A comprehensive introduction to colorimetry from a conceptual perspective. Color for the Sciences is the first book on colorimetry to offer an account that emphasizes conceptual and formal issues rather than applications. Jan Koenderink's introductory text treats colorimetry—literally, “color measurement”—as a science, freeing the topic from the usual fixation on conventional praxis and how to get the “right” result. Readers of Color for the Sciences will learn to rethink concepts from the roots in order to reach a broader, conceptual understanding. After a brief account of the history of the discipline (beginning with Isaac Newton) and a chapter titled “Colorimetry for Dummies,” the heart of the book covers the main topics in colorimetry, including the space of beams, achromatic beams, edge colors, optimum colors, color atlases, and spectra. Other chapters cover more specialized topics, including implementations, metrics pioneered by Schrödinger and Helmholtz, and extended color space. Color for the Sciences can be used as a reference for professionals or in a formal introductory course on colorimetry. It will be especially useful both for those working with color in a scientific or engineering context who find the standard texts lacking and for professionals and students in image engineering, computer graphics, and computer science. Each chapter ends with exercises, many of which are open-ended, suggesting ways to explore the topic further, and can be developed into research projects. The text and notes contain numerous suggestions for demonstration experiments and individual explorations. The book is self-contained, with formal methods explained in appendixes when necessary.

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices. The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic methods. While the mathematics go far beyond the usual in statistics (including differential geometry and even topology), the book is aimed at accessibility by graduate students. There is deliberate focus on ideas over mathematical formulas. J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is a Professor in the Department of Mathematics and Statistics at Florida International University in Miami, has served as Head of School of Mathematical Sciences at the University of Nottingham, and is joint author of the acclaimed book Statistical Shape Analysis.

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