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. Sugihara's mechanism has several potential applications. It can facilitate man-machine communication by extracting object structures automatically from pictures drawn by a designer, which can be particularly useful in the computer-aided design of geometric objects, such as mechanical parts and buildings. It can also be used in the intermediate stage of computer vision systems used to obtain and analyze images in the outside world. The computational mechanism itself is not accompanied by a large database but is composed of several simple procedures based on linear algebra and combinatorial theory. Contents:Introduction. Candidates for Spatial Interpretation. Discrimination between Correct and Incorrect Pictures. Correctness of HiddenPart-Drawn Pictures. Algebraic Structures of Line Drawings. Combinatorial Structures of Line Drawings. Overcoming Superstrictness. Algorithmic Aspects of Generic Reconstructibility. Specification of Unique Shapes. Recovery of Shape from Surface Information. Polyhedrons and Rigidity. Kokichi Sugihara is Professor in the Department of Mathematical Engineering and instrumentation Physics, Faculty of Engineering, the University of Tokyo, Tokyo, Japan. Machine interpretation of Line Drawingsis included in The MIT Press Series in Artificial Intelligence, edited by Patrick Henry Winston and Michael Brady.

Line drawing interpretation is a challenging area with enormous practical potential. At present, many companies throughout the world invest large amounts of money and human resource in the input of paper drawings into computers. The technology needed to produce an image of a drawing is widely available, but the transformation of these images into more useful forms is an active field of research and development. Machine Interpretation of Line Drawing Images - describes the theory and practice underlying the computer interpretation of line drawing images and - shows how line drawing interpretation systems can be developed. The authors show how many of the problems can be tackled and provide a thorough overview of the processes underpinning the interpretation of images of line drawings.

Based on the author’s considerable research, this book contains state-of-the-art reviews of work in drawing interpretation and discrete optimization. It covers both drawings of polyhedral objects as well as complex curved objects.

Drawing is not a talent, it's a skill anyone can learn. This is the philosophy of drawing instructor Brent Eviston based on his more than twenty years of teaching. He has tested numerous types of drawing instruction from centuries old classical techniques to contemporary practices and designed an approach that combines tried and true techniques with innovative methods of his own. Now, he shares his secrets with this book that provides the most accessible, streamlined, and effective methods for learning to draw.

Taking the reader through the entire process, beginning with the most basic skills to more advanced such as volumetric drawing, shading, and figure sketching, this book contains numerous projects and guidance on what and how to practice. It also features instructional images and diagrams as well as finished drawings. With this book and a dedication to practice, anyone can learn to draw!

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

The purpose of computer vision is to make computers capable of understanding environments from visual information. Computer vision has been an interesting theme in the field of artificial intelligence. It involves a variety of intelligent information processing: both pattern processing for extraction of meaningful symbols from visual information and symbol processing for determining what the symbols represent. The term "3D computer vision" is used if visual information has to be interpreted as three-dimensional scenes. 3D computer vision is more challenging because objects are seen from limited directions and some objects are occluded by others. In 1980, the author wrote a book "Computer Vision" in Japanese to introduce an interesting new approach to visual information processing developed so far. Since then computer vision has made remarkable progress: various rangefinders have become available, new methods have been developed to obtain 3D informa tion, knowledge representation frameworks have been proposed, geometric models which were developed in CAD/CAM have been used for computer vision, and so on. The progress in computer vision technology has made it possible to understand more complex 3 D scenes. There is an increasing demand for 3D computer vision. In factories, for example, automatic assembly and inspection can be realized with fewer con straints than conventional ones which employ two-dimensional computer vision.