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10. Workshop Farbbildverarbeitung 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ Please note: all images are sampled down to 72 dpi to reduce this files size 10. Workshop Farbbildverarbeitung 7.– 8. Oktober 2004 Koblenz Detlev Droege Dietrich Paulus (Hrsg.) 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ Vorwort Die Aufnahme, Verarbeitung und Analyse farbiger und mehrkanaliger Bilder gewinnt seit Jahren beständig an Bedeutung, wozu sowohl die verbesserten technischen Mög- lichkeiten als auch die gestiegenen Ansprüche aus den vielfältigen Anwendungsfeldern in der Industrie, der Medizin, der Umwelt und den Medien beitragen. Mit den techni- schen Möglichkeiten und mit den Wünschen der Anwender steigt auch der Forschungs- bedarf in diesem Gebiet. Im Jahr 1995 wurde erstmals der Workshop Farbbildverarbeitung in Koblenz ver- anstaltet, der in der Folge jährlich an verschiedenen Orten stattfand. Dieser Workshop bietet ein Diskussionsforum für Forscher, Anwender und Entwickler, das sich den Pro- blemen der Farbbildaufnahme, den Farbräumen und der Farbmessung genauso widmet wie der Entwicklung von neuen Methoden und Algorithmen zur Analyse mehrkanali- ger Bilder. Seit seiner Gründung bietet der Workshop ein Forum, in dem Hardware-nahe Untersuchungen ebenso wie Software-Entwicklungen, Grundlagen ebenso wie Anwen- dungsberichte ihren Platz finden. Damit ist die Möglichkeit zum Informationsaustausch zwischen denjenigen Gruppen gegeben, die sich intensiv mit dem Thema Farbe be- schäftigen, auch wenn sie aus sonst getrennten Gebieten wie dem Rechnersehen oder der Beleuchtungstechnik kommen. Der 10. Workshop findet im Jahr 2004 zum dritten Mal an seinem Gründungsort statt und wird an der Universität Koblenz-Landau im Institut für Computervisualistik in Zusammenarbeit mit der Firma TiTech Visionsort durchgeführt. Der Unterstützung durch die DAGM und durch die Gesellschaft für Informatik (GI, Fachgruppe 1.0.4) sei an dieser Stelle gedankt. Die Themen des Workshops, wie sie im vorliegenden Tagungsband dokumentiert sind, umfassen alle Bereiche der Erfassung, Verarbeitung, Analyse und Wiedergabe von Farbbildern sowie der Nutzung der industriellen Farbbildverarbeitung zur Quali- tätskontrolle, Inspektion, Robotik und Automatisierung. Koblenz, im September 2004 Detlev Droege Dietrich Paulus Das Titelbild zeigt eine Visualisierung des Median-Cut Algorithmus zur Farbquantisierung nach P. Heckbert (1982). 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ Organisation Der 10. Workshops Farbbildverarbeitung wird von dem Institut für Computervisualistik der Universität Koblenz–Landau, Campus Koblenz, ausgerichtet in Zusammenarbeit mit der Firma TiTech Visionsort GmbH, Andernach, der Gesellschaft für Informatik, Fachgruppe Bildverstehen, Bonn, und der Deutsche Arbeitsgemeinschaft für Musterer- kennung (DAGM e.V.). Tagungsleitung Tagungsvorsitz: Prof. Dr.-Ing. Dietrich Paulus (Universität Koblenz–Landau) Prof. Dr. Lutz Priese (Universität Koblenz–Landau) Dr. Volker Rehrmann (TiTech Visionsort) Programmkommitee PD Dr. Karl-Heinz Franke (TU Ilmenau) Prof. Dr.-Ing. Bernhard Hill (RWTH Aachen) Prof. Dr. Andreas Koschan (University of Tennessee) Prof. Dr.-Ing. Dietrich Paulus (Universität Koblenz-Landau) Prof. Dr. Lutz Priese (Universität Koblenz-Landau) Dr. Volker Rehrmann (TiTech Visionsort, Andernach) Dr. Werner Ritter (Daimler Chrysler, Ulm) Prof. Dr. Wolfgang Slowak (Fachhochschule Koblenz) Prof. Dr. Gerd Stanke (GFaI, Berlin) Veranstalter Institut für Computervisualistik, Universität Koblenz-Landau, Koblenz TiTech Visionsort GmbH, Andernach Gesellschaft für Informatik, Fachgruppe Bildverstehen, Bonn Deutsche Arbeitsgemeinschaft für Mustererkennung e.V. Sponsor TiTech Visionsort GmbH, Andernach 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ Inhaltsverzeichnis Die Metamer-Randdeskriptor-Methode zur Farbkorrektur : : : : : : : : : : : : : : : : : : : 1 Philipp Urban, Rolf-Rainer Grigat Realisierung einer farbmetrischen Anbindung an valenzmetrische Schnittstellen ... 