferation of cheap (even disposable) digital cameras, large scale efforts to digitally scan the world’s written texts, increasing use of imaging in medi- cine, increasing use of visual surveillance systems and the display and transmission of im- ages over the internet. This growth is driving an acute demand for techniques for automatically managing and ex- ploiting this vast resource of data. Intelligent machinery is needed which can search, recog- nize, sort and interpret the contents of images. Additionally, vision systems offer the poten- tial to be the most powerful sensory inputs to robotic devices and are thus set to revolutionize industrial automation, surgery and other medical interventions, the security and military sectors, exploration of our oceans and outer space, transportation and many aspects of our daily lives. Computational intelligence, of which intelligent imaging is a cen- tral part, is also driving and driven by our inner search to understand the workings of the human brain, through the emerging interdisciplinary field of computational neuroscience. Not surprisingly, there is now a large worldwide community of researchers who publish a huge number of new discoveries and techniques each year. There are several excellent texts on vision and pattern recognition available to the reader. However, while these classic texts serve as fine introductions and references to the core mathematical ideas, they cannot hope to keep pace with the vast and diverse outpouring of new research papers. In contrast, this volume is intended to gather together the most recent advances in many aspects of visual pattern recognition, from all over the world. An exceptionally international and interdisci- VI plinary collection of authors have come together to write these book chapters. Some of these chapters provide detailed expositions of a specific technique and others provide a useful tu- torial style overview of some emerging aspect of the field not normally covered in introduc- tory texts. The book will be useful and stimulating to academic researchers and their students and also industrial vision engineers who need to keep abreast of research developments. This book also provides a particularly good way for experts in one aspect of the field to learn about advances made by their colleagues with different research interests. When browsing through this volume, insights into one’s own work are frequently found within a chapter from a different research area. Thus, one aim of this book is to help stimulate cross- fertilization between the multiplying and increasingly disparate branches of the sciences of computer vision and pattern recognition. I wish to thank the many authors and editors who have volunteered their time and material to make this book possible. On this basis, Advanced Robotic Systems International has been able to make this book entirely available to the community as open access. As well as being available on library shelves, any of these chapters can be downloaded free of charge by any researcher, anywhere in the world. We believe that immediate, world-wide, barrier-free, open access to the full text of research articles is in the best interests of the scientific commu- nity. Editor Rustam Stolkin Stevens Institute of Technology USA VII Contents Preface V 1. Real-Time Object Segmentation of the Disparity Map Using Projection-Based Region Merging 001 Dongil Han 2. A Novel Omnidirectional Stereo Vision System via a Single Camera 019 Chuanjiang Luo, Liancheng Su and Feng Zhu 3. Stereo Vision Camera Pose Estimation for On-Board Applications 039 Sappa A., Geronimo D., Dornaika F. and Lopez A. 4. Correcting Radial Distortion of Cameras with Wide Angle Lens Using Point Correspondences 051 Leonardo Romero and Cuauhtemoc Gomez 5. Soft Computing Applications in Robotic Vision Systems 065 Victor Ayala-Ramirez, Raul E. Sanchez-Yanez and Carlos H. Garcia-Capulin 6. Analysis of Video-Based 3D Tracking Accuracy by Using Electromagnetic Tracker as a Reference 091 Matjaz Divjak and Damjan Zazula 7. Photorealistic 3D Model Reconstruction based on the Consistency of Object Surface Reflectance Measured by the Voxel Mask 113 K. K. Chiang, K. L. Chan and H. Y. Ip 8. Collaborative MR Workspace with Shared 3D Vision Based on Stereo Video Transmission 133 Shengjin Wang, Yaolin Tan, Jun Zhou, Tao Wu and Wei Lin VIII 9. Multiple Omnidirectional Vision System and Multilayered Fuzzy Behavior Control for Autonomous Mobile Robot 155 Yoichiro Maeda 10. A Tutorial on Parametric Image Registration 167 Leonardo Romero and Felix Calderon 11. A Pseudo Stereo Vision Method using Asynchronous Multiple Cameras 185 Shoichi Shimizu, Hironobu Fujiyoshi, Yasunori Nagasaka and Tomoichi Takahashi 12. Real-Time 3-D Environment Capture Systems 197 Jens Kaszubiak, Robert Kuhn, Michael Tornow and Bernd Michaelis 13. Projective Rectification with Minimal Geometric Distortion 221 Hsien-Huang P. Wu and Chih-Cheng Chen 14. Non-rigid Stereo-motion 243 Alessio Del Bue and Lourdes Agapito 15. Continuous Machine Learning in Computer Vision - Tracking with Adaptive Class Models 265 Rustam Stolkin 16. A Sensors System for Indoor Localisation of a Moving Target Based on Infrared Pattern Recognition 283 Nikos Petrellis, Nikos Konofaos and George Alexiou 17. Pseudo Stereovision System (PSVS): A Monocular Mirror-based Stereovision System 305 Theodore P. Pachidis 18. Tracking of Facial Regions Using Active Shape Models and Adaptive Skin Color Modeling 331 Bogdan Kwolek 19. Bimanual Hand Tracking based on AR-KLT 351 Hye-Jin Kim, Keun-Chang Kwak