MACHINE VISION – APPLICATIONS AND SYSTEMS Edited by Fabio Solari, Manuela Chessa and Silvio P. Sabatini Machine Vision – Applications and Systems Edited by Fabio Solari, Manuela Chessa and Silvio P. Sabatini Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. As for readers, this license allows users to download, copy and build upon published chapters even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. Notice 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 chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Martina Blecic Technical Editor Teodora Smiljanic Cover Designer InTech Design Team First published March, 2012 Printed in Croatia A free online edition of this book is available at www.intechopen.com Additional hard copies can be obtained from orders@intechopen.com Machine Vision – Applications and Systems, Edited by Fabio Solari, Manuela Chessa and Silvio P. Sabatini p. cm. ISBN 978-953-51-0373-8 Contents Preface IX Chapter 1 Bio-Inspired Active Vision Paradigms in Surveillance Applications 1 Mauricio Vanegas, Manuela Chessa, Fabio Solari and Silvio Sabatini Chapter 2 Stereo Matching Method and Height Estimation for Unmanned Helicopter 23 Kuo-Hsien Hsia, Shao-Fan Lien and Juhng-Perng Su Chapter 3 Fast Computation of Dense and Reliable Depth Maps from Stereo Images 47 M. Tornow, M. Grasshoff, N. Nguyen, A. Al-Hamadi and B. Michaelis Chapter 4 Real-Time Processing of 3D-TOF Data in Machine Vision Applications 73 Stephan Hussmann, Torsten Edeler and Alexander Hermanski Chapter 5 Rotation Angle Estimation Algorithms for Textures and Their Implementations on Real Time Systems 93 Cihan Ulas, Onur Toker and Kemal Fidanboylu Chapter 6 Characterization of the Surface Finish of Machined Parts Using Artificial Vision and Hough Transform 111 Alberto Rosales Silva, Angel Xeque-Morales, L.A. Morales -Hernandez and Francisco Gallegos Funes Chapter 7 Methods for Ellipse Detection from Edge Maps of Real Images 135 Dilip K. Prasad and Maylor K.H. Leung Chapter 8 Detection and Pose Estimation of Piled Objects Using Ensemble of Tree Classifiers 163 Masakazu Matsugu, Katsuhiko Mori, Yusuke Mitarai and Hiroto Yoshii VI Contents Chapter 9 Characterization of Complex Industrial Surfaces with Specific Structured Patterns 177 Yannick Caulier Chapter 10 Discontinuity Detection from Inflection of Otsu’s Threshold in Derivative of Scale-Space 205 Rahul Walia, David Suter and Raymond A. Jarvis Chapter 11 Reflectance Modeling in Machine Vision: Applications in Image Analysis and Synthesis 227 Robin Gruna and Stephan Irgenfried Chapter 12 Towards the Optimal Hardware Architecture for Computer Vision 247 Alejandro Nieto, David López Vilarino and Víctor Brea Sánchez Preface Vision plays a fundamental role for living beings by allowing them to interact with the environment in an effective and efficient way. The Machine Vision goal is to endow computing devices, and more generally artificial systems, with visual capabilities in order to cope with not a priori predetermined situations. To this end, we have to take into account the computing constraints of the hosting architectures and the specifications of the tasks to be accomplished. These elements lead a continuous adaptation and optimization of the usual visual processing techniques, such as ones developed in Computer Vision and Image Processing. Nevertheless, the fast development of off‐the‐shelf processors and computing devices made available to the public a large and low‐cost computational power. By exploiting this contingency, the Vision Research community is now ready to develop real‐time vision systems designed to analyze the richness of the visual signal online with the evolution of complex real‐world situations at an affordable cost. Thus the application field of Machine Vision is not more limited to the industrial environments, where the situations are simplified and well known and the tasks are very specific, but nowadays it can efficiently support system solutions of everyday life problems. This book will focus on both the engineering and technological aspects related to visual processing. The first four chapters describe solutions related to the recovery of depth information in order to solve video surveillance problems and an helicopter landing task (Chp.1 and Chp. 2, respectively), and to propose a high speed calculation of depth maps from stereo images based on FPGAs (Chp. 3) and a Time-of-Flight sensor as an alternative to stereo video camera (Chp. 4). The next three chapters address typical industrial situations: an approach for robust rotation angle estimation for textures alignment is described in Chp. 5, the characterization of the surface finish of machined parts through Hough transform is addressed in Chp. 6 and through structured light patterns in Chp. 7. A new algorithm based on ensemble of trees for object localization and 3D pose estimation that works for piled parts is proposed in Chp. 8. The detection of geometric shapes like ellipses from real images and a theoretical framework for characterization and identification of a discontinuity are addressed in Chp. 9 and X Preface Chp.10, respectively. The automated visual inspection improvement due to reflectance measuring and modeling in the context of image analysis and synthesis is presented in Chp. 11. The last chapter addresses an analysis of different computing paradigms and platforms oriented to image processing Fabio Solari, Manuela Chessa and Silvio P. Sabatini University of Genoa Italy [...]