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ADVANCED IMAGE ACQUISITION, PROCESSING TECHNIQUES AND APPLICATIONS Edited by Dimitrios Ventzas Advanced Image Acquisition, Processing Techniques and Applications Edited by Dimitrios Ventzas 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 Vana Persen 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 [email protected] Advanced Image Acquisition, Processing Techniques and Applications, Edited by Dimitrios Ventzas p. cm. ISBN 978-953-51-0342-4 Contents Preface VII Chapter 1 A Coded Structured Light Projection Method for High-Frame-Rate 3D Image Acquisition 1 Idaku Ishii Chapter 2 High Density Devices Applied to a Gamma-Camera Implementation 17 Griselda Saldana-Gonzalez, Uvaldo Reyes, Humberto Salazar, Oscar Martínez, Eduardo Moreno and Ruben Conde Chapter 3 Adipose Measurement Using Micro MRI 37 Yang Tang and Rex A. Moats Chapter 4 Image Processing Quality Analysis for Particle Based Peptide Array Production on a Microchip 55 Jenny Wagner, Felix Löffler, Tobias Förtsch, Christopher Schirwitz, Simon Fernandez, Heinz Hinkers, Heinrich F. Arlinghaus, Florian Painke, Kai König, Ralf Bischoff, Alexander Nesterov-Müller, Frank Breitling, Michael Hausmann and Volker Lindenstruth Chapter 5 Digital Restoration by Denoising and Binarization of Historical Manuscripts Images 73 Dimitrios Ventzas, Nikolaos Ntogas and Maria-Malamo Ventza Chapter 6 Applications of Image Processing Technique in Porous Material Characterization 109 Ming Gan and Jianhua Wang Chapter 7 Accurate Spectral Measurements and Color Infrared Imagery of Excised Leaves Exhibiting Gaussian Curvature from Healthy and Stressed Plants 123 Christopher R. Little and Kenneth R. Summy Chapter 8 A Comparative Study of Some Markov Random Fields and Different Criteria Optimization in Image Restoration 143 José I. De la Rosa, Jesús Villa, Ma. Auxiliadora Araiza, Efrén González and Enrique De la Rosa Preface This book presents a wide range of applications on image processing that fits areas of engineers such as acquisition, sensing, optimization, medicine, biomedicine, materials, agriculture and text image processing. It is suggested to students, scientists and researchers in the field. Its most important feature is the way the authors allow newcomers with a little background in mathematics to go through all the topics presented without getting lost or discouraged to actually understand image acquisition, enhancement, restoration, preparation, and compression. The mathematical principles are clearly explained by the authors. Another nice distinctive characteristic of the book is the detailed explanation on practical issues and fully developed applications that allow the reader to thoroughly understand how to solve problems professionally and how to implement standard and advanced image processing algorithms. The book covers many topics that one expects to find in a modern image application book such as a coded structured light projection method for high-frame-rate 3D image acquisition, high density devices applied to a gamma-camera implementation, adipose measurement using micro MRI, image processing quality analysis for particle based peptide array production on a micro chip, digital restoration by denoising and binarization of historical manuscripts images, applications of image processing technique in porous material characterization, accurate spectral measurements and color infrared imagery of excised leaves exhibiting Gaussian curvature from healthy and stressed plants and a comparative study of some Markov random fields and different criteria optimization in image restoration. The various algorithms suggested in the book, are a valuable tool for experimenting and helping the reader to grasp the secrets associated with the advanced image processing techniques and algorithms. This book fits the needs of image acquisition and applications engineers to many application fields. Professor Dr. D.E. Ventzas Department of Computer Science and Telecommunications, Technological Educational Institute of Larissa, Greece 0 1 A Coded Structured Light Projection Method for High-Frame-Rate 3D Image Acquisition IdakuIshii HiroshimaUniversity Japan 1.Introduction Three-dimensionalmeasurementtechnologyhasrecentlybeenusedforvariousapplications such as human modeling, cultural properties recording, machine inspection of industrial