Vision Systems Applications Vision Systems Applications Edited by Goro Obinata and Ashish Dutta 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 catalog record for this book is available from the Austrian Library. Vision Systems: Applications, Edited by Goro Obinata and Ashish Dutta p. cm. ISBN 978-3-902613-01-1 1. Vision Systems. 2. Applications. 3. Obinata & Dutta. V Preface Computer Vision is the most important key in developing autonomous navigation systems for interaction with the environment. It also leads us to marvel at the functioning of our own vision system. In this book we have collected the latest applications of vision research from around the world. It contains both the conventional research areas like mobile robot naviga- tion and map building, and more recent applications such as, micro vision, etc. The fist seven chapters contain the newer applications of vision like micro vision, grasping using vision, behavior based perception, inspection of railways and humanitarian demining. The later chapters deal with applications of vision in mobile robot navigation, camera cali- bration, object detection in vision search, map building, etc. We would like to thank all the authors for submitting the chapters and the anonymous re- viewers for their excellent work. Sincere thanks are also due to the editorial members of Advanced Robotic Systems publica- tions for all the help during the various stages of review, correspondence with authors and publication. We hope that you will enjoy reading this book and it will serve both as a reference and study material. Editors Goro Obinata Centre for Cooperative Research in Advanced Science and Technology Nagoya University, Japan Ashish Dutta Dept. of Mechanical Science and Engineering Nagoya University, Japan VII Contents Preface V 1. Micro Vision 001 Kohtaro Ohba and Kenichi Ohara 2. Active Vision based Regrasp Planning for Capture of a Deforming Object using Genetic Algorithms 023 Ashish Dutta, Goro Obinata and Shota Terachi 3. Multi-Focal Visual Servoing Strategies 033 Kolja Kuehnlenz and Martin Buss 4. Grasping Points Determination Using Visual Features 049 Madjid Boudaba, Alicia Casals and Heinz Woern 5. Behavior-Based Perception for Soccer Robots 065 Floris Mantz and Pieter Jonker 6. A Real-Time Framework for the Vision Subsystem in Autonomous Mobile Robots 083 Paulo Pedreiras, Filipe Teixeira, Nelson Ferreira and Luis Almeida 7. Extraction of Roads From Out Door Images 101 Alejandro Forero Guzman and Carlos Parra 8. ViSyR: a Vision System for Real-Time Infrastructure Inspection 113 Francescomaria Marino and Ettore Stella 9. Bearing-Only Vision SLAM with Distinguishable Image Features 145 Patric Jensfelt, Danica Kragic and John Folkesson VIII 10. An Effective 3D Target Recognition Imitating Robust Methods of the Human Visual System 157 Sungho Kim and In So Kweon 11. 3D Cameras: 3D Computer Vision of wide Scope 181 Stefan May, Kai Pervoelz and Hartmut Surmann 12. A Visual Based Extended Monte Carlo Localization for Autonomous Mobile Robots 203 Wen Shang and Dong Sun 13. Optical Correlator based Optical Flow Processor for Real Time Visual Navigation 223 Valerij Tchernykh, Martin Beck and Klaus Janschek 14. Simulation of Visual Servoing Control and Performance Tests of 6R Robot Using Image- Based and Position-Based Approaches 237 M. H. Korayem and F. S. Heidari 15. Image Magnification based on the Human Visual Processing 263 Sung-Kwan Je, Kwang-Baek Kim, Jae-Hyun Cho and Doo-Heon Song 16. Methods of the Definition Analysis of Fine Details of Images 281 S.V. Sai 17. A Practical Toolbox for Calibrating Omnidirectional Cameras 297 Davide Scaramuzza and Roland Siegwart 18. Dynamic 3D-Vision 311 K D. Kuhnert , M. Langer, M. Stommel and A. Kolb 19. Bearing-only Simultaneous Localization and Mapping for Vision-Based Mobile Robots 335 Henry Huang, Frederic Maire and Narongdech Keeratipranon 20. Object Recognition for Obstacles-free Trajectories Applied to Navigation Control 361 W. Medina-Meléndez, L. Fermín, J. Cappelletto, P. Estévez, G. Fernández-López and J. C. Grieco IX 21. Omnidirectional Vision-Based Control From Homography 387 Youcef Mezouar, Hicham Hadj Abdelkader and Philippe Martinet 22. Industrial Vision Systems, Real Time and Demanding Environment: a Working Case for Quality Control 407 J.C. Rodríguez-Rodríguez, A. Quesada-Arencibia and R. Moreno-Díaz jr 23. New Types of Keypoints for Detecting Known Objects in Visual Search Tasks 423 Andrzej gluzek and Md Saiful Islam 24. Biologically Inspired Vision Architectures: a Software/Hardware Perspective 443 Francesco S. Fabiano, Antonio Gentile, Marco La Cascia and Roberto Pirrone 25. Robot Vision in the Language of Geometric Algebra 459 Gerald Sommer and Christian Gebken 26. Algebraic Reconstruction and Post-processing in Incomplete Data Computed Tomography: From X-rays to Laser Beams 487 Alexander B. Konovalov, Dmitry V. Mogilenskikh, Vitaly V. Vlasov and Andrey N. Kiselev 27. AMR Vision System for Perception, Job Detection and Identification in Manufacturing 521 Sarbari Datta and Ranjit Ray 28. Symmetry Signatures for Image-Based Applications in Robotics 541 Kai Huebner and Jianwei Zhang 29. Stereo Vision Based SLAM Issues and Solutions 565 D.C. Herath, K.R.S. Kodagoda and G. Dissanayake 30. Shortest Path Homography-Based Visual Control for Differential Drive Robots 583 G. López-Nicolás, C. Sagüés and J.J. Guerrero 31. Correlation Error Reduction of Images in Stereo Vision with Fuzzy Method and its Application on Cartesian Robot 597 Mehdi Ghayoumi and Mohammad Shayganfar [...]