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Color in Scientific Visualization: Erp Ception and Image-Based Data Display Rochester Institute of Technology RIT Scholar Works Theses 2-24-2008 Color in scientific visualization: erP ception and image-based data display Hongqin Zhang Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Zhang, Hongqin, "Color in scientific visualization: erP ception and image-based data display" (2008). Thesis. Rochester Institute of Technology. Accessed from This Dissertation is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Color in Scientific Visualization: Perception and Image-based Data Display by Hongqin Zhang B.S. Shandong University, 1998 M.S. Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Science, 2001 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Chester F. Carlson Center for Imaging Science Rochester Institute of Technology February 24, 2008 Signature of the Author Accepted by Coordinator, Ph.D. Degree Program Date CHESTER F. CARLSON CENTER FOR IMAGING SCIENCE ROCHESTER INSTITUTE OF TECHNOLOGY ROCHESTER, NEW YORK CERTIFICATE OF APPROVAL Ph.D. DEGREE DISSERTATION The Ph.D. Degree Dissertation of Hongqin Zhang has been examined and approved by the dissertation committee as satisfactory for the dissertation required for the Ph.D. degree in Imaging Science Dr. Mark D. Fairchild, dissertation Advisor Dr. Ethan D. Montag, co-advisor Dr. Andrew M. Herbert Dr. David W. Messinger Date DISSERTATION RELEASE PERMISSION ROCHESTER INSTITUTE OF TECHNOLOGY CHESTER F. CARLSON CENTER FOR IMAGING SCIENCE Title of Dissertation: Color in Scientific Visualization: Perception and Image-based Data Display I, Hongqin Zhang, hereby grant permission to Wallace Memorial Library of R.I.T. to reproduce my thesis in whole or in part. Any reproduction will not be for commer- cial use or profit. Signature Date Color in Scientific Visualization: Perception and Image-based Data Display by Hongqin Zhang Submitted to the Chester F. Carlson Center for Imaging Science in partial fulfillment of the requirements for the Doctor of Philosophy Degree at the Rochester Institute of Technology Abstract Visualization is the transformation of information into a visual display that en- hances users understanding and interpretation of the data. This thesis project has in- vestigated the use of color and human vision modeling for visualization of image-based scientific data. Two preliminary psychophysical experiments were first conducted on uniform color patches to analyze the perception and understanding of different color attributes, which provided psychophysical evidence and guidance for the choice of color space/attributes for color encoding. Perceptual color scales were then designed for univariate and bivariate image data display and their effectiveness was evaluated through three psychophysical experiments. Some general guidelines were derived for effective color scales design. Extending to high-dimensional data, two visualization techniques were developed for hyperspectral imagery. The first approach takes advan- tage of the underlying relationships between PCA/ICA of hyperspectral images and the human opponent color model, and maps the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCbCr, and YUV. The gray world assumption was adopted to automatically set the mapping origins. The rendered images are well 5 color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes. The second approach combines a true color image and a PCA image based on a biologically inspired visual attention model that simulates the center-surround structure of visual receptive fields as the difference between fine and coarse scales. The model was extended to take into account human contrast sensitivity and include high-level information such as the second order statistical structure in the form of local variance map, in addition to low-level features such as color, luminance, and orientation. It generates a topographic saliency map for both the true color image and the PCA image, a difference map is then derived and used as a mask to select in- teresting locations where the PCA image has more salient features than available in the visible bands. The resulting representations preserve consistent natural appearance of the scene, while the selected attentional locations may be analyzed by more advanced algorithms. Acknowledgements My sincere gratitude goes to all the people who have helped me in completion of this thesis. Especially, I would like to thank my advisors, Dr. Mark D. Fairchild and Dr. Ethan D. Montag, for their support, guidance, and understanding, and my committee mem- bers, Dr. Andrew M. Herbert and