parts, and robot vision. The light-section method and coded structured light projection method are well-known active measurementmethods that can accurately obtain three-dimensionalshapesbyprojectinglightpatternsonthemeasurementspace.Theseactive measurement methods have been applied to practical systems in real scenes because they arerobustregardingtexturepatternsonthesurfacesoftheobjectstobeobserved,andhave advantages incalculationtimeandaccuracy. Followingrecentimprovementsinintegration technology,three-dimensionalimagemeasurementsystemsthatcanoperateatarateof30fps ormorehavealreadybeendeveloped[1,2].Inmanyapplicationfields,dynamicanalysistools to observedynamicchangesinhigh-speedphenomenainthree-dimensionalshapesathigh frameratesarerequired. Off-linehigh-speedcamerasthatcancaptureimagesat1000fpsor morehavealreadybeenputintopracticaluseforanalyzinghigh-speedphenomena;however, manyofthemcanonlyrecordhigh-speedphenomenaastwo-dimensionalimagesequences. Inthischapter,weproposeaspatio-temporalselectiontypecodedstructuredlightprojection methodforthree-dimensionalshapeacquisitionatahighframerate. Section2describesthe basic principle and related work on coded structured light projection methods. Section 3 describesourproposedcodedstructuredlightprojectionmethodthatcanalternateatemporal encoding and a spatial encoding adaptively according to the temporal changes of image intensitiessoastoaccuratelyobtainathree-dimensionalshapeofamovingobject.Insection4, severalexperimentswereperformedforseveralmovingobjects,andourproposedalgorithm wasevaluatedbycapturingtheirthree-dimensionalshapesat1000fpsonaverificationsystem comprisinganoff-linehigh-speedcameraandahigh-speedDLPprojector. 2.Codedstructuredlightprojectionmethod Our proposed three-dimensional measurement method can be described in terms of the coded structured light projection method proposedby Posdamer et al. [3]. In this section, we describe its basic principles and the related previous coded structured light projection methods. 2.1Basicprinciple Inthecodedstructuredlightprojectionmethod,aprojectorprojectsmultipleblackandwhite ’zebra’lightpatternswhosewidthsaredifferentontotheobjectstobeobserved,asshownin 2 Advanced Image Acquisition, Processing Techniques and Applications 2 Will-be-set-by-IN-TECH Figure1. Athree-dimensionalimageismeasuredbycapturingtheprojectionimagesonthe objects using acamera whose angle of viewis differentfromthat of the projector. When n patternsareprojectedontheobjects,themeasurementspaceisdividedinto2nverticalpieces, corresponding to the black and white areas of the zebra light patterns. Thereafter, we can obtainthe n-bitdataateachpixelintheprojectionimage,correspondingtothepresenceof thelightpatterns.Thenbitdataiscalledaspacecode,anditindicatestheprojectiondirection. Basedontherelationshipbetweensuchaspacecodeandthemeasurementdirectionsthatare determinedbypixelpositions,we can calculate depthinformationatallpixelsofan image usingtriangulation. 2.2Relatedwork Posdamer et al. [3] have used multiple black and white light patterns with a pure binary codeasshowninFigure1. Inthiscase,seriousencodingerrorsmayoccur,evenwhenthere is a small amount of noise, because the brightness boundaries of the multiple projection patterns with a pure binary code exist at the same positions. To solve this problem, Inokuchi et al. [4] proposeda technique for minimizing encoding errorsusing boundaries introducedintheformofmultiplelightpatterns withagraycode. Bergmann[5]proposed an improved three-dimensional measurement method that combines the gray code pattern projection method and a phase shift method; in this method, the number of projection patterns increases. Caspi et al. [6] proposed an improved gray code pattern projection method that can reduce the number of projection patterns using color patterns. In these methods, three-dimensional shapes can be measured as high-resolution three-dimensional images because depth information is calculated at every pixel; however, it is difficult to accurately measure the three-dimensional shapes of moving objects because multiple light patternsareprojectedatdifferenttimings. Y Projection Pattern Projector Pattern 1 Camera Pattern 2 Z Pattern 3 1 1 1 1 0 0 0 0 = Pattern1(MSB) 7 6 5 4 3 2 1 0 1 1 0 0 1 1 0 0 = Pattern2 1 0 1 0 1 0 1 0 = Pattern3 Space code value (cid:2) 7 6 5 4 3 2 1 0 Fig.1.Codedstructuredlightprojectionmethod.