Understanding how the shape of a three dimensional object may be recovered from shading in a two-dimensional image of the object is one of the most important—and still unresolved—problems in machine vision. Although this important subfield is now in its second decade, this book is the first to provide a comprehensive review of shape from shading. It brings together all of the seminal papers on the subject, shows how recent work relates to more traditional approaches, and provides a comprehensive annotated bibliography.
The book's 17 chapters cover: Surface Descriptions from Stereo and Shading. Shape and Source from Shading. The Eikonal Equation: some Results Applicable to Computer Vision. A Method for Enforcing Integrability in Shape from Shading Algorithms. Obtaining Shape from Shading Information. The Variational Approach to Shape from Shading. Calculating the Reflectance Map. Numerical Shape from Shading and Occluding Boundaries. Photometric Invariants Related to Solid Shape. Improved Methods of Estimating Shape from Shading Using the Light Source Coordinate System. A Provably Convergent Algorithm for Shape from Shading. Recovering Three Dimensional Shape from a Single Image of Curved Objects. Perception of Solid Shape from Shading. Local Shading Analysis Pentland. Radarclinometry for the Venus Radar Mapper. Photometric Method for Determining Surface Orientation from Multiple Images.
Shape from Shading is included in the Artificial Intelligence series, edited by Michael Brady, Daniel Bobrow, and Randall Davis.
About the Editors
Berthold K. P. Horn is Professor of Electrical Engineering and Computer Science at MIT. He has presided over the field of machine vision for more than a decade and is the author of Robot Vision.
Michael J. Brooks is Vice President (Research) at the University of Adelaide.