10 Rico Nestler (ZBS e.V.), Karl-Heinz Franke (ZBS e.V.), Rainer Jahn (ZBS e.V.) Spektral modellierbare Lichtquelle zur Erzeugung beliebiger Spektren durch ... : 18 Markus Schnitzlein, Bernhard Frei, Andreas Willert Farbbasierte Objekterkennung mit einem omnidirektionalen System : : : : : : : : : : 27 A. Maas, P. Heim, J. Kaluza, S. Mitnacht, V. Hong, P. Maillard, F. Occelli, C. Kurucz, D. Paulus Nonparametric Noise Removal in Color Images : : : : : : : : : : : : : : : : : : : : : : : : : : 35 Bogdan Smolka Bestimmungshilfe für Mollusken und Eulenfalter mittels digitaler Bildanalyse : : 43 Fabian Fritzer An Efficient Vector Median Filter Computation : : : : : : : : : : : : : : : : : : : : : : : : : : : 50 V. Hong, L. Csink, S. Bouattour, D. Paulus On Color Image Quantization by the K-Means Algorithm : : : : : : : : : : : : : : : : : : 58 Henryk Palus (Silesian University of Technology) Symmetrie als kognitives Bildmerkmal : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 66 Kai Hübner Shading-Korrektur für Endoskopische Bilder und Fundusbilder : : : : : : : : : : : : : : 74 Annika Hirsch, Christian Münzenmayer, Dietrich Paulus Farbkalibrierung mittels linearer Transformation : : : : : : : : : : : : : : : : : : : : : : : : : : 83 J. Michel, V. Hong, D. Paulus, C. Münzenmayer Einsatz von diskreten Farbsensoren zur Druckbildkontrolle : : : : : : : : : : : : : : : : : 91 Frank Krumbein, Gunter Sieß, Winfried Mahler Robust Color Image Retrieval for the WWW : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 96 Bogdan Smolka Assessment of RGB Encoding for Color Imaging : : : : : : : : : : : : : : : : : : : : : : : : : 103 Úlfar Steingrímsson, Peter Zolliker (EMPA St.Gallen) Ein Entwurfswerkzeug für Farbklassifikatoren in Echtzeitanwendungen : : : : : : : 111 Klaus Arbter und Daniel Kish VI 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ Haustoria Segmentation in Microscope Colour Images of Barley Cells : : : : : : : : 119 Alexander Ihlow, Udo Seiffert Farbanalyse von gebackenen oder frittierten Nahrungsmitteln ... : : : : : : : : : : : : : 127 Walter Hillen, Felix Fischer (Fachhochschule Aachen) Farbauswertung in der Mikroskopie - wo liegen die Herausforderungen ... : : : : : : 135 Peter Schwarzmann 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ Die Metamer-Randdeskriptor-Methode zur Farbkorrektur Philipp Urban1 und Rolf-Rainer Grigat2 1 Ratio Entwicklungen GmbH 2 Technische Universität Hamburg-Harburg, Vision Systems 1 Einleitung Farbkorrektur beschreibt die Transformation zwischen den Sensorantworten eines Bild- aufnahmesytems (z.B.: RGB) und dem CIEXYZ- bzw. CIEL*a*b* Farbraum. In je- dem metameren Farbreproduktionssystem ist dies die erste Farbtransformation nach der Bildaufnahme. Es existieren zwei unterschiedliche Klassen von Methoden zur Farbkorrektur für lineare Bildaufnahmesysteme: Target- und modellbasierte Methoden. Targetbasierte Methoden benutzen eine Menge an Trainings- CIEXYZ / CIEL*a*b* Farben mit korrespondieren- den Sensorantworten, um eine Approximation der Farbkorrekturabbildung zu bestim- men. Die am weitesten verbreitete Methode ist die lineare Transformation zwischen den Sensorantworten und dem CIEXYZ Farbraum. Falls die CIEXYZ Farben linear abhän- gig sind von den Sensorantworten ist diese Transformation optimal. In dieser Arbeit werden lineare Bildaufnahmesysteme mit q 3 Kanälen und Sensorantworten aus der Menge C := [0; 1]q betrachtet. Die lineare Transformation≥ wird durch eine 3 q Matrix A beschrieben, die mittels linearer Regression der Targetfarben bestimmt werden× kann. Die Farbkorrekturabbildung lässt sich in diesem Fall wie folgt schreiben: A c = (X; Y; Z)T (L∗; a∗; b∗)T ; (1) · ! wobei c C eine beliebige Sensorantwort ist. Da die Aufnahme-2 und Beobachtungslichtart i.A. differieren und gängige Bildaufnah- mesysteme die Luther-Bedingung (d.h. die lineare Abhängigkeit der CIE-Normspek- tralwertkurven von den Kanalempfindlichkeiten, vgl. [9][6]) nicht erfüllen, ist die Farb- korrekturabbildung i.A. nicht linear und die Methode resultiert in großen ∆E Fehlern. Eine Verbesserung dieser Methode ist die Benutzung einer polynomialen Abbildung der Ordnung p > 1 PX (c) T ∗ ∗ ∗ T PY (c) = (X; Y; Z) (L ; a ; b ) ; (2) 2 3 ! PZ (c) 4 5 wobei q T x ij (3) Px(c = (c1; : : : ; cq) ) = a(i1;··· ;iq ) cj ; ··· ≤ j=1 i1+ X+iq p Y 2 10. Workshop Farbbildverarbeitung 2004, Koblenz, Online-Proceedings http://www.uni-koblenz.de/icv/fws2004/ für x = X; Y; Z. Die Koeffizienten ax lassen sich mittels mehrdimensionaler (i1;··· ;iq ) polynomialer Regression (MPR) der Targetfarben bestimmen. In den beschriebenen Methoden wurde die Regression in den intensitätslinearen CIEX- YZ Farbraum durchgeführt. Dies minimiert lediglich den „Root Mean Square“ Feh- ler zwischen den transformierten Farben und den korrespondierden Targetfarben und nicht dem Wahrnehmungsfehler. Da die Farbabstandsmetriken jedoch im CIEL*a*b* Farbraum definiert sind, ließe sich eine Verbesserung, im Sinne kleinerer ∆E Werte er- warten, wenn die Regression direkt in den CIEL*a*b* Farbraum durchgeführt werden würde. Hardeberg [3] transformierte R; G; B Sensorantworten zuerst mit der nichtlinearen
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