... 0 .19 5 um 10 0 um 512 pixel The maximum depth resolution is 0.7 81 um, 10 0 um128 bits Figure 18 shows the all-in-focus image in the microscope Compared with Fig .17 , both fabrics can be seen in-focus in one view 14 Figure 17 Microscopic Images for fabrics Vision Systems: Applications Micro Vision Figure 18 All-in-Focus Image with ghost Figure 19 All-in-Focus Image without ghost 15 16 Vision Systems: Applications. .. D-II, Vol.J80-D-II, No.9, pp.229 8-2 307 Masahiro Watanabe and Shree K Nayer (19 96) Minimal Operator Set for Passive Depth from Defocus CVPR'96, pp.43 1- 4 38 Shree K Nayer, Masahiro Watanabe, and Minoru Noguchi (19 95) Real-Time Focus Range Sensor ICCV'95, pp.99 5 -1 0 01 Shree K Nayer, and Yasuo Nakagawa (19 94) Shape from Focus IEEE Trans on PAMI Vol .16 , No.8, pp.82 4-8 31 Sridhar R Kundur and Daniel Raviv (19 96)... all-in-focus and depth images are transmitted into the PC directly 2.3.3 Microscopic System For real micro -applications, a microscopic system is developed with the processing part mentioned before, as shown in Fig .16 Instead of using a dynamic focusing lens, the PIFOC microscope objective nano-positioners and scanners P-7 21. 20, PI-Polytec Co are controlled by a high-speed nano-automation controller E- 612 .C0,... high-speed transmission 60Mbyte/sec ( 512 *480*240Hz) from HSV to the dual-port SDRAM is realized As a result, the performance of the FPGA is good enough to calculate the IQM value with 240Hz, and the total execution performance is less than 20% of the performance of FPGA Figure 12 Sencond Prototype Systems Micro Vision 11 Figure 13 System Configuration (b) Optical Part A micro-zoom lens (Asahi Co MZ-30,... images at four particular focal distances are shown in Fig 9 The objects for demonstration were constructed in a four-step pyramidal shape: first stage, φ 10 mm height 10 mm; second, φ 7mm -1 0 mm; third, φ 4mm -1 0 mm; and top, φ 3mm-5mm In real-usage cases, such as less than 1 mm size, the IQM value could be obtained with the original texture on the object without any artificial texture Micro Vision Figure... planned to use seven images without the first image Figure 14 Ramp Input 12 Vision Systems: Applications Figure 15 Sample Image of Second Prototype System The spatial resolution in this system is 31. 25mu, 16 mm/ 512 pixel The depth resolution is 5.0mm (7 frames with 35mm depth), which can be improved with the input frame number Up to now, the all-in-focus image and the depth image are stored in each memory... combination of the micro zoom lens Figure 10 Sample of All-in-Focus image 10 Vision Systems: Applications Figure 11 Sample of the Depth Image for Sample Object with Texture Based on All-in-Focus Image 2.3.2 Second Prototype This paragraph shows the second prototype of the micro VR camera systems (a) Processing Part Recently, the large-scale FPGA (Field Programmable Gate Array) has dramatically improved... Comparison with Ghost-Filtering A detail analysis indicated that several blurring edges could be observed just around the objects in Fig .18 This ghost is caused by several out-of-focus images In the microscope, the out-of-focus image makes a large blurring region around the real object's location, as can be seen in Fig .18 This blurring region could cause miss-recognition of the all-in-focus area around... operator put the gripper in sight, the in-focus image could allow us to observe the object and the gripper simultaneously, although they are located at different depths 17 Micro Vision Figure 21 Product system for the Micro VR Camera (a) All-in-Focus Image for MEMS device (b) Depth Image Figure 22 Sample View of MEMS device with the product system 18 Vision Systems: Applications 3 Micro Observation In... voltage from -3 0V to +30V to charge the PZT), which directly depends on the number of input frames in the range of variable focusing The “all-in-focus image” and the micro VR environments from one image sequence are shown in Fig 10 and 11 , respectively The “all-in-focus image” gives a clear image to observe the whole object However, the resolution of depth without any interpolation in Fig .11 does not . Library. Vision Systems: Applications, Edited by Goro Obinata and Ashish Dutta p. cm. ISBN 97 8-3 -9 02 61 3-0 1- 1 1. Vision Systems. 2. Applications. 3. Obinata & Dutta. V Preface Computer Vision. Scaramuzza and Roland Siegwart 18 . Dynamic 3D -Vision 311 K D. Kuhnert , M. Langer, M. Stommel and A. Kolb 19 . Bearing-only Simultaneous Localization and Mapping for Vision- Based Mobile Robots 335 Henry. than 15 0Hz high frequency. See details in [10 ]. We applied this lens with the combination of the micro zoom lens. Figure 10 . Sample of All-in-Focus image Vision Systems: Applications 10 Figure