Logo

Machine Interpretation of Line Drawings

Small book cover: Machine Interpretation of Line Drawings

Machine Interpretation of Line Drawings
by

Publisher: The MIT Press
ISBN/ASIN: 0262192543
ISBN-13: 9780262192545
Number of pages: 236

Description:
This book solves a long-standing problem in computer vision, the interpretation of line drawings and, in doing so answers many of the concerns raised by this problem, particularly with regard to errors in the placement of lines and vertices in the images. Sugihara presents a computational mechanism that functionally mimics human perception in being able to generate three-dimensional descriptions of objects from two-dimensional line drawings. The objects considered are polyhedrons or solid objects bounded by planar faces, and the line drawings are single-view pictures of these objects.

Download or read it online for free here:
Download link
(3.7MB, PDF)

Similar books

Book cover: Computer Vision Metrics: Survey, Taxonomy, and AnalysisComputer Vision Metrics: Survey, Taxonomy, and Analysis
by - Springer
Provides an extensive survey of over 100 machine vision methods, with a detailed taxonomy for local, regional and global features. It provides background to develop intuition about why interest point detectors and feature descriptors actually work.
(2725 views)
Book cover: Brain, Vision and AIBrain, Vision and AI
by - InTech
The book provides new ideas, original results and practical experiences regarding service robotics. It is only a small example of this research activity, but it covers a great deal of what has been done in the field recently.
(10177 views)
Book cover: Machine VisionMachine Vision
by - McGraw-Hill
The book is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithm in practical application are provided.
(8781 views)
Book cover: Computer Vision: Models, Learning, and InferenceComputer Vision: Models, Learning, and Inference
by - Cambridge University Press
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use data to learn the relationships between the observed image data and the aspects that we wish to estimate.
(11513 views)