and Jae Jeon Lee 20. An Introduction to Model-Based Pose Estimation and 3-D Tracking Techniques 359 Christophe Doignon IX 21. Global Techniques for Edge based Stereo Matching 383 Yassine Ruichek, Mohamed Hariti and Hazem Issa 22. Local Feature Selection and Global Energy Optimization in Stereo 411 Hiroshi Ishikawa and Davi Geiger 23. A Learning Approach for Adaptive Image Segmentation 431 Vincent Martin and Monique Thonnat 24. A Novel Omnidirectional Stereo Vision System with a Single Camera 455 Sooyeong Yi and Narendra Ahuja 25. Image Processing Techniques for Unsupervised Pattern Classification 467 C. Botte-Lecocq, K. Hammouche, A. Moussa, J G. Postaire, A. Sbihi and A. Touzani 26. Articulated Hand Tracking by ICA-based Hand Model and Multiple Cameras 489 Makoto Kato, Gang Xu and Yen-Wei Chen 27. Biologically Motivated Vergence Control System Based on Stereo Saliency Map Model 513 Sang-Woo Ban and Minho Lee [...]... developed and the video-rate stereo machine has the capability of generating a dense depth map of 256x240 pixels at the frame rate of 30 frames/sec in [1-2] An algorithm proposed from parallel relaxation algorithm for disparity computation [3] results reduction of error rate and enhancement of computational complexity 2 Scene Reconstruction, Pose Estimation and Tracking of problems Also, an algorithm proposed... interface and display And we also implemented embedded system software by constructing necessary device driver with MX21 350MHz microprocessor environment Table 2 shows the logic gates of proposed SMPP module when retargeting FPGA Figure 10 ~13 show real time captured images of stereo camera input and the results of SMPP modules using control pc 10 Scene Reconstruction, Pose Estimation and Tracking. .. 14 Scene Reconstruction, Pose Estimation and Tracking Figure 14 shows control application program operated on control pc This application program communicates to board and hub to calibrate camera and to modify registry of each modules Also it can capture images on the screen which can be useful for debug jobs Figure 15 shows image collecting stereo camera Figure 16 shows implemented embedded system and. .. China (60575024) 20 Scene Reconstruction, Pose Estimation and Tracking addition, two-camera stereo systems are costly and complicated besides having the problem of requiring precise positioning of the cameras Single camera stereo has several advantages over two-camera stereo Because only a single camera and digitizer are used, system parameters such as spectral response, gain, and offset are identical... geometric method using two or more sets of parallel lines in one 22 Scene Reconstruction, Pose Estimation and Tracking image to determine the camera aspect ratio, a scale factor that is the product of the camera and mirror focal lengths, and the principal point Kang (Kang, 2000) describes two methods The first recovers the image center and mirror parabolic parameter from the image of the mirror’s circular... system was placed on a plane desk As both the base of vision system and desk surface are plane, the axis of the mirror is perpendicular to the base of the system and the surface of the desk feckly We make the mirror system coincide with the world system to simplify the model and computations 24 Scene Reconstruction, Pose Estimation and Tracking So the equations of hyperboloid of two sheets in the system... of flat or slanted object, which produce wide range 6 Scene Reconstruction, Pose Estimation and Tracking of distances from camera, the objects need to be recognized as one object Therefore, regular rule is necessary to be applied on the merging algorithm In this chapter, the merging algorithm is such that the two region of depth level is overlapped and its difference of depth level is just one level,... operated on control pc Real-Time Object Segmentation of the Disparity Map Using Projection-Based Region Merging Figure 15 The stereo camera Figure 16 Embedded System and unified FPGA board module 15 16 Scene Reconstruction, Pose Estimation and Tracking 5.4 Software application A household robot has to perform actions like obstacle avoidance or human recognition activity One of systems used widely can recognize... it with various images registered in stereo matching database to secure validity Also we have developed VHDL and on-boarded it to unified FPGA board module to examine various real time tests using stereo camera on various indoor environments for 18 Scene Reconstruction, Pose Estimation and Tracking second step As the result of many experiments, we were able to confirm quality improvement of stereo... we proved the validity of proposed algorithm with C-language level implementation And, after that, we implemented the proposed algorithms with VHDL level and we were able to get result of hardware simulation using Modelsim Finally, the proposed post-processing algorithm is implemented in FPGA We used 320x240 resolution and frame rates of 60 fps, 1/3” CMOS stereo camera, and the full logic is tested . Scene Reconstruction, Pose Estimation and Tracking Scene Reconstruction, Pose Estimation and Tracking Edited by Rustam Stolkin I-TECH Education and Publishing IV Published. Library. Scene Reconstruction, Pose Estimation and Tracking, Edited by Rustam Stolkin p. cm. ISBN 978-3-902613-06-6 1. Vision Systems. 2. Scene Reconstruction. 3. Pose Estimation. 4. Tracking. . results reduction of error rate and enhancement of computational complexity Scene Reconstruction, Pose Estimation and Tracking 2 of problems. Also, an algorithm proposed from depth discontinuities