Dr. David W. Messinger, for their insightful com- ments and guidance. I would also like to thank Dr. Mitch Rosen and Dr. Dave Wyble for their support and advice. It has been a great pleasure and valuable experience to work with Mitch on the skin color profiling project and work with Dave on the LCD/DLP projector colorimetric characterization project. My thanks also go to all the rest of our MCSL family, Dr. Garrett Johnson, Dr. Roy Berns, Lawrence Taplin, Colleen Desimone, Val Hemink, my classmates and friends, Yonghui (Iris) Zhao, Jiangtao (Willy) Kuang, Changmeng Liu, Rod Heckaman, all the graduate students, and our academic and support staff at the Center for Imaging Science. Lastly, my special thanks to my parents and my husband, Honghong Peng, for their love and support. Contents 1 Introduction 19 1.1 Problem Statement . 19 1.2 Research Goals . 22 1.3 Approaches Overview . 22 1.4 Document Structure . 24 2 Perception of Color Attributes on Uniform Color Patches 25 2.1 Background . 25 2.2 Experimental . 28 2.2.1 Color Space Selection . 28 2.2.2 Monitor Setup . 29 2.2.3 Viewing Conditions . 29 2.2.4 Matching Experiment . 30 2.2.5 Judgment Experiment . 33 2.3 Results and Discussion . 34 2.3.1 Matching Experiment . 34 2.3.2 Judgment Experiment . 49 2.4 Conclusions . 58 CONTENTS 8 2.5 Summary . 60 3 Visualization of Univariate and Bivariate Image Data 61 3.1 Introduction . 61 3.2 Review of Theories and Principles . 64 3.2.1 Trumbo’s Principles . 64 3.2.2 Data Type and Visual Representation . 65 3.2.3 Data Spatial Frequency and Human Vision Contrast Sensitivity 66 3.2.4 Task in Visualization . 68 3.2.5 Wainer and Francolini’s Levels of Comprehension . 70 3.3 Perceptual Color Scales Design . 71 3.3.1 Color Space Selection . 71 3.3.2 Univariate Color Scales . 71 3.3.3 Bivariate Color Scales . 72 3.4 Psychophysical Evaluation . 75 3.4.1 Experiment I . 75 3.4.2 Experiment II . 78 3.4.3 Experiment III . 79 3.5 Results and Discussion . 83 3.5.1 Experiment I . 83 3.5.2 Experiment II . 85 3.5.3 Experiment III . 89 3.6 Conclusions . 93 4 Visualization of Multispectral Image Data 95 4.1 Introduction . 95 CONTENTS 9 4.2 The Three Views of Spectral Images . 97 4.3 Visualization Requirements . 99 4.4 Review of Visualization Techniques for Spectral Images . 101 4.4.1 Band Selection Methods . 102 4.4.2 Spectral Weighting Methods . 102 4.4.3 Method Based on IHS Model . 103 4.4.4 Methods Based on Data Projection . 103 4.4.5 Methods Based on Image Fusion . 104 4.4.6 Summary . 107 4.5 Perceptual Display Strategies Based on PCA and ICA . 108 4.5.1 Background . 108 4.5.2 Perceptual rendering strategies . 110 4.5.3 Testing . 113 4.5.4 Discussion . 121 4.5.5 Conclusions . 124 4.6 Hybrid Display Algorithm Based on a Visual Attention Model . 126 4.6.1 Introduction . 126 4.6.2 Visual Attention Model . 127 4.6.3 Hybrid Display Algorithm . 128 4.6.4 Testing Results . 137 4.6.5 Discussion and Conclusions . 137 4.7 Summary . 141 5 Conclusions 143 5.1 Summary . 143 5.2 Future Work . 146 CONTENTS 10 Appendices 148 A MATLAB Scripts 148 A.1 Part I . 148 A.2 Part II . 152 A.3 Part III . 174 List of Figures 1.1 Approaches Overview of the Thesis Work . 23 2.1 The four colors used in the Matching Experiment were chosen at hue angles of 45o, 125o, 195o, and 320o in the CIECAM02 space at Light- ness, J = 70. These colors were chosen to be intermediate to the unique hues (h1, h2, h3, and h4) (the hue positions plotted in the Figure are for illustration only, not necessarily accurate). 30 2.2 Illustration of the Matching Experiment setup. The observers set matches using three different sets of slider controls: RGB, LCH, and (L,r/g,y/b). 32 2.3 The average color difference (CIEDE00) for each of the three control methods by both expert and non-expert observers. The x-axis rep- resents different control methods, and the y-axis represents matching accuracy in terms of color difference CIEDE00 where 1 unit difference is equivalent to 1 just-noticeable-difference (JND). (with error bar at a 95% confidence interval.) . 41 2.4 Average color difference (CIEDE00) and 95% confidence intervals for the main effect for the Matching Experiment: (A) Control method, (B) Observer expertise, and (C) Patch Color (hue angle). 43 LIST OF FIGURES 12 2.5 The average time (s) taken for making a match showing the main ef- fects of (A) Control method, (B) Observer expertise, and (C) Patch Color (hue angle). (with error bar at a 95% confidence interval.) . 47 2.6 The average time (s) taken for making a match by expert and non- expert observers for the three different control methods. (with error bar at a 95% confidence interval.) .
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