... vision, CVGIP: Image Understanding 60(3): 34 3–3 58 22 22 Machine Vision – Applications and Systems Will-be-set-by-IN-TECH Tsai, R (1987) A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses, Robotics and Automation, IEEE Journal of 3(4): 323 –3 44 Weems, C (1991) Architectural requirements of image understanding with respect to parallel... positioning system (GPS) and inertial measurement unit (IMU) The system of the autonomous unmanned helicopter is a 6-DOF system, with 3-axis rotation 24 Machine Vision – Applications and Systems information provided by IMU and 3-axis moving displacement information provided from GPS Oh et al (2006) brought up the tether-guided method for autonomous helicopter landing Many researches used vision systems for controlling... interesting case study is described in section 5 where both disparity and optic flow are used to segment images Finally, in section 6, we present and discuss the system’s performance results 4 4 Machine Vision – Applications and Systems Will-be-set-by-IN-TECH 2 The system: a low-level vision approach The visual cortex is the largest, and probably the most studied part of the human brain The visual cortex... factor and image center for high accuracy 3-d machine vision metrology, IEEE Transactions on Pattern Analysis and Machine Intelligence 10: 71 3–7 20 Lowe, D G (1984) Perceptual Organization and Visual Recognition, PhD thesis, STANFORD UNIV CA DEPT OF COMPUTER SCIENCE Marr, D (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, Henry Holt and Co.,... Computer Vision, Graphics, and Image Processing 29(3): 273 – 285 Koenderink, J & van Doorn, A (1976) Geometry of binocular vision and a model for stereopsis, Biological Cybernetics 21: 2 9–3 5 Kolmogorov, V., Criminisi, A., Blake, A., Cross, G & Rother, C (2005) Bi-layer segmentation of binocular stereo video, Computer Vision and Pattern Recognition, IEEE Computer Society Conference on 2: 40 7–4 14 Bio-Inspired... 2005), and to perform tracking (Harville, 2004) On the other hand, the optic flow is commonly used as a robust feature in motion-based segmentation and tracking (Andrade et al., 2006; Yilmaz et al., 2006) This chapter aims to describe a biological inspired video processing system for being used in video surveillance applications; the degree of similarity between the proposed framework 2 Machine Vision – Applications. .. 630 –6 32 Scharstein, D & Szeliski, R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Int J of Computer Vision 47: 7–4 2 Shi, J & Malik, J (2000) Normalized cuts and image segmentation, Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(8): 888 –9 05 Stauffer, C & Grimson, W (1999) Adaptive background mixture models for real-time tracking, Computer Vision. .. Y, Z ) can be written in terms of the coordinate systems shown in Fig 3 as follows: 8 Machine Vision – Applications and Systems Will-be-set-by-IN-TECH 8 ( X, Y, Z ) =( X L , YL , ZL ) − O L , (8) ( X, Y, Z ) =( XR , YR , ZR ) − OR , (9) where O L = (dx L , dy L , dz L ) and OR = (− dx R , dy R , dz R ) are the origin of the coordinate system of the left and right cameras with respect to the wide-angle... 8), and the short-range scenario in which the depth is in the range between 10 and 50 meters (see Fig 11) 16 Machine Vision – Applications and Systems Will-be-set-by-IN-TECH 16 (b) Left Image, point A (c) Right Image, point A (d) Left Image, point B (e) Right Image, point B (f) Left Image, point C (g) Right Image, point C B C A (a) Cyclopean Image Fig 8 Long-range scenario: Fixation of points A, B and. .. Bio-Inspired Active Vision Paradigms in Surveillance Applications Bio-Inspired Active Vision Paradigms in Surveillance Applications 21 21 Kumar, R K., Ilie, A., Frahm, J.-M & Pollefeys, M (2008) Simple calibration of non-overlapping cameras with a mirror, Computer Vision and Pattern Recognition, IEEE Computer Society Conference on 0: 1–7 Kuon, I & Rose, J (2006) Measuring the gap between fpgas and asics, FPGA . MACHINE VISION – APPLICATIONS AND SYSTEMS Edited by Fabio Solari, Manuela Chessa and Silvio P. Sabatini Machine Vision – Applications and Systems Edited. RobotCub and subsequently adopted by more than 20 laboratories worldwide (see http://www.icub.org/). 2 Machine Vision – Applications and Systems Bio-Inspired Active Vision Paradigms in Surveillance Applications. horizontal and vertical disparities and optic flow (Chessa, Sabatini & Solari, 2009). Structurally, the architecture comprises four processing stages (see 4 Machine Vision – Applications and Systems Bio-Inspired