Pre-Integrated Volume Rendering Martin Kraus and Thomas Ertl MartinĪccelerated Isosurface Extraction Approaches Yarden Livnat Time-Dependent Isosurface Extraction Han-Wei Shen Optimal Isosurface Extraction Paolo Cignoni, Claudio Montani, Roberto Scopigno, and Enrico Puppo ISBN: 0-12-387582-X For information on all Elsevier Butterworth–Heinemann publications visit our Web site at 05 06 07 08 09 10 11 10 9 8 7 6 5 4 3 2 1 Printed in the United States of America TK7882.I6V59 2005 006.6-DC22 2004020457 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Includes bibliographical references and index. Library of Congress Cataloging-in-Publication Data The visualization handbook / edited by Charles D. ![]() ⬁ Recognizing the importance of preserving what has been written, Elsevier prints its books on acid-free paper whenever possible. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (þ44) 1865 843830, fax: (þ44) 1865 853333, e-mail: You may also complete your request on-line via the Elsevier homepage (), by selecting ‘‘Customer Support’’ and then ‘‘Obtaining Permissions.’’ No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. TOKYOĮlsevier Butterworth–Heinemann 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA Linacre House, Jordan Hill, Oxford OX2 8DP, UK Copyright ß 2005, Elsevier Inc. Additionally, information on some grade-specific physical properties and their potential utilization will be necessary for the design of process equipment and the development of various value-added products.Associate Director, Scientific Computing and Imaging Institute Associate Professor, School of Computing University of Utah Salt Lake City, Utahĭirector, Scientific Computing and Imaging Institute Distinguished Professor, School of Computing University of Utah Salt Lake City, UtahĪMSTERDAM. The possible integration of machine vision systems with developed mass models will simultaneously enable the grading of guava with both dimension and mass. The developed mass models and outcome of this study will be beneficial for developing advanced grading machineries. Recent advancements and automation employ mass as a parameter that enhances the overall efficacy of grading operations. The grading process becomes complex when fruits are graded with a similar appearance but difference in mass therefore, mass-based grading of fruit plays a vital role in the design of advanced machineries. Grading is the essential unit operation in postharvest management to achieve dimensional uniformity. Practical Applicationįruits with uniform grades usually have higher demand and consumer preference. The possible applications of established mass models for developing an integrated and effective grading system and the prospective utilization of graded fruits for processing into a variety of value-added products are also discussed. The higher coefficient of determination ( R 2) and low mean relative deviation (MRD) indicated that quadratic models based on geometric mean diameter ( R 2 ≥.984, MRD = 2.32) and ellipsoidal volume ( R 2 ≥.986, MRD = 2.28) can effectively predict the mass of guava fruits. ![]() It was observed that predictions of mass models fitted on ungraded fruit lots were found superior to fitted on individual grades. The model equations were also fitted on ungraded fruits samples for comparison purpose. The fruits were graded based on the maximum equatorial diameter in three grades that is, large ( Φ = 66–75 mm), medium ( Φ = 54–65 mm), small ( Φ = 43–53 mm), and mass modeling was performed. Allahabad safeda), and the development of predictive linear and nonlinear (linear, quadratic, power, and S-curve) models to determine the mass of guava. ![]() The present study focused on measuring physical characteristics (dimensions, projected area, and volume) of guava (cv. The correlation between physical parameters of guava like axial dimensions, projected area, volume, and mass is essential for developing postharvest machineries especially grading systems.
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